Reviewerly/exHarmony huggingface dataset:
Arabic speech recognition using end‐to‐end deep learning

Arabic automatic speech recognition (ASR) methods with diacritics have the ability to be integrated with other systems better than Arabic ASR methods without diacritics. In this work, the application of state-of-the-art end-to-end deep learning approaches is investigated to build a robust diacritised Arabic ASR. These approaches are based on the Mel-Frequency Cepstral Coefficients and the log Mel-Scale Filter Bank energies as acoustic features. To the best of our knowledge, end-to-end deep learning approach has not been used in the task of diacritised Arabic automatic speech recognition. To fill this gap, this work presents a new CTC-based ASR, CNN-LSTM, and an attention-based end-to-end approach for improving diacritisedArabic ASR. In addition, a word-based language model is employed to achieve better results. The end-to-end approaches applied in this work are based on state-of-the-art frameworks, namely ESPnet and Espresso. Training and testing of these frameworks are performed based on the Standard Arabic Single Speaker Corpus (SASSC), which contains 7 h of modern standard Arabic speech. Experimental results show that the CNN-LSTM with an attention framework outperforms conventional ASR and the Joint CTC-attention ASR framework in the task of Arabic speech recognition. The CNN-LSTM with an attention framework could achieve a word error rate better than conventional ASR and the Joint CTC-attention ASR by 5.24% and 2.62%, respectively.
Embodied gesture interaction for immersive maps

With the increasing availability of head-mounted displays for virtual reality and augmented reality, we can create immersive maps in which the user is closer to the data. Embodiment is a key concept, allowing the user to act upon virtual objects in an immersive environment. Our work explores the use of embodied interaction for immersive maps. We propose four design considerations for embodied maps and embodied gesture interaction with immersive maps: object presence, consistent physics, human body skills, and direct manipulation. We present an example of an immersive flow map with a series of novel embodied gesture interactions, which adhere to the proposed design considerations. The embodied interactions allow users to directly manipulate immersive flow maps and explore origin-destination flow data in novel ways. Authors of immersive maps can use the four proposed design considerations for creating embodied gesture interactions. The discussed example interactions apply to diverse types of immersive maps and will hopefully incite others to invent more embodied interactions for immersive maps.
Balanced Open Set Domain Adaptation via Centroid Alignment

Open Set Domain Adaptation (OSDA) is a challenging domain adaptation setting which allows the existence of unknown classes on the target domain. Although existing OSDA methods are good at classifying samples of known classes, they ignore the classification ability for the unknown samples, making them unbalanced OSDA methods. To alleviate this problem, we propose a balanced OSDA methods which could recognize the unknown samples while maintain high classification performance for the known samples. Specifically, to reduce the domain gaps, we first project the features to a hyperspherical latent space. In this space, we propose to bound the centroid deviation angles to not only increase the intra-class compactness but also enlarge the inter-class margins. With the bounded centroid deviation angles, we employ the statistical Extreme Value Theory to recognize the unknown samples that are misclassified into known classes. In addition, to learn better centroids, we propose an improved centroid update strategy based on sample reweighting and adaptive update rate to cooperate with centroid alignment. Experimental results on three OSDA benchmarks verify that our method can significantly outperform the compared methods and reduce the proportion of the unknown samples being misclassified into known classes.
Normalized Avatar Synthesis Using StyleGAN and Perceptual Refinement

We introduce a highly robust GAN-based framework for digitizing a normalized 3D avatar of a person from a single unconstrained photo. While the input image can be of a smiling person or taken in extreme lighting conditions, our method can reliably produce a high-quality textured model of a person’s face in neutral expression and skin textures under diffuse lighting condition. Cutting-edge 3D face reconstruction methods use non-linear morphable face models combined with GAN-based decoders to capture the likeness and details of a person but fail to produce neutral head models with unshaded albedo textures which is critical for creating relightable and animation-friendly avatars for integration in virtual environments. The key challenges for existing methods to work is the lack of training and ground truth data containing normalized 3D faces. We propose a two-stage approach to address this problem. First, we adopt a highly robust normalized 3D face generator by embedding a non-linear morphable face model into a StyleGAN2 network. This allows us to generate detailed but normalized facial assets. This inference is then followed by a perceptual refinement step that uses the generated assets as regularization to cope with the limited available training samples of normalized faces. We further introduce a Normalized Face Dataset, which consists of a combination photogrammetry scans, carefully selected photographs, and generated fake people with neutral expressions in diffuse lighting conditions. While our prepared dataset contains two orders of magnitude less subjects than cutting edge GAN-based 3D facial reconstruction methods, we show that it is possible to produce high-quality normalized face models for very challenging unconstrained input images, and demonstrate superior performance to the current state-of-the-art.
Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies—Current Status and Next Steps

