Reviewerly/exHarmony huggingface dataset:
Bug Resolution Prediction for Open-Source Software Using Ensembles of Instance Selection Algorithms

Studies about maintenance effort estimation of open-source software have investigated the impact of single instance selection on the performance machine learning techniques. However, Ensemble of Instance Selection (EIS) has not been investigated for this topic, especially for bug resolution prediction. This empirical study considers the impact of EIS on the performance of k-Nearest Neighbor, Support Vector Machine, and Multinomial Naïve Bayes techniques. A set of 27 classifiers are built using Bagging and Random Feature Subset Ensembles based on AllkNN single instance selection algorithm on three datasets. The results are presented, together with a comparative analysis of the built classifiers' performance. The results show that the investigated EIS algorithms improve the performance of ML classifiers and outperform the single instance selection algorithm-based classifiers.
Speech Command Recognition Systems Based on two Different Artificial Intelligence Approaches

Speech recognition using artificial intelligence (AI) is widely used in everyday life, whether it's voice assistants for smartphones or smart speakers. Our voice command recognition systems, which are based on Deep Learning, are designed to recognize 10 specific German commands, which contain a maximum of two words, and convert them into a readable text. The two AI approaches used in this work are Bidirectional Long Short-Term Memory (BiLSTM) and convolutional neural network (ConvNet). These Networks have recently shown significant performance improvements in image and speech analysis. Our Models are realized with MATLAB® and finally optimized with suitable parameters. To enhance both recognition systems we used Mel Frequency Cepstral Coefficients (MFCC) Feature, instead of using raw wave signals. As a result, both approaches have reached a remarkable recognition accuracy. The best obtained validation accuracy was from the BiLSTM network, which has reached 98.22% and only one false positive classification by using it on a test data set. The purpose of this paper is to provide a comparative analysis and evaluation of two different deep learning approaches for a speech commands recognition system.
A Combined Learning Based Classifier Using Decision Tree and Logistic Regression

Overweight and obesity are pointed out as a serious threat to the health of people. According to a 2016 report by WHO, 1.9 billion people worldwide are overweight and 650 million are obese. Because the obesity causes various cardiovascular diseases, diabetes, hypertension, and musculoskeletal disorders, it is important to assess the risk of obesity in order to prepare its prevention policies. To deal with this problem, logistic regressions and decision trees are widely applied and then employed as primary classifiers. This paper intends to present a combined learning approach to classifying the risk of overweight and obesity. In our framework, logistic regression models are cascaded onto decision tree classifiers. This feature would be applicable to improving the predictive capability of primary classifiers. Experimental results, using an open dataset provided by Korea Disease Control and Prevention Agency, show that the proposed scheme improves the classification accuracy by 5% to 27%.
Deep Generative Model with Isolation Forest (DGM-IF) for Unsupervised Anomaly Detection in Wireless Sensor Network and Internet of Things

Anomaly detection is crucial for maintaining the reliability and security of wireless sensor networks and loT systems. Conventional methods require labeled data, often unavailable in these systems, making unsupervised techniques essential. Deep learning has shown promise in unsupervised anomaly detection tasks, including wireless sensor networks and loT applications. The Isolation Forest, an unsupervised anomaly detection algorithm, isolates anomalies based on in-herent properties. Combining deep learning with the Isolation Forest can improve accuracy and effectiveness in detecting anomalies. This paper presents the Deep Generative Model with Isolation Forest (DGM-IF), a novel unsupervised anomaly detection method for wireless sensor networks and loT. DGM-IF integrates deep generative models with the Isolation Forest algorithm to learn a robust representation of normal data and identify anomalies. The model generates synthetic data based on the learned distribution and employs the Isolation Forest to separate deviating data points. The proposed technique is assessed using real-world datasets and benchmarked against cutting-edge methods, proving its efficacy in detecting anomalies. The DGM-IF approach has the potential to significantly enhance the reliability and security of wireless sensor networks and IoT systems by identifying potential threats and attacks.
A temporal Human Activity Recognition Based on Stacked Auto Encoder and Extreme Learning Machine

Human Activity Recognition (HAR) is one of the most important research areas in the fields of health and human-machine interaction. The creation of several artificial intelligence-based models for activity identification has resulted in poor long-term performance in the actual world since these methods are unable to extract spatial and temporal information. Though, Deep learning is starting to replace well-established hand-crafted techniques that rely on expertly built feature extraction and classification techniques in the field of HAR. Nevertheless, it is challenging to acquire an overview of the suitability of these discrete implementations of custom deep architectures for issues ranging from the recognition of manipulative gestures to the identification and segmentation of physical activities. Given these constraints, we develop an innovative Stacked Auto Encoder (SAE) and Extreme Learning Machine (ELM) architecture based on temporal feature to produce a new model for HAR.. This feature is connected to speed of movement and fed to a new neural network architecture as temporal stream. A key step in this approach is the selection of features that characterize the complexity of human actions in time. Results show that the proposed model outperforms many of the existing deep and machine learning techniques.
A Privacy-Preserving Federated Learning for IoT Intrusion Detection System

