Reviewerly/exHarmony huggingface dataset:
Machine learning model optimization for removal of steroid hormones from wastewater

In the past few decades, there has been a significant focus on detecting steroid hormones in aquatic environments due to their influence on the endocrine system. Most compounds of these pollutants are the natural steroidal estrogens, i.e., estrone (E1), 17β-Estradiol (E2), and the synthetic estrogen 17α-Ethinylestradiol (EE2). The Moving-Bed Biofilm Reactor (MBBR) technique is appropriate for eliminating steroid hormones. This study centers on creating a model to estimate the effectiveness of the MBBR system regarding its ability to eliminate E1, E2, and EE2. The results were modeled with artificial neural networks (ANNs). The Particle Warm Optimization (PSO) and Levenberg Marquardt (LM) algorithms were selected for network training. The models incorporated five input parameters, encompassing the COD loading rate, initial levels of E1, E2, and EE2 steroid hormones, and Hydraulic Retention Time (HRT). The optimum removal conditions (three steroid hormones and COD) were determined using the optimized ANN based on both PSO and LM algorithms. The optimal transfer functions for the hidden and output layers were identified as tan-sigmoid and linear, respectively. The best ANN structures (Neurons in input, hidden, and output layers) and correlation coefficients (R) were 5:9:4, with R = 0.9978, and 5:10:4, with R = 0.9982 for the trained networks with LM and PSO algorithms, respectively. Eventually, the input parameters' importance was ranked using sensitivity analysis (SA) through Pearson correlation and developed ANNs.
Narratives of Change to Platform Governance on DTube, an Emerging Blockchain-Based Video-Sharing Platform

Several blockchain-based social media (BSM) platforms have emerged as alternatives to mainstream social media (MsSM) platforms in the recent past. Using blockchain architecture as a mechanism for governance and economics, these second-generation alternative social media (ASM) platforms aim to create social media platforms without advertising revenue or algorithmic governance. BSM platforms are relatively new and less popular than other platforms. They are, therefore, understudied in the disciplines of media studies, communication studies, platform studies, or cultural studies. This article traces changes to platform governance on DTube, a blockchain-based video-sharing platform. Using platform biography as a systematic framework and methodological approach, this article aims to understand the ever (re)configuring assemblage of technology, people, media, and economics on DTube, between 2019 and 2022. Platform governance on DTube changed from being manually driven by community members to a semi-automated process using bots. Although characteristics of blockchain technology influenced platform governance, the changes over time reiterated that user practices (not the technology itself) determined the platform’s functions. In the reconfiguration of platform governance, DTube articulated minor structures of feeling that aimed to produce an inclusive space over rapid scaling. Furthermore, it hints toward “agonistic pluralism” to govern a (transactional) community with differential ties.
Generating Sparse Counterfactual Explanations for Multivariate Time Series

Since neural networks play an increasingly important role in critical sectors, explaining network predictions has become a key research topic. Counterfactual explanations can help to understand why classifier models decide for particular class assignments and, moreover, how the respective input samples would have to be modified such that the class prediction changes. Previous approaches mainly focus on image and tabular data. In this work we propose SPARCE, a generative adversarial network (GAN) architecture that generates SPARse Counterfactual Explanations for multivariate time series. Our approach provides a custom sparsity layer and regularizes the counterfactual loss function in terms of similarity, sparsity, and smoothness of trajectories. We evaluate our approach on real-world human motion datasets as well as a synthetic time series interpretability benchmark. Although we make significantly sparser modifications than other approaches, we achieve comparable or better performance on all metrics. Moreover, we demonstrate that our approach predominantly modifies salient time steps and features, leaving non-salient inputs untouched.
A Comparative Analysis of Long Covid in the French Press and Twitter

This research focuses, in the media public sphere, on the modalities of construction and progressive visibility of persistent symptoms of certain forms of Covid-19, designated by the category “Long Covid”. The aim is to analyze the process according to which this particular form of the disease appeared in the media in June 2020, was the subject of debates and contradictory - even conflicting - positions of the actors, until its institutional and public recognition in 2022. The article conducts a comparative analysis on the French national and regional press and on the digital social network Twitter.
FBPFormer: Dynamic Convolutional Transformer for Global-Local-Contexual Facial Beauty Prediction