Diagnosis and Gleason grading of prostate cancer in biopsies are critical for the clinical management of men with prostate cancer. Despite this, the high grading variability among pathologists leads to the potential for under- and overtreatment. Artificial intelligence (AI) systems have shown promise in assisting pathologists to perform Gleason grading, which could help address this problem. In this mini-review, we highlight studies reporting on the development of AI systems for cancer detection and Gleason grading, and discuss the progress needed for widespread clinical implementation, as well as anticipated future developments.Patient summaryThis mini-review summarizes the evidence relating to the validation of artificial intelligence (AI)-assisted cancer detection and Gleason grading of prostate cancer in biopsies, and highlights the remaining steps required prior to its widespread clinical implementation. We found that, although there is strong evidence to show that AI is able to perform Gleason grading on par with experienced uropathologists, more work is needed to ensure the accuracy of results from AI systems in diverse settings across different patient populations, digitization platforms, and pathology laboratories.
Quantum-inspired firefly algorithm integrated with cuckoo search for optimal path planning

The firefly algorithm and cuckoo search are the meta-heuristic algorithms efficient to determine the solution for the searching and optimization problems. The current work proposes an integrated concept of quantum-inspired firefly algorithm with cuckoo search (IQFACS) that adapts both algorithms’ expedient attributes to optimize the solution set. In the IQFACS algorithm, the quantum-inspired firefly algorithm (QFA) ensures the diversification of fireflies-based generated solution set using the superstitions quantum states of the quantum computing concept. The cuckoo search (CS) algorithm uses the Lévy flight attribute to escape the QFA from the premature convergence and stagnation stage more effectively than the quantum principles. Here, the proposed algorithm is applied for the application of optimal path planning. Before using the proposed algorithm for path planning, the algorithm is tested on different optimization benchmark functions to determine the efficacy of the proposed IQFACS algorithm than the firefly algorithm (FA), CS, and hybrid FA and CS algorithm. Using the proposed IQFACS algorithm, path planning is performed on the satellite images with vegetation as the focused region. These satellite images are captured from Google Earth and belong to the different areas of India. Here, satellite images are converted into morphologically processed binary images and considered as maps for path planning. The path planning process is also executed with the FA, CS, and QFA algorithms. The performance of the proposed algorithm and other algorithms are accessed with the evaluation of simulation time and the number of cycles to attain the shortest path from defined source to destination. The error rate measure is also incorporated to analyze the overall performance of the proposed IQFACS algorithm over the other algorithms.
Boosting Fast Adversarial Training With Learnable Adversarial Initialization

Adversarial training (AT) has been demonstrated to be effective in improving model robustness by leveraging adversarial examples for training. However, most AT methods are in face of expensive time and computational cost for calculating gradients at multiple steps in generating adversarial examples. To boost training efficiency, fast gradient sign method (FGSM) is adopted in fast AT methods by calculating gradient only once. Unfortunately, the robustness is far from satisfactory. One reason may arise from the initialization fashion. Existing fast AT generally uses a random sample-agnostic initialization, which facilitates the efficiency yet hinders a further robustness improvement. Up to now, the initialization in fast AT is still not extensively explored. In this paper, focusing on image classification, we boost fast AT with a sample-dependent adversarial initialization, i.e., an output from a generative network conditioned on a benign image and its gradient information from the target network. As the generative network and the target network are optimized jointly in the training phase, the former can adaptively generate an effective initialization with respect to the latter, which motivates gradually improved robustness. Experimental evaluations on four benchmark databases demonstrate the superiority of our proposed method over state-of-the-art fast AT methods, as well as comparable robustness to advanced multi-step AT methods. The code is released at https://github.com//jiaxiaojunQAQ//FGSM-SDI.
Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints

