Reviewerly/exHarmony huggingface dataset:
An intelligent identification and classification system of decoration waste based on deep learning model

Authors:
raw_author_name: Jing Bai
raw_affiliation_string: The Institute for Sustainable Development, Macau University of Science and Technology, Macau 999078, China
raw_author_name: Jiafei Ning
raw_affiliation_string: Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Shenzhen 518055, China; School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China

Abstract: Efficient sorting and recycling of decoration waste are crucial for the industry's transformation, upgrading, and high-quality development. However, decoration waste can contain toxic materials and has greatly varying compositions. The traditional method of manual sorting for decoration waste is inefficient and poses health risks to sorting workers. It is therefore imperative to develop an accurate and efficient intelligent classification method to address these issues. To meet the demand for intelligent identification and classification of decoration waste, this paper applied the deep learning method You Only Look Once X (YOLOX) to the task and proposed an identification and classification framework of decoration waste (YOLOX-DW framework). The proposed framework was validated and compared using a multi-label image dataset of decoration waste, and a robot automatic sorting system was constructed for practical sorting experiments. The research results show that the proposed framework achieved a mean average precision (mAP) of 99.16 % for different components of decoration waste, with a detection speed of 39.23 FPS. Its classification efficiency on the robot sorting experimental platform reached 95.06 %, indicating a high potential for application and promotion. This provides a strategy for the intelligent detection, identification, and classification of decoration waste.
Colorectal cancer detection based on convolutional neural networks (CNN) and ranking algorithm

Authors:
raw_author_name: S. Jothilakshmi
raw_affiliation_string: Department of Information Technology, Annamalai University, Chidambaram, 608002, India
raw_author_name: S. Suthir
raw_affiliation_string: Amrita School of Computing, Amrita VishwaVidyapeetham, Chennai, 601103, India

Abstract: With the development of targeted therapies, many treatments are based on molecular studies, which require sampling tumor tissue from paraffin blocks for sequencing. An automated solution could potentially reduce the workload of pathologists by acting as a screening device and may reduce the subjectivity in diagnosis. In tissue-based diagnostics, most of the work still needs to be done manually by a pathologist using a microscope to examine stained slides. The foundation of such tasks is to accurately distinguish cancer/malignant cells from normal/benign cells. However, the determination of tumor content is poorly reproducible with significant variation. As the size of tumor regions can be very small, pathologists are often required to use high magnification for detecting tumor cells. This requirement significantly increases the workload for pathologists. As digital pathology datasets have become publicly available and have opened up the possibility of evaluating the feasibility of applying deep learning techniques to improving the efficiency and quality of histologic diagnosis. The model proposed in this work is an application to detect colorectal cancer based on the Convolutional Neural Network and Ranking algorithm. Based on the performance evaluation, it is found that the proposed model is yielding better results in diagnosis of Colorectal Cancer than the existing methods in terms of Recall, Precision and Accuracy.
Pre-training Question Embeddings for Improving Knowledge Tracing with Self-supervised Bi-graph Co-contrastive Learning

Authors:
raw_author_name: Wentao Wang
raw_affiliation_string: College of Computer Science and Engineering, Northwest Normal University, China
raw_author_name: Huifang Ma
raw_affiliation_string: College of Computer Science and Engineering, Northwest Normal University, Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, and Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, China
raw_author_name: Yan Zhao
raw_affiliation_string: College of Computer Science and Engineering, Northwest Normal University, China
raw_author_name: Zhixin Li
raw_affiliation_string: Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, China

Abstract: Learning high-quality vector representations (aka. embeddings) of educational questions lies at the core of knowledge tracing (KT), which defines a task of estimating students’ knowledge states by predicting the probability that they correctly answer questions. Although existing KT efforts have leveraged question information to achieve remarkable improvements, most of them learn question embeddings by following the supervised learning paradigm. In this article, we propose a novel question embedding pre-training method for improving knowledge tracing with self-supervised Bi -graph Co -contrastive learning ( BiCo ). Technically, on the basis of self-supervised learning paradigm, we first select two similar but distinct views (i.e., representing objective and subjective semantic perspectives) as the semantic source of question embeddings. Then, we design a primary task (structure recovery) together with two auxiliary tasks (question difficulty recovery and contrastive learning) to further enhance the representativeness of questions. Finally, extensive experiments conducted on two real-world datasets show BiCo has a higher expressive power that enables KT methods to effectively predict students’ performances.
Exploratory machine learning with unknown unknowns

