Reviewerly/exHarmony huggingface dataset:
3D Human Skeleton Estimation from Single RGB Image Based on Fusion of Predicted Depths from Multiple Virtual-Viewpoints

Estimating 3D human pose from a single RGB image is still challenging in computer vision. Driven by multi-view approach, we propose a model to estimate multiple virtual-view skeletons from a single real-view image which are then fused to derive the final 3D human skeleton. Our network is composed of two stages. The first-stage is a two-streams network, where a Real-Net stream predicts 2D image coordinates and relative depth for each joint from the real-view image and a Virtual-Net stream predicts the relative depths in virtual viewpoints for the same joints. Our second-stage network consists of depth-denoising module and fusion network, where the outputs of the first-stage network are concatenated, depth-denoised, and converted into the predicted 3D human skeleton. The experimental results show that our technique has achieved a performance of MPJPE=47.48 mm, which is comparable to other state-of-the-art methods based on a much longer image sequence (9~243 frames).
Out-of-Vocabulary Word Detection in Spoken Dialogues Based on Joint Decoding with User Response Patterns

We detect out-of-vocabulary (OOV) words from spoken utterances. A straightforward cascade model, where syllable recognition and word segmentation are simply concatenated, often fails to correctly segment syllables around OOV words. Because OOV words in spoken dialogues with systems do not appear in the training data, some reasonable constraints in such situations are necessary. In this study, we propose a method combining two ideas to detect OOV words. First, we jointly decode syllable sequences and word boundaries in them using user response patterns, which exist in the user responses to explicit confirmations from a spoken dialogue system. These patterns serve as constraints on vocabulary in utterances and the positions of OOV words. We represent OOV words as arbitrary syllable sequences and use finite-state transducers (FSTs) containing both OOV words and in-vocabulary words as constraints during decoding. Joint decoding with these constraints can also reduce syllable recognition errors. Second, we automatically select the better results between the baseline cascade model and the joint model through model selection. This effectively improves the detection performance as not all user utterances follow the patterns. Experimental results show improvement in the F-score for OOV word detection.
A Scalable Recommendation System with Hybrid Similarity Matrix Using Accelerated Particle Swarm Optimization

Collaborative filtering (CF) is one of the most popular recommendation approaches that recommends relevant items to users according to their past interactions to help users with the information overload from the large volume of online multimedia content. However, while the traditional similarity measures using solely rating information suffer from data sparsity and cold-start problems, the features of items are biased and do not reflect the true quality of the items. Existing hybrid neighborhood-based models have mitigated these problems by adopting both the transaction history data and profiles of users and items. Previous works on aggregating rating-based and content-based data into an unified hybrid similarity matrix in neighborhood-based models have shown noticeable success. Correspondingly, this paper proposes adopting a variant of particle swarm optimization to automatically adjust the influence of each data type on the combined similarity matrix, which can boost the performance of neighborhood-based systems significantly by at least 1.64% in terms of predicting accuracy and 0.77% in the ranking task compared to the earlier related hybrid methods. Additionally, to deal with the huge timing disadvantage of this optimization algorithm, we propose a tweak to the particle swarm optimization to train the model only on a small batch of the training set, utilizing the existing information included in distinct similarity matrices.
Distributed Computation of Heat Kernel Smoothing Using Series Expansion Method

Heat kernel smoothing (HKS) is one of important methods to reduce noise corrupted in the irregular data collected from various complex network. In this paper, the distributed computation of HKS method using series expansion is studied. First, the relation between HKS computation and matrix exponential is presented and the centralized computation of HKS method is derived. Then, two series expansion methods are used to implement the HKS operator in a distributed manner. They include Taylor series expansion (TSE) and Laguerre series expansion (LSE). The TSE method corresponds to a maximally-flat graph filter method, and LSE method corresponds to the weighted-least-squares graph filter method. Next, the realization structures of two series expansion methods are described by choosing the Laplacian matrix as graph shift operator. Finally, the denoising experiments of data collected from sensor network and social network are used to demonstrate the effectiveness of HKS methods in terms of the improvement of signal to noise ratio.
ASR Model Adaptation for Rare Words Using Synthetic Data Generated by Multiple Text-To-Speech Systems

