Reviewerly/exHarmony huggingface dataset:
Digital literacy skills among African library and information science professionals – an exploratory study

Purpose This study aims to assess the digital literacy of African library and information science (LIS) professionals. Digital literacy skills are required in the digital library environment. Design/methodology/approach Relevant literature was reviewed on this topic; previous study questionnaires, various university LIS curriculum, content analysis and job advertising were used for preparing the survey questionnaire. This study used an ex post facto research design. Purposive sampling was adopted in this study, and data were collected from Google form by sharing the research questionnaire by hyperlink which was undertaken from March 15, 2022 to May 24, 2022. The data were collected by a structured questionnaire. A four-point Likert scale was used to measure how library professionals acquired digital literacy skills. Content analysis of more than 500 job vacancies over the past three years (2018–2022) of library job advertisements was taken randomly. Findings This study’s findings show that the majority of the good at basic level of digital literacy skills at the same time have less in advanced digital literacy skills. The finding of this study shows the majority of them have to upgrade and learn about advanced digital literacy skills. Practical implications This study recommends adopting a new syllabus and updating a LIS curriculum based on the library’s technological development. Furthermore, this study’s potential result suggests more practical classes instead of theoretical study and it is useful to the library schools, associations, MOOC providers and lifelong learners. Originality/value This study is conducted with 102 African library professionals from 13 countries. This study discusses futuristic digital literacy skills and basic literacy skills. This study is beneficial to those who are preparing a new curriculum in LIS fields.
Turbid Underwater Image Enhancement Based on Parameter-Tuned Stochastic Resonance

In turbid water, the attenuation and scattering of light caused by scatterers make underwater optical images degraded, blurred, and contrast reduced, limiting the extraction and analysis of information from images. To address such problems, a turbid underwater image enhancement method based on parameter-tuned stochastic resonance (SR) is proposed in this article. First, an SR algorithm framework for underwater image enhancement is constructed, including the dimensionality reduction and normalization of input images, the solution and parameter optimization of the SR system, the dimensionality upgrading of output images, etc. This framework can apply the SR's ability to enhance weak signals to the enhancement of turbid underwater images. Second, to measure the performance of the system, a synthetic turbid underwater image data set (UWCHIC) is constructed using the underwater imaging model and an image set with simulated scatterers. Based on this data set, the relationship between various image quality evaluation metrics and system parameters is analyzed, and then the suitable no-reference (NR) metrics for system performance evaluation are selected and an adaptive parameter tuning strategy of the SR system is proposed to guide the image enhancement. Lastly, the proposed method is evaluated on the UWCHIC, a dataset to evaluate underwater image restoration methods (TURBID), marine underwater environment database (MUED), and underwater image enhancement benchmark (UIEB) data sets and the turbid underwater images captured from natural waters. Different experimental evaluations demonstrated that the proposed method not only effectively enhances the visual quality of turbid underwater images but also improves the performance of downstream vision tasks.
Predicting Performance Improvement of Human Activity Recognition Model by Additional Data Collection

The development of a machine-learning-based human activity recognition (HAR) system using body-worn sensors is mainly composed of three phases: data collection, model training, and evaluation. During data collection, the HAR developer collects labeled data from participants wearing inertial sensors. In the model training phase, the developer trains the HAR model on the collected training data. In the evaluation phase, the developer evaluates the trained HAR model on the collected test data. When the HAR model cannot achieve the target recognition accuracy, the developer iterates the above procedures by taking certain measures, including collecting additional training data, until the re-trained model achieves the target accuracy. However, collecting labeled data for HAR requires additional time and incurs high monetary costs. In addition, it is difficult to determine the amount and type of data to collect for achieving the target accuracy while reducing costs. To address this issue, this paper proposes a new method that predicts the performance improvement of the current HAR model, i.e., it determines the level of performance improvement achievable by re-training the HAR model with additional data, before collecting the additional data. Thus, the method enables the HAR developer to establish a strategy for additional data collection by providing advice such as "If labeled data for the Walking and Running activities from two additional participants is collected, the HAR accuracy of the current HAR model for Walking will improve by 20%." To achieve this, a neural network called AIP-Net is proposed to estimate the improvement in performance by analyzing the feature space of the current HAR model using the proposed entropy-based attention mechanism. The performance of AIP-Net was evaluated on eight HAR datasets using leave-one-dataset-out cross-validation.
Towards a Dynamic Inter-Sensor Correlations Learning Framework for Multi-Sensor-Based Wearable Human Activity Recognition

