Reviewerly/exHarmony huggingface dataset:
Trustworthy multi-view clustering via alternating generative adversarial representation learning and fusion

Authors:
raw_author_name: Wenqi Yang
raw_affiliation_string: School of Computer Science, China University of Geosciences, Wuhan, 430074, China
raw_author_name: Minhui Wang
raw_affiliation_string: Department of Pharmacy, Lianshui People’s Hospital of Kangda College Affiliated to Nanjing Medical University, Huai’an, 223300, China
raw_author_name: Chang Tang
raw_affiliation_string: School of Computer Science, China University of Geosciences, Wuhan, 430074, China
raw_author_name: Xiao Zheng
raw_affiliation_string: School of Computer, National University of Defense Technology, Changsha, 410073, China
raw_author_name: Xinwang Liu
raw_affiliation_string: School of Computer, National University of Defense Technology, Changsha, 410073, China
raw_author_name: Kunlun He
raw_affiliation_string: Medical Engineering Laboratory, Chinese People’s Liberation Army General Hospital, Beijing, 100853, China

Abstract: Multi-view clustering (MVC) has attached extensive attention as it provides an effective approach to deal with the unlabeled data in real-world applications. To enjoy the strong feature extraction capacity of deep learning, traditional shallow MVC methods are further extended to deep version. Though achieve superiorities in many fields, most existing deep MVC models are still limited by their lacking assurance of the trustworthiness and robustness. This drawback also leads to the possible poor clustering performance. To tackle this drawback, we propose an alternating generative adversarial learning strategy based network, termed as Alternating Generative Adversarial Representation Learning (AGARL) network for multi-view clustering, aiming to drive different views to fall into the same semantic space for the further alignment and information fusion. Various from the traditional generative adversarial learning strategy, the proposed alternating adversarial representation learning strategy successes in further aligning the distributions of the view-specific representations and exploring the cross-view consistency credited by its setting each view as the reference alternately. Then a consensus representative latent representation can be learned through an attention-like sub-network for all views. Finally, a self-supervised clustering module is employed in the proposed network to guide the learning of cluster assignment. In addition, comprehensive experiments on numerous benchmark MVC datasets have been performed to demonstrate the effectiveness of the proposed network compared to other state-of-the-art methods.
Passive Continuous Variable Measurement-Device-Independent Quantum Key Distribution Predictable with Machine Learning in Oceanic Turbulence

Authors:
raw_author_name: Jianmin Yi
raw_affiliation_string: School of Automation, Central South University, Changsha 410083, China
raw_author_name: Hao Wu
raw_affiliation_string: School of Automation, Central South University, Changsha 410083, China
raw_author_name: Ying Guo
raw_affiliation_string: School of Automation, Central South University, Changsha 410083, China; School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract: Building an underwater quantum network is necessary for various applications such as ocean exploration, environmental monitoring, and national defense. Motivated by characteristics of the oceanic turbulence channel, we suggest a machine learning approach to predicting the channel characteristics of continuous variable (CV) quantum key distribution (QKD) in challenging seawater environments. We consider the passive continuous variable (CV) measurement-device-independent (MDI) QKD in oceanic scenarios, since the passive-state preparation scheme offers simpler linear elements for preparation, resulting in reduced interaction with the practical environment. To provide a practical reference for underwater quantum communications, we suggest a prediction of transmittance for the ocean quantum links with a given neural network as an example of machine learning algorithms. The results have a good consistency with the real data within the allowable error range; this makes the passive CVQKD more promising for commercialization and implementation.
Uniform approximation of common Gaussian process kernels using equispaced Fourier grids

Authors:
raw_author_name: Alex Barnett
raw_affiliation_string: Center for Computational Mathematics, Flatiron Institute, New York, NY, 10010, United States of America
raw_author_name: Philip Greengard
raw_affiliation_string: Department of Statistics, Columbia University, New York, NY, 10027, United States of America
raw_author_name: Manas Rachh
raw_affiliation_string: Center for Computational Mathematics, Flatiron Institute, New York, NY, 10010, United States of America

Abstract: The high efficiency of a recently proposed method for computing with Gaussian processes relies on expanding a (translationally invariant) covariance kernel into complex exponentials, with frequencies lying on a Cartesian equispaced grid. Here we provide rigorous error bounds for this approximation for two popular kernels—Matérn and squared exponential—in terms of the grid spacing and size. The kernel error bounds are uniform over a hypercube centered at the origin. Our tools include a split into aliasing and truncation errors, and bounds on sums of Gaussians or modified Bessel functions over various lattices. For the Matérn case, motivated by numerical study, we conjecture a stronger Frobenius-norm bound on the covariance matrix error for randomly-distributed data points. Lastly, we prove bounds on, and study numerically, the ill-conditioning of the linear systems arising in such regression problems.
Floating Offshore Photovoltaics across Geographies: An Enhanced Model of Water Cooling