Automatic speech recognition (ASR) for rare words is difficult as there are little relevant text-audio data pairs to train an ASR model. To obtain more text-audio pairs, text-only data are fed to Text-To-Speech (TTS) systems to generate synthetic audio. Previous works use a single TTS system conditioned on multiple speakers to produce different speaker voices to improve the output data’s speaker diversity, and they show that training an ASR model on the more diverse data can avoid overfitting and improve the model’s robustness. As an alternative way to improve the diversity, we study the speaker embedding distribution of audios synthesized by different TTS systems and found that the audios synthesized by different TTS systems have different speaker distributions even when they are conditioned on the same speaker. Inspired by this, this paper proposes to condition multiple TTS systems repeatedly on a single speaker to synthesize more diverse speaker data, so ASR models can be trained more robustly. When we apply our method to a rare word dataset partitioned from National Speech Corpus SG, which contains mostly road names and addresses in its text transcripts, experiments show that a pretrained ASR model adapted to our multi-TTS-same-SPK data gives relatively 9.8% lower word error rate (WER) compared to the ASR models adapted to same-TTS-multi-SPK data of the same data size, and our overall adaptation improves the model’s WER from 57.6% to 16.5% without using any real audio as training data.
Implementation of OCR system on extracting information from Vietnamese book cover images.

This paper describes an OCR system for extracting information on Vietnamese book cover images. The set system takes as input the book cover image and outputs the book title, author, publisher and other information. The system is composed of three modules including text detection, text recognition and information synthesis. For the text detection module, we experiment with EAST and SAST models. For the text recognition module, we experimented with CRNN, SVTR, Transformer OCR models. In the last step, we use Yolov4 for object detection then to process the information. Our system extracts book cover information with accuracy per word up to 84.06% and on WER and CER score respectively 22.67% and 22.41%.
Continual Local Updates for Federated Learning with Enhanced Robustness to Link Noise

Communication errors caused by noisy links can negatively impact the accuracy of federated learning (FL) algorithms. To address this issue, we introduce an FL algorithm that is robust to communication errors while concurrently reducing the communication load on clients. To formulate the proposed algorithm, we consider a weighted least-squares regression problem as a motivating example. We recast this problem as a distributed optimization problem over a federated network, which employs random scheduling to enhance communication efficiency, and solve the reformulated problem via the alternating direction method of multipliers. Unlike conventional FL approaches employing random scheduling, the proposed algorithm grants the clients the ability to continually update their local model estimates even when they are not selected by the server to participate in FL. This allows for more frequent and ongoing client involvement, resulting in performance improvement and enhanced robustness to communication errors compared to when the local updates are only performed when the respective clients are selected by the server. We demonstrate the effectiveness and performance gains of the proposed algorithm through simulations.
Mismatched Semi-supervised Learning with Feature Similarity Consistency

Effective learning from a small amount of labeled data and a large amount of unlabeled data remains a prominent issue in machine learning. Current semi-supervised learning methods often neglect mismatched problems within class distribution, and do not fully use feature-level encoding for unlabeled data. In this article, we introduce a novel semi-supervised learning method called FSimC, which effectively uses information from unlabeled data at both the semantic- and feature-levels and is successfully applied to mismatched distribution scenarios. At the semantic-level, FSimC consists of two classifiers predicting pseudo and complementary labels for unlabeled data. The mutual alignment of the predictive distributions of the two classifiers results in more accurate pseudo and complementary labels. Then, the two classifiers undergo separate consistency regularization. At the feature-level, we create a cache to store the feature encodings of labeled data. Next, we compute the feature similarity vectors between feature encodings of the weakly and strongly augmented unlabeled data with feature encodings in the cache. Finally, we perform feature similarity consistency on the feature similarity vectors. Extensive experiments are conducted on various benchmark datasets, and the results demonstrate that FSimC achieves outstanding performance in matched and mismatched distribution scenarios. The code is publicly available at https://github.com/GZHU-DVL/FSimC.
Modeling Suprasegmental Information Using Finite Difference Network for End-to-End Speaker Verification

In recent years, using raw waveforms as input to deep networks has been widely explored for speaker verification systems that process speech signals at the segmental level. A critical issue of such an approach is that the front-end network with a small kernel fails to capture the suprasegmental information, such as the intonation patterns and prosody that span longer than one second. This paper proposes a novel framework that can capture the segmental and suprasegmental information after the first convolutional layer. Concretely, suprasegmental information is obtained from the first-order finite difference of two consecutive suprasegmental envelopes estimated by Hilbert transforms. Experimental evaluations on the Voxceleb dataset show that combining segmental and suprasegmental features can reduce the EER of an end-to-end system by 27%. To our best knowledge, this is the first attempt to incorporate suprasegmental information for end-to-end speaker verification.
Artificial Hummingbird Algorithm for Dynamic Partition of Logistics Network

