Reviewerly/exHarmony huggingface dataset:
Semi-supervised Histological Image Segmentation via Hierarchical Consistency Enforcement

Acquiring pixel-level annotations for histological image segmentation is time- and labor- consuming. Semi-supervised learning enables learning from the unlabeled and limited amount of labeled data. A challenging issue is the inconsistent and uncertain predictions on unlabeled data. To enforce invariant predictions over the perturbations applied to the hidden feature space, we propose a Mean-Teacher based hierarchical consistency enforcement (HCE) framework and a novel hierarchical consistency loss (HC-loss) with learnable and self-guided mechanisms. Specifically, the HCE takes the perturbed versions of the hierarchical features from the encoder as input to the auxiliary decoders, and encourages the predictions of the auxiliary decoders and the main decoder to be consistent. The HC-loss facilitates the teacher model to generate reliable guidance and enhances the consistency among all the decoders of the student model. The proposed method is simple, yet effective, which can easily be extended to other frameworks. The quantitative and qualitative experimental results indicate the effectiveness of the hierarchical consistency enforcement on the MoNuSeg and CRAG datasets.
Memory optimization at Edge for Distributed Convolution Neural Network

Abstract Internet of Things (IoT) edge intelligence has emerged by optimizing the deep learning (DL) models deployed on resource‐constraint devices for quick decision‐making. In addition, edge intelligence reduces network overload and latency by bringing intelligent analytics closer to the source. On the other hand, DL models need a lot of computing resources. As a result, they have high computational workloads and memory footprint, making it impractical to deploy and execute on IoT edge devices with limited capabilities. In addition, existing layer‐based partitioning methods generate many intermediate results, resulting in a huge memory footprint. In this article, we propose a framework to provide a comprehensive solution that enables the deployment of convolutional neural networks (CNNs) onto distributed IoT devices for faster inference and reduced memory footprint. This framework considers a pretrained YOLOv2 model, and a weight pruning technique is applied to the pre‐trained model to reduce the number of non‐contributing parameters. We use the fused layer partitioning method to vertically partition the fused layers of the CNN and then distribute the partition among the edge devices to process the input. In our experiment, we have considered multiple Raspberry Pi as edge devices. Raspberry Pi with a neural computing stick is a gateway device to combine the results from various edge devices and get the final output. Our proposed model achieved inference latency of 5 to 7 seconds for to fused layer partitioning for five devices with a 9% improvement in memory footprint.
What drives SMEs to adopt smart technologies in Korea? Focusing on technological factors

As a country highly dependent on international trade, Korea has made efforts to achieve sustainable industrial competitiveness in the recent era of international supply chain transformation. The Korean government supports small and medium-sized enterprises (SMEs) in their acceptance of key technologies of the 4th Industrial Revolution with the same purpose, but sufficient results have not yet been obtained to enable SMEs to respond flexibly to the global supply chain structure transformation. This study attempts to understand the cause of this status by examining the insufficient recognition of the technological characteristics of the key technologies of the 4th Industrial Revolution. Specifically, this study aims to ascertain Korean SMEs' perceptions of technological characteristics, and their acceptance of the key technologies of the 4th Industrial Revolution. By empirically analyzing their effects on supply chain innovation performance, this study suggests implications related to the need to improve the Korean government's policy. In this study, the key technologies of the 4th Industrial Revolution are collectively called smart technology, and their technological characteristics are divided into technological innovation, process integration, and technological convergence. The recognition of the process integration and technological convergence of smart technology was found to be significant, and the results of this empirical analysis have implications for improving the Korean government's policy. • We study the effects of technological characteristics of smart technologies and supply chain innovation on SMEs in Korea. • Korean SMEs' improved recognition on technological characteristics of smart technologies will help to achieve higher performance in technology adoption and supply chain innovation. • Korea government needs to improve the current policy by reflecting the appropriate recognition of smart technologies.
An Integrated Automatic Writing Evaluation and SVVR Approach to Improve Students’ EFL Writing Performance

