Reviewerly/exHarmony huggingface dataset:
Contemporary Research Trends in Mobile Learning

This study attempts to conduct a bibliometric analysis of the structure and development of mobile learning research. For this, 7829 publications included in the Elsevier SCOPUS database between 1984 and 2021 were examined using bibliometric analysis by identifying key research areas, most influential authors, co-authorship status of countries, and organizations. As a result of this study, most topics related to mobile learning were Computer Science. “Mobile Learning” was the most used keyword followed by “e-learning” and “higher education”. Top performing organizations were in Taiwan. Taiwan was the major contributor in m-learning publications’ co-citation with other co-authorship countries.
Low-Resourced Machine Translation for Senegalese Wolof Language

Natural language processing (NLP) research has made great advancements in recent years with major breakthroughs that have established new benchmarks. However, these advances have mainly benefited a certain group of languages commonly referred to as resource-rich such as English and French. Majority of other languages with weaker resources are then left behind which is the case for most African languages including Wolof. In this work, we present a parallel Wolof/French corpus of 100,000 sentences on which we conducted experiments on machine translation models based on recurrent neural networks (RNNs) in different data configurations. We noted performance gains with the models trained on subworded data as well as those trained on the French-English language pair compared to those trained on the French-Wolof pair under the same experimental conditions.
Information Security Policy Compliance: Usefulness and Ease of Use

Various information security (IS) standards and frameworks have been proposed with the objective of safeguarding an organization’s information assets. The Information Security Policy (ISP) is one of the tools for managing the IS. ISP compliance continues to be a topic of interest in the IS area. This paper presents a quantitative, descriptive and cross-sectional research of the critical factors in ISP compliance. Data were collected from 103 security professionals. The relationships between the factors perceived ease of use (PEU), perceived usefulness (PU), security knowledge (SK) and security culture (SC) and ISP Compliance Intention (INT) were analysed. The results show that PU and PEU factors are strongly related to INT, while the SK and SC factors are significant related to INT.
Classification Performance in the Bio-inspired Asymmetric and Symmetric Networks

Recent developments of deep learning, machine learning, and artificial intelligence have a great influence on the wide areas of technologies. Classification is a core technology in their processing. This paper aims to make clear the classification performance for the bio-inspired asymmetric and symmetric networks. First, the bio-inspired asymmetric network is shown to have superior performance for tracing features compared to the symmetric one. Second, the classification characteristics of the asymmetric and symmetric networks are derived based on the independence of their outputs. Further, it is shown that generation of extended bases in the bio-inspired layered networks improves classification performance. Finally, the higher-dimensional mapping code generated as the extended bases are applied to the modified XOR problem.
An accelerated sine mapping whale optimizer for feature selection

An improved whale optimization algorithm (SWEWOA) is presented for global optimization issues. Firstly, the sine mapping initialization strategy (SS) is used to generate the population. Secondly, the escape energy (EE) is introduced to balance the exploration and exploitation of WOA. Finally, the wormhole search (WS) strengthens the capacity for exploitation. The hybrid design effectively reinforces the optimization capability of SWEWOA. To prove the effectiveness of the design, SWEWOA is performed in two test sets, CEC 2017 and 2022, respectively. The advantage of SWEWOA is demonstrated in 26 superior comparison algorithms. Then a new feature selection method called BSWEWOA-KELM is developed based on the binary SWEWOA and kernel extreme learning machine (KELM). To verify its performance, 8 high-performance algorithms are selected and experimentally studied in 16 public datasets of different difficulty. The test results demonstrate that SWEWOA performs excellently in selecting the most valuable features for classification problems.
Convolutional neural network and 2D logistic-adjusted-Chebyshev-based zero-watermarking of color images

Abstract Robust zero-watermarking is a protection of copyright approach that is both effective and distortion-free, and it has grown into a core of research on the subject of digital watermarking. This paper proposes a revolutionary zero-watermarking approach for color images using convolutional neural networks (CNN) and a 2D logistic-adjusted Chebyshev map (2D-LACM). In this algorithm, we first extracted deep feature maps from an original color image using the pre-trained VGG19. These feature maps were then fused into a featured image, and the owner's watermark sequence was incorporated using an XOR operation. Finally, 2D-LACM encrypts the copyright watermark and scrambles the binary feature matrix to ensure security. The experimental results show that the proposed algorithm performs well in terms of imperceptibility and robustness. The BER values of the extracted watermarks were below 0.0044 and the NCC values were above 0.9929, while the average PSNR values of the attacked images were 33.1537 dB. Also, it is superior to other algorithms in terms of robustness to conventional image processing and geometric attacks.
A lightweight object detection framework for underwater imagery with joint image restoration and color transformation