Authors:
raw_author_name: Sara Golroodbari
raw_affiliation_string: Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
raw_author_name: Wilfried van Sark
raw_affiliation_string: Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands

Abstract: Solar photovoltaics (PV) continues to grow rapidly across the world and now accounts for a very considerable proportion of all non-fossil-fuel electricity. With the continuing urgency of greenhouse gas abatement, the growth of solar PV is inevitable. Competition with other land uses and the desire to optimize the efficiency of the panels by making use of water cooling are compelling arguments for offshore floating PV (OFPV), a trend that could also benefit from the existing infrastructure recently built for offshore wind farms. Building on our earlier work, we present a larger dataset (n = 82) located around the globe to assess global yield (dis)advantages while also accounting for a modified form of water cooling of the offshore panels. Using our results regarding the Köppen–Geiger (KG) classification system and using a statistical learning method, we demonstrate that the KG climate classification system has limited validity in predicting the likely gains from OFPV. Finally, we also explore a small subset of sites to demonstrate that economics, alongside geography and technology, impacts the feasibility of locating PV panels offshore.
BALQUE: Batch active learning by querying unstable examples with calibrated confidence

Authors:
raw_author_name: Dajiang Liu
raw_affiliation_string: College of Computer Science, Chongqing University, Chongqing 400044, China
raw_author_name: Jiaxing Shang
raw_affiliation_string: College of Computer Science, Chongqing University, Chongqing 400044, China
raw_author_name: Linjiang Zheng
raw_affiliation_string: College of Computer Science, Chongqing University, Chongqing 400044, China
raw_author_name: Jiang Zhong
raw_affiliation_string: College of Computer Science, Chongqing University, Chongqing 400044, China
raw_author_name: Weiwei Cao
raw_affiliation_string: Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Guanghan, China
raw_author_name: Hong Sun
raw_affiliation_string: Flight Technology and Flight Safety Research Base, Civil Aviation Flight University of China, Guanghan, China
raw_author_name: Wu Xie
raw_affiliation_string: Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China

Abstract: Active learning alleviates labeling costs by selecting and labeling the most informative examples from an unlabeled pool. However, most existing active learning approaches estimate informativeness with uncalibrated confidence, resulting in unreliable informativeness estimation. These approaches generally ignored two significant issues caused by uncalibrated confidence methods. Firstly, the average uncalibrated confidence generated by modern neural networks is usually higher than the accuracy. Secondly, examples located near the decision boundaries are unstable during prediction when the target model updates parameters in the last several epochs, even throughout the training process. This phenomenon, caused by the forgetting characteristic of neural networks, has a significant impact on some specific models that estimate the informativeness by predicted probability vectors or pseudo labels. To address these issues, in this paper, we propose a novel active learning approach to reliably estimate informativeness with calibrated confidence. Specifically, we integrate the intermediate predictions for each unlabeled example, generated by the target model during the training process, to generate calibrated confidence. The calibrated confidence can capture a tendentious label from an indecisive subset of the class space. We show that the calibrated confidence with tendentiousness can maintain the ability of correct predictions. The empirical results demonstrate that our approach outperforms the state-of-the-art active learning methods on image classification tasks.
Introspective GAN: Learning to grow a GAN for incremental generation and classification

Authors:
raw_author_name: Chen He
raw_affiliation_string: Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China
raw_author_name: Ruiping Wang
raw_affiliation_string: Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China
raw_author_name: Shiguang Shan
raw_affiliation_string: Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China
raw_author_name: Xilin Chen
raw_affiliation_string: Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China

Abstract: Lifelong learning, the ability to continually learn new concepts throughout our life, is a hallmark of human intelligence. Generally, humans learn a new concept by knowing what it looks like and what makes it different from the others, which are correlated. Those two ways can be characterized by generation and classification in machine learning respectively. In this paper, we carefully design a dynamically growing GAN called Introspective GAN (IntroGAN) that can perform incremental generation and classification simultaneously with the guidance of prototypes, inspired by their roles of efficient information organization in human visual learning and excellent performance in other fields like zero-shot/few-shot/incremental learning. Specifically, we incorporate prototype-based classification which is robust to feature change in incremental learning and GAN as a generative memory to alleviate forgetting into a unified end-to-end framework. A comprehensive benchmark on the joint incremental generation and classification task is proposed and our method demonstrates promising results. Additionally, we conduct comprehensive analyses over the properties of IntroGAN and verify that generation and classification can be mutually beneficial in incremental scenarios, which is an inspiring area to be further exploited. The code is available at https://github.com/TonyPod/IntroGAN.
Digital simulation of convex mixtures of Markovian and non-Markovian single qubit Pauli channels on NISQ devices