Writing is a challenging task for English Foreign Language (EFL) instruction. Based on artificial intelligence technology, Automatic Writing Evaluation (AWE) has received considerable attention from the EFL research community in recent years, since it can provide timely and personalized feedback to EFL writing learners. However, researchers have pointed out that while AWE can provide satisfactory feedback on vocabulary use and grammar, it is relatively inadequate at providing efficient feedback on organization, coherence, and content. Spherical Video-based Virtual Reality (SVVR) can provide a highly immersive and in-depth interaction learning environment that makes up for this shortcoming. Authentic experiences help enhance EFL writing learners’ perceptions and understanding of context, and assist students in creating constructive internal connections between their personal experiences and the topic of their writing, thus improving their writing quality. Therefore, the current study proposed an approach which integrated SVVR and AWE to investigate its effects on EFL writing. To investigate the effectiveness of the proposed approach, a quasi-experiment was carried out in a university’s EFL writing course. The experimental group (37 students) used the SVVR–AWE approach, while the control group (39 students) used the conventional approach with AWE. The results showed that the learning method not only considerably enhanced the students’ EFL writing performance, but also raised their motivation, self-efficacy, and sense of presence, as well as reduced their EFL writing anxiety. Furthermore, interviews were performed and a thematic inductive qualitative analysis of the interview data was conducted to investigate the impact of this learning method on students’ learning behaviors and perceptions.
Accurate determination of Pb and Bi contents and Pb isotopic composition in geological and cosmochemical samples by inductively coupled plasma quadrupole mass spectrometry

A method for accurately determining Pb and Bi contents in geochemical and cosmochemical solid samples using inductively coupled plasma quadrupole mass spectrometry (ICP–QMS) is described. Pb and Bi contents are determined using isotope dilution and calibration methods, respectively. For this purpose, an accurate determination scheme of Pb isotopic ratios is established using standard-sample bracketing and matrix-matching methods due to the significantly differences of Pb isotope ratios in commercial standard solution, geochemical and cosmochemical samples. Lead is chosen as the most appropriate internal standard element to reduce the matrix effect when determining Bi content. Our developed analytical procedure was applied to geological and cosmochemical reference samples to evaluate the accuracy of our data. The Pb isotopic composition data obtained from this study are in good agreement with the literature values obtained using multicollector inductively coupled plasma mass spectrometry and thermal ionization mass spectrometry, which ensures the accuracy of the Pb isotopic ratios obtained by our method. The Bi content data obtained from this study for a cosmochemical sample are in excellent agreement with neutron activation values in literature.
Power and Energy Applications Based on Quantum Computing: The Possible Potentials of Grover’s Algorithm

In quantum computing, calculations are achieved using quantum mechanics. Typically, two main phenomena of quantum mechanics (i.e., superposition and entanglement) allow quantum computing to solve some problems more efficiently than classical algorithms. The most well-known advantage of quantum computing is the speedup of some of the calculations, which have been performed before by classical applications. Scientists and engineers are attempting to use quantum computing in different fields of science, e.g., drug discovery, chemistry, computer science, etc. However, there are few attempts to use quantum computing in power and energy applications. This paper tries to highlight this gap by discussing one of the most famous quantum computing algorithms (i.e., Grover’s algorithm) and discussing the potential applications of this algorithm in power and energy systems, which can serve as one of the starting points for using Grover’s algorithm in power and energy systems.
FUSSNet: Fusing Two Sources of Uncertainty for Semi-supervised Medical Image Segmentation

In recent years, various semi-supervised learning (SSL) methods have been developed to deal with the scarcity of labeled data in medical image segmentation. Especially, many of them focus on the uncertainty caused by a lack of knowledge (about the best model), i.e. epistemic uncertainty (EU). Besides EU, another type of uncertainty, aleatoric uncertainty (AU), originated from irreducible errors or noise, also commonly exists in medical imaging data. While previous SSL approaches focus on only one of them (mostly EU), this study shows that SSL segmentation models can benefit more by considering both sources of uncertainty. The proposed FUSSNet framework is featured by a joint learning scheme, which combines the EU-guided unsupervised learning and AU-guided supervised learning. We assess the method on two benchmark datasets for the segmentation of left atrium (LA) and pancreas, respectively. The experimental results show that FUSSNet outperforms the state-of-the-art semi-supervised segmentation methods by over 2% on Dice score for pancreas data and almost reaches the accuracy obtained in fully supervised setting for LA data.
A binary tree seed algorithm with selection-based local search mechanism for huge-sized optimization problems