Multi-sensor-based wearable human activity recognition (WHAR) is a research hotspot in the field of ubiquitous computing. Extracting effective features from multi-sensor data is essential to improve the performance of activity recognition. Despite the excellent achievements of previous works, the challenge remains for modelling the dynamic correlations between sensors. In this paper, we propose a lightweight yet efficient GCN-based dynamic inter-sensor correlations learning framework called DynamicWHAR for automatically learning the dynamic correlations between sensors. DynamicWHAR is mainly composed of two modules: Initial Feature Extraction and Dynamic Information Interaction. Firstly, Initial Feature Extraction module performs data-to-feature transformation to extract the initial features of each sensor. Subsequently, Dynamic Information Interaction module explicitly models the specific interaction intensity between any two sensors, and performs dynamic information aggregation between sensors by the learned interaction intensity. Extensive experiments on four diverse WHAR datasets and two different resource-constrained devices validate that DynamicWHAR outperforms the SOTA models in both recognition performance and computational complexity.
Institutional perspective of financial sector development: A multidimensional assessment

Financial sector development is a multidimensional process that plays a vital role in the economic growth and development of a country. This study investigates the effect of institutional quality on multidimensional financial sector development, and its dimensions, such as, depth, access, and efficiency. We used a panel dataset of 85 emerging and developing economies from 1996 to 2018 for analysis. Our findings based on 2SLS estimation demonstrate that institutional quality has a significant positive effect on the progress of the financial sector, especially its depth, access, and efficiency. The breakdown analysis shows that most of the key components (control of corruption, government effectiveness, regulatory quality, rule of law, and voice and accountability) of institutional quality enhance the financial sector development. Our empirical results are robust across alternative measures of institutional quality, split-sample analysis, alternative instrument, and estimator. This paper also offers useful policy implications to the stakeholders in emerging and developing economies. • The effect of institutional quality on financial development and its dimensions is investigated.· Results are drawn from 85 emerging and developing economies. • Institutional quality promotes financial development and its dimensions. • Most of the components of institutional quality are fostering financial development.
Multi-modal cyber security based object detection by classification using deep learning and background suppression techniques

Foreground object recognition in video is critical in many computer vision applications and automated video surveillance systems. Object detection and tracking are critical steps in navigation, object recognition and surveillance schemes. Object detection is process of separating foreground and background items in photographs. In this paper, we proposes a framework for achieving these tasks with the enhanced cyber security control facility. This proposed algorithmperformed in 2 stages: multi-object detection utilizing the Cyber secure Probabilistic Gaussian Mixture Model (Cy_SPGMM) and background suppression and another stage is multiple moving objects tracking utilizing Kernel convoluted moving window with Kalman filter (KCMW_KF). It can, however, deal with a variety of video sequences in the MOT 20 dataset. The experimental findings reveal that the proposed algorithm detects and tracks foreground objects in complex and dynamic scenarios with high accuracy, robustness, and efficiency. This method also produces smoothened images without noise.
Oral squamous cell carcinoma diagnosis in digitized histological images using convolutional neural network

Diagnostic methods of oral squamous cell carcinoma (SCC) using artificial intelligence (AI) and digital-histopathologic images have been developed. However, previous AI training methods have focused on the cellular atypia given by the training of high-magnification images, and little attention has been paid to structural atypia provided by low-power wide fields. Since oral SCC has histopathologic types with bland cytology, both cellular atypia and structural atypia must be considered as histopathologic features. This study aimed to investigate AI ability to judge oral SCC in a novel training method considering cellular and structural atypia and their suitability.We examined digitized histological whole-slide images from 90 randomly selected patients with tongue SCC who attended a dental hospital. Image patches of 1000 × 1000 pixels were cut from whole-slide images at 0.3125-, 1.25-, 5-, and 20-fold magnification, and 90,059 image patches were used for training and evaluation. These image patches were resized into 224 × 224, 384 × 384, 512 × 512, and 768 × 768 pixels, and the differences in input size were analyzed. EfficientNet B0 was utilized as the convolutional neural network model. Gradient-weighted class activation mapping (Grad-CAM) was used to elucidate its validity.The proposed method achieved a peak accuracy of 99.65% with an input size of 512 × 512 pixels. Grad-CAM suggested that AI focused on both cellular and structural atypia of SCC, and tended to focus on the region surrounding the basal layer.Training AI regarding both cellular and structural atypia using various magnification images simultaneously may be suitable for the diagnosis of oral SCC.
An infrared image denoising model with unidirectional gradient and sparsity constraint on biomedical images

