Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning based Underwater Structural Pole Inspection

The most challenging problem in inspection planning is the structural coverage in an environment with obstacles. This paper presents a coverage path planning framework based on reinforcement learning using an autonomous underwater vehicle (AUV). This approach exploits the knowledge from the model and generates an optimal path to move from the initial position to the nearest area of interest (AOI). Then, it starts to perform a sweep of the exterior boundary of a three-dimensional (3D) structure in the workspace, including concerning the complete coverage of the given AOI and avoiding obstacles. In this model, a non-linear action selection strategy is used to provide a meaningful exploration, contributing to more stability in the learning process. A reward function is designed by taking into consideration multiple objectives to satisfy the sub-goal requirements. The simulation result indicates the effectiveness of the approach in planning the inspection path. The AUV behaves as a boustrophedon motion when covering the AOI and can achieve maximum cumulative reward while reaching the learning goal.
Enhancing Differential Evolution Algorithm: Adaptation for CEC 2017 and CEC 2021 Test Suites

Differential evolution (DE) has proved its significance for optimizing various real-world applications and standard benchmarks. In this work, a self-adaptive version of DE is proposed namely LSHADESPA by employing three major modifications, i) proportional shrinking population mechanism for reducing computational burden, ii) simulated annealing-based scaling factor (F) for improving the exploration properties, and iii) oscillating inertia weight-based crossover rate (CR) for a balancing exploitation and exploration. The proposed algorithm has been experimentally tested on IEEE CEC 2017 and IEEE CEC 2021 benchmarks. For performance evaluation, a comparison with respect to JADE, SaDE, SHADE, LSHADE, MVMO, and others has been performed. Experimental and statistical results affirm the superior performance of the proposed LSHADESPA algorithms with respect to other algorithms.
VPass: An Open-Source COVID-19 Vaccine Passport, and Vaccine Hesitancy

The use of technology is one of the keys to combating the covid-19 pandemic. This paper proposes and demonstrates an implementation of a digital vaccine passport /certificate is, called VPass, for taking non-essential services. This passport will represent someone's vaccination status while preserving all personal data safe. The developed application is platform-independent and accessible using any device connected to the internet. This application also keeps an offline copy in a device or a printed copy of a vaccine passport for convenience. A quick response (QR) code will show the COVID-19 vaccination status. All data stored and transmitted between the front (to the end user) and backend (to and from the server) are fully encrypted. This paper presents the technical detail of implementing a digital vaccine passport for COVID-19. This application could also be used for keeping other vaccination records/certificates.
Anomaly Detection with Convolutional Autoencoder for Predictive Maintenance

Predictive maintenance is set to prevent downtime and failures of equipment and processes to meet the quality and availability requirements of several industrial, commercial, and even residential activities. This paper proposes CAE-AD, a novel convolutional autoencoder anomaly detection method that relies only on normal operation data for training the intelligent classi-fier. The method also accommodates a sliding window algorithm for generating the input from sensor readings, which accounts for the dynamic characteristics of the data. The anomaly detection is accomplished by comparing the convolutional autoencoder reconstruction error to a threshold value to segregate between normal and anomalous predictions. The threshold value is found by minimizing the False Positive Rates and False Negative Rates. Using a benchmark water pump sensor time-series data, the model successfully classified all water pump breakdowns and correctly identified 98.8% of anomalous data and 94.8 % of normal data using a chosen best window length of the past 37 sensor readings.
MVI-DCGAN Insights into Heterogenous EO and Passive RF Fusion

As technology trends towards automation, deep neural network (DNN) based methods become more and more desirable from a technological, economical, and societal standpoint. However, owing to the way that these black box technologies operate, it can be difficult to troubleshoot potential errors, especially when dealing with data that the human mind cannot intuitively understand. For this reason, the use of explainable artificial intelligence (XAI) is integral to obtaining interpretability and understanding of these systems' techniques. The paper explores some of the known uses of XAI in Generative Adversarial Networks (GANs); i.e., in processing electro-optical (EO) and passive radiofrequency (Passive RF) data to achieve heterogenous sensor fusion. GANs are capable of generating realistic images, music text, and other forms of data, and the use of deep convolutional generative adversarial networks (DCGANs) to process such information provides “richer” corrective feedback from which the model can train from. Using the DCGAN approach, tone can provide visualizations from different types of neural networks and use them as a training source for the multiple visualizations input (MVI) DCGAN. The MVI-DCGAN uses these visualizations in order to track the vehicle target and further differentiate between other overlay visualization data and the generated overlay input visualizations. The paper demonstrates multiple sources of visualization input from different neural networks for the training of the MVI-DCGAN for a more robust training and directing the discriminator towards focusing on the P-RF aspects of the visualizations.
Evaluating the performance of the Quantum Approximate Optimisation Algorithm to solve the Quadratic Assignment Problem