Traditional underwater detectors struggle to match the demands of visual observation and icon detection because of the underwater optical pictures' color distortion, uneven illumination, and excessive noise levels. This research proposes a lightweight deepwater target detection network with three essential parts. Firstly, a lightweight attention underwater image repair and enhancement network is proposed. In this paper, a lightweight deep-water object detection network is proposed, which consists of three key components, namely image restoration, color transformation, and object detection. First, we propose a lightweight attention module for the problems of color distortion and uneven illumination in underwater images. This module pays more attention to the areas with a greater influence of underwater color when dealing with different types of poor-quality images. Second, to reduce the computational complexity of object detection, we propose a color conversion module based on the Laplacian pyramid decomposition. This module incorporates multi-scale features to effectively address the loss of details in the brightest or darkest regions. Finally, we propose a multi-scale dense residual object detection network. The network applies a multi-scale kernel to an improved residual module to realize the extraction and fusion of feature information of multiple receptive fields. Furthermore, to deal with the problem of underwater small-sample object detection, we propose a mutually exclusive loss function. The mutual exclusion loss promotes the update of network parameters so that the classes with different characteristics of samples generated by the network are mutually exclusive. We ran tests on the Pycharm platform to show that the suggested lightweight joint underwater object identification algorithm outperformed the most sophisticated ones. The proposed system was put to the test in an intricate underwater setting, achieving 92.56 percent accuracy and 19.2 frames per second.
A performance analysis of transformer-based deep learning models for Arabic image captioning

Image captioning has become a fundamental operation that allows the automatic generation of text descriptions of images. However, most existing work focused on performing the image captioning task in English, and only a few proposals exist that address the image captioning task in Arabic. This paper focuses on understanding the factors that affect the performance of machine learning models performing Arabic image captioning (AIC). In particular, we focus on transformer-based models for AIC and study the impact of various text-preprocessing methods: CAMeL Tools, ArabertPreprocessor, and Stanza. Our study shows that using CAMeL Tools to preprocess text labels improves the AIC performance by up to 34-92% in the BLEU-4 score. In addition, we study the impact of image recognition models. Our results show that ResNet152 is better than EfficientNet-B0 and can improve BLEU scores performance by 9-11%. Furthermore, we investigate the impact of different datasets on the overall AIC performance and build an extended version of the Arabic Flickr8k dataset. Using the extended version improves the BLEU-4 score of the AIC model by up to 148%. Finally, utilizing our results, we build a model that significantly outperforms the state-of-the-art proposals in AIC by up to 196-379% in the BLUE-4 score.
The Logic of Separation Logic: Models and Proofs

Abstract The standard semantics of separation logic is restricted to finite heaps. This restriction already gives rise to a logic which does not satisfy compactness, hence it does not allow for an effective, sound and complete axiomatization. In this paper we therefore study both the general model theory and proof theory of the separation logic of finite and infinite heaps over arbitrary (first-order) models. We show that we can express in the resulting logic finiteness of the models and the existence of both countably infinite and uncountable models. We further show that a sound and complete sequent calculus still can be obtained by restricting the second-order quantification over heaps to first-order definable heaps.
A new computationally efficient method to tune BERT networks – transfer learning

Abstract BERT is a pre-trained language model that achieves state-of-the-art performance on natural language processing (NLP) tasks. Once it was published, it quickly became one of the most popular models in the NLP field. The official recommended method for applying BERT to downstream tasks is fine-tuning. However, we argue that transfer learning is also a very practical approach to applying BERT, and this approach has its own advantages compared to fine-tuning. In this paper, we explore the advantages of the transfer learning approach through the method that uses transfer learning and fine-tuning approaches separately to apply BERT to the same eight GLUE benchmark tasks and compare the training time spent and model performance scores, such as accuracy or F1 score, of the two approaches. Finally, this paper finds that among all eight GLUE tasks and on small training sets, transfer learning saves 30% to 50% of the time compared to a fine-tuning approach to train the models on the data of downstream tasks and can achieve very similar performance. Besides, for the same amount of time, transfer learning can obtain higher performance scores than fine-tuning on larger training sets. In conclusion, on small training sets, transfer learning has a huge advantage in time consumption under the premise of approximate performance, and it also performs better on large training sets under the same length of time, compared to fine-tuning.
Self-supervised feature matched virtual try-on

