Miracle-dz/UniFAR-FTUs huggingface dataset:
5G Mobile Communication Applications: A Survey and Comparison of Use Cases

The mobile demands and future business context are anticipated to be resolved by the fifth-generation (5G) of mobile communication systems. It is expected to provide an utterly mobile device, connected society, and support the demanding services of various use cases (UCs). This is intended to meet the demand requirement by providing services at tens of Gbps in terms of data rates, higher mobility range, lower latencies, and massive connectivity density devices per square kilometer. A comprehensive and up-to-date survey of the different developed and proposed use cases is presented in this paper. The first part of the paper presents the overview of the new 5G Architecture by introducing new features such as the new radio interface (New Radio), an overview of the 5G Core Network, minimum requirements, and the Radio Access Network, 5G spectrum requirements and other fundamentals of the network. Secondly, a detailed review of the developed and proposed use cases for 5G communications by the standards development organizations (SDO) and other key players in mobile communication is provided. Thirdly, we went ahead to propose spectrum bands for the deployment of the various use cases based on the low-, mid-, and high-band spectrum and further classified the use cases with respect to their relevance and family, identifying the IMT-2020 test environments and the usage scenarios derived by the 3GPP, fourthly, the channel capacity and the bandwidth of the spectrum was studied, simulated and compared to ascertain the spectrum proposed in this paper for each UC family. Hence, this paper serves as a guideline for understanding the future 5G deployment scenarios in various environments. This would allow system developers to design and implement 5G channel characterization models specific to the usage scenarios to meet the system requirements.
Nonparametric Model-Based Online Junction Temperature and State-of-Health Estimation for Insulated Gate Bipolar Transistors

Insulated gate bipolar transistor (IGBT) is widely used in power equipment, it generally works in complex circuit profiles and it is very difficult to measure or predict the thermal parameters of the module in real-time and evaluate the corresponding health status in the transient process. This paper develops a novel approach for solder-layer condition monitoring of IGBTs. In the approach a time-series nonparametric model of a power module is constructed, the current power and ambient temperature data are used to deduce the health state junction and case temperature. Three groups of time-series insulated gate bipolar transistors (IGBTs) data are used to train and verify the time-series nonparametric model for online conditions, the results show that the developed method has high accuracy. Compared with traditional methods, the time series non-parametric model method not only saves characteristic experiments but also saves the process of mathematical model construction. Besides, the proposed method also has the advantages of strong generalization and low equipment requirements which is useful for actual working conditions. Thereafter, another nonparametric model is built, the predicted junction temperature is used to estimate the collector voltage in the health state, and the percentage deviation of the measured collector voltage from the estimated voltage is used to do the state-of-health estimation of the IGBT and its accuracy is verified by the experiment result.
PyGModels: A Python package for exploring Probabilistic Graphical Models with Graph Theoretical Structures

Probabilistic Graphical Models (PGMs) are a marriage between “graphs” from graph theory and “probability” from statistics and probability theory. While PGMs are widely used in many fields, we noticed that most existing PGM libraries are implement in a way that doesn’t take full advantage of their graphical nature. PyGModels ’ value proposition is that it faithfully implements the graphical nature of PGMs, thereby giving PyGModels ’ instantiated objects both graph-theoretical and statistical properties, which allows users to explore and test inference algorithms that are rooted in graph theory or statistics. PyGModels also implements several algorithms of interest on Lauritzen-Wermuth-Frydenberg (LWF) chain graphs, also known as mixed graphs.
eigentools: A Python package for studying differential eigenvalue problems with an emphasis on robustness

1 Department of Physics and Astronomy, Bates College 2 Department of Mathematics, MIT 3 School of Natural Sciences, Institute for Advanced Study 4 CIERA, Northwestern University 5 Department of Astrophysical and Planetary Sciences, University of Colorado, Boulder 6 School of Mathematics and Statistics, University of Sydney 7 Department of Engineering Sciences and Applied Mathematics, Northwestern University DOI: 10.21105/joss.03079
An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data