The Internet of Things (IoT) is an impending area with applications in numerous fields. The number of IoT devices has seen exponential growth, increasing apprehensions around security. Cyberattacks are of rising concern because of the expanded attack surface of threats that have plagued networks. Adding to that are insecure practices among users who may not know to protect their IoT devices. Therefore, IoT security has become fundamental, especially as IoT devices carry sensitive data. This paper provides a proof of concept of an Intelligent Intrusion Detection System for IoT. We centered our work on a privacy-preserving approach offering a Federated Learning (FL) based solution for intrusions recognition combining network and energy data. Our model has achieved high accuracy while preserving a short running time in multiple FL rounds.
A Fully Bayesian Inference Approach for Multivariate McDonald's Beta Mixture Model with Feature Selection

Mixture models are widely used in unsupervised machine learning applications where annotating a large amount of data is not feasible. They have succeeded in various real-world problems, including medical applications, human activity recognition, and anomaly detection. This paper proposes a fully Bayesian analysis of the multivariate McDonald's Beta mixture model (McDBMM) using Gibbs sampling method and Metropolis-Hastings to estimate parameters. In addition, we integrated a feature selection technique which simultaneously determines the most relevant features for our mixture model. This allows for the simultaneous selection of the most relevant features, improving the accuracy and efficiency of the unsupervised learning process. Our approach is evaluated on challenging applications, including lung cancer image analysis and human activity recognition. Experimental results indicate that our proposed method is an effective solution compared to the Gaussian mixture model (GMM).
Design of an Automatic Subtype Classifier of Breast Carcinoma Using XResNet with Cyclic Scheduling

This paper reports the design of an automatic system to classify breast cancer subtypes using an XResNet deep learning model. The prior solutions to this problem suffer from low accuracy or cost of training. Our designed system effectively classifies the breast cancer histopathology patches cropped from whole slide images while being affordable and accurate. The reported system effectively detects cancer's localized or invasive nature. The patient's prognosis and treatment schedule depend on the multi-classification of the images. The presented system achieves state-of-the-art accuracy on the multiclass BACH dataset. On the BACH dataset, we achieved the highest accuracy of 89.95% and balanced accuracy of 87.8% in only 30 epochs matching the state-of-the-art systems. Comparisons of the designed system with recent works establish the model's efficacy, making it worthy for the state-of-the-art breast cancer diagnosis routine. The designed system can be a valuable tool for medical professional for improving the survival of patients.
IoT Gas Sensors Array for Unobtrusive Tracking of Cooking Activity

Recent research on remote tracking environments has strengthened smart home IoT ecosystems by the integration of multiple sensing tools that capture not only contextual data in a private setting, but also information about its residents. This shift paves the way for remote health industries, as information traditionally out of reach is available 24/7. Gas sensing, moving away from privacy-invasive tracking paradigms, emerges within this context, inspiring the monitoring of activities of daily living (ADLs) that could facilitate the remote healthcare supervision of the elderly. In this paper, we present how a gas sensing array based on low-cost commercial metal oxide (MOX) gas sensors has been assembled for the development of Principal Component Analysis (PCA) model which detects cooking activity within a household. Our resulting unobtrusive tracking system requiring no user input and posing no privacy concerns, suitable to other ADL use cases, highlights how AI-equipped IoT cloud infrastructures and accurate gas sensors are called to revolutionise remote healthcare.
Recent Challenges, Issues, and Initiatives for Cloud Computing Adoption in Healthcare: A Survey

There are many opportunities and advantages that cloud computing adoption can bring to the healthcare field, as experts believe that it can help improve the level of healthcare services significantly and contribute to its regular and continuous innovation. This paper provides a comprehensive review of the current literature on the common limitations and problems facing the cloud in healthcare. A brief introduction to cloud computing and the benefits of its adoption is also provided, in addition to a review of the most important security protection standards applied in this field. This paper highlights the most important problems and challenges that still exist in cloud-based healthcare systems. Finally, relevant recent research is reviewed and the most recent initiatives that contribute to solving problems for healthcare applications are identified. It was concluded that these solutions and initiatives are not comprehensive in nature as they partially address issues related to data security in the cloud.
IoT-Based Surveillance Camera Distribution Using Triangle Geometry

