Reviewerly/exHarmony huggingface dataset:
Multi-View Graph Learning by Joint Modeling of Consistency and Inconsistency

Authors:
raw_author_name: Youwei Liang
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China
raw_author_name: Dong Huang
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China
raw_author_name: Chang-Dong Wang
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Philip S. Yu
raw_affiliation_string: Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA

Abstract: Graph learning has emerged as a promising technique for multi-view clustering due to its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency between views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm that can obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on 12 multi-view datasets have demonstrated the robustness and efficiency of the proposed approach. The code is available at https://github.com/youweiliang/Multi-view_Graph_Learning.
Tchebichef Transform Domain-Based Deep Learning Architecture for Image Super-Resolution

Authors:
raw_author_name: Ahlad Kumar
raw_affiliation_string: Dhirubhai Ambani-Institute of Information and Communication Technology#TAB#
raw_author_name: Harsh Vardhan Singh

Abstract: Recent advances in the area of artificial intelligence and deep learning have motivated researchers to apply this knowledge to solve multipurpose applications in the area of computer vision and image processing. Super-resolution (SR), in the past few years, has produced remarkable results using deep learning methods. The ability of deep learning methods to learn the nonlinear mapping from low-resolution (LR) images to their corresponding high-resolution (HR) images leads to compelling results for SR in diverse areas of research. In this article, we propose a deep learning-based image SR architecture in the Tchebichef transform domain. This is achieved by integrating a transform layer into the proposed architecture through a customized Tchebichef convolutional layer (TCL). The role of TCL is to convert the LR image from the spatial domain to the orthogonal transform domain using Tchebichef basis functions. The inversion of the transform mentioned earlier is achieved using another layer known as the inverse TCL (ITCL), which converts back the LR images from the transform domain to the spatial domain. It has been observed that using the Tchebichef transform domain for the task of SR takes the advantage of high and low- frequency representation of images that makes the task of SR simplified. Furthermore, a transfer learning-based approach is adopted to enhance the quality of images by considering Covid19 medical images as an additional experiment. It is shown that our architecture enhances the quality of X-ray and CT images of COVID-19, providing a better image quality that may help in clinical diagnosis. Experimental results obtained using the proposed Tchebichef transform domain SR (TTDSR) architecture provides competitive results when compared with most of the deep learning methods employed using a fewer number of trainable parameters.
RCT: Resource Constrained Training for Edge AI

Authors:
raw_author_name: Tian Huang
raw_affiliation_string: Agency for Science Technology and Research (A&#x002A;STAR), Institute of High Performance Computing, Fusionopolis, Singapore
raw_author_name: Tao Luo
raw_affiliation_string: Agency for Science Technology and Research (A&#x002A;STAR), Institute of High Performance Computing, Fusionopolis, Singapore
raw_author_name: Ming Yan
raw_affiliation_string: Agency for Science Technology and Research (A&#x002A;STAR), Centre for Frontier AI Research, Fusionopolis, Singapore
raw_author_name: Joey Tianyi Zhou
raw_affiliation_string: Agency for Science Technology and Research (A&#x002A;STAR), Centre for Frontier AI Research, Fusionopolis, Singapore
raw_author_name: Rick Siow Mong Goh
raw_affiliation_string: Agency for Science Technology and Research (A&#x002A;STAR), Institute of High Performance Computing, Fusionopolis, Singapore

Abstract: Efficient neural network training is essential for in situ training of edge artificial intelligence (AI) and carbon footprint reduction in general. Train neural network on the edge is challenging because there is a large gap between limited resources on edge and the resource requirement of current training methods. Existing training methods are based on the assumption that the underlying computing infrastructure has sufficient memory and energy supplies. These methods involve two copies of the model parameters, which is usually beyond the capacity of on-chip memory in processors. The data movement between off-chip and on-chip memory incurs large amounts of energy. We propose resource constrained training (RCT) to realize resource-efficient training for edge devices and servers. RCT only keeps a quantized model throughout the training so that the memory requirement for model parameters in training is reduced. It adjusts per-layer bitwidth dynamically to save energy when a model can learn effectively with lower precision. We carry out experiments with representative models and tasks in image classification, natural language processing, and crowd counting applications. Experiments show that on average, 8-15-bit weight update is sufficient for achieving SOTA performance in these applications. RCT saves 63.5%-80% memory for model parameters and saves more energy for communications. Through experiments, we observe that the common practice on the first/last layer in model compression does not apply to efficient training. Also, interestingly, the more challenging a dataset is, the lower bitwidth is required for efficient training.
Understanding via Exploration: Discovery of Interpretable Features With Deep Reinforcement Learning

