Reviewerly/exHarmony huggingface dataset:
Improving Cross-Lingual Speech Synthesis with Triplet Training Scheme

Recent advances in cross-lingual text-to-speech (TTS) made it possible to synthesize speech in a language foreign to a monolingual speaker. However, there is still a large gap between the pronunciation of generated cross-lingual speech and that of native speakers in terms of naturalness and intelligibility. In this paper, a triplet training scheme is proposed to enhance the cross-lingual pronunciation by allowing previously unseen content and speaker combinations to be seen during training. Proposed method introduces an extra fine-tune stage with triplet loss during training, which efficiently draws the pronunciation of the synthesized foreign speech closer to those from the native anchor speaker, while preserving the non-native speaker’s timbre. Experiments are conducted based on a state-of-the-art baseline cross-lingual TTS system and its enhanced variants. All the objective and subjective evaluations show the proposed method brings significant improvement in both intelligibility and naturalness of the synthesized cross-lingual speech.
Selective Scale Cascade Attention Network for Breast Cancer Histopathology Image Classification

Convolutional Neural Networks (CNNs) approaches are widely applied to histopathological image analysis due to the breakthrough performance achieved. However, it remains challenging because complex backgrounds obscure the most discriminative region. In this paper, we propose selective scale cascade attention network (SSCA) to learning discriminative features for breast histopathological image classification. Based on the backbone, SSCA mainly consists of three modules: 1) cross scale attention module leverage deeper level features to generate an attention map that locates the discriminative part in high-resolution features and conduct multi-scale classification. 2) cascade attention modules gradually identify fine-grained cues and increase their weight through a cascade of attention. 3) selective scale fusion module dynamically adjusts the weight of each scale feature, i.e., the select scale depends on the characteristics of the cancer subtypes. Extensive experiments show that our proposed consistently outperforms the existing state-of-the-art methods on the public BreakHis dataset.
AdverSparse: An Adversarial Attack Framework for Deep Spatial-Temporal Graph Neural Networks

Spatial-temporal graph have been widely observed in various domains such as neuroscience, climate research, and transportation engineering. The state-of-the-art models of spatialtemporal graphs rely on Graph Neural Networks (GNNs) to obtain explicit representations for such networks and to discover hidden spatial dependencies in them. These models have demonstrated superior performance in various tasks. In this paper, we propose a sparse adversarial attack framework AdverSparse to illustrate that when only a few key connections are removed in such graphs, hidden spatial dependencies learned by such spatial-temporal models are significantly impacted, leading to various issues such as increasing prediction errors. We formulate the adversarial attack as an optimization problem and solve it by the Alternating Direction Method of Multipliers (ADMM). Experiments show that AdverSparse can find and remove key connections in these graphs, leading to malfunctioning models, even in models capable of learning hidden spatial dependencies.
Polyphone Disambiguation and Accent Prediction Using Pre-Trained Language Models in Japanese TTS Front-End

Although end-to-end text-to-speech (TTS) models can generate natural speech, challenges still remain when it comes to estimating sentence-level phonetic and prosodic information from raw text in Japanese TTS systems. In this paper, we propose a method for polyphone disambiguation (PD) and accent prediction (AP). The proposed method incorporates explicit features extracted from morphological analysis and implicit features extracted from pre-trained language models (PLMs). We use BERT and Flair embeddings as implicit features and examine how to combine them with explicit features. Our objective evaluation results showed that the proposed method improved the accuracy by 5.7 points in PD and 6.0 points in AP. Moreover, the perceptual listening test results confirmed that a TTS system employing our proposed model as a front-end achieved a mean opinion score close to that of synthesized speech with ground-truth pronunciation and accent in terms of naturalness.
Decentralized Bilevel Optimization for Personalized Client Learning

Decentralized optimization with multiple networked clients/learners has advanced machine learning significantly over the past few years. When data distributions at different nodes/locations are heterogeneous, consensus-based decentralized algorithms ignore distinctive features of local data samples. In this paper, we propose a decentralized client adaptation strategy for personalized learning by taking local client data structures into account. It turns out that optimizing the model parameters can be formulated as a decentralized bilevel programming problem. Motivated by this application, we propose a stochastic primal-dual framework for solving decentralized bilevel nonconvex problems and show that the devised algorithm achieves the Karush–Kuhn–Tucker (KKT) points for this class of problems at a rate of $\mathcal{O}(1/\sqrt {nT} )$, where n denotes the number of total learners and T the total number of iterations. Multiple experiments show the superiority of our proposed method compared to state-of-the-art methods in terms of both training speed and testing accuracy for decentralized learning problems on real datasets.
Adaptive Pseudo Labeling for Source-Free Domain Adaptation in Medical Image Segmentation