Authors:
raw_author_name: I. J. David
raw_affiliation_string: School of Chemistry and Physics, University of KwaZulu-Natal, Durban, 4001, South Africa
raw_author_name: I. Sinayskiy
raw_affiliation_string: School of Chemistry and Physics, University of KwaZulu-Natal, Durban, 4001, South Africa
raw_author_name: F. Petruccione
raw_affiliation_string: School of Chemistry and Physics, University of KwaZulu-Natal, Durban, 4001, South Africa

Abstract: Abstract Quantum algorithms for simulating quantum systems provide a clear and provable advantage over classical algorithms in fault-tolerant settings. There is also interest in quantum algorithms and their implementation in Noisy Intermediate Scale Quantum (NISQ) settings. In these settings, various noise sources and errors must be accounted for when executing any experiments. Recently, NISQ devices have been verified as versatile testbeds for simulating open quantum systems and have been used to simulate simple quantum channels. Our goal is to solve the more complicated problem of simulating convex mixtures of single qubit Pauli channels on NISQ devices. We consider two specific cases: mixtures of Markovian channels that result in a non-Markovian channel (M + M = nM) and mixtures of non-Markovian channels that result in a Markovian channel (nM + nM = M). For the first case, we consider mixtures of Markovian single qubit Pauli channels; for the second case, we consider mixtures of Non-Markovian single qubit depolarising channels, which is a special case of the single qubit Pauli channel. We show that efficient circuits, which account for the topology of currently available devices and current levels of decoherence, can be constructed by heuristic approaches that reduce the number of CNOT gates used in our circuit. We also present a strategy for regularising the process matrix so that the process tomography yields a completely positive and trace-preserving (CPTP) channel. Key points This work simulates the convex mixtures of single qubit Markovian and non-Markovian quantum channels on NISQ devices provided by the IMBQE. The circuits used to implement the channels take into account the topolgy of the quantum device used as well as the number of CNOT gates used. We present a strategy for regularising the process matrix to ensure the quantum process tomography yields a CPTP channel. Something that is not correctly implemented in Qiskit. A method is outlined for finding mixtures of non-Markovian depolarising channels that yield a Markovian depolarising channel. It is also shown that, one cannot convexly mix two Markovian depolarising channels that leads to a non-Markovian depolarising channel.
Construction of a network intelligence platform for privacy protection and integrated big data mining

Authors:
raw_author_name: Yuan Guo
raw_affiliation_string: School of Advanced Manufacturing Engineering, Hefei University, Hefei, Anhui, China

Abstract: Motivation: With the enhancement of people’s awareness of the protection of personal privacy information, how to provide better services on the premise of protecting users’ privacy has become an urgent problem to be solved. Therefore, it is a necessary motivation to build a network intelligent platform for privacy protection and integrated big data mining. Objective: In view of the existing network platform of data privacy leakage, low efficiency of data mining and user satisfaction is not high, this paper will adopt advanced privacy technology, to ensure the confidentiality of users’ personal information and security, to enhance the user trust and use experience, to better meet the needs of users. Methods: In order to better protect the privacy of users, the network intelligent platform should adopt more advanced privacy protection technology. This paper uses the differential privacy algorithm to reduce the risk of data leakage and abuse, and ensure the accuracy and efficiency of data analysis and mining. In the design of the platform, the performance of the platform is fully taken into account to realize the secure storage and efficient processing of data, with good scalability and flexibility to meet the growing user needs and business needs. The performance of the network intelligent platform is also analyzed by experimental simulation. Result: The experimental results of this article indicated that in a network intelligent platform based on privacy protection and integrated big data mining, its data transmission encryption score was 9.5; the data storage encryption score was 9.8; the score of access control mechanism was 9.3; the privacy protection score was 9.6; the response time was 80 ms; the processing speed was 121GB/h; the user satisfaction rating was 6.6. Conclusion: This indicated that the network intelligent platform had good platform performance and user friendliness while ensuring data security and privacy protection. It could efficiently conduct data mining and ensure data security and privacy.
A hybrid stacking classifier with feature selection for handling imbalanced data