Today’s sedentary life leads to a plethora of lifestyle-related illnesses. This has led to the quest to predict diseases before they occur. In the past, research on stress prediction was carried out conventionally in a laboratory-based environment. However, recent studies are focusing on developing non-invasive ways to predict stress with the help of wearable devices. Generally, the models developed for stress prediction do not provide accurate results because the stress patterns are highly subjective and vary from person to person. Therefore, person-dependent models may achieve higher accuracies. These models, however, have to be trained with collected data over a comparatively longer period of time. In this paper, an Adaptive Neuro-Fuzzy Inference System aided Fire Works Grey Wolf Optimization (ANFIS-FWGWO) classification algorithm has been proposed for stress prediction. In particular, the proposed machine learning framework has been implemented to predict computer users’ stress by using a sensor-integrated keyboard data. Various physiological parametric data were acquired during two different phases for the experimentation, and the received data was analyzed using an efficient machine learning framework. Specifically, the proposed framework encompasses various techniques such as data preprocessing for data smoothing and the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection algorithm for identifying the important features on the data set. From the experimental analysis, it is concluded that the ANFIS-FWGWO classification algorithm discriminates the stress subjects with a high degree of accuracy when compared with existing classification algorithms.
Simple PV Modeling Under Variable Operating Conditions

Photovoltaic (PV) panels are increasingly used to convert sunlight into electricity, as a source of sustainable energy. It can be used in a wide variety of applications ranging from the well-known power generation to the emerging energy harvesting in Internet of Things (IoT). Hence, an accurate model is required to evaluate and predict the performance of the PV panel. However, the non-linear characteristics of PV panels make the modeling of their electrical response a challenging task. In the literature, most of the previous PV models have been developed for large wattage PVs under high irradiance, or for small wattage PVs under lower irradiance. Those that can model both usually require more information, including the I-V curves data at different irradiances, which is not always provided by the manufacturers. Therefore, this paper presents a simple PV modeling that can be applied for different wattage panels at different operating levels of irradiance, using only the commonly provided datasheet values at standard test condition (STC). The model uses the characteristic points translation technique to translate the short circuit current, open circuit voltage and maximum power voltage points, at STC to other operating conditions. These translated values are then used by the parameter extraction technique to extract the model’s parameters. The proposed model’s techniques can model the losses across the resistors at low irradiance, which reduces the error. The accuracy of the proposed model is validated using two representative commercial PV panels. Results are generated for the proposed model and other comparative works. The results show that the proposed model can improve the accuracy over the other compared works, with a consistent percentage difference of below 5% across all levels of irradiances for both panels.
Robust Vehicle Classification Based on Deep Features Learning

This paper aims to introduce a scientific Semi-Supervised Fuzzy C-Mean (SSFCM) clustering approach for passenger cars classification based on the feature learning technique. The proposed method is able to classify passenger vehicles in the micro, small, middle, upper middle, large and luxury classes. The performance of the algorithm is analyzed and compared with an unsupervised fuzzy C-means (FCM) clustering algorithm and Swiss expert classification dataset. Experiment results demonstrate that the classification of SSFCM algorithm has better correlation with expert classification than traditional unsupervised algorithm. These results exhibit that SSFCM can reduce the sensitivity of FCM to the initial cluster centroids with the help of labeled instances. Furthermore, SSFCM results in improved classification performance by using the resampling technique to deal with the multi-class imbalanced problem and eliminate the irrelevant and redundant features.
Interpretable Pneumonia Detection by Combining Deep Learning and Explainable Models With Multisource Data

With the rapid development of AI techniques, Computer-aided Diagnosis has attracted much attention and has been successfully deployed in many applications of health care and medical diagnosis. For some specific tasks, the learning-based system can compare with or even outperform human experts’ performance. The impressive performance owes to the excellent expressiveness and scalability of the neural networks, although the models’ intuition usually cannot be represented explicitly. Interpretability is, however, very important, even the same as the diagnosis precision, for computer-aided diagnosis. To fill this gap, our approach is intuitive to detect pneumonia interpretably. We first build a large dataset of community-acquired pneumonia consisting of 35389 cases (distinguished from nosocomial pneumonia) based on actual medical records. Second, we train a prediction model with the chest X-ray images in our dataset, capable of precisely detecting pneumonia. Third, we propose an intuitive approach to combine neural networks with an explainable model such as the Bayesian Network. The experiment result shows that our proposal further improves the performance by using multi-source data and provides intuitive explanations for the diagnosis results.
Deep Reinforcement Learning-Based Smart Joint Control Scheme for On/Off Pumping Systems in Wastewater Treatment Plants