Domain adaptation is common but challenging in signal processing tasks due to the intrinsic discrepancy, especially in difficult-to-label medical image segmentation application scenarios. Pseudo labeling methods are widely utilized to compensate for the scarcity of annotation. However, most existing methods set the fixed thresholds to select highly-confident predictions as pseudo labels, inevitably generating false labels with noise. In this paper, we combine the dual-classifiers consistency and predictive category-aware confidence to form a novel regularization for pseudo-label denoising. The dual-classifiers consistency helps promote the robustness of pseudo labels. Meanwhile, category-aware confidence is utilized as adaptive pixel-wise weights, avoiding the need for handcrafted thresholds. The adapted model is refined by the rectified pseudo labels without source domain samples. The proposed method is model-independent and thus can be plug-and-play to improve existing UDA methods. We validated it on the cross-modality medical image segmentation and obtained more competitive results.
Disentangled Feature-Guided Multi-Exposure High Dynamic Range Imaging

Multi-exposure high dynamic range (HDR) imaging aims to generate an HDR image from multiple differently exposed low dynamic range (LDR) images. It is a challenging task due to two major problems: (1) there are usually misalignments among the input LDR images, and (2) LDR images often have incomplete information due to under-/over-exposure. In this paper, we propose a disentangled feature-guided HDR network (DFGNet) to alleviate the above-stated problems. Specifically, we first extract and disentangle exposure features and spatial features of input LDR images. Then, we process these features through the proposed DFG modules, which produce a high-quality HDR image. Experiments show that the proposed DFGNet achieves outstanding performance on a benchmark dataset. Our code and more results are available at https://github.com/KeuntekLee/DFGNet.
Unsupervised and Untrained Underwater Image Restoration Based on Physical Image Formation Model

Underwater images suffer from degradation caused by light scattering and absorption. Training a deep neural network to restore underwater images is challenging due to the labor-intensive data collection and the lack of paired data. To this end, we propose an unsupervised and untrained underwater image restoration method based on the layer disentanglement and the underwater image formation model. Specifically, our network disentangles an underwater image into four components, i.e., the scene radiance, the direct transmission map, the backscatter transmission map, and the global background light, which are further combined to reconstruct the underwater image in a self-supervised manner. Our method can avoid using paired training data and large-scale datasets, benefiting from the unsupervised and untrained characteristics. Extensive experiments demonstrated that our method obtains promising performance compared with six methods on three real-world underwater image databases.
Multi-Pose Virtual Try-On Via Self-Adaptive Feature Filtering

With the growing trend of virtual try-on, multi-pose tasks attract researchers due to their higher commercial value. Prior methods lack an effective geometric deformation to maintain the original image details resulting in many details loss in the head and garment. To address this problem, we propose a new multi-pose virtual try-on network, which can fit a garment to the corresponding area of a person in arbitrary poses. First, the target pose’s body-semantic distribution is predicted by the target pose point. Second, the in-shop garment and human body are warped based on a human pose to solve the unnatural alignment and the lack of body details by the Deformation Module (DM). Finally, the human body in the given pose and garment is fine generated by the Filtering Synthesis Network (FSN). Compared to state-of-the-art methods with objective experiments on the MPV dataset, the proposed method achieves the best performance in metrics and the rich details in visual results.
Sain: Similarity-Aware Video Frame Interpolation

Video frame interpolation (VFI) aims to synthesize an intermediate frame between two consecutive original frames. Most existing methods simply linearly combine the warped frames, leading to a loss of image texture. Since moving objects usually have similarities in consecutive frames, we propose a similarity-aware video frame interpolation method (SAIN) that searches patches with similar texture in the embedding space from input frames to extract features and capture image details. To gather the frame details and restore image texture, SAIN incorporates an implicit neural representation learning from similar patches to enrich image details and refine outputs in frame synthesis networks. Experiments demonstrate that SAIN preserves image texture and enhances interpolated image quality significantly.
Modeling Human Memory in Multi-Object Tracking with Transformers