Breast tumor segmentation is an important step in the diagnostic procedure of physicians and computer-aided diagnosis systems. We propose a two-step deep learning framework for breast tumor segmentation in breast ultrasound (BUS) images which requires only a few manual labels. The first step is breast anatomy decomposition handled by a semi-supervised semantic segmentation technique. The input BUS image is decomposed into four breast anatomical structures, namely fat, mammary gland, muscle and thorax layers. Fat and mammary gland layers are used as constrained region to reduce the search space for breast tumor segmentation. The second step is breast tumor segmentation performed in a weakly-supervised learning scenario where only image-level labels are available. Breast tumors are first recognized by a classification network and then segmented by the proposed class activation mapping and deep level set (CAM-DLS) method. For breast anatomy decomposition, the proposed framework achieves Dice similarity coefficient (DSC) of 83.0 ± 11.8%, 84.3 ± 10.0%, 80.7 ± 15.4% and 91.0 ± 11.4% for fat, mammary gland, muscle and thorax layers, respectively. For breast tumor recognition, the proposed framework achieves sensitivity of 95.8%, precision of 92.4%, specificity of 93.9%, accuracy of 94.8% and F1-score of 0.941. For breast tumor segmentation, the proposed framework achieves DSC of 77.3% and intersection-over-union (IoU) of 66.0%. In conclusion, the proposed framework could efficiently perform breast tumor recognition and segmentation simultaneously in a weakly-supervised setting with anatomical constraints.
STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation

How to learn a better speech representation for end-to-end speech-to-text translation (ST) with limited labeled data?Existing techniques often attempt to transfer powerful machine translation (MT) capabilities to ST, but neglect the representation discrepancy across modalities.In this paper, we propose the Speech-TExt Manifold Mixup (STEMM) method to calibrate such discrepancy.Specifically, we mix up the representation sequences of different modalities, and take both unimodal speech sequences and multimodal mixed sequences as input to the translation model in parallel, and regularize their output predictions with a selflearning framework.Experiments on MuST-C speech translation benchmark and further analysis show that our method effectively alleviates the cross-modal representation discrepancy, and achieves significant improvements over a strong baseline on eight translation directions.
Theory of Estimation-of-Distribution Algorithms

Estimation-of-distribution algorithms (EDAs) are general metaheuristics used in optimization that represent a more recent alternative to classical approaches such as evolutionary algorithms. In a nutshell, EDAs typically do not directly evolve populations of search points but build probabilistic models of promising solutions by repeatedly sampling and selecting points from the underlying search space. Recently, significant progress has been made in the theoretical understanding of EDAs. This chapter provides an up-to-date overview of the most commonly analyzed EDAs and the most recent theoretical results in this area. In particular, emphasis is put on the runtime analysis of simple univariate EDAs, including a description of typical benchmark functions and tools for the analysis. Along the way, open problems and directions for future research are described.
Image Denoising by Wavelet Based Thresholding Method

In the present day, visual data transferred in digital imageries is fetching a popular technique of communication, however the picture acquired after transfer communication is frequently distorted by noise. Before it can be used in applications, the received image must be processed. Picture denoising is the process of manipulating picture data in order to create a visibly excellent picture. We can characterize signals with a precise degree of scarcity using wavelet transforms. Wavelet thresholding is a signal estimating approach that uses the wavelet transform's ability to de-noise signals. Noise suppression in medical imaging is a very delicate and challenging endeavour. The trade-off between noise reduction and picture feature preservation must be adjusted in such a way that the diagnostically useful image content is enhanced. The wavelet thresholding method has been widely utilised to de-noise medical images. The goal is to convert the image information into a wavelet basis, where the large coefficients reflect the signal and the smaller coefficients indicate the noise. The noise in the data can be reduced by adjusting these coefficients appropriately. The goal of this thesis is to compare the performance of several thresholding strategies such as Sure Shrink, Visu Shrink, and Bayes Shrink.
Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation

Incremental few-shot semantic segmentation (IFSS) targets at incrementally expanding model's capacity to segment new class of images supervised by only a few samples. However, features learned on old classes could significantly drift, causing catastrophic forgetting. Moreover, few samples for pixel-level segmentation on new classes lead to notorious overfitting issues in each learning session. In this paper, we explicitly represent class-based knowledge for semantic segmentation as a category embedding and a hyper-class embedding, where the former describes exclusive semantical properties, and the latter expresses hyper-class knowledge as class-shared semantic properties. Aiming to solve IFSS problems, we present EHNet, i.e., Embedding adaptive-update and Hyper-class representation Network from two aspects. First, we propose an embedding adaptive-update strategy to avoid feature drift, which maintains old knowledge by hyper-class representation, and adaptively update category embeddings with a class-attention scheme to involve new classes learned in individual sessions. Second, to resist overfitting issues caused by few training samples, a hyper-class embedding is learned by clustering all category embeddings for initialization and aligned with category embedding of the new class for enhancement, where learned knowledge assists to learn new knowledge, thus alleviating performance dependence on training data scale. Significantly, these two designs provide representation capability for classes with sufficient semantics and limited biases, enabling to perform segmentation tasks requiring high semantic dependence. Experiments on PASCAL-5i and COCO datasets show that EHNet achieves new state-of-the-art performance with remarkable advantages.
NWPU-Captions Dataset and MLCA-Net for Remote Sensing Image Captioning

