Reviewerly/exHarmony huggingface dataset:
VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer Dataset

In this paper, we have presented a novel deep neural network architecture involving transfer learning approach, formed by freezing and concatenating all the layers till block4 pool layer of VGG16 pre-trained model (at the lower level) with the layers of a randomly initialized naïve Inception block module (at the higher level). Further, we have added the batch normalization, flatten, dropout and dense layers in the proposed architecture. Our transfer network, called VGGIN-Net, facilitates the transfer of domain knowledge from the larger ImageNet object dataset to the smaller imbalanced breast cancer dataset. To improve the performance of the proposed model, regularization was used in the form of dropout and data augmentation. A detailed block-wise fine tuning has been conducted on the proposed deep transfer network for images of different magnification factors. The results of extensive experiments indicate a significant improvement of classification performance after the application of fine-tuning. The proposed deep learning architecture with transfer learning and fine-tuning yields the highest accuracies in comparison to other state-of-the-art approaches for the classification of BreakHis breast cancer dataset. The articulated architecture is designed in a way that it can be effectively transfer learned on other breast cancer datasets.
Neural Collaborative Graph Machines for Table Structure Recognition

Recently, table structure recognition has achieved impressive progress with the help of deep graph models. Most of them exploit single visual cues of tabular elements or simply combine visual cues with other modalities via early fusion to reason their graph relationships. However, neither early fusion nor individually reasoning in terms of multiple modalities can be appropriate for all varieties of table structures with great diversity. Instead, different modalities are expected to collaborate with each other in different patterns for different table cases. In the community, the importance of intrainter modality interactions for table structure reasoning is still unexplored. In this paper, we define it as heterogeneous table structure recognition (HeteroTSR) problem. With the aim offilling this gap, we present a novel Neural Collaborative Graph Machines (NCGM) equipped with stacked collaborative blocks, which alternatively extracts intramodality context and models inter-modality interactions in a hierarchical way. It can represent the intrainter modality relationships of tabular elements more robustly, which significantly improves the recognition performance. We also show that the proposed NCGM can modulate collaborative pattern of different modalities conditioned on the context of intramodality cues, which is vital for diversified table cases. Experimental results on benchmarks demonstrate our proposed NCGM achieves state-of-the-art performance and beats other contemporary methods by a large margin especially under challenging scenarios.
Investigation of improving the pre-training and fine-tuning of BERT model for biomedical relation extraction

Recently, automatically extracting biomedical relations has been a significant subject in biomedical research due to the rapid growth of biomedical literature. Since the adaptation to the biomedical domain, the transformer-based BERT models have produced leading results on many biomedical natural language processing tasks. In this work, we will explore the approaches to improve the BERT model for relation extraction tasks in both the pre-training and fine-tuning stages of its applications. In the pre-training stage, we add another level of BERT adaptation on sub-domain data to bridge the gap between domain knowledge and task-specific knowledge. Also, we propose methods to incorporate the ignored knowledge in the last layer of BERT to improve its fine-tuning.The experiment results demonstrate that our approaches for pre-training and fine-tuning can improve the BERT model performance. After combining the two proposed techniques, our approach outperforms the original BERT models with averaged F1 score improvement of 2.1% on relation extraction tasks. Moreover, our approach achieves state-of-the-art performance on three relation extraction benchmark datasets.The extra pre-training step on sub-domain data can help the BERT model generalization on specific tasks, and our proposed fine-tuning mechanism could utilize the knowledge in the last layer of BERT to boost the model performance. Furthermore, the combination of these two approaches further improves the performance of BERT model on the relation extraction tasks.
Residue calibrated least-squares unwrapping algorithm for noisy and steep phase maps

This work proposes a robust unwrapping algorithm for noisy and steep phase maps based on the residue calibrated least-squares method. The proposed algorithm calculates and calibrates the residues in the derivative maps to get a noise-free Poisson equation. Moreover, it compensates for the residuals between the wrapped and unwrapped phase maps iteratively to eliminate approximation errors and the smoothing effect of the least-squares method. The robustness and efficiency of the proposed algorithm are validated by unwrapping simulated and experimentally wrapped phase maps. Compared with the other three typical algorithms, the proposed algorithm has the most effective performance in noisy and steep phase unwrapping, providing a reliable alternative for practical applications.
Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning

