Reviewerly/exHarmony huggingface dataset:
An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation

Although the cost-reference particle filter (CRPF) has a good advantage in solving the state estimation problem with unknown noise statistical characteristics, its estimation accuracy is still affected by the lack of particle diversity and sensitivity to the particles' initial value. In order to solve these problems of the CRPF, this paper proposed an intelligent cost-reference particle filter algorithm based on multi-population cooperation. A multi-population cooperative resampling strategy based on ring structure was designed. The particles were divided into multiple independent populations upon initialization, and each population generated particles with a different initial distribution. The particles in each population were divided into three different particle sets with high, medium and low weights by the golden section ratio according to the weight. The particle sets with high and medium weights were retained. Then, a cooperative strategy based on Gaussian mutation was designed to resample the low-weight particle set of each population. The high-weight particles of the previous population in the ring structure were randomly selected for Gaussian mutation to replace the low-weight particles in the current population. The low-weight particles of all populations were resampled in turn. The simulation results show that the intelligent CRPF based on multi-population cooperation proposed in this paper can reduce the sensitivity of the CRPF to the particles' initial value and improve the particle diversity in resampling. Compared with the general CRPF and intelligent CRPF with adaptive MH resampling (MH-CRPF), the RMSE and MAE of the proposed method are lower.
Evolution of Walsh Transforms with Genetic Programming

The design of Boolean functions which exhibit high-quality cryptography properties is a crucial aspect when implementing secure stream ciphers. To this end, several methods have been proposed to search for secure Boolean functions, and, among those, evolutionary algorithms play a prominent role. In this paper, Genetic Programming (GP) is applied for the evolution of Boolean functions in order to maximize one essential property for strong cryptography functions, namely non-linearity. Differently from other approaches, the evolution happens in the space of Walsh Transforms, instead of using a direct representation of the Boolean functions. Specifically, we evolve coefficients of the Walsh Transform to obtain a generic Walsh spectrum, from which it is possible, through spectral inversion, to obtain a pseudo-Boolean function that, consequently, can be mapped to (one of) the nearest Boolean one. Since that function might not be unique, we propose a strategy in which balancedness, another important cryptography property, is preserved as much as possible. To show that the evolutionary search is actually effective in this task, we evolved Boolean functions from 6 to 16 variables. The results show that not only GP is effective in evolving Boolean functions with high non-linearity, but also that balanced functions are discovered.
2D-3D Facial Image Analysis for Identification of Facial Features Using Machine Learning Algorithms With Hyper-Parameter Optimization for Forensics Applications

Recognizing a face is a remarkable process that humans naturally use. Computer vision has tried to resemble this ability of human vision as a biometric tool to identify humans. Commercial and law enforcement applications are increasingly using face recognition technology to identify people. Currently, it is one of the most sought-after detection methods used in forensics for criminal identification purposes. Owing to similarities in the appearance of certain faces, especially in criminal cases, this problem poses a great challenge in forensic investigation and detection. The novelty of this work lies in the development of a framework for face recognition using 2D facial images gathered from various sources to generate a 3D face mesh using 468 MediaPipe landmarks which detects multiple faces in real-time. This leads to the generation of input feature vectors being formulated utilizing Euclidean/Geodesic distances and their ratios between the landmarks. These feature vectors are then trained into various classifiers that can provide the correct matching decision in an unrestricted environment such as large pose, expression, and occlusion variations. These quantitative similarity measures can then be presented as statistical evidence to identify criminals in forensic investigations. This two-dimensional to three-dimensional annotation provides a higher quality of three-dimensional reconstructed face models without the need for any additional three-dimensional morphable models. The proposed methods were validated and tested to achieve comparable recognition performance using hyperparameter optimization. Regarding accuracy, the statistical results show that Extreme gradient boosting is the best classification model that provides the best accuracy (78%) for predicting facial images compared with Adaptive Boosting (77%), Random Forest (75%), Bernoulli Naive Bayes (68%), Decision Tree (57%), Logistic Regression (71%), Light Gradient Boosting Model (58%), Extra Tree Classifier (57%), Support Vector Machine (58%), and Nearest Centroid (62%) classifiers which can be further increased by considering a greater number of images in the dataset implying at the potential of further research for scale-up implementation.
Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research

