Reviewerly/exHarmony huggingface dataset:
When Sparse Neural Network Meets Label Noise Learning: A Multistage Learning Framework

Authors:
raw_author_name: Yan Yan
raw_affiliation_string: Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China
raw_author_name: Jing-Hao Xue
raw_affiliation_string: Department of Statistical Science, University College London, London, U.K.
raw_author_name: Biao Wang
raw_affiliation_string: Zhejiang Laboratory, Hanzhou, China
raw_author_name: Hanzi Wang
raw_affiliation_string: Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China

Abstract: Recent methods in network pruning have indicated that a dense neural network involves a sparse subnetwork (called a winning ticket), which can achieve similar test accuracy to its dense counterpart with much fewer network parameters. Generally, these methods search for the winning tickets on well-labeled data. Unfortunately, in many real-world applications, the training data are unavoidably contaminated with noisy labels, thereby leading to performance deterioration of these methods. To address the above-mentioned problem, we propose a novel two-stream sample selection network (TS3-Net), which consists of a sparse subnetwork and a dense subnetwork, to effectively identify the winning ticket with noisy labels. The training of TS3-Net contains an iterative procedure that switches between training both subnetworks and pruning the smallest magnitude weights of the sparse subnetwork. In particular, we develop a multistage learning framework including a warm-up stage, a semisupervised alternate learning stage, and a label refinement stage, to progressively train the two subnetworks. In this way, the classification capability of the sparse subnetwork can be gradually improved at a high sparsity level. Extensive experimental results on both synthetic and real-world noisy datasets (including MNIST, CIFAR-10, CIFAR-100, ANIMAL-10N, Clothing1M, and WebVision) demonstrate that our proposed method achieves state-of-the-art performance with very small memory consumption for label noise learning. Code is available at https://github.com/Runqing-forMost/TS3-Net/tree/master.
TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations

Authors:
raw_author_name: Zijian Li
raw_affiliation_string: School of Computer, Guangdong University of Technology, Guangzhou, China
raw_author_name: Ruichu Cai
raw_affiliation_string: Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application and the School of Computer, Guangdong University of Technology, Guangzhou, China
raw_author_name: Fengzhu Wu
raw_affiliation_string: School of Computer, Guangdong University of Technology, Guangzhou, China
raw_author_name: Hao Gu
raw_affiliation_string: Tencent Technology (Shenzhen) Company Ltd., Shenzhen, China
raw_author_name: Yuexing Hao
raw_affiliation_string: College of Human Ecology, Cornell University, Ithaca, NY, USA
raw_author_name: Yuguang Yan
raw_affiliation_string: School of Computer, Guangdong University of Technology, Guangzhou, China

Abstract: Sequential recommendation aims to choose the most suitable items for a user at a specific timestamp given historical behaviors. Existing methods usually model the user behavior sequence based on transition-based methods such as Markov chain. However, these methods also implicitly assume that the users are independent of each other without considering the influence between users. In fact, this influence plays an important role in sequence recommendation since the behavior of a user is easily affected by others. Therefore, it is desirable to aggregate both user behaviors and the influence between users, which are evolved temporally and involved in the heterogeneous graph of users and items. In this article, we incorporate dynamic user-item heterogeneous graphs to propose a novel sequential recommendation framework. As a result, the historical behaviors as well as the influence between users can be taken into consideration. To achieve this, we first formalize sequential recommendation as a problem to estimate conditional probability given temporal dynamic heterogeneous graphs and user behavior sequences. After that, we exploit the conditional random field to aggregate the heterogeneous graphs and user behaviors for probability estimation and employ the pseudo-likelihood approach to derive a tractable objective function. Finally, we provide scalable and flexible implementations of the proposed framework. Experimental results on three real-world datasets not only demonstrate the effectiveness of our proposed method but also provide some insightful discoveries on the sequential recommendation.
FAT: Frequency-Aware Transformation for Bridging Full-Precision and Low-Precision Deep Representations