The exponential growth of the internet and a multi-fold increase in social media users in the last decade have resulted in a massive growth of unstructured data.Aspect-Based Sentiment Analysis (ABSA) is challenging because it performs a fine-grain analysis; it is a text analysis technique where the opinions group is based on the aspect.The Aspect Extraction (AE) task is one of the core subtasks of ABSA; it helps to identify aspect terms in the text, comments, or reviews.The challenge of the Arabic AE task increases due to the complexity of the Arabic language.This work aims to develop the Arabic AE task by proposing transfer learning using state-of-art pre-trained contextual language models.We concatenate the Bidirectional Encoder Representation from Transformers (BERT) language model and contextualize string embeddings (Flair embedding) as a stacked embeddings layer for better word representation for Arabic language.Then, we extend it with different deep learning network architectures.For Arabic AE, the model is developed by concatenating the Arabic contextual language model, AraBERT, and Flair embedding as a contextual stacked embeddings layer with an extended layer, BiLSTM-CRF or BiGRU-CRF, for sequence labeling.Our proposed models are called BF-BiLSTM-CRF and BF-BiGRU-CRF.The proposed model is evaluated using the Arabic Hotel's reviews dataset.For performance evaluation, we used the F1 score.The experimental results show that the proposed BF-BiLSTM-CRF configuration outperformed the baseline and other models by achieving an F1score of 79.7%.
A Teaching Factory Paradigm for Personalized Perception of Education based on Extended Reality (XR)

Engineering education is key element to the evolution of the global society. The new generations of engineers, following the Industrial Revolutions, and more specifically the upcoming Industry 5.0, require new skills and competencies in order to facilitate this transformation. The scope of the new Industrial Revolution will be to reinforce the Human and Machine Interface (HMI), based on foundations built during the Industry 4.0. Therefore, classic learning models will have to be adapted to this reality. Furthermore, the recent global pandemic has facilitated the development of solutions/platforms for the virtual collaboration for various aspects of everyday life, including professional meetings, educational lectures, academic collaborations, and personal relationships. Taking into consideration the above-mentioned, the key to the creation of more robust and resilient education models, lies on the development of new platforms utilizing cutting-edge technologies, such as Cloud, 5G, Artificial Intelligence (AI), Extended Reality (XR), and Blockchain. Following the mass personalization paradigm implemented in modern manufacturing, a new paradigm, the “Personalized Perception” is introduced, promoting the constant adaptation of education to the skills and competencies of students and generate highly capable engineers by elevating their personal attributes. Therefore, the contribution of this paper is focused on the design and development of a Cloud-based platform supporting the collaboration between engineering students, academic organizations, research organizations, and industrial companies as well. The proposed platform engages technologies such as XR, and Blockchain in order to provide suitable communication channels for collaboration, virtual co-existence, and secure information exchange. Cloud Computing and 5G are utilized in order to support the seamless information flow. Moreover, AI algorithms can be used for supporting simulation tools and for the quantifying student skills and competencies. Consequently, this decentralized platform, promotes the collaboration of the involved parties on a 24/7 basis, providing access to professional tools, complementary to physical presence.
Parrondo's paradox in quantum walks with deterministic aperiodic sequence of coins

Parrondo's effect is a well-known apparent paradox where a combination of biased random walks displays a counterintuitive reversal of the bias direction. We show that Parrondo's effect can occur not only in the case of one-dimensional discrete quantum walks with random or deterministic periodic sequence of two- or multistate quantum coins but also in the case of one-dimensional discrete quantum walks with deterministic aperiodic sequence of two-state quantum coins. Moreover, we show how Parrondo's effect affects the time evolution of the walker-coin quantum entanglement.
Real-time estimation of upper limbs kinematics with IMUs during typical industrial gestures

In the context of Industry 4.0, collaborative robotics can be improved by implementing appropriate reactive behavior schemes between human and the robot within the shared workspace. Inertial Measurement Units (IMUs) are widely used for real-time capturing of the human motion in clinical and sports applications, while less attention has been dedicated to IMUs for human-robot interactions in manufacturing industrial field. The aim of the present study was to validate an IMU-based method for real-time estimation of upper limbs kinematics during typical gestures required for industrial assembly tasks. To avoid ferro-magnetic disturbances typical of manufacturing environments, the magnetometers were excluded from the estimate of IMUs orientations. Shoulder and elbow angles were assessed in real-time during pick and place tasks through three IMUs fixed on the upper body of six participants. Results were validated through a stereophotogrammetric motion capture system. Errors associated to the proposed method were moderate and amounted to 2.4 and 3.5 deg on average for shoulder elevation and elbow flexion-extension angles, respectively, confirming their suitability for an industrial context of human-robot collaboration.
Comparative Analysis of Recent Architecture of Convolutional Neural Network

