Reviewerly/exHarmony huggingface dataset:
The (+)-extended twisted generalized Reed-Solomon code

Authors:
raw_author_name: Canze Zhu
raw_affiliation_string: School of Computer Science and Technology, Dongguan University of Technology, Dongguan, 523000, China
raw_author_name: Qunying Liao
raw_affiliation_string: College of Mathematical Science, Sichuan Normal University, Chengdu, 610066, China

Abstract: In this paper, we give a parity check matrix of the (+)-extended twisted generalized Reed Solomon code, and then not only prove that it is MDS or NMDS, but also determine the weight distribution. Especially, based on Schur method, we show that the (+)-extended twisted generalized Reed Solomon code is neither the generalized Reed Solomon code nor extended generalized Reed Solomon code. Furthermore, we present necessary and sufficient conditions for the (+)-twisted generalized Reed Solomon code and the (+)-extended twisted generalized Reed Solomon code to be self-orthogonal, respectively. Finally, several classes of self-dual (+)-twisted generalized Reed Solomon code codes and almost self-dual (+)-extended twisted generalized Reed Solomon codes are constructed.
Boundary fusion multi-scale enhanced network for gland segmentation in colon histology images

Authors:
raw_author_name: YuBing Luo
raw_affiliation_string: Shanxi Medical Imaging and Data Analysis Engineering Research Center, School of Big Data, North University of China, Taiyuan 03000, China
raw_author_name: PinLe Qin
raw_affiliation_string: Shanxi Medical Imaging and Data Analysis Engineering Research Center, School of Big Data, North University of China, Taiyuan 03000, China
raw_author_name: Rui Chai
raw_affiliation_string: Shanxi Medical Imaging and Data Analysis Engineering Research Center, School of Big Data, North University of China, Taiyuan 03000, China
raw_author_name: ShuangJiao Zhai
raw_affiliation_string: Shanxi Medical Imaging and Data Analysis Engineering Research Center, School of Big Data, North University of China, Taiyuan 03000, China
raw_author_name: JunYi Yan
raw_affiliation_string: Shanxi Medical Imaging and Data Analysis Engineering Research Center, School of Big Data, North University of China, Taiyuan 03000, China

Abstract: Morphological features of glands serve as essential diagnostic criteria for colon cancer, and physicians must accurately segment glands in histopathological sections to grade the cancer. However, owing to the diverse gland morphologies and blurred edges of malignant glands, the contextual representations of edges have received limited attention in current segmentation methods, leading to less-than-ideal segmentation results, particularly in malignant cases. We present an edge-based multi-scale enhanced network (BFMSE-Net) for gland segmentation to address these issues. This network is based on U-net and fuses an edge fusion module (BFM) and a multi-scale enhancement module (MSEM). The BFM extracts detailed edge features, fuses edge information, and enhances edge pixel values, which effectively facilitates the segmentation of glands with blurred edges and adhesions. MSEM can handle complex scenarios where gland size and shape significantly change because it adaptively gathers information from various receptive fields in the region of interest. The method applied to evaluate this proposed network was to compare it to current methods through experiments, applying the gland segmentation (GlaS) and colorectal adenocarcinoma gland (CRAG) datasets. The experimental results demonstrate that our proposed method achieves clear gland segmentation, with shape similarity indices of 72.617 and 69.792 on the GlaS and CRAG datasets, respectively. These results show an improvement of 20.627 and 76.978, respectively, over current state-of-the-art methods. In conclusion, BFMSE-Net, which clearly focuses on edge representation, can accurately segment various types of glands.
Hierarchical visual-semantic interaction for scene text recognition

Authors:
raw_author_name: Xin Tang
raw_affiliation_string: Huawei Technologies Ltd., Shenzhen, China
raw_author_name: Jun Wang
raw_affiliation_string: Ping An Healthcare Technology, Beijing, China; Ping An Technology, Shenzhen, China
raw_author_name: Guotong Xie
raw_affiliation_string: Ping An Healthcare Technology, Beijing, China; Ping An Technology, Shenzhen, China
raw_author_name: Junlin Hu
raw_affiliation_string: School of Software, Beihang University, Beijing, China