Authors:
raw_author_name: Peng Zhao
raw_affiliation_string: National Key Laboratory for Novel Software Technology, Nanjing University, China; School of Artificial Intelligence, Nanjing University, China
raw_author_name: Yu-Jie Zhang
raw_affiliation_string: National Key Laboratory for Novel Software Technology, Nanjing University, China; School of Artificial Intelligence, Nanjing University, China
raw_author_name: Zhi-Hua Zhou
raw_affiliation_string: National Key Laboratory for Novel Software Technology, Nanjing University, China; School of Artificial Intelligence, Nanjing University, China

Abstract: In conventional supervised learning, a training dataset is given with ground-truth labels from a known label set, and the learned model will classify unseen instances to known labels. This paper studies a new problem setting in which there are unknown classes in the training data misperceived as other labels, and thus their existence appears unknown from the given supervision. We attribute the unknown unknowns to the fact that the training dataset is badly advised by the incompletely perceived label space due to the insufficient feature information. To this end, we propose the exploratory machine learning, which examines and investigates training data by actively augmenting the feature space to discover potentially hidden classes. Our method consists of three ingredients including rejection model, feature exploration, and model cascade. We provide theoretical analysis to justify its superiority, and validate the effectiveness on both synthetic and real datasets.
Human image animation via semantic guidance

Authors:
raw_author_name: Congwei Guo
raw_affiliation_string: Tiangong University School of Computer Science and Technology, Tianjin, 300387, China
raw_author_name: Yongken Ke
raw_affiliation_string: National Demonstration Center for Experimental Engineering Training Education, Tianjin, 300387, China; Tiangong University School of Computer Science and Technology, Tianjin, 300387, China; Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tianjin, 300387, China
raw_author_name: Zhenkai Wan
raw_affiliation_string: National Demonstration Center for Experimental Engineering Training Education, Tianjin, 300387, China
raw_author_name: Minrui Jia
raw_affiliation_string: National Demonstration Center for Experimental Engineering Training Education, Tianjin, 300387, China
raw_author_name: Kai Wang
raw_affiliation_string: Tiangong University School of Computer Science and Technology, Tianjin, 300387, China; Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tianjin, 300387, China
raw_author_name: Shuai Yang
raw_affiliation_string: Tiangong University School of Computer Science and Technology, Tianjin, 300387, China; Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tianjin, 300387, China

Abstract: Image animation creates visually compelling effects by animating still source images according to driving videos. Recent work performs animation on arbitrary objects using unsupervised methods and can relatively robustly perform motion transfer on human bodies. However, the complex representation of motion and unknown correspondence between human bodies often lead to issues such as distorted limbs and missing semantics, which make human animation challenging. In this paper, we propose a semantically guided, unsupervised method of motion transfer, which uses semantic information to model motion and appearance. Specifically, we use a pre-trained human parsing network to encode the rich and diverse foreground semantic information, thus generating fine details. Secondly, we use a cross-modal attention layer to learn the semantic region's correspondence between human bodies to guide the network in selecting appropriate input features, prompting the network to generate accurate results. Experiments demonstrate that our method outperforms state-of-the-art methods in motion-related metrics, while effectively addressing the problems of semantic missing and unclear limb structures prevalent in human motion transfer. These improvements can facilitate its applications in various fields, such as education and entertainment.
A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows

Authors:
raw_author_name: Agnimitra Dasgupta
raw_affiliation_string: Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, 90089, CA, USA
raw_author_name: Dhruv V. Patel
raw_affiliation_string: Department of Mechanical Engineering, Stanford University, Stanford, 94305, CA, USA
raw_author_name: Deep Ray
raw_affiliation_string: Department of Mathematics, University of Maryland, College Park, 20742, MD, USA