• Unidirectional gradient constraint is proposed to describe the image priori knowledge. • The across-stripe directional gradient is keep to retain the recovered image details. • Wavelet transform is introduced to analyze the distribution of Wavelet coefficients. The biomedical images denoising is a fundamental issue in the computer vision and image super-resolution. However, the stripe noises and random noises often degrade the biomedical image quality. In this work, we develop a novel infrared image destriping model with unidirectional gradient and sparsity constraint. According to our observation, the stripe noises often exists in the vertical image gradient, thus, the unidirectional gradient constraint is proposed to describe the image priori knowledge. The across-stripe directional gradient is keep to retain the recovered image details. The Wavelet transform is introduced to reveal the distribution of Wavelet coefficients. We test the proposed model on the simulated infrared and real infrared biomedical images. Experimental results show that our model can suppress the stripe noises and preserve the image structure simultaneously. With the promising results obtained by our model, believing that visual detection on biomedical image has a great potential is reasonable.
Real-Time Robotic Mirrored Behavior of Facial Expressions and Head Motions Based on Lightweight Networks

The ability of a humanoid robot to imitate facial expressions with simultaneous head motions is crucial to natural human–robot interaction. This mirrored behavior from human beings to humanoid robots has high demands of similarity and real-time performance. To fulfill these needs, this article proposes a real-time robotic mirrored behavior of facial expressions and head motions based on lightweight networks. First, a humanoid robot that can change the state of its facial organs and neck through servo displacement is developed to achieve the mirrored behavior of facial expressions and head motions. Second, to overcome the high latency caused by deep learning models running in embedded devices, a lightweight deep learning network is constructed for detecting facial feature points, which can reduce model size and improve running speed without affecting the performance of the model. Finally, a mapping relationship of 68 facial feature points to optimal servo displacements is established to realize the mirrored behavior from human beings to humanoid robots. The experimental results show that the facial feature point recognition method based on the lightweight model performs better than other state-of-the-art methods, and our head motion tracking method can maintain high accuracy compared with the gold standard optical motion capture system NOKOV. Overall, our method ensures the accurate and real-time generation of robot mirrored behavior and has a certain reference value for the efficient and natural interaction between humans and robots.
Artificial intelligence based liver portal tract region identification and quantification with transplant biopsy whole-slide images

Liver fibrosis staging is clinically important for liver disease progression prediction. As the portal tract fibrotic quantity and size in a liver biopsy correlate with the fibrosis stage, an accurate analysis of portal tract regions is clinically critical. Manual annotations of portal tract regions, however, are time-consuming and subject to large inter- and intra-observer variability. To address such a challenge, we develop a Multiple Up-sampling and Spatial Attention guided UNet model (MUSA-UNet) to segment liver portal tract regions in whole-slide images of liver tissue slides. To enhance the segmentation performance, we propose to use depth-wise separable convolution, the spatial attention mechanism, the residual connection, and multiple up-sampling paths in the developed model. This study includes 53 histopathology whole slide images from patients who received liver transplantation. In total, 6,012 patches derived from 30 images are used for our deep learning model training and validation. The remaining 23 whole slide images are utilized for the model testing. The average liver portal tract segmentation performance of the developed MUSA-UNet is 0.94 (Precision), 0.85 (Recall), 0.89 (F1 Score), 0.89 (Accuracy), 0.80 (Jaccard Index), and 0.91 (Fowlkes-Mallows Index), respectively. The clinical Scheuer fibrosis stage presents a strong correlation with the resulting average portal tract fibrotic area (R = 0.681, p<0.001) and portal tract percentage (R = 0.335, p = 0.02) computed from the MUSA-UNet segmentation results. In conclusion, our developed deep learning model MUSA-UNet can accurately segment portal tract regions from whole-slide images of liver tissue biopsies, presenting its promising potential to assist liver disease diagnosis in a computational manner.
Novel features to detect gender from handwritten documents

Gender detection from handwritten documents is a crucial research area in many disciplines such as psychology, pyelography, graphology, and forensic analysis. Furthermore, this task is challenging due to the high similarity and overlap between individuals' handwriting. The performance of the document recognition and analysis systems, depends on the extracted features from handwritten documents, which can be a challenging task as this depends on extracting the most relevant information from row text. In this paper, a set of gender-related features suggested by a graphologist, to detect the gender of the writers, have been proposed. These features include margins, space between words, pen-pressure and handwriting irregularity. Both SVM and ANN classifiers have been used to train, validate and test the proposed approach on two different data sets: our data set FSHS and ICDAR2013 dataset. The proposed method has achieved high classification rates of 94.7% and 97.1% using SVM and ANN respectively. Meanwhile, our method outperformed state-of-arts methods when applied to the ICDAR2013 dataset with classification rates of 91.4% and 92.5% using SVM and ANN respectively.
PET Image Denoising Using Unsupervised Domain Translation