Abstract: Proper interaction between visual and semantic features is crucial to obtain a powerful feature representation for scene text recognition (STR). The existing interaction methods usually treat visual and semantic features as distinct tokens and use transformers to learn contextual information and prior language knowledge, and they achieve promising performance for STR task. However, there still remain several issues needed to be further addressed such as the imbalance number and mis-alignment between visual and semantic features, and the necessarily of stacking transformers to a progressive improvement in accuracy. To this aim, this paper proposes a novel interaction manner namely hierarchical visual-semantic interaction (HVSI) which contains three novel modules including a hierarchical visual-semantic interaction module, fusion module, and visual-semantic alignment module. The hierarchical visual-semantic interaction module employs multiple visual-semantic interaction blocks in various scales to enhance the representation power of visual features and semantic features. To better exploit multi-scale visual and semantic features, the fusion module is introduced to fuse multiple semantic features based on attention mechanisms. Furthermore, our HVSI presents a simple plug-in block named visual-semantic alignment module to alleviate mis-alignment of semantic features by mapping them into a unified semantic space, which helps improve the performance of HVSI. Extensive experiments on multiple benchmarks including English and Chinese text recognition datasets show that our method obtains state-of-the-art or competitive performances.
GNaN: A natural neighbor search algorithm based on universal gravitation

Authors:
raw_author_name: Juntao Yang
raw_affiliation_string: The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
raw_author_name: Lijun Yang
raw_affiliation_string: School of Computer Science and Engineering, Southwest Minzu University, Chengdu, China; The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
raw_author_name: Jinghui Zhang
raw_affiliation_string: The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
raw_author_name: Qiwen Liang
raw_affiliation_string: The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
raw_author_name: Wentong Wang
raw_affiliation_string: The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
raw_author_name: Dongming Tang
raw_affiliation_string: School of Computer Science and Engineering, Southwest Minzu University, Chengdu, China; The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
raw_author_name: Tao Liu
raw_affiliation_string: School of Computer Science and Engineering, Southwest Minzu University, Chengdu, China; The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China

Abstract: The natural neighbor (NaN) method and its search algorithm (NaN-Searching) are widely used in many fields, including pattern recognition and image processing. NaN-Searching fundamentally overcomes the problem of the conventional nearest neighbor algorithm in selecting parameters for datasets with arbitrary shapes and achieves good results. However, this algorithm uses the conventional distance metric as the neighbor judgment criterion, which cannot accurately reflect the overall structure of the dataset in the process of neighbor search. Inspired by Newton's law of universal gravitation, we propose a NaN search algorithm based on universal gravitation (GNaN-Searching). Our algorithm calculates gravitation using the structural features of data points in the dataset, it utilizes the gravitation between data as the neighbor judgment criterion, and inherits the no-parameter and dynamic neighborhood characteristics of the NaN search algorithm. Experimental results show that the natural neighborhood graph obtained by our method has a high performance in the representation of manifold data. We also applied the new method to clustering and outlier detection and achieved satisfactory results.
Frequency-aware feature aggregation network with dual-task consistency for RGB-T salient object detection

Authors:
raw_author_name: Heng Zhou
raw_affiliation_string: Institute of Systems Engineering, AMS, Beijing 100141, China; School of Electronic Engineering, Xidian University, Xi'an 710071, China
raw_author_name: Chunna Tian
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xi'an 710071, China
raw_author_name: Zhenxi Zhang
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xi'an 710071, China
raw_author_name: Chengyang Li
raw_affiliation_string: Institute of Systems Engineering, AMS, Beijing 100141, China; School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
raw_author_name: Yongqiang Xie
raw_affiliation_string: Institute of Systems Engineering, AMS, Beijing 100141, China
raw_author_name: Zhongbo Li
raw_affiliation_string: Institute of Systems Engineering, AMS, Beijing 100141, China

Abstract: RGB-Thermal salient object detection (SOD) aims to merge two spectral images to segment visually appealing objects. Current methods primarily extract salient object information in the pixel perspective. However, biological and psychological research indicates notable frequency sensitivity of the human visual system (HVS). The high-frequency (HF) and low-frequency (LF) information in images are processed by different neural channels, which has been overlooked in SOD. In this study, we argue that the objective of RGB-T SOD is not only to enhance feature representation in the pixel-aware but also to emulate human visual mechanisms. To our best knowledge, we explore RGB-T SOD from the frequency perspective for the first time. Specifically, we first present a frequency-aware multi-spectral feature aggregation module (FMFA) to exploit the separability and complementarity of frequency-aware features, generating and making full use of LF and HF cues. FMFA improves the feature representation of RGB-T from the frequency perspective and provides stronger frequency cues for boundary auxiliary tasks. Then, we develop an HF-guided signed distance map prediction module (HF-SDM) with dual-task consistency to effectively alleviate the coarse mask caused by blur boundary. HF-SDM employs the geometric relationship of objects which boosts the interaction between salient regions and boundaries. As a result, the model can gain sharper boundaries for salient objects. Finally, we propose a frequency-aware feature aggregation network (FFANet) incorporated with dual-task learning. Extensive experiments on RGB-T SOD datasets demonstrate that our proposed method outperforms other state-of-the-art methods. Ablation studies and visualizations further verify the effectiveness and interpretability of our method.
Optimal Fuzzy Intensification System for Contrast Distorted Medical Images