Convolutiona neural network (CNN) is one of the best neural networks for classification, segmentation, natural language processing (NLP), and video processing. The CNN consists of multiple layers or structural parameters. The architecture of CNN can be divided into three sections: convolution layers, pooling layers, and fully connected layers. The application of CNN became most demanding due to its ability to learn features from images automatically, involving massive amount of training data and high computational resources like GPUs. Due to the availability of the above-stated resources, multiple CNN architectures have been reported. This study focuses on the working of convolution, pooling, and the fully connected layers of CNN architecture, origin of architectures, limitation, benefits of reported architectures, and comparative analysis of contemporary architecture concerning the number of parameters, architectural depth, and significant contribution.
FeNet: Feature Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution

In the field of remote sensing, due to memory consumption and computational burden, the single-image super-resolution (SISR) methods based on deep convolution neural networks (CNNs) are limited in practical application. To address this problem, we propose a lightweight feature enhancement network (FeNet) for accurate remote-sensing image super-resolution (SR). Considering the existence of equipment with extremely poor hardware facilities, we further design a lighter FeNet-baseline with about 158K parameters. Specifically, inspired by lattice structure, we construct a lightweight lattice block (LLB) as a nonlinear feature extraction function to improve the expression ability. Here, channel separation operation makes the upper and lower branches of the LLB only responsible for half of the features, and the weight coefficients calculated through the attention mechanism enable the upper and lower branches to communicate efficiently. Based on LLB, the feature enhancement block (FEB) is designed in a nested manner to obtain expressive features, where different layers are responsible for the features with different texture richness, and then features from different layers are sequentially fused from deep to shallow. Model parameters and multi-adds operations are used to evaluate network complexity, and extensive experiments on two remote-sensing and four SR benchmark test datasets show that our methods can achieve a good tradeoff between complexity and performance. Our code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wangzheyuan-666/FeNet</uri> .
Distributed GGIW-CPHD-Based Extended Target Tracking Over a Sensor Network

Multiple extended target tracking (METT) is a common and challenging problem. Various solutions for METT have been proposed, however, most of them focus on the single-sensor or centralized multi-sensor scenarios. In this letter, we explore the multi-sensor METT problem in a distributed fusion framework. Specifically, there are two stages in the implementation process: 1) to perform a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gamma Gaussian Inverse Wishart Cardinalized Probability Hypothesis Density</i> (GGIW-CPHD) filter for each sensor node, and 2) to perform a fusion by resorting to the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Generalized Covariance Intersection</i> (GCI) fusion rule. In the fusion stage, we derive an approximate GGIW mixture form of the fused spatial density. Lastly, simulation experiments via a consensus sensor network are provided to verify the effectiveness of the proposed approach.
Hybrid differential evolution and particle swarm optimization for Multi-visit and Multi-period workforce scheduling and routing problems

This research proposed an optimization method (Hybrid Differential Evolution and Particle Swarm Optimization, HDEPSO) using a solution technique based on two well-known techniques, Differential Evolution (DE) and Particle Swarm Optimization (PSO), to tackle a multi-visit and multi-period workforce scheduling and routing problem (MMWSRP) in field service operation of a sugarcane mill company in Thailand. The HDEPSO can be used for planning of routes and maintenance work for each sugarcane harvester to be provided by service teams of mechanical, hydraulic, and electrical technicians. The members of the service teams will be determined according to their skills and skill levels and service routes for each individual service team so that the operation cost is minimized. At first, mixed integer programing was used to determine the best solution. This technique is, however, not suitable for large-size problems. A HDEPSO was therefore developed to solve the MMWSRP and then tested against the mixed integer programing for small-size problems and it was found that both methods were equally effective. However, for larger-size problems, shortcomings of the mixed-integer technique became obvious whereas the HDEPSO was much more advantageous. The HDEPSO was also tested against the DE and PSO. The computational results show that the objective value of the proposed method was decreased by 4.94% and 7.45% compared with the DE and the PSO, respectively.
What is Event Knowledge Graph: A Survey

Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are also an essential kind of knowledge in the world, which trigger the spring up of event-centric knowledge representation form like Event KG (EKG). It plays an increasingly important role in many downstream applications, such as search, question-answering, recommendation, financial quantitative investments, and text generation. This paper provides a comprehensive survey of EKG from history, ontology, instance, and application views. Specifically, to characterize EKG thoroughly, we focus on its history, definitions, schema induction, acquisition, related representative graphs/systems, and applications. The development processes and trends are studied therein. We further summarize prospective directions to facilitate future research on EKG.
Impact of Image Resizing on Deep Learning Detectors for Training Time and Model Performance