Authors:
raw_author_name: Jiawen Wei
raw_affiliation_string: School of Automation, Central South University, Changsha, China
raw_author_name: Zhifeng Qiu
raw_affiliation_string: School of Automation, Central South University, Changsha, China
raw_author_name: Fangyuan Wang
raw_affiliation_string: School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
raw_author_name: Wenwei Lin
raw_affiliation_string: School of Automation, Central South University, Changsha, China
raw_author_name: Ning Gui
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, China

Abstract: Understanding the environments through interactions has been one of the most important human intellectual activities in mastering unknown systems. Deep reinforcement learning (DRL) has already been known to achieve effective control through human-like exploration and exploitation in many applications. However, the opaque nature of deep neural network (DNN) often hides critical information about feature relevance to control, which is essential for understanding the target systems. In this article, a novel online feature selection framework, namely, the dual-world-based attentive feature selection (D-AFS), is first proposed to identify the contribution of the inputs over the whole control process. Rather than the one world used in most DRL, D-AFS has both the real world and its virtual peer with twisted features. The newly introduced attention-based evaluation (AR) module performs the dynamic mapping from the real world to the virtual world. The existing DRL algorithms, with slight modification, can learn in the dual world. By analyzing the DRL's response in the two worlds, D-AFS can quantitatively identify respective features' importance toward control. A set of experiments is performed on four classical control systems in OpenAI Gym. Results show that D-AFS can generate the same or even better feature combinations than the solutions provided by human experts and seven recent feature selection baselines. In all cases, the selected feature representations are closely correlated with the ones used by underlying system dynamic models.
BASS: Broad Network Based on Localized Stochastic Sensitivity

Authors:
raw_author_name: Ting Wang
raw_affiliation_string: Department of Radiology, Guangzhou First People&#x2019;s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
raw_author_name: Mingyang Zhang
raw_affiliation_string: Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
raw_author_name: Jianjun Zhang
raw_affiliation_string: Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
raw_author_name: Wing W Y Ng
raw_affiliation_string: Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
raw_author_name: C L Philip Chen
raw_affiliation_string: Brain and Affective Cognitive Research Center, Pazhou Laboratory, Guangzhou, China

Abstract: The training of the standard broad learning system (BLS) concerns the optimization of its output weights via the minimization of both training mean square error (MSE) and a penalty term. However, it degrades the generalization capability and robustness of BLS when facing complex and noisy environments, especially when small perturbations or noise appear in input data. Therefore, this work proposes a broad network based on localized stochastic sensitivity (BASS) algorithm to tackle the issue of noise or input perturbations from a local perturbation perspective. The localized stochastic sensitivity (LSS) prompts an increase in the network's noise robustness by considering unseen samples located within a Q -neighborhood of training samples, which enhances the generalization capability of BASS with respect to noisy and perturbed data. Then, three incremental learning algorithms are derived to update BASS quickly when new samples arrive or the network is deemed to be expanded, without retraining the entire model. Due to the inherent superiorities of the LSS, extensive experimental results on 13 benchmark datasets show that BASS yields better accuracies on various regression and classification problems. For instance, BASS uses fewer parameters (12.6 million) to yield 1% higher Top-1 accuracy in comparison to AlexNet (60 million) on the large-scale ImageNet (ILSVRC2012) dataset.
Confusion-Based Metric Learning for Regularizing Zero-Shot Image Retrieval and Clustering

Authors:
raw_author_name: Binghui Chen
raw_affiliation_string: Chaoyang District, Beijing, China
raw_author_name: Weihong Deng
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Biao Wang
raw_affiliation_string: Chaoyang District, Beijing, China
raw_author_name: Lei Zhang
raw_affiliation_string: Department of Computing, The Hong Kong Polytechnic University, Hong Kong, SAR, Hung Hom, China