The performance of the Quantum Approximate Optimisation Algorithm (QAOA) in solving the Quadratic Assignment Problem (QAP) is evaluated, with the Variational Quantum Eigensolver (VQE) as a benchmark. The QAP is directly revelant to numerous industry scenarios. The QAP, a Combinatorial Optimisation Problem (COP), is classified as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{NP}$</tex> -Hard. This classification means CPU time increases exponentially as the problem size scales when solving the QAP using deterministic optimisation techniques. Therefore, this work investigates the QAOA in search of a non-deterministic optimisation technique to efficiently obtain solutions to the QAP. This research compares two warm start techniques to solve QAP instances of sizes 3 to 7. The metrics of comparison - that measure efficiency and solution quality - were introduced in previous work on this topic. For the QAOA, the impact of the p-value, a determination of circuit depth, is investigated. Of the two quantum hybrid heuristics, the VQE retrieves solutions in a shorter computational time with a smaller circuit size, which allows for solving instances with a larger problem size. Compared to the VQE, the QAOA performs better in terms of feasibility as the problem size scales. The quantum warm start method results implies that the QAOA may not maintain higher solution quality for instances larger than size 4. Still, further investigation should be conducted once quantum devices with more qubits and higher quantum volumes are available.
Learning Disentangled Representations Using Dormant Variations

A disentangled representation is one in which each variable in the latent space is sensitive to one single generative factor and is relatively dormant to other factors. Disentanglement results in an incisive latent representation of the image which can be used for downstream tasks such as reinforcement learning and supervised learning. The discrete generative factors in image datasets are hard to capture in the form of a latent space and in order to perform efficient interpolations it requires smooth and continuous latent spaces in order to address this by disentangling the important factors of the input image in the latent space. Subsequently post training the model should be able to generate different versions of the input image by varying features/attributes. A technique Hybrid Optimized GAN using Dormant Variants (HOGDV) is proposed which can be deployed in multiple places if the number is made variable and works on a wide variety of data distribution.
Proficiency Prediction System for Online Learning Based on Recurrent Neural Networks

In recent years, online learning systems have become increasingly popular among students and teachers because they are unlimited by time and space. A brand-new online learning system called QSticker, which is based on a line bot, has also been proposed to improve the online learning environment. However, there is still a problem in the online learning system that teachers and students cannot communicate face-to-face in time and care about students' learning status, so if there is not a proper analysis of students' performance, it will lead to poor learning conditions. There have been many pieces of research about knowledge tracing in the past. Nonetheless, we found that the knowledge tracing models cannot optimally predict students' proficiency in knowledge concepts in some online learning environments such as QSticker. Therefore, we proposed a Proficiency Prediction System based on Gated Recurrent Unit (GRU). Since many students have similar trajectories in learning, the system uses the most straightforward exercise answering behavior data, including the correctness of his answer and the knowledge concept correlations. It then calculates other characteristics, such as the difficulty of each knowledge concept, to predict students' proficiency in all knowledge concepts in the course. The accomplished experiments show that our model can achieve 71% accuracy on the collected dataset. With the help of this system, we can predict the difficulties students may encounter in the learning process. In addition, to be practically used in teaching scenarios, we also designed an analysis platform for this system.
Enhanced RecycleNet for Efficient Waste Classification

Segregation of recyclable waste items is one of the crucial aspects of smart cities and their industrial applications. CNN-based machine learning models are widely used to predict and classify image datasets. Traditional deep learning models are fast in training the image dataset, but the classification accuracy is usually too low. Different densely connected CNN architectures are widely used to improve the accuracy in the image waste classification. Despite the remarkable accuracy in such densely connected models, these models often suffer from high computational complexity during the training phase. To overcome this computational complexity, DenseNet121 has been developed, which reduces the training time due to its unique dense block architecture. RecycleNet is a modification of DenseNet121 where the skip connections in the dense block architecture are changed to reduce the computational complexity. In this paper, we propose a unique model called Enhanced RecycleNet, where the skip connections between the dense block architecture are reduced to one-third than in the DenseNet121 model. This unique architecture has improved the model's performance by 46.3% and decreased the trainable parameters from 7 million to about 2.4 million.
Serverless Architecture: Scalability, Implementations and Open Issues