Abstract Virtual try-on is a technology that enables users to preview the effect of wearing a target garment without wearing the actual garment. However, existing image-based virtual try-on methods often require additional human parsing or segmentation operations to generate intermediate representations required for garment deformation and texture fusion. These operations not only increase the computational complexity and memory consumption, but also limit the real-time and portability of virtual try-on. Additionally, inaccurate parsing results can lead to misleading final generated images. To overcome these challenges, we propose a self-supervised feature matched virtual try-on network, which can directly generate high-quality try-on results from human body images and target clothing images without any additional input. Specifically, we design an optical flow warp module, which focuses on the optical flow changes between the person image and the clothing image to achieve accurate clothing alignment and deformation. Furthermore, a feature refine warp module is designed to enhance the features of the extracted optical flow information and the original character segmentation and analysis operations, reducing the influence of background clutter features on the content, and ensuring that the wrinkles and deformation of the replacement clothes are close to the original clothes. The feature match module is developed to calculate the feature matching loss of the converted clothing and the generated results of the teacher network and the student network, and the corresponding knowledge is distilled and passed to the student network to assist in self-supervised training. We conduct experiments on the VITON dataset and show that our model can generate high quality and high resolution, and our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively.
The metasedimentary-hosted Nyangoubé gold prospect, northwest Côte d’Ivoire: Geochemical and mineralogical characterization of associated hydrothermal alteration

The Nyangoubé gold prospect, located in northwestern northwest of Côte d'Ivoire in Africa, is a feature of the Bagoé furrow of the Birimian of the West African Craton. This study is aimed at characterizing the geochemical and mineralogical signatures of hydrothermal alterations associated with the gold mineralization of Nyangoubé gold prospect to provide guidelines for mining exploration. Microscopic petrographic analysis and geochemical characteristics from elemental contents analyzed by X-ray fluorescence (XRF) and inductive coupled-plasma mass spectrometer (ICP-MS) were studied using alteration diagrams and by calculating mass balances and describing thin sections. The results indicate that the host rocks have been affected by silicification, carbonation, sericitization, chloritization, sulphidation and albitization. Hydrothermal alterations associated with the mineralization systems resulted in the destruction of plagioclase in the metasediments studied. The latter was replaced by sericite, chlorite, carbonates, quartz, and sulphides in varying proportions, depending on the intensity of each type of alteration linked to the formation of each mineral. The mass balance calculations show a gradual increase in the concentrations of Au, W, V, As and Pb as well as K2O, CaO, Na2O and Fe2O3 which could be vectoring parameters towards gold mineralization. The mineralogical assemblage as sericite-chlorite-pyrite, chlorite-pyrite±sericite, carbonate-sericite and chlorite-carbonate revealed by hydrothermal alteration trends in the host rocks could also help identify potential gold corridors in the area of study and its peripheries.
Incorrect Application of Yilmaz–Poli (2022) Initialisation Method in dePater–Mitici 2023 paper entitled “A mathematical framework for improved weight initialization of neural networks using Lagrange multipliers”

In this letter to the editor we report on a methodological error made in the article entitled "A mathematical framework for improved weight initialization of neural networks using Lagrange multipliers" by dePater and Mitici recently appeared in this journal. The error relates to the incorrect application of a weight initialisation method we derived, published last year in this same journal.
Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas

Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease with five predominant histologic subtypes. Fully supervised convolutional neural networks can improve the accuracy and reduce the subjectivity of LUAD histologic subtyping using hematoxylin and eosin (H&E)-stained whole slide images (WSIs). However, developing supervised models with good prediction accuracy usually requires extensive manual data annotation, which is time-consuming and labor-intensive. This work proposes three self-supervised learning (SSL) pretext tasks to reduce labeling effort. These tasks not only leverage the multi-resolution nature of the H&E WSIs but also explicitly consider the relevance to the downstream task of classifying the LUAD histologic subtypes. Two tasks involve predicting the spatial relationship between tiles cropped from lower and higher magnification WSIs. We hypothesize that these tasks induce the model to learn to distinguish different tissue structures presented in the images, thus benefiting the downstream classification. The third task involves predicting the eosin stain from the hematoxylin stain, inducing the model to learn cytoplasmic features relevant to LUAD subtypes. The effectiveness of the three proposed SSL tasks and their ensemble was demonstrated by comparison with other state-of-the-art pretraining and SSL methods using three publicly available datasets. Our work can be extended to any other cancer type where tissue architectural information is important. The model could be used to expedite and complement the process of routine pathology diagnosis tasks. The code is available at https://github.com/rina-ding/ssl_luad_classification.
Window Token Transformer: Can learnable window token help window-based transformer build better long-range interactions?