Abstract: Deep metric learning turns to be attractive in zero-shot image retrieval and clustering (ZSRC) task in which a good embedding/metric is requested such that the unseen classes can be distinguished well. Most existing works deem this "good" embedding just to be the discriminative one and race to devise the powerful metric objectives or the hard-sample mining strategies for learning discriminative deep metrics. However, in this article, we first emphasize that the generalization ability is also a core ingredient of this "good" metric and it largely affects the metric performance in zero-shot settings as a matter of fact. Then, we propose the confusion-based metric learning (CML) framework to explicitly optimize a robust metric. It is mainly achieved by introducing two interesting regularization terms, i.e., the energy confusion (EC) and diversity confusion (DC) terms. These terms daringly break away from the traditional deep metric learning idea of designing discriminative objectives and instead seek to "confuse" the learned model. These two confusion terms focus on local and global feature distribution confusions, respectively. We train these confusion terms together with the conventional deep metric objective in an adversarial manner. Although it seems weird to "confuse" the model learning, we show that our CML indeed serves as an efficient regularization framework for deep metric learning and it is applicable to various conventional metric methods. This article empirically and experimentally demonstrates the importance of learning an embedding/metric with good generalization, achieving the state-of-the-art performances on the popular CUB, CARS, Stanford Online Products, and In-Shop datasets for ZSRC tasks.
Flying in Dynamic Scenes With Multitarget Velocimetry and Perception-Enhanced Planning

Authors:
raw_author_name: Chih-Yung Wen
raw_affiliation_string: Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China
raw_author_name: Fei Gao
raw_affiliation_string: FAST (Field Autonomous System and Computing) Lab, Zhejiang University, Hangzhou, China
raw_author_name: Peng Lu
raw_affiliation_string: Department of Mechanical Engineering, University of Hong Kong, Hong Kong, SAR, China

Abstract: Micro drones have been widely applied in applications, such as aerial cinematography and environment exploration. Due to the presence of numerous demanding scenes, such as crowded obstacles and dynamic objects, remotely controlling a drone poses a significant challenge for humans. As a result, there is an urgent need for highly autonomous flight capabilities. This article presents a fully autonomous flight system in a complex dynamic environment, showing satisfactory performance in real-world tests, and outperforms the state-of-the-art works in both dynamic object perception, and flight safety and efficiency. A lightweight but effective multiobject velocimetry based on a cross-correlation algorithm and local points feature is proposed, with a robust image-based object classifier as the front end. Also, we plan the flight trajectory considering the camera's field of view and the uncertainty in the dynamic object's constant velocity model. At last, we further explore the benefits of vehicle's active yaw control for improving perception quality and flight safety.
Data, language and graph-based reasoning methods for identification of human malicious behaviors in nuclear security

Authors:
raw_author_name: Zhan Li
raw_affiliation_string: Department of Nuclear Engineering and Management, School of Engineering, The University of Tokyo, Tokyo, Japan
raw_author_name: Xingyu Song
raw_affiliation_string: Department of Nuclear Engineering and Management, School of Engineering, The University of Tokyo, Tokyo, Japan
raw_author_name: Shi Chen
raw_affiliation_string: Department of Nuclear Engineering and Management, School of Engineering, The University of Tokyo, Tokyo, Japan
raw_author_name: Kazuyuki Demachi
raw_affiliation_string: Department of Nuclear Engineering and Management, School of Engineering, The University of Tokyo, Tokyo, Japan

Abstract: Identification of human malicious behaviors is a demanding problem in nuclear security due to its potential to cause huge personnel and economic casualties. With the visibly rapid development of deep learning technology especially computer vision, the automatic identification of human malicious behaviors become feasible, offering significant savings in human resources. However, there is few research focusing on the explainable reasoning process based on the calculation results of computer vision. In this paper, an innovative framework is proposed, which contains two modules called computer vision module and reasoning module. Among them, the computer vision module could extract and detect human actions by some computer vision technologies, while these actions would be comprehensively utilized in reasoning module to derive the reasoning results. Furthermore, within the reasoning module, three novel reasoning methods are developed which are data-based reasoning method, language-based reasoning method and graph-based reasoning method. For evaluating the practicality and effectiveness of these methods, experiments are conducted on four typical scenarios in nuclear security, which are normal status, fence climbing, wire cutting and weapon holding. The results show that the proposed language-based reasoning method turns out to be the best one, which obtain a higher precision value of 0.7917 and a perfect recall value of 1.0000.
Diagnostic Performance of Artificial Intelligence–Based Computer-Aided Detection Software for Automated Breast Ultrasound

Authors:
raw_author_name: Mi-ri Kwon
raw_affiliation_string: Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea (M.K., I.Y., H.-A.L.)
raw_author_name: Mi Yeon Lee
raw_affiliation_string: Division of Biostatistics, Department of R&D Management, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (M.Y.L.)