Authors:
raw_author_name: Chaofan Tao
raw_affiliation_string: Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong
raw_author_name: Quan Chen
raw_affiliation_string: School of Microelectronics, Southern University of Science and Technology, Shenzhen, China
raw_author_name: Zhaoyang Zhang
raw_affiliation_string: Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
raw_author_name: Ping Luo
raw_affiliation_string: Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong
raw_author_name: Ngai Wong
raw_affiliation_string: Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong

Abstract: Learning low-bitwidth convolutional neural networks (CNNs) is challenging because performance may drop significantly after quantization. Prior arts often quantize the network weights by carefully tuning hyperparameters such as nonuniform stepsize and layerwise bitwidths, which are complicated since the full- and low-precision representations have large discrepancies. This work presents a novel quantization pipeline, named frequency-aware transformation (FAT), that features important benefits: 1) instead of designing complicated quantizers, FAT learns to transform network weights in the frequency domain to remove redundant information before quantization, making them amenable to training in low bitwidth with simple quantizers; 2) FAT readily embeds CNNs in low bitwidths using standard quantizers without tedious hyperparameter tuning and theoretical analyses show that FAT minimizes the quantization errors in both uniform and nonuniform quantizations; and 3) FAT can be easily plugged into various CNN architectures. Using FAT with a simple uniform/logarithmic quantizer can achieve the state-of-the-art performance in different bitwidths on various model architectures. Consequently, FAT serves to provide a novel frequency-based perspective for model quantization.
Using Decomposed Error for Reproducing Implicit Understanding of Algorithms

Authors:
raw_author_name: Caitlin A. Owen
raw_affiliation_string: Department of Information Science, University of Otago, Dunedin, New Zealand caitlin.owen@otago.ac.nz
raw_author_name: Grant Dick
raw_affiliation_string: Department of Information Science, University of Otago, Dunedin, New Zealand grant.dick@otago.ac.nz
raw_author_name: Peter A. Whigham
raw_affiliation_string: Department of Information Science, University of Otago, Dunedin, New Zealand peter.whigham@otago.ac.nz

Abstract: Abstract Reproducibility is important for having confidence in evolutionary machine learning algorithms. Although the focus of reproducibility is usually to recreate an aggregate prediction error score using fixed random seeds, this is not sufficient. Firstly, multiple runs of an algorithm, without a fixed random seed, should ideally return statistically equivalent results. Secondly, it should be confirmed whether the expected behaviour of an algorithm matches its actual behaviour, in terms of how an algorithm targets a reduction in prediction error. Confirming the behaviour of an algorithm is not possible when using a total error aggregate score. Using an error decomposition framework as a methodology for improving the reproducibility of results in evolutionary computation addresses both of these factors. By estimating decomposed error using multiple runs of an algorithm and multiple training sets, the framework provides a greater degree of certainty about the prediction error. Also, decomposing error into bias, variance due to the algorithm (internal variance), and variance due to the training data (external variance) more fully characterises evolutionary algorithms. This allows the behaviour of an algorithm to be confirmed. Applying the framework to a number of evolutionary algorithms shows that their expected behaviour can be different to their actual behaviour. Identifying a behaviour mismatch is important in terms of understanding how to further refine an algorithm as well as how to effectively apply an algorithm to a problem.
Emerging Scientific Topic Discovery by Analyzing Reliable Patterns of Infrequent Synonymous Biterms

Authors:
raw_author_name: Junfeng Wu
raw_affiliation_string: School of Information Technology, Deakin University, Blackburn South, Australia
raw_author_name: Guangyan Huang
raw_affiliation_string: School of Information Technology, Deakin University, Blackburn South, Australia
raw_author_name: Hui Zheng
raw_affiliation_string: Data61, CSIRO, Victoria, Australia
raw_author_name: Guang-Li Huang
raw_affiliation_string: School of Information Technology, Deakin University, Blackburn South, Australia
raw_author_name: Borui Cai
raw_affiliation_string: School of Information Technology, Deakin University, Blackburn South, Australia
raw_author_name: Chi-Hung Chi
raw_affiliation_string: Nanyang Technological University, Singapore
raw_author_name: Jing He
raw_affiliation_string: Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, U.K.