Compared with the vanilla transformer, the window-based transformer offers a better trade-off between accuracy and efficiency. Although the window-based transformer has made great progress, its long-range modeling capabilities are limited due to the size of the local window and the window connection scheme. To address this problem, we propose a novel Window Token Transformer (WTT). The core mechanism of WTT is the addition of a window token for summarizing window information in each local window. We refer to this type of token interaction as Window Token Attention. These window tokens will interact spatially with the tokens in each window to enable long-range modeling. In order to preserve the hierarchical design of the window-based transformer, we design Feature Inheritance Module (FIM) in each phase of WTT to deliver the local window information from the previous phase to the window token in the next phase. In addition, we have designed a Global–Local Feedforward Network (GLFFN) in WTT, which can enhance the local awareness of the network while preserving the global awareness. Extensive experiments have shown that our WTT achieves competitive results with low parameters in image classification and downstream tasks.
CrowdDCNN: Deep convolution neural network for real-time crowd counting on IoT edge

Ensuring the safety and security of crowded places is a major concern for both the government and the public. Accurately and quickly estimating the number of people in a crowd is crucial for public safety, urban planning, and traffic monitoring. The existing methods have a minimum mean square error of 0.89 and require high storage space, making them inappropriate for crowd counting using low-computation and small-storage devices like single-board computers. Furthermore, these methods suffer from prediction time lag and are not suitable for live streaming. To tackle these challenges, this paper proposes a Deep Convolution Neural Network-based 'CrowdDCNN' model for crowd counting. This model reduces the value of mean square error by 0.29 and the size of the model by 80.01%. Additionally, the prediction time was decreased to 700 ms. Further, the 'NoLag' algorithm introduced in this paper is efficient in live crowd counting. Its O(1) time and space complexity make it appropriate for all devices, including single-board computers, laptops, and GPUs. The reported 0-p value during statistical analysis using Wilcoxon test, Friedman rank test, and paired t-test validates the superiority of the proposed model.
Polarization-based Camouflaged Object Detection

Camouflaged Object Detection (COD) is a challenging task aiming to locate the “indistinguishable” object with high similarity to the background. Existing COD methods are highly dependent on the differences in extrinsic appearances while neglecting inherent physical characteristics. In this letter, we introduce polarization information to enlarge the discrepancies between objects and their surroundings for the COD task. We construct a polarization-based dataset PCOD that comprises 639 challenging real-world scenes, along with a CNN-based network PolarNet tailored to process polarization images. Our method achieves state-of-the-art performance on PCOD and other COD datasets, and shows enhanced qualitative and quantitative effectiveness compared with existing COD methods.
Fault diagnosis strategy of industrial process based on multi-source heterogeneous information and deep learning

With the progress of technology, modern industrial monitoring data not only includes traditional process data but also includes video data. In order to make full use of these multi-source heterogeneous data to achieve better accuracy of fault classification, feature extraction and feature fusion of different modalities is vital important. For feature extraction, because the contribution rate of process data at different times is different to fault classification, in order to obtain the temporal features that have a great impact on fault classification, the Batch Normalization Long Short-Term Memory based on Self-Attention mechanism (BN-LSTM-SA) is used for feature extraction of process data. Since temporal and spatial features are important indicators of video classification, the Two Stream Shifted Windows 3D Convolution Transformer (TSSCT) model is used to extract temporal and spatial features. Cross Multi-head Self-Attention(CMSA) model is designed for feature fusion, which fully utilize the correlation and complementarity of multi-source heterogeneous information and can reasonably allocate the weight of different modalities. Experiments show that our proposed method is superior to the unimodal model or the existing multimodal fusion model and prove the effectiveness of the proposed method.
Families of bosonic suppression laws beyond the permutation symmetry principle

Abstract Exact cancellation of quantum amplitudes in multiphoton interferences with Fock states at the input, the so-called suppression or zero transmission laws generalizing the Hong–Ou–Mandel dip, are useful tools in quantum information and computation. It was recently suggested that all bosonic suppression laws follow from a common permutation symmetry both in the input quantum state and in the unitary matrix of the interferometer. By using the recurrence relations for the interference of Fock states, we find a wealth of suppression laws on the beamsplitter and tritter interferometers that do not follow from the permutation symmetry principle. In general, our results point on the existence of whole families of suppression laws with only a fraction of them being accounted for by the permutation symmetry principle, suggested as the general principle behind all the suppression laws.