Authors:
raw_author_name: Bharath Subramani
raw_affiliation_string: Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, India
raw_author_name: Ashish Kumar Bhandari
raw_affiliation_string: Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna, India

Abstract: Contrast enhancement plays a significant role in medical imaging technology for clinical diagnosis and observing the patient's health conditions. However, medical images may suffer from different degradations, such as poor visibility and low contrast, which are significant challenges for the automatic diagnosis of diseases. This article develops an ameliorated fuzzy transformation model to significantly enhance the contrast by adjusting the imbalanced intensity levels of medical images. The proposed optimal fuzzy intensification system (OFIS) utilizes the entire dynamic range by expanding the segmented intensity level of the input image. The fuzzy expansion process can enrich the diagnostic details of the processed image. The moth flame optimization is incorporated to optimize the gamma parameter to improve the clear visibility by adjusting the local contrast of the dark regions. Experiments on benchmark datasets reveal that the proposed method has superior performance in enhancing contrast and visibility of the local details of abnormalities in medical images compared with existing methods.
An integrated approach for magnification independent breast cancer classification

Authors:
raw_author_name: Venugopal P
raw_affiliation_string: School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

Abstract: Breast cancer (BC) is the second most common cancer worldwide, and its diagnosis and treatment rely heavily on histopathology image analysis. Moreover, continuous evaluation of deep learning techniques in medical imaging modalities is essential for improving early diagnosis. The Break-His dataset is the de facto standard for such analysis. Researchers have actively explored various approaches to classifying these images into binary, four, and eight based on the magnification-independent (MI) and magnification-dependent (MD). However, existing processes necessitate separate models for each classification based on the magnification factor and entail training multiple models. It leads to increased training time and model complexity. Moreover, model performance in eight-class is relatively low compared to other classifications. This paper introduces a modified Swin-Transformer V2 architecture for multi-labeled and eight-class classification on BC histopathology images. Further, the eight-class output was reconfigured to operate in an integrated approach, allowing the model to predict binary, four, and eight classes simultaneously for both MI and MD. On the Break-His dataset, the proposed model achieved an accuracy of 98.27%, 97.95%, 98.97%, and 99.16% for eight-class, malignant, benign, and binary classification tasks, respectively. Additionally, the multi-labeled approach demonstrated a performance improvement in the eight-class category compared to current leading models, achieving an enhancement of up to 5%, and the model was trained in just 2 h. Overall, the proposed model can improve the prognosis of breast cancer patients by leading to more accurate diagnosis and treatment. The implemented code can be accessed at https://github.com/kbnarayanavit/integrated-approach.
Explainable deep learning for attack intelligence and combating cyber-physical attacks

Authors:
raw_author_name: Muna Al-Hawawreh
raw_affiliation_string: Deakin University, Melbourne, Australia
raw_author_name: Nour Moustafa
raw_affiliation_string: University of New South Wales, Canberra, Australia

Abstract: Cyber-physical control loops comprising sensors, actuators and controllers pose the most valued and critical part of the industrial Internet of Things (IIoT) as it regulates the state of the physical process, such as water treatment or gas flow. Thus, any malicious activities could lead to physical damage, affecting human safety. Cyber-physical attacks against the physical process are difficult to detect using existing threats and attack intelligence due to the (1) lack of such intelligence for the physical process and operational technology systems and (2) such attacks affect the process parameters and states. Artificial Intelligence (AI)-based attack intelligence is required. This study proposes an attack intelligence framework for identifying cyber–physical attacks and extracting attack intelligence. We propose an attribution module for attack identification using various machine and deep learning algorithms. We also utilize Explainable AI (XAI) to improve the explainability of the attack attribution module and extract attack intelligence. Our proposed framework is evaluated and tested using a gas pipeline dataset as a use case. We demonstrate that the proposed framework improves the understanding of attacks and provides attack rules, assisting security analysts in securing critical physical processes.
Light-modulated graphene-based φ <sub>0</sub> Josephson junction and −φ <sub>0</sub> to φ <sub>0</sub> transition