Abstract: Emerging scientific topics are those topics that the number of related articles was small in the past but has grown dramatically in recent years. Automatic discovery of emerging scientific topics has become increasingly necessary because of the exponentially increasing of research papers. Such discovery enables broad applications, such as optimizing resource allocations for promising research areas, predicting future technology trends, finding knowledge gaps and new concepts, and recommending personalized research directions. In this paper, we provide a framework of emerging topic discovery methods using Infrequent Synonymous Biterm (ISB), which automatically extracts the dedicated knowledge from the infrequent patterns of synonymous biterms in a corpus (e.g., paper titles); each term in a synonymous biterm represents a collaborating supertopic, whose collaboration originates an emerging topic. In particular, we propose an Analyzing Reliable Patterns of Infrequent Synonymous Biterms (ARPISB) method, which guarantees the quality of the result emerging topics by adaptively giving larger weights to more reliable ISB. Extensive experiments on five subfields' scholarly papers demonstrate the significant and robust improvement of the accuracy of emerging scientific topic discovery.
Negative Instance Guided Self-Distillation Framework for Whole Slide Image Analysis

Authors:
raw_author_name: Linhao Qu
raw_affiliation_string: Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai, China
raw_author_name: Zhijian Song
raw_affiliation_string: Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai, China
raw_author_name: Manning Wang
raw_affiliation_string: Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai, China

Abstract: Histopathology image classification is an important clinical task, and current deep learning-based whole-slide image (WSI) classification methods typically cut WSIs into small patches and cast the problem as multi-instance learning. The mainstream approach is to train a bag-level classifier, but their performance on both slide classification and positive patch localization is limited because the instance-level information is not fully explored. In this article, we propose a negative instance-guided, self-distillation framework to directly train an instance-level classifier end-to-end. Instead of depending only on the self-supervised training of the teacher and the student classifiers in a typical self-distillation framework, we input the true negative instances into the student classifier to guide the classifier to better distinguish positive and negative instances. In addition, we propose a prediction bank to constrain the distribution of pseudo instance labels generated by the teacher classifier to prevent the self-distillation from falling into the degeneration of classifying all instances as negative. We conduct extensive experiments and analysis on three publicly available pathological datasets: CAMELYON16, PANDA, and TCGA, as well as an in-house pathological dataset for cervical cancer lymph node metastasis prediction. The results show that our method outperforms existing methods by a large margin. Code will be publicly available.
A Robust Multilabel Method Integrating Rule-Based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance

Authors:
raw_author_name: Qiongdan Lou
raw_affiliation_string: School of Artificial Intelligence and Computer Science, Wuxi, China
raw_author_name: Zhaohong Deng
raw_affiliation_string: School of Artificial Intelligence and Computer Science, Wuxi, China
raw_author_name: Zhiyong Xiao
raw_affiliation_string: School of Artificial Intelligence and Computer Science, Wuxi, China
raw_author_name: Kup-Sze Choi
raw_affiliation_string: Centre for Smart Health, Hong Kong Polytechnic University, Hung Hom, Hong Kong
raw_author_name: Shitong Wang
raw_affiliation_string: School of Artificial Intelligence and Computer Science, Wuxi, China

Abstract: Model transparency, label correlation learning and robustness to label noise are crucial for multilabel learning. However, few existing methods consider these three characteristics simultaneously. To address this challenge, we propose the robust multilabel Takagi-Sugeno-Kang fuzzy system (R-MLTSK-FS) with three mechanisms. First, we design a soft label learning mechanism to reduce the effect of label noise by explicitly measuring the interactions between labels, which is also the basis of the other two mechanisms. Second, rule-based multi-output TSK FS is used as the base model to efficiently model the inference relationship between features and soft labels in a more transparent way than many existing multilabel models. Third, to further improve the performance of multilabel learning, we build a correlation enhancement learning mechanism based on the soft label space and the fuzzy feature space. Extensive experiments are conducted to demonstrate the superiority of the proposed method.
GeoLinter: A Linting Framework for Choropleth Maps