IoT systems provide excellent solutions to many applications in both civilian and military aspects. This is even facilitated by the advent of many types of cameras with the diminishing cost of communication devices. We focus on the surveillance system that collaboratively monitors an intended area. A good design of such systems should ensure complete coverage with the ability to detect and track objects. Existing surveillance systems produce sensing coverage redundancy with extra communication overhead and cost. This paper presents an IoT-based surveillance system that exhibits many features. First, it provides an efficient distributed network clustering algorithm based on the Field of View (FoV) of cameras. Second, maximum area coverage can be achieved with a minimum number of camera sensors. Simulation results show that our proposed camera distribution algorithm outperforms other existing techniques in terms of the needed number of cameras, coverage percentage, and cluster size. We also believe that the efficiency of our algorithm can be improved with the involvement of the reinforcement learning approach where dynamic camera adaptation can be achieved based on the traffic distribution and changing conditions in the surveillance area.
Online Signature Verification Using Logistic Regression

Online signatures are one of the widely accepted biometrics used for the purpose of authentication and identification. Although there are several approaches and algorithms used in online signature verification systems, nowadays machine learning algorithms are leading the field. In this paper, an online signature verification system based on logistic regression is presented. The SVC2004 database was used to test the accuracy of the system by applying different combinations of features. The results showed that we could achieve accuracies between 91.7% and 98.08%.
Moving Horizon Planning and Control Under Uncertainties with Guarantees – Combining Operational Choices and Motion Primitives

We present a method for enhancing autonomous system capabilities by optimizing both planning and control layers over a moving horizon. Traditional reliance on simplified models is overcome by reformulating the planning problem into a robust optimization issue and leveraging multiple operational choices. Constraint satisfaction and collision avoidance are assured through a technique called constraint back off, which adapts to the system's operational mode, derived from precise motion primitives. The path planning problem is then converted into a solvable mixed-integer linear optimization problem. Using existing robust model predictive control strategies for the control layer allows for handling uncertainties and rapid response to changing obstacles. Simulation results validate the efficacy of our approach.
A Blockchain-Based Modular Architecture for Managing Multiple and Quantum-Safe Encryption Algorithms

The development of Quantum Computing has brought great advantages in terms of computational power that can be seen as an opportunity or as a potential threat to currently implemented systems. The security of a platform can be easily broken if its founding algorithms are not quantum-safe. For this reason, it is crucial to understand how quantum computers work and how much time is needed to switch to quantum-safe platforms. The main contribution of this paper consists of a software architecture for modular Blockchains to let multiple encryption algorithms coexist in order to mine new quantum-safe blocks without discarding old, validated, quantum-broken ones. Old blocks will still be unsafe for post-quantum cryptography, but this is not a threat to the chain integrity.
Hypercomplex Multilayer Perceptron for Planetary Orbits Prediction

This contribution is oriented towards proposing a novel strategy to simulate and predict planetary orbits based on exploiting the optimized performance of hypercomplex neural networks. These structures perform a learning process based on quaternion algebra thus leading to lower the number of epochs required to obtain an adequate degree of approximation. Moreover, we propose a strategy for predicting the outcome of the learning phase by instantiating suitable hypercomplex auxiliary networks to predict the trends of the main weights of the network.
Flying Objects Classification Using Trajectory Characterization

This paper introduces a method for classifying and recognizing flying objects using trajectory features and artificial neural networks (ANN). Initially, the video sequence undergoes processing through a Gaussian mixture model (GMM) to detect and track the flying objects. Then, we will extract trajectory features from our dataset and use them to feed ANN for flying object classification. These features include turning angle, speed, acceleration and centroid distance function. The classical ANN is applied for feature vector classification to discriminate between birds and drones. Experimental results are conducted to showcase the effectiveness of the proposed method in classifying drones and birds. Moreover, the automated approach can be valuable in aiding military services to differentiate drones from other objects.
Modelling and Simulation of a Mamdani Fuzzy PID Controller Using Non-Uniformly Distributed Fuzzy Sets

From the literature, it seems to the authors that there is no relevant work related to modelling of a Fuzzy PID (FPID) controller using Non-Uniformly Distributed Fuzzy Sets (NUD FSs). Fuzzy controllers with NUD FSs have more potential to deliver superior performance when the dynamic systems are nonlinear. This paper attempts to model and develop a three-input general Mamdani FPID controller using NUD FSs. Algebraic Product (AP) t-norm, Maximum (Max) t-conorm, Larsen Product (LP) inference, and Centre of Gravity (CoG) defuzzification are considered for obtaining the analytical structures of the controller. The applicability and usefulness of the proposed FPID controller are demonstrated through the simulation of two nonlinear plants.
Data-Based Solar Radiation Forecasting with Pre-Processing Using Variational Mode Decomposition