Neural networks that perform domain translation have been successfully used for image restoration in settings where paired datasets are not available for network training. Here we present an unsupervised domain translation technique for PET image denoising which restores noisy real PET images by learning shared latent space information across a simulated clean image domain and a real noisy image domain. For training and validating the network in unsupervised mode, we used noisy human brain PET scans in combination with a set of noiseless simulated images based on the BrainWeb digital phantom. Using the peak signal-to-noise ratio as our evaluation metric, we show here that this unsupervised domain translation technique leads to quantitative improvements over Gaussian filtering. Unsupervised domain translation also leads to improvements in visual image quality relative to Gaussian filtering as evidenced by relative degrees of enhancement in gray matter intensities in the denoised brain PET images.
Data Distribution-Aware Online Client Selection Algorithm for Federated Learning in Heterogeneous Networks

Federated learning (FL) has received significant attention as a practical alternative to traditional cloud-centric machine learning (ML). The performance (e.g., accuracy and convergence time) of FL is hampered by the selection of clients having non-independent and identically distributed (non-IID) data. In addition, a long convergence time is inevitable if clients with poor computation or communication capabilities participate in the FL procedure (i.e., the straggler problem). To minimize convergence time while guaranteeing high learning accuracy, we first formulate an optimization problem on client selection. As a practical solution, we devise a data distribution-aware online client selection (DOCS) algorithm. In DOCS, the FL server finds several clusters having near IID data and then uses a multi-armed bandit (MAB) technique to select the cluster with the lowest convergence time. The evaluation results demonstrate that DOCS can reduce the convergence time by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10\% \!\sim\! 41\%$</tex-math></inline-formula> and improve the learning accuracy by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4\% \!\sim\! 13\%$</tex-math></inline-formula> compared to the traditional client selection schemes.
ParaTTS: Learning Linguistic and Prosodic Cross-Sentence Information in Paragraph-Based TTS

Recent advancements in neural end-to-end text-to-speech (TTS) models have shown high-quality, natural synthesized speech in a conventional sentence-based TTS. However, it is still challenging to reproduce similar high quality when a whole paragraph is considered in TTS, where a large amount of contextual information needs to be considered in building a paragraph-based TTS model. To alleviate the difficulty in training, we propose to model linguistic and prosodic information by considering cross-sentence, embedded structure in training. Three sub-modules, including linguistics-aware, prosody-aware and sentence-position networks, are trained together with a modified Tacotron2. Specifically, to learn the information embedded in a paragraph and the relations among the corresponding component sentences, we utilize linguistics-aware and prosody-aware networks. The information in a paragraph is captured by encoders and the inter-sentence information in a paragraph is learned with multi-head attention mechanisms. The relative sentence position in a paragraph is explicitly exploited by a sentence-position network. Trained on a storytelling audio-book corpus (4.08 hours), recorded by a female Mandarin Chinese speaker, the proposed TTS model demonstrates that it can produce rather natural and good-quality speech paragraph-wise. The cross-sentence contextual information, such as break and prosodic variations between consecutive sentences, can be better predicted and rendered than the sentence-based model. Tested on paragraph texts, of which the lengths are similar to, longer than, or much longer than the typical paragraph length of the training data, the TTS speech produced by the new model is consistently preferred over the sentence-based model in subjective tests and confirmed in objective measures.
Mapless Navigation With Safety-Enhanced Imitation Learning

Mapless navigation is a popular approach for guiding a robot in an unknown environment. However, current learning-based methods for mapless navigation cannot guarantee safe exploration during training. In this article, we aim to safely train a mapless navigation policy with 2-D LiDAR inputs using imitation learning (IL). We propose a novel IL training scheme based on the dataset aggregation with extra safety enhancement. In order to avoid collisions during training, we design a controlled exploration strategy for demonstration data collection. In this strategy, the output of the novice and expert policy are fused adaptively to control the robot. In order to improve the obstacle avoidance capability of the trained policy, we design a safety-enhanced loss function for network training. This proposed loss function focuses on the obstacle avoidance by prioritizing the training samples collected in different states. In addition, inspired by the symmetrical pattern of the navigation task, we propose a new data augmentation technique, which increases the training speed significantly. In both simulation and real-world experiments, our method demonstrates the remarkable performance in terms of training safety and navigation success rate compared with other IL-based approaches.