Facial Beauty Prediction (FBP) is subjective and varies from person to person, which makes it difficult to obtain a unified and objective evaluation. Previous efforts adopt conventional convolution neural networks to extract local facial features and calculate corresponding facial attractiveness scores, ignoring the global facial features. To address this issue, we propose a dynamic convolution vision transformer named FBPFormer which aims to focus on both local facial features and the global facial information of the human face. Specifically, we first build a lightweight convolution network to produce pseudo facial attribute embedding. To inject the global facial information into the transformer, the parameters of encoders are dynamically generated by the embedding of each instance. Therefore, these dynamic encoders can fuse and further fuse local facial features and global facial information while encoding query, key, and value vectors. Furthermore, we design an instance-level dynamic exponential loss to dynamically adjust the optimization objectives of the model. Extensive experiments show our method achieves competitive performance, demonstrating its effectiveness in the FBP task.
WSDTN a Novel Dataset for Arabic Word Sense Disambiguation

Word sense disambiguation (WSD) task aims to find the exact sense of an ambiguous word in a particular context. It is crucial for many applications, including machine translation, information retrieval, and semantic textual similarity. Arabic WSD faces significant challenges, primarily due to the scarcity of resources, which hinders the development of robust deep learning models. Additionally, the semantic sparsity of context further complicates the task, as Arabic words often exhibit multiple meanings. In this paper, we propose WSDTN, a manually annotated corpus, designed to fill this gap and to enable the automatic disambiguation of Arabic words. It consists of 27530 sentences collected from different resources and spanning different domains, each with a target word and its appropriate sense. We present the novel corpus itself, its creation procedure for reproducibility and a transformer based model to disambiguate new words and evaluate the performance of the corpus. The experimental results show that the baseline approach achieves an accuracy of around 90%. The corpus is publically available upon request and is open for extension.
A Transformer-Based Framework for Biomedical Information Retrieval Systems

With the increasing amount of electronic biomedical research available, it is becoming difficult for scientists and researchers to effectively access and process this information. In this study, a framework is proposed for biomedical information retrieval using state-of-the-art transformer-based natural language processing methods, focusing on discovering genetic relationships and mechanisms that contribute to diseases and other health conditions. Data processing, model evaluation and algorithm transparency through the use explainable artificial intelligence are proposed as the key components of the methodology. The framework is explained and demonstrated in the context of COVID-19 in a topical and systematic manner, and is designed so that it can be adapted for use in other biomedical settings.
ASP Loss: Adaptive Sample-Level Prioritizing Loss for Mass Segmentation on Whole Mammography Images

Alarming statistics on the mortality rate for breast cancer are a clear indicator of the significance of computer vision tasks related to cancer identification. In this study, we focus on mass segmentation, which is a crucial task for cancer identification as it preserves critical properties of the mass, such as shape and size, vital for identification tasks. While achieving promising results, existing approaches are mostly hindered by pixel class imbalance and various mass sizes that are inherent properties of masses in mammography images. We propose to alleviate this limitation on segmentation methods via a novel modification of the common hybrid loss, which is a weighted sum of the cross entropy and dice loss. The proposed loss, termed Adaptive Sample-Level Prioritizing (ASP) loss, leverages the higher-level information presented in the segmentation mask for customizing the loss for every sample, to prioritize the contribution of each loss term accordingly. As one of the variations of U-Net, AU-Net is selected as the baseline approach for the evaluation of the proposed loss. The ASP loss could be integrated with other existing mass segmentation approaches to enhance their performance by providing them with the ability to address the problems associated with the pixel class imbalance and diverse mass sizes specific to the domain of breast mass segmentation. We tested our method on two publicly available datasets, INbreast and CBIS-DDSM. The results of our experiments show a significant boost in the performance of the baseline method while outperforming state-of-the-art mass segmentation methods.
An Improved Lightweight YOLOv5 for Remote Sensing Images

Achieving real-time accurate detection in remote sensing images, which exhibit features such as high resolution, small targets, and complex backgrounds, remains challenging due to the substantial computational demands of existing object detection models. In this paper, we propose an improved remote sensing image small object detection method based on YOLOv5. In order to preserve high-resolution features, we remove the Focus module from the YOLOv5 network structure and introduce RepGhostNet as a feature extraction network to enhance both accuracy and speed. We adopt the BiFormer prediction head for more flexible computational allocation and content perception, and employ the Normalized Wasserstein Distance (NWD) metric to alleviate IoU’s sensitivity to small objects. Experimental results show that our proposed method achieves mAP scores of 75.54% and 75.65% on the publicly available VEDAI and DIOR remote sensing image datasets, respectively, with significantly fewer parameters and FLOPs. Our approach effectively balances accuracy and speed compared to other models.
Robust dynamic control algorithm for uncertain powered wheelchairs based on sliding neural network approach