When tracking objects, humans rely on a memory mechanism, memorize the track of an object then look for it in the current scene. In this paper, we propose Memory-based Multi-object Tracking with Transformers (MMTT) to mimic human behavior in multi-object tracking. Unlike Re-ID-based methods, MMTT solves multi-object tracking in an explicit way, with a Track Encoder to extract track memory, a Detection Encoder to extract detection interactions, and a Memory Decoder to simulate the "look" process. The design of MMTT has the ability to model both spatial and temporal in-formation of a single track. We evaluate on commonly used MOT datasets and the experimental results demonstrate its superior effectiveness. We hope this paper can provide a novel direction for the MOT task. The code and models will be made publicly available upon acceptance.
Syntax-Based Graph Matching for Knowledge Base Question Answering

Semantic parsing is a mainstream method of knowledge base question answering task that first generates a set of logical forms according to question and knowledge base (KB), and then selects the most matching one to get answers. However, existing selection methods are usually based on word-level matching, which cannot capture the structural information or solve the long-term dependency problem of entities. To solve this problem, we propose a syntax-based graph matching method, which explicitly models both question and logical form as graphs, and performs matching at both word-level and structure-level. The multi-level matching strategy not only captures the structural and semantic relations between entities but also explores their intrinsic relations. Such a design can greatly minimize the gap between question and most relevant knowledge from KB, and hence can reason more accurately. We conduct extensive experiments on several benchmarks and demonstrate the effectiveness of our proposed method.
Code-Mixed Hinglish to English Language Translation Framework

Indians, like many other non-English speakers around the world, avoid using single code in their social media conversations. They use transliteration and blend multiple languages to exhibit their linguistic proficiency by randomly merging English words (English-Hindi, English-Spanish, etc.). As a result, a large amount of unstructured text is generated because of the wide use of social media applications. Code-mixing (CM) is a fast-evolving field of study in the domain of text mining. The present situation of various social media posts, blogs, and reviews have a large use of code-mixed messages, due to its modern yet localized way of speaking. Linguistic codes from various languages are used for different purposes. Code-mixed Hindi and English is a typical practice observed in India's day-to-day language usage. Most people have already started to consider this mixing as a new language which has given birth to a brand new language termed “Hinglish”. Hinglish is majorly used among the younger generation, as observed in the code-mixed data obtained via social sites and various other platforms. This mixing of languages stands as a new challenge to the concept of machine translation. It is important to recognize the foreign elements in a language and process them appropriately. As a result, a translation mechanism is needed to assist monolingual users, as well as for easier comprehension by language processing models. This paper proposes a pipelined mechanism for machine translation of a bi-lingual language i.e. Hinglish to monolingual English in this paper.
A Novel Framework for Mixed Reality–Based Control of Collaborative Robot: Development Study

Background Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research. Objective This study aims to present the development of a novel framework for the collaborative robot using mixed reality. Methods The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality–based collaborative system. Results A thorough experiment was conducted to validate the novel framework for mixed reality–based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5–degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot. Conclusions Opportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance.
Intelligent Professional Competitive Basketball Training (IPCBT): from Video based Body Tracking to Smart Motion Prediction

Intelligent professional competitive basketball training from the video based body tracking to smart motion prediction is studied in the paper. The research results of human motion analysis can be applied in many fields, such as intelligent monitoring system, virtual reality, human-computer interaction and motion analysis that plays a role in medicine and sports. The window- based human representation model is one of the more commonly used models in current human detection methods. The model represents the core human body as a rectangular area or a combination of several areas with a fixed relative position relationship, and does not describe the details of the human body's limbs and torso in detail. With these modelling steps, the proposed IPCBT is defined. The smart data analysis is applied fore the video information analysis. The robustness of the designed model is provided.
to indicate satisfaction of university students in information (the case study of State Public Scientific and Technological Library of the Russian Academy of Science Siberian Branch)