Tree seed algorithm (TSA) is a recently proposed metaheuristic algorithm for solving continuous optimization problems. In order to use TSA in binary optimization problems, the SimLogicTSA method was developed by adding logic gates and Jaccard's similarity measure to this algorithm by Cinar and Kiran. Although SimLogicTSA is generally successful in small, medium, and large size problems, it has not been successful in the huge-sized problems by stucking into local minima. To overcome this problem, a new local search mechanism called enhanced local search module (ELSM) is proposed and the SimLogicTSA-ELSM algorithm is suggested by implementing the ELSM mechanism to the original SimLogicTSA algorithm. The proposed ELSM mechanism consists of a swap operator and logic-based gates. To analyze the contribution of the ELSM mechanism to the algorithm, firstly, the original SimLogicTSA and SimLogicTSA-ELSM algorithms were compared on the Cap and M* problem sets. The obtained results showed that the proposed algorithm produced more successful results than the original SimLogicTSA. Then, the proposed SimLogicTSA-ELSM is compared with many state-of-art algorithms in the literature by using different performance metrics on Cap and M* problem sets. The results show that SimLogicTSA-ELSM outperforms the compared algorithms in nearly all cases. Especially, the performance of the SimLogicTSA-ELSM stands out in huge-sized problems.
A Multi-Label Classification with an Adversarial-Based Denoising Autoencoder for Medical Image Annotation

Medical image annotation aims to automatically describe the content of medical images. It helps doctors to understand the content of medical images and make better informed decisions like diagnoses. Existing methods mainly follow the approach for natural images and fail to emphasize the object abnormalities, which is the essence of medical images annotation. In light of this, we propose to transform the medical image annotation to a multi-label classification problem, where object abnormalities are focused directly. However, extant multi-label classification studies rely on arduous feature engineering, or do not solve label correlation issues well in medical images. To solve these problems, we propose a novel deep learning model where a frequent pattern mining component and an adversarial-based denoising autoencoder component are introduced. Extensive experiments are conducted on a real retinal image dataset to evaluate the performance of the proposed model. Results indicate that the proposed model significantly outperforms image captioning baselines and multi-label classification baselines.
Heterogeneous deep graph convolutional network with citation relational BERT for COVID-19 inline citation recommendation

The outbreak of COVID-19 brings almost the biggest explosions of scientific literature ever. Facing such volume literature, it is hard for researches to find desired citation when carrying out COVID-19 related research, especially for junior researchers. This paper presents a novel neural network based method, called citation relational BERT with heterogeneous deep graph convolutional network (CRB-HDGCN), for COVID-19 inline citation recommendation task. The CRB-HDGCN contains two main stages. The first stage is to enhance the representation learning of BERT model for COVID-19 inline citation recommendation task through CRB. To achieve the above goal, an augmented citation sentence corpus, which replaces the citation placeholder with the title of the cited papers, is used to lightly retrain BERT model. In addition, we extract three types of sentence pair according citation relation, and establish sentence prediction tasks to further fine-tune the BERT model. The second stage is to learn effective dense vector of nodes among COVID-19 bibliographic graph through HDGCN. The HDGCN contains four layers which are essentially all sub neural networks. The first layer is initial embedding layer which generates initial input vectors with fixed size through CRB and a multilayer perceptron. The second layer is a heterogeneous graph convolutional layer. In this layer, we expand traditional homogeneous graph convolutional network into heterogeneous by subtly adding heterogeneous nodes and relations. The third layer is a deep attention layer. This layer uses trainable project vectors to reweight the node importance simultaneously according to both node types and convolution layers, which further promotes the performance of learnt node vectors. The last decoder layer recovers the graph structure and let the whole network trainable. The recommendation is finally achieved by integrating the high performance heterogeneous vectors learnt from CRB-HDGCN with the query vectors. We conduct experiments on the CORD-19 and LitCovid datasets. The results show that compared with the second best method CO-Search, CRB-HDGCN improves MAP, MRR, P@100 and R@100 with 21.8%, 22.7%, 37.6% and 21.2% on CORD-19, and 29.1%, 25.9%, 15.3% and 11.3% on LitCovid, respectively.
MGNet: Mutual-guidance network for few-shot semantic segmentation

Few-shot semantic segmentation has recently drawn attention for its remarkable potential to segment the regions of different object classes with only a few labeled samples as guidance. Although recent methods have achieved impressive performance, there exist two critical bottleneck problems to be solved. First, most existing methods typically model a target class only using information from the foreground regions of support images, which actually does not adequately exploit the background region information of support images. Second, segmentation performance will be greatly affected when there is a large intra-class variation between the support and query images of the same class. To address these problems, we propose a mutual-guidance network (MGNet) for few-shot semantic segmentation to enhance the discriminative ability of class-specific prototypes. More specifically, the prototype learning module is first devised to learn the class-specific prototype of the foreground and background regions. Then, with non-parametric metric learning, the deep features of the query image are matched with multiple learned prototypes. Finally, to make good use of the ground truth mask of the support image, a reverse auxiliary learning module is constructed to reinforce the learned prototype. Extensive experiments on two standard benchmarks PASCAL-5i and COCO-20i are shown that the proposed method can yield competitive segmentation results with state-of-the-art methods. Surprisingly, our model achieves state-of-the-art results under both 1-shot and 5-shot tasks on more challenging COCO-20i when ResNet-101 is used as the backbone network.
An Anchor-Free Lightweight Deep Convolutional Network for Vehicle Detection in Aerial Images