With the rapid development of modern information technology, various advanced logistics distribution models and strategies have been proposed. All related stake holders have paid much attention to city express delivery. Achieving the ideal supply and demand matching of logistics resources is one of the important goals in smart cities. A dynamic logistics distribution network model based on Artificial Hummingbird Algorithm (AHA) is proposed in this paper. Dynamic Partition Network (DPN) can effectively divide a distribution area such that the total distribution consumption is minimized. It is a novel logistics region partition framework that can develop the optimal strategy for each distribution task. AHA is improved with a new coding method and the idea of the Grey Wolf Optimizer, which improves the performance of the model solver and speeds up the output of a better distribution scheme. Extensive experiments on real-world logistics and distribution tasks show that the dynamic division model is able to make substantial improvement in dividing distribution areas over existing technologies.
Weapon Detection Using Swin Transformer

Smart Camera along with Artificial Intelligence to solve complex social problems is increasingly popular. Security and safety issues are complex and tending to be related to crimes using weapons. Recently, Transformer Architectures have gradually become popular in Computer Vision problems. This paper focuses on training a weapon detection system based on the Swin Transformer model, combined with another method: Mask R-CNN, Cascade Mask R-CNN, Mask RepPoints-V2 to create better, faster object recognition solutions. The research focuses on 3 main objects of weapon such as Pistol, Rifle, Knife. Due to the restriction of available dataset, WeaponData_VN dataset were built and described.
A Transfer Learning approach for handwritten drug names recognition

In many developing countries, handwritten medical prescriptions remain a prevalent mode of documentation within healthcare systems. However, the legibility of these prescriptions often poses a significant challenge, leading to potential errors in patient care. To address this critical issue, our work focuses on developing a model for the recognition of medical handwritten prescriptions. The model architecture is composed of Convolutional Neural Networks (CNNs) and Bi-directional Long-short term memory (Bi-LSTMs) and a Connectionist temporal classification (CTC) Layer to compute loss between predicted sequences and ground truth sequences. However, the availability of annotated medical handwriting data is constrained, particularly due to privacy concerns. In response, we delve into transfer learning techniques to harness existing knowledge and enhance model performance. By leveraging transfer learning, we aim to optimize our recognition model using fine-tuning strategies. This study contributes to the field of medical handwriting recognition by exploring the potential of transfer learning in enhancing model accuracy and applicability within developing healthcare contexts.
A Spatial-temporal Gated Network for Credit Card Fraud Detection

Timely and accurate credit card fraud detection (CCFD) is concerned by all financial institutions. Existing CCFD methods generally employ aggregated or raw features as their representations to train their detection models. Yet such features tend to fall short of effectively exposing some critical characteristics of frauds. In this work, we propose a spatial-temporal gated network (STGN) to automatically learn new informative transactional representations containing users’ transactional behavioral information for CCFD. A special gated recurrent neural net unit is constructed with a time-aware gate and location-aware gate to extract users’ spatial and temporal transactional behaviors. A spatial-temporal attention module is designed to expose the transaction motive of users in their historical transactional behaviors, which allows the proposed model to better extract the fraudulent characteristics from successive transactions with time and location information. A representation interaction module is offered to make rational decisions and learn compositive transactional representations. A real-world transaction dataset is used in our experiments to verify the efficacy of the learned new representations. The results demonstrate that our proposed model outperforms the state-of-the-art ones, thus greatly advancing the field of CCFD.
Coarse-Age Loss: A New Training Method Using Coarse-Age Labeled Data for Speaker Age Estimation

We propose Coarse-age loss for a new training method that uses coarse-age labeled speech samples for numerical speaker age estimation. The estimation performance of existing approaches can be degraded by a mismatch in the acoustic characteristics between the training and test phases. The age estimation model must be fine-tuned to suit the test condition. It is easier to collect coarse-age labeled samples, such as ten-year step size, than exact-age labeled speech samples due to privacy concerns. However, special care is needed in using coarse-age labeled samples because they cannot be used as is for training numerical age estimation models. A conventional method converts the coarse-age labels into their median value as tentatively-correct labels (e.g., twenties is converted to 24.5 years old). However, errors between the correct and the tentatively-correct labels negatively influence the model training phase. A key idea of our method proposed here is that the coarse-age labels indicate continuous correct- and incorrect-age ranges. Coarse-age loss trains the model so that it does not misestimate the incorrect range by minimizing a sum of posteriors corresponding to the incorrect range without using exact-age labels. In experiments, the proposed method reduces the mean absolute errors by up to 30.1 % and 10.8 % compared to the baseline and the conventional method, respectively. Also, the results indicated that somewhat broad coarse-age labels, such as the ten-year step, are suitable for the proposed method.
Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images