In this paper, we propose a deep reinforcement learning (DRL) based predictive control scheme for reducing the energy consumption and energy cost of pumping systems in wastewater treatment plants (WWTP), in which the pumps are operated in a binary mode, using on/off signals. As global energy consumption increases, the efficient operation of energy-intensive facilities has also become important. A WWTP in Busan, Republic of Korea is used as the target of this study. This WWTP is a large energy-consuming facility, and the pumping station accounts for a significant portion of the energy consumption of the WWTP. The framework of the proposed scheme consists of a deep neural network (DNN) model for forecasting wastewater inflow and a DRL agent for controlling the on/off signals of the pumping system, where proximal policy optimization (PPO) and deep Q-neural network (DQN) are employed as the DRL agents. To implement smart control with DRL, a reward function is designed to consider the energy consumption amount and electricity price information. In particular, new features and penalty factors for pump switching, which are essential for preventing pump wear, are also considered. The performance of our designed DRL agents is compared with those of WWTP experts and conventional approaches such as scheduling method and model predictive control (MPC), in which integer linear programming (ILP) optimization is employed. Results show that the designed agents outperform the other approaches in terms of compliance with operating rules and reducing energy costs.
Thermodynamic functions for carbon dioxide in the ideal gas state

Thermodynamic functions for carbon dioxide were compu ted by Kassel [1] / whose values were corrected to more recent values of the physical constan ts and extended from 3,500° to 5,000° K by Wagman , et al. [2]. Values by H. L . Jolmston have been given by Ellenwood, Kulik , and Gay [3]. A ubsequen t calculation by Gratch [4] differed appreciably, showing a somewhat different behavior at elevated temperatme. With improved spectroscopic constan ts, it bas become possible to carry out a recalculation of greater accuracy. Values obtained for the specific heat in the main are intermediate between the previous calculations.
Time-Constrained Task Allocation and Worker Routing in Mobile Crowd-Sensing Using a Decomposition Technique and Deep Q-Learning

Mobile crowd-sensing (MCS) is a data collection paradigm, which recruits mobile users with smart devices to perform sensing tasks on a city-wide scale. In MCS, a key challenge is task allocation, especially when MCS applications are time-sensitive, and the platform needs to consider task completion order (since a worker may perform multiple tasks and different task completion orders lead to different travel costs and response times, i.e., the times needed to arrive at the task venues), requirements of tasks (such as deadline and required sensor) and workers heterogeneity. In other words, the task allocation problem consists of multiple task completion order problems, which is challenging to solve due to the large solution space. Therefore, in this paper, we first formulate the considered problem into two related integer linear programming problems (i.e., assignment and task completion order problems) using a decomposition technique in order to reduce the problem size and enable the use of diverse searching strategies. Then, a deep Q-learning (DQN)-based algorithm, namely assignment DQN with a local search (A-DQN w/ LS), is proposed to determine the task–worker assignments, which iteratively employs an asymmetric traveling salesman (ATSP) heuristic to find the task completion orders of the workers. The local optimizer is applied at the end of the A-DQN algorithm to deal with the computation time and local optima. Simulation results show that the proposed method outperforms existing approaches under different sensing dynamics in terms of total cost.
A Novel Technique for Short-Term Load Forecasting Using Sequential Models and Feature Engineering

With the advent of smart grid, load forecasting is emerging as an essential technology to implement optimal planning and control of grid assets. Ergo in recent years, a significant thrust can be witnessed for the research towards the improvement of the prediction of the energy demand. However thus far there has not been any one technique in the literature that is shown to give best forecasts for a variety of sites; almost all the papers published on load forecasting, report their best results on just one of the dataset. This problem accentuates further when the training data does not have enough data points to learn patterns over all the seasons. Hence to devise a load forecasting technique that can yield the best estimates on diverse datasets, especially when the training data is limited, is a big challenge, which is addressed in this paper. The paper presents a novel combination of deep learning with feature engineering for short-term load forecasting. The proposed architecture, named as Deep Derived Feature Fusion (DeepDeFF), is based on the sequential model in conjunction with the hand-crafted derived features in order to aid the model for better learning and predictions. The raw data and the hand-crafted features are trained at separate levels, then their respective outputs are combined to make the final prediction. The efficacy and robustness of the proposed methodology is evaluated on diverse datasets from five countries with completely different patterns. The extensive experiments and results demonstrate that the proposed technique is superior to the existing state of the art.
A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