Abstract: We propose a novel modular inference approach combining two different generative models — generative adversarial networks (GAN) and normalizing flows — to approximate the posterior distribution of physics-based Bayesian inverse problems framed in high-dimensional ambient spaces. We dub the proposed framework GAN-Flow. The proposed method leverages the intrinsic dimension reduction and superior sample generation capabilities of GANs to define a low-dimensional data-driven prior distribution. Once a trained GAN-prior is available, the inverse problem is solved entirely in the latent space of the GAN using variational Bayesian inference with normalizing flow-based variational distribution, which approximates low-dimensional posterior distribution by transforming realizations from the low-dimensional latent prior (Gaussian) to corresponding realizations of a low-dimensional variational posterior distribution. The trained GAN generator then maps realizations from this approximate posterior distribution in the latent space back to the high-dimensional ambient space. We also propose a two-stage training strategy for GAN-Flow wherein we train the two generative models sequentially. Thereafter, GAN-Flow can estimate the statistics of posterior-predictive quantities of interest at virtually no additional computational cost. The synergy between the two types of generative models allows us to overcome many challenges associated with the application of Bayesian inference to large-scale inverse problems, chief among which are describing an informative prior and sampling from the high-dimensional posterior. GAN-Flow does not involve Markov chain Monte Carlo simulation, making it particularly suitable for solving large-scale inverse problems. We demonstrate the efficacy and flexibility of GAN-Flow on various physics-based inverse problems of varying ambient dimensionality and prior knowledge using different types of GANs and normalizing flows. Notably, one of the applications we consider involves a 65,536-dimensional inverse problem of phase retrieval wherein an object is reconstructed from sparse noisy measurements of the magnitude of its Fourier transform.
Autoencoder-based joint image compression and encryption

Authors:
raw_author_name: Benxuan Wang
raw_affiliation_string: Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
raw_author_name: Kwok-Tung Lo
raw_affiliation_string: Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

Abstract: Recently, learning-based methods have developed rapidly, with promising results in many areas. And deep learning models are expected to be the next-generation optimal image compression solutions. This paper proposes a new image compression and encryption framework that integrates encryption algorithms with a deep-learning compression network. Our work employs Auto-Encoder (AE) based compression network as the backbone. After extracting semantic features from the learning network, encryption layers are added to introduce end-to-end learning to image encryption. And for a higher level of visual security, the parameters of the synthesis network are replaced by a new parameter matrix based on a logistic map controlled by a secret key. The encryption key of the system is derived from the image content, which will be embedded in the deep feature vectors to save the cost of sending the key for different images. Another secret key, which will be shared among other images, is used for controlling the embedding process. And to learn the entropy model from the scrambled feature maps, an attention scheme is exploited in estimating parameters to achieve more effective compression. Experimental results and the security analysis using Kodak dataset show that the proposed scheme can achieve a sufficiently high-security level with high compression efficiency.
Non-contact sensing for anomaly detection in wind turbine blades: A focus-SVDD with complex-valued auto-encoder approach

Authors:
raw_author_name: Gaëtan Frusque
raw_affiliation_string: Laboratory of Intelligent Maintenance and Operations Systems, EPFL, Lausanne, Switzerland
raw_author_name: David Flynn
raw_affiliation_string: James Watt School of Engineering, Autonomous Systems and Connectivity Division, University of Glasgow, University Avenue, Glasgow, G12 8QQ, UK
raw_author_name: Olga Fink
raw_affiliation_string: Laboratory of Intelligent Maintenance and Operations Systems, EPFL, Lausanne, Switzerland