In recent years, deep neural networks (DNNs) trained with transformed data have been applied to various applications such as privacy-preserving learning, access control, and adversarial defenses. However, the use of transformed data decreases the performance of models. Accordingly, in this paper, we propose a novel method for fine-tuning models with transformed images under the use of the vision transformer (ViT). The proposed domain adaptation method does not cause the accuracy degradation of models, and it is carried out on the basis of the embedding structure of ViT. In experiments, we confirmed that the proposed method prevents accuracy degradation even when using encrypted images with the CIFAR-10 and CIFAR-100 datasets.
Study on Face Landmark-based Analysis for Synthetic Media Identification Generated by Adversarial Generative Networks

With the development of deep learning technology, artificially created images and videos, known as deepfakes, have become a problem. It is very difficult for human visual and auditory senses to distinguish them from real ones. Therefore, there is a growing risk of them being used for crimes such as bullying, defamation, extortion, and fraud on social media. StyleGAN is one of the generators for such fake content and is known for its ability to generate high-quality images with a great degree of realism. This study aims to distinguish between real and fake images generated by StyleGAN. We introduce effective image processing techniques into the detector which is CNN model designed by fine-tuning the XceptionNet. We examined two methods: one that extracts color attributes from the image and the other that distinguishes based on landmarks. We evaluated the accuracy of distinguishing between real and fake images by computer simulation.
Multi-Frequency Feature Enhancement for Multi-Granularity Visual Classification

Multi-granularity visual classification is a challenging task derived from traditional image recognition. Previous methods commonly use the features from the final convolutional layer to perform multi-granularity visual classification. However, the features required for different granularity label classification are not consistent. The finer the granularity of the label, the more detailed the features are needed. So, the key to multi-granularity visual classification is to extract effective features for different granularity levels. Generally, the high-frequency parts of natural images usually encode detailed information, while low-frequency parts often encode global structures. Therefore, mapping the output features of convolutional layers into high-frequency and low-frequency parts may enhance feature learning with multi-granularity. In this paper, we decompose the output features from convolutional layers into high-frequency and low-frequency counterparts, and use the combinations of the high-frequency and low-frequency features from various convolutional layers to learn different granularity labels. We evaluate the proposed method on the three public available datasets, including CUB-200-2011, Stanford Cars, and FGVC-Aircraft datasets. The experimental results demonstrate the effectiveness of the proposed method.
Improved Hierarchical Attention Networks for Cyberbullying Detection via Social Media Data

Nowadays, online bullying, called cyberbullying, has become one of the most common problems faced by young people in society. The text, containing and spreading amounts of information, is a key medium in the social media and social networks. The natural language processing (NLP) method as the most common method deals with textual information. In the field of cyberbullying detection, extracting information from texts is a way to predict online bullying phenomenon presently. In this work, we propose a transformer based hierarchical neural network (THNN). Compared to existing methods, ours has the following advantages: (i) it captures longer context information for longer time intervals; (ii) it reflects and mimics the hierarchical structure of conversation media clearly; (iii) it has less computational time.
Explainable AI (XAI) for AI-Acceptability: The Coming Age of Digital Management 5.0

This paper delves into the nascent paradigm of Explainable AI (XAI) and its pivotal role in enhancing the acceptability of growing AI systems that are shaping the Digital Management 5.0 era. XAI holds significant promise, promoting compliance with legal and ethical standards and offering transparent decision-making tools. The imperative of interpretable AI systems to counter the black box effect and adhere to data protection laws like GDPR is highlighted. This paper aims to achieve a dual objective. Firstly, it provides an in-depth understanding of the emerging XAI paradigm, helping practitioners and academics project their future research trajectories. Secondly, it proposes a new taxonomy of XAI models with potential applications that could facilitate AI acceptability. Although the academic literature reflects a crucial lack of exploration into the full potential of XAI, existing models remain mainly theoretical and lack practical applications. By bridging the gap between abstract models and the pragmatic implementation of XAI in management, this paper breaks new ground by launching the scientific foundations of XAI in the upcoming era of Digital Management 5.0.
Comparison of Classic and Recent Multi-Agent Path Finding Methods via MAPFame

A Multi-Agent Path Finding (MAPF) problem aims to plan paths for multiple agents given a prescribed map and ensure they do not conflict with each other and travel the shortest distance or lowest cost in the minimal time. MAPF is useful in many practical applications, e.g., automated warehouses and intelligent factories. It has been widely-studied in the past decade. Existing MAPF algorithms have evolved from those solving single-agent path finding problems. When realized in different programming languages, they tend to deliver varying results regarding execution time and solution quality. Many kinds of simulated maps are used but some of them are not directly related to actual application environment. In this paper, we experimentally compare existing MAPF algorithms based on an open-source simulation platform called Multi-Agent Path Finding based on Advanced Methods and Evaluation (MAPFame). We analyze and test the effects of obstacle density, different maps, and agents counts on their performance indices. Hence, our research outcomes can be used by practitioners to select a right method for their particular applications.