Resizing images is a critical pre-processing step in computer vision. Principally, deep learning models train faster on small images. A larger input image requires the neural network to learn from four times as many pixels, and this increase the training time for the architecture. In this work, we presented the evolution of effects of image resizing on model training time and performance. This study is applied on a vehicle dataset. We used You Look Only Once based architectures which include YOLOv2, YOLOv3, YOLOv4, and YOLOv5 with pretrained models to perform object detection. YOLO is designed to detect objects with high accuracy and high speed, which is an advent for real-time applications. Data augmentation method is used in this research to reduce overfitting problems, which approximates the data probability by manipulating the input samples. The experimental results show that if the input image size varies, then it has effects on the training time of the CNN based images classification. Additionally, this research reviewed image resizing and its impacts on the models’ performance in terms of accuracy, precision, and recall.
Series-Constellation Feature Based Blind Modulation Recognition for Beyond 5G MIMO-OFDM Systems With Channel Fading

Due to the shortage of radio spectrum in the current 5G and upcoming 6G systems, the cognitive radio (CR) technique is indispensable for spectrum management and can put the unutilized spectrum to good use. As the core technology of CR, blind modulation recognition (BMR) plays a pivotal role in improving spectral efficiency. However, the BMR research on MIMO-OFDM systems still lacks enough attention. Given the prosperity of deep learning, we propose a series-constellation multi-modal feature network (SC-MFNet) to recognize the modulation types of MIMO-OFDM subcarriers. Without any prior information, a blind signal separation algorithm is employed to reconstruct the impaired transmitted signal. Considering the insufficient features of signal series, we propose a segment accumulated constellation diagram (SACD) strategy to produce the striking constellation features. Moreover, the proposed multi-modal feature fusion network is employed to collect the advantages of series and SACD features, which are extracted by one-dimensional convolution (Conv1DNet) branch and improved EfficientNet branch, respectively. Experimental results demonstrate that in a realistic non-cooperative cognitive communication scenario where prior information is exempted, the proposed SC-MFNet outperforms the traditional feature-based methods and the state-of-the-art neural networks which are based on either constellation features or series features.
Generative adversarial U-Net for domain-free few-shot medical diagnosis

• We proposed a new data augmentation method for few-shot medical diagnosis. • The proposed method can be used in different modality medical image data. • Extensive experiments are conducted to demonstrate the superior of the proposed method. In interactive remote medical diagnosis, medical diagnosis support system plays an important role in the pre-screening stage. Deep learning methods are widely used in such system especially medical imaging area to provide an initial diagnosis to support medical professional. However, the shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. Without a sufficient number of training samples, deep learning based models are very likely to suffer from over-fitting problem. The common solution is image manipulation such as image rotation , cropping, or resizing. Those methods can help relieve the over-fitting problem as more training samples are introduced. However, they do not really introduce new images with additional information and may lead to data leakage as the test set may contain similar samples which appear in the training set. To address this challenge, we propose to generate diverse images with generative adversarial network . In this paper, we develop a novel generative method named generative adversarial U-Net, which utilizes both generative adversarial network and U-Net. Different from existing approaches, our newly designed model is domain-free and generalizable to various medical images. Extensive experiments are conducted over eight diverse datasets including computed tomography (CT) scan, pathology, X-ray, etc. The visualization and quantitative results demonstrate the efficacy and good generalization of the proposed method on generating a wide array of high-quality medical images.
Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements

3D interacting hand reconstruction is essential to facilitate human-machine interaction and human behaviors understanding. Previous works in this field either rely on auxiliary inputs such as depth images or they can only handle a single hand if monocular single RGB images are used. Single-hand methods tend to generate collided hand meshes, when applied to closely interacting hands, since they cannot model the interactions between two hands explicitly. In this paper, we make the first attempt to reconstruct 3D interacting hands from monocular single RGB images. Our method can generate 3D hand meshes with both precise 3D poses and minimal collisions. This is made possible via a two-stage framework. Specifically, the first stage adopts a convolutional neural network to generate coarse predictions that tolerate collisions but encourage pose-accurate hand meshes. The second stage progressively ameliorates the collisions through a series of factorized refinements while retaining the preciseness of 3D poses. We carefully investigate potential implementations for the factorized refinement, considering the trade-off between efficiency and accuracy. Extensive quantitative and qualitative results on large-scale datasets such as InterHand2.6M demonstrate the effectiveness of the proposed approach.