Cloud service providers have been developing new solutions in response to the needs of the cloud computing paradigm. Serverless architecture is a solution that delegates the server administration to the cloud service provider, however, each provider’s implementation comes with scalability trade-offs. This manuscript approaches serverless architecture by breaking down the architecture into its parts and reviewing its role and impact on serverless architecture. A set of limitations are given by the cloud service provider to consider at the moment of choosing their serverless implementation nevertheless, the discussion in this manuscript highlights the preference for AWS over other cloud providers, thus leaving the features offered by the other providers untested, and the use of frameworks to enhance parallelism or concurrency capabilities in the serverless implementation.
A Novel Approach for Federated Learning with Non-IID Data

Federated learning (FL) is an emerging technique used to collaboratively train a global machine learning model while keeping the data localized on the user devices. The main obstacle to FL's practical implementation is the Non-Independent and Identical (Non-IID) data distribution across users, which slows convergence and degrades performance. To tackle this fundamental issue, we propose a method (called ComFed) that enhances the whole training process on both the client and server sides. The key idea of ComFed is to simultaneously utilize client-variance reduction techniques to facilitate server aggregation and global adaptive update techniques to accelerate learning. Our experiments show that ComFed can improve state-of-the-art algorithms dedicated to Non-IID data.
CILDI: Class Incremental Learning with Distilled Images

Lifelong learning aims to develop machine learning systems that can learn new tasks while preserving the performance on previously learned tasks. Learning new tasks in most proposals, implies to keeping examples of previously learned tasks to retrain the model when learning new tasks, which has an impact in terms of storage capacity. In this paper, we present a method that adds new capabilities, in an incrementally way, to an existing model keeping examples from previously learned classes but avoiding the problem of running out of storage by using distilled images to condensate sets of images into a single image. The experimental results on four data sets confirmed the effectiveness of CILDI to learn new classes incrementally across different tasks and obtaining a performance close to the state-of-the-art algorithms for class incremental learning using only one distilled image per learned class and beating the state-of-the-art on the four data sets when using 10 distilled images per learned class, while using a smaller memory footprint than the competing approaches.
Hybrid Joint Embedding with Intra-Modality Loss for Image-Text Matching

Image-text(caption) matching has become a regular evaluation of joint-embedding models that combine vision and language. This task comprises ranking the data of one modality (images) based on a Text query (Image Retrieval), and ranking texts by relevance for an image query (Text Retrieval). The current joint embedding approaches use symmetric similarity measurement, due to that order embedding is not taken in consideration. In addition to that, in image-text matching, the used losses ignore the intra similarity in a certain modality that explores the relation between the candidates in the same modality. In spite of, the important role of intra information in the embedding. In this paper, we proposed a hybrid joint embedding approach that combines between distance preserving which based on symmetric distance and order preserving that based on asymmetric distance to improve image-text matching. In addition to that we propose an intra loss function to enrich the embedding with intra-modality information. We evaluate our embedding approach on the baseline model on Flickr30K dataset. The proposed loss shows a significant enhancement in matching task.
Empirical Evaluation of Word Representation Methods in the Context of Candidate-Job Recommender Systems

In this paper, we have evaluated our hybrid two-way recommendation system with expert-ranked resumes and job descriptions. The aim of the paper is to compare the lists produced by the recommendation system with human-ranked lists for candidate and job descriptions. Firstly, we set up four scenarios such as the matching of resume to resumes, job to jobs, resume to jobs, and job to resumes, and prepared a human ranking based on the content similarity on a total of 400 documents. Based on this annotated corpus we tested our system to calculate the cosine-similarity-based ranking for each scenario using the Global Vectors for Word Embeddings and Term Frequency-Inverse Document Frequency representations. Finally, we compared the similarities of human ranked lists and system-ranked lists by using the Rank Biased Overlap (RBO) similarity score. In both methods, GloVe and TF-IDF, the median RBO between human-ranked lists and system ranked are greater than 0.5. The highest median score is achieved on TF-IDF with a slight difference compared to GloVe apart from the ranking of resume-to-resume scenario where the variation between the two methods is considerable. This is due to the similarity between human-ranked lists and program-generated lists.
A Methodology for Selective Protection of Matrix Multiplications: A Diagnostic Coverage and Performance Trade-off for CNNs Executed on GPUs