Abstract: This study aimed to evaluate the diagnostic performance of radiologists following the utilization of artificial intelligence (AI)-based computer-aided detection software (CAD) in detecting suspicious lesions in automated breast ultrasounds (ABUS).ABUS-detected 262 breast lesions (histopathological verification; January 2020 to December 2022) were included. Two radiologists reviewed the images and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. ABUS images were classified as positive or negative using AI-CAD. The BI-RADS category was readjusted in four ways: the radiologists modified the BI-RADS category using the AI results (AI-aided 1), upgraded or downgraded based on AI results (AI-aided 2), only upgraded for positive results (AI-aided 3), or only downgraded for negative results (AI-aided 4). The AI-aided diagnostic performances were compared to radiologists. The AI-CAD-positive and AI-CAD-negative cancer characteristics were compared.For 262 lesions (145 malignant and 117 benign) in 231 women (mean age, 52.2 years), the area under the receiver operator characteristic curve (AUC) of radiologists was 0.870 (95% confidence interval [CI], 0.832-0.908). The AUC significantly improved to 0.919 (95% CI, 0.890-0.947; P = 0.001) using AI-aided 1, whereas it improved without significance to 0.884 (95% CI, 0.844-0.923), 0.890 (95% CI, 0.852-0.929), and 0.890 (95% CI, 0.853-0.928) using AI-aided 2, 3, and 4, respectively. AI-CAD-negative cancers were smaller, less frequently exhibited retraction phenomenon, and had lower BI-RADS category. Among nonmass lesions, AI-CAD-negative cancers showed no posterior shadowing.AI-CAD implementation significantly improved the radiologists' diagnostic performance and may serve as a valuable diagnostic tool.
An Implicit-Explicit Prototypical Alignment Framework for Semi-Supervised Medical Image Segmentation

Authors:
raw_author_name: Chunna Tian
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xi&#x0027;an, China
raw_author_name: Zhenxi Zhang
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xi&#x0027;an, China
raw_author_name: Xinbo Gao
raw_affiliation_string: Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China
raw_author_name: Heng Zhou
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xi&#x0027;an, China
raw_author_name: Ran Ran
raw_affiliation_string: Department of Medical Oncology, The First Affiliated Hospital of Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China
raw_author_name: Zhicheng Jiao
raw_affiliation_string: Department of Diagnostic Imaging, Brown University, Providence, RI, USA

Abstract: Semi-supervised learning methods have been explored to mitigate the scarcity of pixel-level annotation in medical image segmentation tasks. Consistency learning, serving as a mainstream method in semi-supervised training, suffers from low efficiency and poor stability due to inaccurate supervision and insufficient feature representation. Prototypical learning is one potential and plausible way to handle this problem due to the nature of feature aggregation in prototype calculation. However, the previous works have not fully studied how to enhance the supervision quality and feature representation using prototypical learning under the semi-supervised condition. To address this issue, we propose an implicit-explicit alignment (IEPAlign) framework to foster semi-supervised consistency training. In specific, we develop an implicit prototype alignment method based on dynamic multiple prototypes on-the-fly. And then, we design a multiple prediction voting strategy for reliable unlabeled mask generation and prototype calculation to improve the supervision quality. Afterward, to boost the intra-class consistency and inter-class separability of pixel-wise features in semi-supervised segmentation, we construct a region-aware hierarchical prototype alignment, which transmits information from labeled to unlabeled and from certain regions to uncertain regions. We evaluate IEPAlign on three medical image segmentation tasks. The extensive experimental results demonstrate that the proposed method outperforms other popular semi-supervised segmentation methods and achieves comparable performance with fully-supervised training methods.
Study of the seismic activity of the Almalik-Angren industrial zone based on lineament analysis

Authors:
raw_author_name: Lola SİCHUGOVA
raw_affiliation_string: Astronomical Institute of Uzbek Academy of Sciences
raw_author_name: Dilbarkhon FAZİLOVA
raw_affiliation_string: Astronomical Institute of Uzbek Academy of Sciences

Abstract: In this work, an automated lineament analysis was carried out to search for earthquake precursors for the territory of the Almalyk-Angren industrial zone in Uzbekistan. The seven events with a magnitude of about 3 were selected for analysis. The Landsat 8 satellite images were processed using the automated lineament detection method in the LEFA software. The processing steps included detecting line elements in raster images, calculating the characteristics of the spatial distribution of line elements, and combining collinear linear elements into lineaments. The analyses of the cyclicality of precursors before and after earthquakes were based on the study of the distribution of the lineament trend in the study area using rose diagrams and lineament density maps. The results showed a change in the dynamics of the lineament structure. The statistics of the number of lineaments showed that their increase begins almost 20 days before the event, reaches its maximum about 1 – 2 days before the earthquake, decreases starting from 14 days after the earthquake, and has a minimum value of 1 – 2 months. The main trends observed in the lineament map showed the dominant trend in NS, WE, NW-SE directions.