Recently, the burgeoning demands for captioning-related applications have inspired great endeavors in the remote sensing community. However, current benchmark datasets are deficient in data volume, category variety, and description richness, which hinders the advancement of new remote sensing image captioning approaches, especially those based on deep learning. To overcome this limitation, we present a larger and more challenging benchmark dataset, termed NWPU-Captions. NWPU-Captions contains 157,500 sentences, with all 31,500 images annotated manually by 7 experienced volunteers. The superiority of NWPU-Captions over current publicly available benchmark datasets not only lies in its much larger scale but also in its wider coverage of complex scenes and the richness and variety of describing vocabularies. Further, a novel encoder-decoder architecture, multi-level and contextual attention network (MLCA-Net), is proposed. MLCA-Net employs a multi-level attention module to adaptively aggregate image features of specific spatial regions and scales and introduces a contextual attention module to explore the latent context hidden in remote sensing images. MLCA-Net improves the flexibility and diversity of the generated captions while keeping their accuracy and conciseness by exploring the properties of scale variations and semantic ambiguity. Finally, the effectiveness, robustness, and generalization of MLCA-Net are proved through extensive experiments on existing datasets and NWPU-Captions.
TinyViT: Fast Pretraining Distillation for Small Vision Transformers

Vision transformer (ViT) recently has drawn great attention in computer vision due to its remarkable model capability. However, most prevailing ViT models suffer from huge number of parameters, restricting their applicability on devices with limited resources. To alleviate this issue, we propose TinyViT, a new family of tiny and efficient small vision transformers pretrained on large-scale datasets with our proposed fast distillation framework. The central idea is to transfer knowledge from large pretrained models to small ones, while enabling small models to get the dividends of massive pretraining data. More specifically, we apply distillation during pretraining for knowledge transfer. The logits of large teacher models are sparsified and stored in disk in advance to save the memory cost and computation overheads. The tiny student transformers are automatically scaled down from a large pretrained model with computation and parameter constraints. Comprehensive experiments demonstrate the efficacy of TinyViT. It achieves a top-1 accuracy of 84.8% on ImageNet-1k with only 21M parameters, being comparable to Swin-B pretrained on ImageNet-21k while using 4.2 times fewer parameters. Moreover, increasing image resolutions, TinyViT can reach 86.5% accuracy, being slightly better than Swin-L while using only 11% parameters. Last but not the least, we demonstrate a good transfer ability of TinyViT on various downstream tasks. Code and models are available at https://github.com/microsoft/Cream/tree/main/TinyViT .
Self-supervised Learning: A Succinct Review

Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervised learning (SSL) is a type of un-supervised learning that helps in the performance of downstream computer vision tasks such as object detection, image comprehension, image segmentation, and so on. It can develop generic artificial intelligence systems at a low cost using unstructured and unlabeled data. The authors of this review article have presented detailed literature on self-supervised learning as well as its applications in different domains. The primary goal of this review article is to demonstrate how images learn from their visual features using self-supervised approaches. The authors have also discussed various terms used in self-supervised learning as well as different types of learning, such as contrastive learning, transfer learning, and so on. This review article describes in detail the pipeline of self-supervised learning, including its two main phases: pretext and downstream tasks. The authors have shed light on various challenges encountered while working on self-supervised learning at the end of the article.
Information security and technical issues of cloud storage services: a qualitative study on university students in Hong Kong

Purpose Although cloud storage services can bring users valuable convenience, they can be technically complex and intrinsically insecure. Therefore, this research explores the concerns of academic users regarding cloud security and technical issues and how such problems may influence their continuous use in daily life. Design/methodology/approach This qualitative study used a semi-structured interview approach comprising six main open-ended questions to explore the information security and technical issues for the continuous use of cloud storage services by 20 undergraduate students in Hong Kong. Findings The analysis revealed cloud storage service users' major security and technical concerns, particularly synchronization and backup issues, were the most significant technical barrier to the continuing personal use of cloud storage services. Originality/value Existing literature has focused on how cloud computing services could bring benefits and security and privacy-related risks to organizations rather than security and technical issues of personal use, especially in the Asian academic context.