Abstract: The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally lead to severe and disastrous consequences.Therefore, inspection during the manufacturing process is crucial to ensure consistent fabrication of composite materials.Non-contact sensing techniques, such as Frequency Modulated Continuous Wave (FMCW) radar, are becoming increasingly popular as they offer a full view -cross sectional analysis -of these complex structures during assembly and curing.In this paper, we enhance the quality assurance of WTB manufacturing utilising FMCW radar as a non-destructive sensing modality.Additionally, a novel anomaly detection pipeline is developed that offers the following advantages: (1) We use the analytic representation of the Intermediate Frequency signal of the FMCW radar as a feature to disentangle material-specific and round-trip delay information from the received wave.(2) We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD).This methodology involves defining the limit boundaries of the dataset after removing healthy data features, thereby focusing on the attributes of anomalies.(3) The proposed method employs a complex-valued autoencoder to remove healthy features and we introduces a new activation function called Exponential Amplitude Decay (EAD).EAD takes advantage of the Rayleigh distribution, which characterises an instantaneous amplitude signal.The effectiveness of the proposed method is demonstrated through its application to collected data, where it shows superior performance compared to other state-of-the-art unsupervised anomaly detection methods.This method is expected to make a significant contribution not only to structural health monitoring but also to the field of deep complex-valued data processing and SVDD application.The code and dataset will be made publicly available.
A critical review of multi-output support vector regression

Authors:
raw_author_name: Nguyen Khoa Tran
raw_affiliation_string: Heinrich Heine University Düsseldorf, Universitätsstraße 1, Düsseldorf, 40225, North Rhine-Westphalia, Germany

Abstract: Single-output regression is a widely used statistical modeling method to predict an output based on one or more features of a datapoint. If a dataset has multiple outputs, they can be predicted independently from each other although this disregards potential correlations and thus may negatively affect the predictive performance. Therefore, multi-output regression methods predict multiple outputs simultaneously. One way to approach single-output regression is by using methods based on support vectors such as support vector regression (SVR) or least-squares SVR (LS-SVR). Based on these two, previous works have devised multi-output support vector regression methods. In this review, we introduce a unified notation to summarize the single-output support vector regression methods SVR and LS-SVR as well as state-of-the-art multi-output support vector regression methods. Furthermore, we implemented a workflow for subject- and record-wise bootstrapping and nested cross-validation experiments, which we used for an exhaustive evaluation of all single- and multi-output support vector regression methods on synthetic and non-synthetic datasets. Although the reviewed papers claim that their multi-output methods improve regression performance, we find that none of them outperform both single-output methods SVR and LS-SVR for various reasons. Due to these results, we reflected about the general concept of support vector regression and then concluded that support vector regression methods do not appear to be suitable for the task of multi-output regression.
ELTS-Net: An enhanced liver tumor segmentation network with augmented receptive field and global contextual information

Authors:
raw_author_name: Xiaoyue Guo
raw_affiliation_string: College of Engineering, Peking University, Beijing 100871, China; Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
raw_author_name: Zidong Wang
raw_affiliation_string: Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK
raw_author_name: Peishu Wu
raw_affiliation_string: Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
raw_author_name: Fuad E. Alsaadi
raw_affiliation_string: Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
raw_author_name: Nianyin Zeng
raw_affiliation_string: Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China

Abstract: The liver is one of the organs with the highest incidence rate in the human body, and late-stage liver cancer is basically incurable. Therefore, early diagnosis and lesion location of liver cancer are of important clinical value. This study proposes an enhanced network architecture ELTS-Net based on the 3D U-Net model, to address the limitations of conventional image segmentation methods and the underutilization of image spatial features by the 2D U-Net network structure. ELTS-Net expands upon the original network by incorporating dilated convolutions to increase the receptive field of the convolutional kernel. Additionally, an attention residual module, comprising an attention mechanism and residual connections, replaces the original convolutional module, serving as the primary components of the encoder and decoder. This design enables the network to capture contextual information globally in both channel and spatial dimensions. Furthermore, deep supervision modules are integrated between different levels of the decoder network, providing additional feedback from deeper intermediate layers. This constrains the network weights to the target regions and optimizing segmentation results. Evaluation on the LiTS2017 dataset shows improvements in evaluation metrics for liver and tumor segmentation tasks compared to the baseline 3D U-Net model, achieving 95.2% liver segmentation accuracy and 71.9% tumor segmentation accuracy, with accuracy improvements of 0.9% and 3.1% respectively. The experimental results validate the superior segmentation performance of ELTS-Net compared to other comparison models, offering valuable guidance for clinical diagnosis and treatment.