This paper presents a hybrid method for accurately predicting Global Horizontal Irradiance (GHI) over the following 24 hours to forecast energy production from a photo-voltaic system in a positive energy building. The input data is preprocessed using the Variational Mode Decomposition (VMD) method to extract wide-bandwidth features and decompose them into smooth modes focused on specific frequency ranges. The Salp Swarm Algorithm (SSA) is utilized to identify the optimal VMD parameters for accurate extraction. The data analysis is employed to identify the most critical modes of input features. The model's efficiency is further enhanced by performing a residual preprocessing step between the observed solar radiance data and the decomposed modes. The Stacking technique (ST) is employed to predict the 24-hour GHI modes and the residual, which are summed to reconstruct the final signal. The proposed method's performance is evaluated using the Normalized Root Mean Square Error (NRMSE) and Normalized Mean Absolute Error (NMAE) metrics on three years of available data (2019–2022) in Rabat, and compared with the model based on raw data. The results show that the proposed method achieved promising results with an NRMSE of 1.35% and NMAE of 0.82% on a cloudy day.
Output Feedback Reinforcement Learning for Temperature Control in a Fused Deposition Modelling Additive Manufacturing System

The development of effective closed-loop control algorithms is one of the main challenges in Additive Man-ufacturing (AM). Many parameters of AM processes need continuous monitoring and regulation, with temperature being one of the most important. We investigate the design of an output-feedback controller of the temperature process within the extruder of a Fused Deposition Modelling (FDM) AM system. Based on a state space approach, and using input-output measurements, we first design a model-based linear quadratic tracking controller, followed by an equivalent model-free, data-driven version. We demonstrate these approaches using a simulator of the temperature evolution in the extruder of the AM system, based on a model validated and identified in recent literature. Our findings show that a comparable performance to the model-based case is possible using only measured data, generated through probing control explorations during the simulations.
A Pre-Distillation Strategy for Object Detection Task

In this paper, we propose a novel model compression method based on knowledge distillation, that is called Pre-Distillation, for object detection task. First, the full model is “squashed” (by the instructor) to generate key information (a.k.a. attention mechanism), and it is also referred to as dataset condensation; it is then provided for lightweight model learning (for the student). Students can study and preview the course through lightweight models with key information that is compressed by the instructor. Additionally, students can also be able to improve their efficiency in learning and acquire integrative understanding of the subject through supplementary materials in which the they will be observing hidden information within the ground truth. This paper is verified and quantified by the public databases of Pascal VOC. The results show that the previewing distillation strategy developed in this paper has higher accuracy. The accuracy of ResNet-101 is 74.02%, which is 2.28% higher than that of the un-distilled 71.74%. The method in this paper is an algorithm-based performance improvement method. Our proposed scheme is a high-efficiency distillation strategy that does not need to change the network architecture and also can avoid the occurrence of model overfitting and speed up model training.
An Optimal Method for Testing Jobs' Execution in MapReduce Based Systems

Over the past few years, parallel and distributed processing using the MapReduce programming paradigm has gained considerable attention. The purpose of such a model is to ease parallel computing and concurrent processing of large data stored on the Hadoop platform. However, MapReduce may experience performance and timing problems while using more complex and time-consuming data processing jobs. This paper focuses on improving MapReduce's performance. As a first step, we propose optimizing MapReduce jobs. The system conformance is then tested using a distributed test application. Finally, the test process is optimized using the Markov Decision Process.
Path Tracking for Wheeled Mobile Robot Using Non Linear Model Predictive Control in Indoor Environment

Wheeled Mobile Robots (WMR) with the assistance of Information Communication Technology (ICT) can navigate and perform some tasks in an uncontrolled environment. They can experience some problems in task management if they can not localize themself in indoor/outdoor environments. Different algorithms can be exploited to evaluate the proper control law to track a default path. In this paper, a path-tracking algorithm for a WMR has been tested for an indoor environment, with a delimited area spanned by localization tags. The WMR must check some waypoints during its path until the last one, where the WMR re-start the simulation. In this work, the control law applied for the experiments is the Non-Linear Model Predictive Controller (NMPC). The control algorithm is tested using two experiments, the first one is based on the robot movements in a simulation environment using the Gazebo tools, and the second one is related to a real context where the mobile robot moves in an indoor environment.