The ability of CNNs to efficiently and accurately perform complex functions, such as object detection, has fostered their adoption in safety-related autonomous systems. These algorithms require high computational performance platforms that exploit high levels of parallelism. The detection, control and mitigation of random errors in these underlying high computational platforms become a must according to functional safety standards. In this paper, we propose protecting, with a catalog of diagnostic techniques, the most computationally expensive operation of the CNNs, the matrix multiplication. However, this protection entails a performance penalty, and the complete CNN protection may be unaffordable for those systems operating with strict real-time constraints. This paper proposes a three-stage methodology to selectively protect CNN layers to achieve the required diagnostic coverage and performance trade-off: i) sensitivity analysis to misclassification per CNN layers using a statistical fault injection campaign, ii) layer-by- layer performance impact and diagnostic coverage analysis, and iii) selective layer protection. Furthermore, we propose a strategy to effectively compute the achievable diagnostic coverage of large matrices implemented on GPUs. Finally, we apply the proposed methodology and strategy in Tiny YOLO-v3, an object detector based on CNNs.
The Usability of Derived Function Features in Online Signature Verification

Handwritten signatures are one of the most commonly used biometrics. Because signatures are widely accepted, their verification is a fundamental problem. The aim of signature verification is to decide about the origin of the signature so the ability to detect forgeries. Until offline signature verification is based on the scanned image of the signatures, online signature verification applies different electronic devices to capture the signatures. Online signatures also contain dynamic information such as the pressure or inclination angle of the pen, so it is much more challenging to forge online signatures than offline ones. In addition, it is possible to define and calculate further derived features based on the captured ones. The captured features are usually function features, which means they assign a value to each signature point or specified sets of signature points. This work aims to compare the usability of common derived function features using a dynamic time warping (DTW) based solution.
Explainable Machine Learning Models for Pneumonia Mortality Risk Prediction Using MIMIC-III Data

To gain trust, machine learning (ML) models used in high stake applications such as clinical decision support need to provide explainable behaviours and outputs. To assess whether interpretable explanations can be obtained without sacrificing prediction performance, we compare using “black box” versus “glass box” models for predicting the mortality risk of patients diagnosed with pneumonia, using data in the MIMIC-III dataset. We examine five types of black box models: random forest (RF), support vector machine (SVM), gradient boosting classifier (GBC), AdaBoost (ADA), and multilayer perceptron (MLP), and three types of glassbox models: K-nearest neighbor (KNN), explainable boosting machine (EBM), and generalized additive models (GAM). When trained using 417 features, a black box RF model performs best with AUC of 0.896. With the feature set size reduced to 19, an EBM model performs the best with AUC 0.872. Both models exceed the AUC of 0.661, the best previously reported for the task. Our results suggest that ML models with inbuilt explainability may provide prediction power as attractive as black box models.
A Cooperative Population-Based Method for Solving the Max-Min Knapsack Problem with Multi-scenarios

In this paper, we study the max-min knapsack problem with multi-scenarios, where a cooperative population based method is designed for approximately solving it. Its instance is represented by a knapsack of fixed capacity, a set of items (with weights and profits) and possible scenarios related to overall items. Its goal is to select a subset of items whose total weight fills the knapsack, and whose total profit is maximized in the worst scenario according the whole scenarios. The designed method is based upon the grey wolf optimizer, where a series of local searches are employed for highlighting the performance of the method. It starts with a reference set of positions related to the wolves, which is provided with a random greedy procedure. In order to enhance the behavior of the standard version, a series of exploring strategies is employed. Next, in order to avoid premature convergence, a drop and rebuild strategy is added hopping to exploit new unexplored subspaces. Finally, the behavior of the method is computationally analyzed on benchmark instances of the literature, where its provided results are compared to the best results available in the literature. Encouraging results have been obtained.
Agent-Based Document Expansion for Information Retrieval Based on Topic Modeling of Local Information

With the advent of data ecosystems finding information in distributed and federated catalogs and marketplaces becomes more and more important. One of the problems in data search and search in general is the mismatch between the terminology of users and of the searched items, be it dataset metadata or web pages. The paper proposes an agent-based approach to document expansion (ADE). The idea is to represent documents with agents that exploit local information collected from user searches and relevant signals to improve the representation of the document in a search index and subsequently to improve the search performance of the system. The agents collect terms from relevant queries and perform topic modeling on these terms and publish different variants expanded with the topic terms to the search index. We find that the approach achieves good improvement in search performance and is a valuable tool because is places no burden on the information retrieval pipeline and is complementary to other document expansion and information retrieval approaches.