Abstract Recurrent neural networks (RNNs) are neural network architectures with hidden state and which use feedback loops to process a sequence of data that ultimately informs the final output. Therefore, RNN models can recognize sequential characteristics in the data and help to predict the next likely data point in the data sequence. Leveraging the power of sequential data processing, RNN use cases tend to be connected to either language models or time-series data analysis. However, multiple popular RNN architectures have been introduced in the field, starting from SimpleRNN and LSTM to deep RNN, and applied in different experimental settings. In this chapter, we will present six distinct RNN architectures and will highlight the pros and cons of each model. Afterward, we will discuss real-life tips and tricks for training the RNN models. Finally, we will present four popular language modeling applications of the RNN models –text classification, summarization, machine translation, and image-to-text translation– thereby demonstrating influential research in the field.
Source Free Semi-Supervised Transfer Learning for Diagnosis of Mental Disorders on fMRI Scans

The high prevalence of mental disorders gradually poses a huge pressure on the public healthcare services. Deep learning-based computer-aided diagnosis (CAD) has emerged to relieve the tension in healthcare institutions by detecting abnormal neuroimaging-derived phenotypes. However, training deep learning models relies on sufficient annotated datasets, which can be costly and laborious. Semi-supervised learning (SSL) and transfer learning (TL) can mitigate this challenge by leveraging unlabeled data within the same institution and advantageous information from source domain, respectively. This work is the first attempt to propose an effective semi-supervised transfer learning (SSTL) framework dubbed S3TL for CAD of mental disorders on fMRI data. Within S3TL, a secure cross-domain feature alignment method is developed to generate target-related source model in SSL. Subsequently, we propose an enhanced dual-stage pseudo-labeling approach to assign pseudo-labels for unlabeled samples in target domain. Finally, an advantageous knowledge transfer method is conducted to improve the generalization capability of the target model. Comprehensive experimental results demonstrate that S3TL achieves competitive accuracies of 69.14%, 69.65%, and 72.62% on ABIDE-I, ABIDE-II, and ADHD-200 datasets, respectively. Furthermore, the simulation experiments also demonstrate the application potential of S3TL through model interpretation analysis and federated learning extension.
Ethical Challenges in the Development of Virtual Assistants Powered by Large Language Models

Virtual assistants (VAs) have gained widespread popularity across a wide range of applications, and the integration of Large Language Models (LLMs), such as ChatGPT, has opened up new possibilities for developing even more sophisticated VAs. However, this integration poses new ethical issues and challenges that must be carefully considered, particularly as these systems are increasingly used in public services: transfer of personal data, decision-making transparency, potential biases, and privacy risks. This paper, an extension of the work presented at IberSPEECH 2022, analyzes the current regulatory framework for AI-based VAs in Europe and delves into ethical issues in depth, examining potential benefits and drawbacks of integrating LLMs with VAs. Based on the analysis, this paper argues that the development and use of VAs powered by LLMs should be guided by a set of ethical principles that prioritize transparency, fairness, and harm prevention. The paper presents specific guidelines for the ethical use and development of this technology, including recommendations for data privacy, bias mitigation, and user control. By implementing these guidelines, the potential benefits of VAs powered by LLMs can be fully realized while minimizing the risks of harm and ensuring that ethical considerations are at the forefront of the development process.
Two-Round Multi-Signatures from Okamoto Signatures

Multi-signatures (MS) are a special type of public-key signature (PKS) in which multiple signers participate cooperatively to generate a signature for a single message. Recently, applications that use an MS scheme to strengthen the security of blockchain wallets or to strengthen the security of blockchain consensus protocols are attracting a lot of attention. In this paper, we propose an efficient two-round MS scheme based on Okamoto signatures rather than Schnorr signatures. To this end, we first propose a new PKS scheme by modifying the Okamoto signature scheme and prove the unforgeability of our PKS scheme under the discrete logarithm assumption in the algebraic group model (AGM) and the non-programmable random oracle model (ROM). Next, we propose a two-round MS scheme based on the new PKS scheme and prove the unforgeability of our MS scheme under the discrete logarithm assumption in the AGM and the non-programmable ROM. Our MS scheme is the first one to prove security among two-round MS based on Okamoto signatures.
BTSD: A curated transformation of sentence dataset for text classification in Bangla language

The Bangla Transformation of Sentence Classification dataset addresses the resource gap in natural language processing (NLP) for the Bangla language by providing a curated resource for Bangla sentence classification. With 3,793 annotated sentences, the dataset focuses on categorizing Bangla sentences into Simple, Complex, and Compound classes. It serves as a benchmark for evaluating NLP models on Bangla sentence classification, promoting linguistic diversity and inclusive language models. Collected from publicly accessible Facebook pages, the dataset ensures balanced representation across the categories. Preprocessing steps, including anonymization and duplicate removal, were applied. Three native Bangla speakers independently assessed the Transformation of Sentence labels, enhancing the dataset's reliability. The dataset empowers researchers, practitioners, and developers to build accurate and robust NLP models tailored to the Bangla language. It offers insights into Bangla syntax and structure, benefiting linguistic research. The dataset can be used to train models, uncover patterns in Bangla language usage, and develop effective NLP applications across domains.
Impact of Boundary Control Methods on Bound-constrained Optimization Benchmarking