Authors:
raw_author_name: Asha Abraham
raw_affiliation_string: Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology Kattankulathur, Chennai, India
raw_author_name: R. Kayalvizhi
raw_affiliation_string: Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology Kattankulathur, Chennai, India
raw_author_name: Habeeb Shaik Mohideen
raw_affiliation_string: Department of Genetic Engineering, College of Engineering and Technology, SRM Institute of Science and Technology Kattankulathur, Chennai, India

Abstract: Nowadays, cancer has become more alarming. This paper discusses the most significant Ovarian Cancer, Epithelial Ovarian Cancer (EOC), due to the low survival rate. The proposed algorithm for this work is a ‘Multi classifier ShapRFECV based EOC’ (MSRFECV-EOC) subtype analysis technique that utilized the EOC data from the National Centre for Biotechnology Information and Cancer Cell Line Encyclopedia websites for early identification of EOC using Machine Learning Techniques. This approach increases the data size, balances different classes of the data, and cuts down the enormous number of features unrelated to the disease of interest to prevent overfitting. To incorporate these functionalities, in the data preprocessing stage, OC-related gene names were taken from the Cancermine database and other OC-related works. Moreover, OC datasets were merged based on OC genes, and missing values of EOC subtypes were identified and imputed using Iterative Logistic Imputation. Synthetic Minority Oversampling Technique with an Edited Nearest Neighbors approach is applied to the imputed dataset. Next, in the Feature Selection phase, the most significant features for subtypes of EOC were identified by applying the Shapley Additive Explanations based on the Recursive Feature Elimination Cross-Validation (ShapRFECV) algorithm, preserving predefined features while selecting new EOC features. Eventually, an accuracy of 97% was achieved with Optuna-optimized Random Forest, which outperformed the existing models. SHAP plotted the most prominent features behind the classification. The Pickle tool saves much training time by preserving hidden parameter values of the model. In the final phase, by using the Stratified K Fold Stacking Classifier, the accuracy was improved to 98.9%.
MTC-Net: Multi-scale feature fusion network for medical image segmentation

Authors:
raw_author_name: Shujun Ren
raw_affiliation_string: Department of Computer Science and Technology, Shandong University of Science and Technology, Qingdao, China
raw_author_name: Yuanhong Wang
raw_affiliation_string: Department of Computer Science and Technology, Shandong University of Science and Technology, Qingdao, China

Abstract: Image segmentation is critical in medical image processing for lesion detection, localisation, and subsequent diagnosis. Currently, computer-aided diagnosis (CAD) has played a significant role in improving diagnostic efficiency and accuracy. The segmentation task is made more difficult by the hazy lesion boundaries and uneven forms. Because standard convolutional neural networks (CNNs) are incapable of capturing global contextual information, adequate segmentation results are impossible to achieve. We propose a multiscale feature fusion network (MTC-Net) in this paper that integrates deep separable convolution and self-attentive modules in the encoder to achieve better local continuity of images and feature maps. In the decoder, a multi-branch multi-scale feature fusion module (MSFB) is utilized to improve the network’s feature extraction capability, and it is integrated with a global cooperative aggregation module (GCAM) to learn more contextual information and adaptively fuse multi-scale features. To develop rich hierarchical representations of irregular forms, the suggested detail enhancement module (DEM) adaptively integrates local characteristics with their global dependencies. To validate the effectiveness of the proposed network, we conducted extensive experiments, evaluated on the public datasets of skin, breast, thyroid and gastrointestinal tract with ISIC2018, BUSI, TN3K and Kvasir-SEG. The comparison with the latest methods also verifies the superiority of our proposed MTC-Net in terms of accuracy. Our code on https://github.com/gih23/MTC-Net.
A negative selection algorithm with human-in-the-loop for anomaly detection

Authors:
raw_author_name: Chunling Li
raw_affiliation_string: Jiangsu Provincial Institute of Technology Transfer (Changzhou University), Jiangsu Province, China; The School of Business, Changzhou University, Jiangsu Province, China

Abstract: The existing negative selection algorithms can not improve their detection performance by human intervention during the testing process. This paper proposes a negative selection algorithm with human-in-the-loop for anomaly detection. It uses self-sample clusters to train detectors with a nonrandom strategy. Its detectors and self-sample clusters fully cover state space without overlapping each other. It locally adjusts detectors and self-sample clusters with human intervention to improve its detection performance during the testing process. Experiments were performed on two synthetic datasets and the Iris dataset from the UCI repository to assess its performance. The results show that it outperforms the other anomaly detection methods in most cases.