&lt;abstract&gt;&lt;p&gt;The dynamic model of mobile wheelchair technology requires developing and implementing an intelligent control system to improve protection, increasing performance efficiency, and creating precise maneuvering in indoor and outdoor spaces. This work aims to design a robust tracking control algorithm based on a reference model for operating the kinematic model of powered wheelchairs under the variation of system parameters and unknown disturbance signals. The control algorithm was implemented using the pole placement method in combination with the sliding mode control (PP-SMC) approach. The design also adopted a neural network approach to eliminate system uncertainties from perturbations. The designed method utilized the sinewave signal as an essential input signal to the reference model. The stability of a closed-loop control system was achieved by adopting the Goa reaching law. The performance of the proposed tracking control system was evaluated in three scenarios under different conditions. These included assessing the tracking under normal operation conditions, considering the tracking performance by changing the dynamic system's parameters and evaluating the control system in the presence of uncertainties and external disturbances. The findings demonstrated that the proposed control method efficiently tracked the reference signal within a small error based on mean absolute error (MAE) measurements, where the range of MAE was between 0.08 and 0.12 in the presence of uncertainties or perturbations.&lt;/p&gt;&lt;/abstract&gt;
Efficient multi-objective evolutionary neural architecture search for U-Nets with diamond atrous convolution and Transformer for medical image segmentation

Deep encoder–decoder neural networks like U-Nets have made significant contributions to the development of computer vision applications such as image segmentation. Neural architecture search (NAS) has the potential to further automatically adjust the architectures of U-Nets for various medical image segmentation tasks. Most of the NAS techniques focus on optimizing segmentation accuracies of network architectures. In real-world medical image segmentation scenarios, two main challenges are poor medical image quality and diverse deployment devices with different computing capabilities. A large architecture designed only for the high segmentation accuracy is difficult to run on various deployment devices. To address these challenges, this paper proposes a multi-objective evolutionary neural architecture search method (CTU-NAS) for U-Nets with diamond atrous convolution and Transformer for medical image segmentation. A hybrid U-Net architecture (CTU-Net) with diamond atrous convolution and Transformer modules is designed as the supernet of CTU-NAS. Then a channel search strategy based on sorting and selection is applied to speed up the search for subnets by precisely selecting and training the most important channels more times. In addition, CTU-NAS employs a dual acceleration mechanism based on weight sharing and surrogate model to lower the cost of evaluations of subnets. CTU-NAS applies a multi-objective evolutionary algorithm to balance between the segmentation accuracy and the number of parameters. Experimental results on two medical segmentation datasets show that CTU-NAS is capable of quickly generating a group of excellent network architectures with different sizes and their performances also outperform or come close to those of the manually designed networks.
Cryptanalysis Of A Type Of White-Box Implementations Of The SM4 Block Cipher

Abstract The SM4 block cipher is a Chinese national standard and an ISO international standard. Since white-box cryptography has many real-life applications nowadays, a few white-box implementations of SM4 has been proposed, among which a type of constructions is dominated, which uses a linear or affine diagonal block encoding to protect the original three 32-bit branches entering a round function and uses its inverse as the input encoding to the S-box layer. In this paper, we analyse the security of this type of constructions against Lepoint et al.’s collision-based attack method. Our experiment under a small fraction of (encodings, round key) combinations shows that the rank of the concerned linear system is much less than the number of the involved unknowns, meaning these white-box SM4 implementations should resist Lepoint et al.’s method, but we leave it as an open problem whether there are such encodings that the rank of the corresponding linear system is slightly less than the number of the involved unknowns, in which scenario Lepoint et al.’s method may be used to recover a round key for the case with linear encodings and to remove most white-box operations until mainly some Boolean masks for the case with affine encodings.
Quantum teleportation and phase quantum estimation in a two-qubit state influenced by dipole and symmetric cross interactions

Abstract In this paper, we address the problem of quantum teleportation in single and two-qubit scenarios based on the Heisenberg XYZ two-qubit chain model under the influence of atomic dipole and Kaplan-Shekhtman-Entin-Wohlman-Aharony (KSEWA) interactions. Using the concepts of fidelity threshold and average fidelity in classical and quantum, we reveal the effectiveness of the current channel to remain in the quantum limits. In addition, we investigate the quantum estimation of the encoded phase in single and two-qubit scenarios at the teleportation destination. By using different variables of the system, the average fidelity of the quantum teleportation and the quantum estimation of the encoded phase in one and two teleported qubit(s) can be improved.
Image encryption algorithm based on hyperchaotic system and dynamic DNA encoding