Despite initial indifference towards boundary control methods (BCM) in the context of metaheuristic algorithm design, benchmarking, and execution, our research demonstrates their critical importance. This study investigates how the choice of a particular BCM can profoundly influence the performance of competitive algorithms. We analyzed the top three algorithms from the 2017 and 2020 IEEE CEC competitions, posing the following question: Could a change in BCM usage alter an algorithm's overall performance and, consequently, its ranking among competitors? Our findings reveal that paying attention to BCMs can lead to significant improvements.
Deep Learning: Basics and Convolutional Neural Networks (CNNs)

Abstract Deep learning belongs to the broader family of machine learning methods and currently provides state-of-the-art performance in a variety of fields, including medical applications. Deep learning architectures can be categorized into different groups depending on their components. However, most of them share similar modules and mathematical formulations. In this chapter, the basic concepts of deep learning will be presented to provide a better understanding of these powerful and broadly used algorithms. The analysis is structured around the main components of deep learning architectures, focusing on convolutional neural networks and autoencoders.
Empowerment contributes to exploration behaviour in a creative video game

Studies of human exploration frequently cast people as serendipitously stumbling upon good options. Yet these studies may not capture the richness of exploration strategies that people exhibit in more complex environments. Here we study behaviour in a large dataset of 29,493 players of the richly structured online game 'Little Alchemy 2'. In this game, players start with four elements, which they can combine to create up to 720 complex objects. We find that players are driven not only by external reward signals, such as an attempt to produce successful outcomes, but also by an intrinsic motivation to create objects that empower them to create even more objects. We find that this drive for empowerment is eliminated when playing a game variant that lacks recognizable semantics, indicating that people use their knowledge about the world and its possibilities to guide their exploration. Our results suggest that the drive for empowerment may be a potent source of intrinsic motivation in richly structured domains, particularly those that lack explicit reward signals.
The Forecasting About Bitcoin and Other Digital Currency Markets: The Effects of Data Mining and Other Emerging Technologies

We examine the bitcoin currency's predictability over prediction timeframes of 1 to 15 minutes. As a result, we evaluate a variety of machine learning methods and discover that, although all models perform better than a random predictor, Bidirectional recurrent neural networks and extreme Gradient Boosting Classifier (XGB Classifier) are particularly effective for the investigated prediction tasks. We employ a wide range of features, including technological, asset-based, interest-based, and block chain technology characteristics. Our findings indicate that technical characteristics continue to be the most important for the majority of techniques, followed by a few blockchain- and interest-based features. Furthermore, we see that reliability rises with increasing prediction horizons. Although a quantile-based long-short trading strategy may provide market return of up to 34% before transaction costs, the very brief holding durations result in negative yields after transaction expenses are incurred.
Voice in the Machine: Ethical Considerations for Language-Capable Robots

Parsing the promise of language-capable robots.
Semantic segmentation using Firefly Algorithm-based evolving ensemble deep neural networks

Automatic segmentation of salient objects in real-world images has gained increasing interests owing to its popularity in diverse real-world applications, such as autonomous driving, medical diagnosis, aviation security, and underwater surveillance. In this research, we propose Firefly Algorithm (FA)-enhanced evolving ensemble deep networks for semantic segmentation and visual saliency prediction. An improved FA model is proposed to optimize network hyper-parameters. Specifically, it employs mutation operators and a neighbouring search strategy with granular search steps to establish search intensification. It also emphasizes search diversification by adopting multiple dynamic hybrid leaders and diverse adaptive sine and cosine search trajectories in full and randomly selected sub-dimensions to overcome stagnation. Because of its competent segmentation performance, DeepLabV3+ is fine-tuned using transfer learning with FA-based hyper-parameter identification. We optimize the learning rate, momentum and weight decay of the transfer learning network. A number of optimized DeepLabV3+ networks with distinguishing learning configurations are yielded. An ensemble model is subsequently constructed by incorporating three optimized base networks to further strengthen segmentation performance. Evaluated using diverse challenging semantic segmentation and saliency prediction tasks using underwater and medical image data sets, our evolving ensemble deep network illustrates significant superiority over other state-of-the-art deep networks and existing studies. The proposed FA model also outperforms other search methods in solving diverse mathematical landscapes with statistical significance.
Federated Learning for IoT Intrusion Detection