Vehicle object detection in aerial scenes has important applications in both military and civilian fields. Recently, deep learning has shown clear advantages in object detection, and the detection performance has been continuously improved. However, these deep object detection algorithms rely on anchor-based approaches accompanied by complex convolutional operations. In this paper, we establish a lightweight aerial vehicle object detection algorithm based on the method of anchor-free. The anchor-free based object detection method effectively gets rid of the limitation of detection model capability by the size of fixed anchor box, which reduces the set of parameters and provides a more flexible solution space. In addition, the proposed lightweight object feature extraction network effectively reduces the computational cost of the model, while improving the feature extraction capability of small objects. Besides, we use channel stacking to improve the object feature extraction capability of the lightweight network, and introduce the attention mechanism in the detection model to improve the efficiency of resource utilization. We evaluate the proposed detection algorithm on both the public aerial dataset and our collected aerial dataset, and the results show that our algorithm has significant advantages over other detection algorithms in detection accuracy and efficiency. The proposed detection algorithm achieves 89.1% and 92.6% mAP on the Munich dataset and the created dataset, and the detection time for each image is 1.21s and 0.036s, respectively.
DASFTOT: Dual attention spatiotemporal fused transformer for object tracking

The Siamese network method is widely applied in the field of object tracking. The transformer-based tracker achieves state-of-the-art tracking results. However, these methods cannot effectively fuse local and global features of video images and cannot pay more attention to tracking objects spatiotemporally. In this paper, we proposed a new object tracking method (DASFTOT), which includes a backbone network, transformer mechanism and bounding prediction box. First, we use a 3D CNN to extract motion information. Second, we superimpose important temporal and spatial information through a dual attention spatiotemporal fused transformer (DASFT) to fuse local and global spatiotemporal features and calculate the correlation between templates and search regions. Third, to improve the robustness of tracking, we dynamically update part of the template frame. Finally, we position the tracking object through a bounding prediction box. To verify the effectiveness of the proposed tracker (DASFTOT), experiments on the GOT-10K, LaSOT, TrackingNet, VOT2020 and OTB100 benchmark datasets demonstrated that the proposed tracker was highly comparable to other state-of-the-art methods.
TKGAT: Graph attention network for knowledge-enhanced tag-aware recommendation system

In recent practices, sparsity problems often arise in recommendation systems, resulting in weak generalization ability. To alleviate this problem, tag-aware recommendation systems (TRS) leverage personalized tags to enhance the modeling of user preferences and item characteristics. However, current tag-aware methods suffer from arbitrary user behaviors as they limit the additional information only to user tags. In this paper, we investigate a more general scenario, namely Knowledge-enhanced Tag-aware Recommendation System (KTRS) which involves auxiliary knowledge compared with TRS. Correspondingly, we propose a novel recommendation model for KTRS, called TKGAT. It firstly constructs a collaborative recommendation graph and then learns heterogeneous representation via an multi-layer multi-head attention mechanism. Experiments conducted on real-world datasets demonstrate that the proposed system outperforms the state-of-the-art recommendation methods, and show effectiveness of the auxiliary knowledge.
Robust structural similarity index measure for images with non-Gaussian distortions

• Introduction of a new robust structure similarity index based on p th order statistics. • A simple and low cost special case: l1-norm based robust structure similarity index. • Extension of this measure to colour images. • Demonstration of the success of new index on various types of distortions. Structural Similarity Index Measure (SSIM) has been a very successful tool in image processing and computer vision . After almost two decades, it is still the most popular method for quantifying the similarity between original and distorted images with applications in image quality assessment , image retrieval , video coding, computer vision, image encryption and data-hiding. Despite its general success, there are some types of distortions, such as when the distortion is of non-Gaussian character, where SSIM does not sustain its success. We provide a generalized version of SSIM utilizing l p -norm ( 1 ≤ p ≤ 2 ) based sample moments as opposed to the classical SSIM which uses l 2 -norm based sample moments. In particular, we study the l 1 -norm special case and our simulations on well-known image-databases show superior performance compared to SSIM and related measures.