Authors:
raw_author_name: Miao Yu
raw_affiliation_string: New Energy Technology Engineering Laboratory of Jiangsu Province, School of Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, CHINA
raw_author_name: Hong Wang
raw_affiliation_string: New Energy Technology Engineering Laboratory of Jiangsu Province, School of Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, CHINA
raw_author_name: Deliang Cao
raw_affiliation_string: New Energy Technology Engineering Laboratory of Jiangsu Province, School of Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, CHINA
raw_author_name: Xingao Li
raw_affiliation_string: New Energy Technology Engineering Laboratory of Jiangsu Province, School of Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, CHINA
raw_author_name: Fenghua Qi
raw_affiliation_string: School of Electronic Engineering, Nanjing Xiaozhuang University, Nanjing, 211171, CHINA

Abstract: We investigate the chiral edge states-induced Josephson current–phase relation in a graphene-based Josephson junction modulated by the off-resonant circularly polarized light and the staggered sublattice potential. By solving the Bogoliubov–de Gennes equation, a φ 0 Josephson junction is induced in the coaction of the off-resonant circularly polarized light and the staggered sublattice potential, which arises from the fact that the center of-mass wave vector of Cooper pair becomes finite and the opposite center of-mass wave vector to compensate is lacking in the nonsuperconducting region. Interestingly, when the direction of polarization of light is changed, − φ 0 to φ 0 transition generates, which generalizes the concept of traditional 0– π transition. Our findings provide a purely optical way to manipulate a phase-controllable Josephson device and guidelines for future experiments to confirm the presence of graphene-based φ 0 Josephson junction.
Domain Adaptation for In-Air to Underwater Image Enhancement via Deep Learning

Authors:
raw_author_name: Yang Tang
raw_affiliation_string: Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
raw_author_name: Gary G. Yen
raw_affiliation_string: School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA
raw_author_name: Qiyu Sun
raw_affiliation_string: Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

Abstract: Underwater image enhancement has drawn a lot of attention due to its significance in ocean exploration. Since it is extremely hard to obtain a degraded image and its clear version in the same scene, most deep learning-based methods utilize synthetic datasets for training. However, synthetic datasets often fail to describe the natural appearance and show poor capabilities of generalization to real scenes. In this study, we present an in-air to underwater image enhancement (IUIE) framework that overcomes the limitations of underwater synthetic datasets. Specifically, the presented framework consists of a color correction step and a domain adaptation step. In the color correction step, we propose a color correction mechanism by utilizing the white balance algorithm to eliminate color bias and produce a more natural appearance. In the domain adaptation step, the network can reduce the domain gap between the in-air domain and the underwater domain and obtain a better generalization in real scenes. To accommodate spatially varying textures and edges, we propose a residual adaptive module in the domain adaptation network. Experimental results on the test data indicate that our IUIE framework not only obtains more remarkable UIQM and BRISQUE scores than the previous methods do but also achieves superior results in visual perception.
Visualization of object-oriented software in a city metaphor: Comprehending the implemented variability and its technical debt

Authors:
raw_author_name: Johann Mortara
raw_affiliation_string: Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis; Scalable and Pervasive softwARe and Knowledge Systems
raw_author_name: Philippe Collet
raw_affiliation_string: Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis; Scalable and Pervasive softwARe and Knowledge Systems
raw_author_name: Anne-Marie Dery-Pinna
raw_affiliation_string: Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis; Scalable and Pervasive softwARe and Knowledge Systems

Abstract: While many large-scale software systems intensively implement variability to reuse software and speed up development, they often do not document it, hampering its comprehension. This is especially the case for variability-rich object-oriented (OO) systems that heavily rely on existing OO mechanisms (i.e., inheritance, overloading and some patterns) to implement it in a single codebase. With no traceability information, the variability is buried in the codebase, hampering its identification, analysis, and understanding. While variability management becomes increasingly difficult over the system evolution, the implementation mechanisms also bring additional complexity to the codebase, which eventually leads to technical debt, threatening even more the software quality. In this article, we report on the design and evaluation of an extensible visualization, VariCity, that exhibits zones of high density of OO variability implementations. It relies on the city metaphor to represent the classes of the system as buildings whose dimensions are used to show variability metrics inherent to the implementation classes. They are linked together through streets depicting usage relationships and grouping in neighborhoods classes using each other. The extensibility of VariCity is demonstrated with VariMetrics, which highlights OO quality metrics on the buildings, revealing quality-critical classes concentrating variability implementations. We evaluate the visualization capacity to reveal zones concentrating variability implementations and being quality-critical by applying it to multiple variability-intensive open source software systems. We also report on a controlled experiment comparing the gain brought by the visualization to the use of an IDE.