The number of Internet of Things (IoT) devices has increased considerably in the past few years, resulting in a large growth of cyber attacks on IoT infrastructure. As part of a defense in depth approach to cybersecurity, intrusion detection systems (IDSs) have acquired a key role in attempting to detect malicious activities efficiently. Most modern approaches to IDS in IoT are based on machine learning (ML) techniques. The majority of these are centralized, which implies the sharing of data from source devices to a central server for classification. This presents potentially crucial issues related to privacy of user data as well as challenges in data transfers due to their volumes. In this article, we evaluate the use of federated learning (FL) as a method to implement intrusion detection in IoT environments. FL is an alternative, distributed method to centralized ML models, which has seen a surge of interest in IoT intrusion detection recently. In our implementation, we evaluate FL using a shallow artificial neural network (ANN) as the shared model and federated averaging (FedAvg) as the aggregation algorithm. The experiments are completed on the ToN_IoT and CICIDS2017 datasets in binary and multiclass classification. Classification is performed by the distributed devices using their own data. No sharing of data occurs among participants, maintaining data privacy. When compared against a centralized approach, results have shown that a collaborative FL IDS can be an efficient alternative, in terms of accuracy, precision, recall and F1-score, making it a viable option as an IoT IDS. Additionally, with these results as baseline, we have evaluated alternative aggregation algorithms, namely FedAvgM, FedAdam and FedAdagrad, in the same setting by using the Flower FL framework. The results from the evaluation show that, in our scenario, FedAvg and FedAvgM tend to perform better compared to the two adaptive algorithms, FedAdam and FedAdagrad.
Improving Mortality Risk Prediction with Routine Clinical Data: A Practical Machine Learning Model Based on eICU Patients

Mortality risk prediction helps clinicians make better decisions in patient healthcare. However, existing severity scoring systems or algorithms used in intensive care units (ICUs) often rely on laborious manual collection of complex variables and lack sufficient validation in diverse clinical environments, thus limiting their practical applicability. This study aims to evaluate the performance of machine learning models that utilize routinely collected clinical data for short-term mortality risk prediction.Using the eICU Collaborative Research Database, we identified a cohort of 12,393 ICU patients, who were randomly divided into a training group and a validation group at a ratio of 9:1. The models utilized routine variables obtained from regular medical workflows, including age, gender, physiological measurements, and usage of vasoactive medications within a 24-hour period prior to patient discharge. Four different machine learning algorithms, namely logistic regression, random forest, extreme gradient boosting (XGboost), and artificial neural network were employed to develop the mortality risk prediction model. We compared the discrimination and calibration performance of these models in assessing mortality risk within 1-week time window.Among the tested models, the XGBoost algorithm demonstrated the highest performance, with an area under the receiver operating characteristic curve (AUROC) of 0.9702, an area under precision and recall curves (AUPRC) of 0.8517, and a favorable Brier score of 0.0259 for 24-hour mortality risk prediction. Although the model's performance decreased when considering larger time windows, it still achieved a comparable AUROC of 0.9184 and AUPRC of 0.5519 for 3-day mortality risk prediction.The findings demonstrate the feasibility of developing a highly accurate and well-calibrated model based on the XGBoost algorithm for short-term mortality risk prediction with easily accessible and interpretative data. These results enhance confidence in the application of the machine learning model to clinical practice.
An Entity Event Knowledge Graph for Human Resources Management in Public Administration: the case of education personnel

Public administrations (PA) around the globe produce and handle vast amounts of data that are mainly the outcome of interactions of limited types of core-entities (e.g., citizens, businesses). Usually, the information involving the core entities are scattered in numerous siloed databases developed by different departments and divisions, thus hindering PAs from providing a comprehensive view of their core entities and their interactions. One type of core entity with a prominent role within PA involved in many temporal interactions (e.g., appointments, assignments) is the “public servant”. A special characteristic of public servants, compared to other types of entities, is that PAs need to systematically track them from the human resources point of view, recording for example information about their employment status throughout time. Recently, knowledge graphs have been proposed for structuring large collections of data in a meaningful way. Furthermore, in order to focus on the dynamics and changes over time of the data, a new approach of entity-event knowledge graphs has been introduced that captures the dynamics of knowledge considering events as first-class entities for knowledge representation. The aim of this paper is: 1) to propose an entity-event knowledge graph for the holistic governance of human resources management data in public administration and 2) apply it to the case of education personnel management.