Complex networks (CNs) have gained much attention in recent years due to their importance and popularity. The rapid growth in the size of CNs leads to more difficulties in the analysis of CNs tasks. Community Detection (CD) is an important multidisciplinary research area where many machine/deep learning-based methods have been applied to map CNs into a low-dimensional representation for extracting information similarity among members of CNs. Currently, Deep Learning (DL) is one of the promising methods to extract knowledge and learn information from high dimensional space and represent it in low dimensional space. However, designing an accurate and efficient DL-based CD method especially when dealing with large CNs is always an on-going research endeavor to pursue. Meta-Heuristic (MH) algorithms have shown their potentials in improving DL models in terms of solution quality and computational cost. In addition, parallel computing is a feasible solution for building efficient DL models. The algorithmic principle of MH is parallel in nature; however, its computation framework in DL training that is reported in the literature is not really implemented in a parallel computing setup. In this paper, we present a systematic review of CD in CNs from conventional machine learning to DL methods and point out the gap of applying DL-based CD methods in large CNs. In addition, the relevant studies on DL with parallel and MH approaches are reviewed and their implications on DL models are highlighted to prospect effective solutions to overcome the challenges of DL-based CD methods. We also point out research challenges in the field of CD and suggest possible future research directions.
spaghetti: spatial network analysis in PySAL

The role spatial networks, such as streets, play on the human experience cannot be overstated. All of our daily activities fall along, or in close proximity to, roads, bike paths, and subway systems to name a few. Therefore, when performing spatial analysis in many cases considering network space, as opposed to Euclidean space, allows for a more precise representation of daily human action and movement patterns. For example, people generally cannot get to work by driving in a straight line directly from their home, but move along paths within networks. To this end, spaghetti (spatial graphs: networks, topology, & inference), a sub-module embedded in the wider PySAL ecosystem, was developed to address network-centric research questions with a strong focus on spatial analysis (Gaboardi et al., 2018; Rey et al., 2015; Rey & Anselin, 2007).
Optimized Uniform Scalar Quantizers of Ternary ADCs for Large-Scale MIMO Communications

Large-scale multiple-input multiple-output (LS-MIMO), a promising technology for the future wireless networks to boost the network capacity, unavoidably faces hardware cost and power consumption issues due to a large number of high-resolution power-hungry analog-to-digital converters (ADCs) at the receiver side. The low-resolution ADCs have recently been proven to be an effective solution to cut down the system cost. This paper studies the LS-MIMO communication systems where three-level ADCs, so-called Ternary ADCs (T-ADCs), are employed to achieve better energy efficiency in some LS-MIMO configurations. The truncation limit of the uniform scalar quantizer of the T-ADCs is optimized to minimize the quantization distortion on the LS-MIMO received signal, and thus its performance approaches that of the well-known Lloyd-Max algorithm while offering lower complexity and better robustness. Furthermore, our proposed solution is independent of the number of antennas. The LS-MIMO systems’ performance with the proposed T-ADCs is substantially improved over the conventional 3- $\sigma $ uniform scalar quantizer in three different performance metrics - achievable rates, iterative decoding thresholds of pragmatic protograph low-density parity-check codes (LDPC), and the frame error rate (FER)/bit error rate (BER). Our energy efficiency investigation reveals that proposed T-ADCs attain better energy efficiency than that of the 1-bit ADCs and 2-bit ADCs when the ratio between the receiving antennas and the transmitting antennas is low and medium as the power consumption of ADCs and the transmission power are better balanced in comparison with one-bit ADCs and two-bit ADCs.
ArabicDialects: An Efficient Framework for Arabic Dialects Opinion Mining on Twitter Using Optimized Deep Neural Networks

The rapid development of tools for communication such as social networks, tweeting and Whatsapp has generated a large mass of important textual data. Also, the COVID-19 pandemic has inflamed social networks, hence the automatic analysis of opinions has become paramount. The purpose of this paper is to analyze Arabic tweets in terms of positivity, negativity, or neutrality.In analyzing the opinions of the Arabic language, a real challenge is encountered, which lies in the use of different dialects (Egyptian, Saudian, Maghrebian, Gulfian, Levantine, Syrian $\ldots $ ). In this paper, we introduce two major components: The first employs six machine learning (ML) methods, including Decision Trees (DT), Logistic Regression (LR), k Nearest Neighbors (K-NN), Random Forests (RF), Support Vector Machines (SVM), and Nave Bayes (NB), with the TF-IDF method acting as the feature extraction.While, the second part consists of testing three variants of Deep Learning (DL) based on multiplicative Long Short Term Memory (mLSTM), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) by applying word embedding as the input vector. The experimental study was validated using three Arabic language corpora (TEAD, ATSAD, and ASTD) and two learning modes (Hold out and 10-folds cross validation). The obtained results in terms of Accuracy (ACC), Precesion (PREC), Recall (REC), and F1-score (F1) show a clear performance for DL techniques based on a 10-folds strategy compared to the state-of-the-art. The experiments shown in the paper reveal that the proposed DL models accomplished the best results.