Abstract Since the existing DNA encryption algorithms only have fixed DNA coding and decoding rules and a single algorithm that cannot meet more complex and more secure encryption requirements, an image encryption algorithm based on hyperchaotic system and dynamic DNA coding is proposed. The algorithm proposes two new operation methods in the DNA operation rules, namely the equal-additive column transformation and the equal-subtractive column transformation. which combine the SHA-256 function and the external key to generate the initial value of the chaotic system to ensure that key and plaintext of algorithm are correlated. In the encryption process, the plaintext image is firstly converted into a two-dimensional matrix for rotation and scrambling. Then the chaotic sequence generated by the hyperchaotic Chen system is used to perform DNA dynamic encoding, decoding and operation on the scrambled matrix and block matrix generated by logistic chaotic sequence. Finally, the encrypted matrix is scrambled in one dimension. Simulation results show that the improved encryption algorithm has a larger key space, breaks the strong correlation between image pixel layers, and can effectively resist multiple attacks.
The V-Lab VR Educational Application Framework

This paper presents the V-Lab, a VR application development framework for educational scenarios mainly involving scientific processes executed in laboratory environments such as chemistry and biology laboratories. This work is an extension of the Onlabs simulator which has been developed by the Hellenic Open University as a distance teaching enabler for similar subjects, helping to alleviate the need for access to the physical laboratory infrastructure; thus, shortening training periods of students in the laboratory and making their training during the periods of physical presence more productive and secure. The extensions of the Onlabs to deliver an enhanced and modular framework that can be extended to multiple educational scenarios is the work performed within the context of the European project XR2Learn (Leveraging the European XR industry technologies to empower immersive learning and training).
Comparison of deep learning classification models for facial image age estimation in digital forensic investigations

There has been a significant rise in digital forensic investigations containing Indecent Images of Children (IIoC), and one of the major challenges faced by investigators is the time-consuming task of manually investigating images for illicit content. In the UK, law enforcement maintains and uses a standard national repository of IIoC, known as CAID (Child Abuse Image Database), to identify known illegal images by matching their image hashes and metadata. The CAID plays a significant role in making IIoC investigations faster and more effective. However, all images that are not matched through using CAID require manual analysis. Every image has to be viewed and verified as IIoC by investigators. The victim age estimation in the images (i.e., determining whether they are juvenile or adult as this would change the course of the investigation) is a crucial part of this verification process and takes time due to a large number of images to inspect, therefore impacting the speed of the investigation, and consequently victims. This is a time-consuming and challenging task for human investigators. Previous work has demonstrated that deep learning has the capability to estimate age with high accuracy in images. This reduces the number of images that will need to be manually processed, thereby finishing the investigation faster. However, in terms of practical implementation in IIoC investigations, there is an absence of a comparative study using the same datasets to establish the most appropriate deep learning model and classification approach to use. This is important as different models have different capabilities and previous works utilise various binary, multi-class, and regression approaches. It is not yet known which is the most accurate for use in digital forensic investigations. In this paper, we construct an extensive dataset before experimenting with four pre-trained deep learning models: VGG16, ResNet50, Xception, and InceptionV3. We have identified that binary classification works best for the identification of images as a child or adult, with the ResNet50 obtaining the best results in terms of accuracy (91.70%) on unseen images.
Feature selection using a sinusoidal sequence combined with mutual information

Data classification is the most common task in machine learning, and feature selection is the key step in the classification task. Common feature selection methods mainly analyze the maximum correlation and minimum redundancy between feature factors and tags while ignoring the impact of the number of key features, which will inevitably lead to waste in subsequent classification training. To solve this problem, a feature selection algorithm (SSMI) based on the combination of sinusoidal sequences and mutual information is proposed. First, the mutual information between each feature and tag is calculated, and the interference information in high-dimensional data is removed according to the mutual information value. Second, a sine function is constructed, and sine ordering is carried out according to the mutual information value and feature mean value between different categories of the same feature. By adjusting the period and phase value of the sequence, the feature set with the largest difference is found, and the subset of key features is obtained. Finally, three machine learning classifiers (KNN, RF, SVM) are used to classify key feature subsets, and several feature selection algorithms (JMI, mRMR, CMIM, SFS, etc.) are compared to verify the advantages and disadvantages of different algorithms. Compared with other feature selection methods, the SSMI algorithm obtains the least number of key features, with an average reduction of 15 features. The average classification accuracy has been improved by 3% on the KNN classifier. On the HBV and SDHR datasets, the SSMI algorithm achieved classification accuracy of 81.26% and 83.12%, with sensitivity and specificity results of 76.28%, 87.39% and 68.14%, 86.11%, respectively. This shows that the SSMI algorithm can achieve higher classification accuracy with a smaller feature subset.