The information needs of students were explored with the demand analysis methods. The study was targeted at students as a social group. The results confirmed the sociological findings of students’ low satisfaction with information support provided by the largest libraries. More specifically, the target group was regular library users who access library resources predominantly for the purposes of learning. In this segment, the high level of satisfaction is identified as related to university curricula and education goals (completing assignments, deepening and consolidating knowledge), medium level of satisfaction is linked to personal development and gaining learning skills, while the low level of satisfaction is linked to searching for further learning and career prospects and various aspects of social networking. The year of study. The structure of demand for educational anв professional documents depends on the curricula, and chronology limitation are determined by educational standards and faculty recommendations. Based on the study findings, the author suggests that the diverse newest and aссessible learning materials and possibility to develop certain learning skills through attending educational events and consultations make the advantages of larger libraries.
Safe sample screening rules for multicategory angle-based support vector machines

Support vector machines are popular techniques for classification problems, where the optimal separating hyperplane only depends on a subset of training data. To reduce computational costs, safe sample screening rules are proposed in the literature, which enable us to remove redundant samples prior to the training phase. However, existing works on safe sample screening rules mainly focus on binary classification. The multicategory angle-based support vector machine (MASVM) is a computationally efficient method for multicategory classification problems, which constructs a decision function without the sum-to-zero constraint. To further reduce computational costs in linear MASVM, two safe sample screening methods are proposed: the gap safe rule (MAGSR) and the dual screening with variational inequalities (MADVI). A two-stage screening framework combining MAGSR and MADVI together is then developed. Extensive simulations and real applications show the great advantage of the proposed methods in computation, compared with existing approaches.
Harmonizing ABC System with ERP System for Operational Effectiveness Improvement during Covid-19 Pandemic

Relying on modern technological systems has always been one of the most important means used to mitigate the negative effects of crises facing the economies of countries, especially if these systems are harmonized and integrated to achieve a competitive advantage for the company and improve operational effectiveness. The aim of this study is to measure the impact of harmonizing activity-based-costing system with enterprise resource planning in improving the operational effectiveness of manufacturing companies listed on the Amman Stock Exchange by implementing industry type as a mediating variable. In order to achieve the study objectives, a designed questionnaire was developed and distributed to specialists in the accounting department, enterprise resource planning system managers, and costing managers within the study population. The study revealed that industry type as a mediating variable has modified the role of enterprise resource planning system integration with the activity-based-costing system in improving operations effectiveness, especially in the food, supplies, and agricultural sectors, and it is recommended for managers to develop and improve their current systems by attracting software specialists to build the integration between information systems.
Design and Implementation of Virtual Reality Interactive Product Software Based on Artificial Intelligence Deep Learning Algorithm

The aim of this study is to improve the interactive needs of artificial intelligence in the virtual reality environment. Based on the in-depth study of the interactive needs of virtual reality, a virtual reality interactive glove based on nine-axis inertial sensor and realized by artificial intelligence deep learning algorithm is designed. The AI deep learning algorithms employed include the KNN, SVM, Fuzzy, PNN, and DTW algorithms. Static gesture recognition is relatively simple, dynamic gesture recognition needs to a dynamic real-time gesture sequence data starting point and end point planning, by building the directed graph structure, quickly retrieving the global optimal solution, and determining gesture starting point, with dynamic planning to solve the minimum distance between two points, avoid the graph, and improve efficiency. The results showed that by 50 gestures such as select object, attract object, zoom object, rotate object, shoot small box, exhale menu, and close the menu, the recognition rate is 100%, 94%, 96%, 100%, 92%, 100%, and 100%. The motion data of finger and palm are captured by nine axis sensor, and the gesture recognition is carried out by using artificial intelligence deep learning algorithm. It is proved that the artificial intelligence deep learning algorithm can effectively realize the design of virtual reality interactive product software.
Image Denoising with Deep Unfolding And Normalizing Flows

Many application domains, spanning from low-level computer vision to medical imaging, require high-fidelity images from noisy measurements.State-of-the-art methods for solving denoising problems combine deep learning with iterative modelbased solvers, a concept known as deep algorithm unfolding or unrolling.By combining a-priori knowledge of the forward measurement model with learned proximal image-to-image mappings based on deep networks, these methods yield solutions that are both physically feasible (data-consistent) and perceptually plausible (consistent with prior belief).However, current proximal mappings based on (predominantly convolutional) neural networks only implicitly learn such image priors.In this paper, we propose to make these image priors fully explicit by embedding deep generative models in the form of normalizing flows within the unfolded proximal gradient algorithm, and training the entire algorithm in an end-to-end fashion.We demonstrate that the proposed method outperforms competitive baselines on image denoising.