Authors:
raw_author_name: Fan Lei
raw_affiliation_string: School of Computing,; School of Computing, Informatics &#x0026; Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
raw_author_name: Arlen Fan
raw_affiliation_string: School of Computing,; School of Computing, Informatics &#x0026; Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
raw_author_name: Alan M. MacEachren
raw_affiliation_string: Informatics & Decision Systems Engineering, Arizona State University.; School of Computing,
raw_author_name: Ross Maciejewski
raw_affiliation_string: School of Computing,; School of Computing, Informatics &#x0026; Decision Systems Engineering, Arizona State University, Tempe, AZ, USA

Abstract: Visualization linting is a proven effective tool in assisting users to follow established visualization guidelines. Despite its success, visualization linting for choropleth maps, one of the most popular visualizations on the internet, has yet to be investigated. In this paper, we present GeoLinter, a linting framework for choropleth maps that assists in creating accurate and robust maps. Based on a set of design guidelines and metrics drawing upon a collection of best practices from the cartographic literature, GeoLinter detects potentially suboptimal design decisions and provides further recommendations on design improvement with explanations at each step of the design process. We perform a validation study to evaluate the proposed framework's functionality with respect to identifying and fixing errors and apply its results to improve the robustness of GeoLinter. Finally, we demonstrate the effectiveness of the GeoLinter - validated through empirical studies - by applying it to a series of case studies using real-world datasets.
A Dual-Attention Learning Network With Word and Sentence Embedding for Medical Visual Question Answering

Authors:
raw_author_name: Hongfang Gong
raw_affiliation_string: School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, China

Abstract: Research in medical visual question answering (MVQA) can contribute to the development of computer-aided diagnosis. MVQA is a task that aims to predict accurate and convincing answers based on given medical images and associated natural language questions. This task requires extracting medical knowledge-rich feature content and making fine-grained understandings of them. Therefore, constructing an effective feature extraction and understanding scheme are keys to modeling. Existing MVQA question extraction schemes mainly focus on word information, ignoring medical information in the text, such as medical concepts and domain-specific terms. Meanwhile, some visual and textual feature understanding schemes cannot effectively capture the correlation between regions and keywords for reasonable visual reasoning. In this study, a dual-attention learning network with word and sentence embedding (DALNet-WSE) is proposed. We design a module, transformer with sentence embedding (TSE), to extract a double embedding representation of questions containing keywords and medical information. A dual-attention learning (DAL) module consisting of self-attention and guided attention is proposed to model intensive intramodal and intermodal interactions. With multiple DAL modules (DALs), learning visual and textual co-attention can increase the granularity of understanding and improve visual reasoning. Experimental results on the ImageCLEF 2019 VQA-MED (VQA-MED 2019) and VQA-RAD datasets demonstrate that our proposed method outperforms previous state-of-the-art methods. According to the ablation studies and Grad-CAM maps, DALNet-WSE can extract rich textual information and has strong visual reasoning ability.
Co-Propagation of QKD &amp; 6 Tb/s (60 × 100G) DWDM Channels With ∼17 dBm Total WDM Power in Single and Multi-Span Configurations

Authors:
raw_author_name: P. Gavignet
raw_affiliation_string: Orange Innovation, Lannion, France
raw_author_name: F. Mondain
raw_affiliation_string: Orange Innovation, Lannion, France
raw_author_name: R. I. Woodward
raw_affiliation_string: Toshiba Europe Ltd., Cambridge, U.K.
raw_author_name: J. F. Dynes
raw_affiliation_string: Toshiba Europe Ltd., Cambridge, U.K.
raw_author_name: A. J. Shields
raw_affiliation_string: Toshiba Europe Ltd., Cambridge, U.K.

Abstract: We report co-propagation experiments of the quantum channel (at 1310 nm) of a Quantum Key Distribution (QKD) system with Dense Wavelength Division Multiplexing (DWDM) data channels in the 1550 nm range. Two configurations are assessed. The first one is a single span configuration where various lengths of Standard Single Mode Fiber (SSMF) (from 20 to 70 km) are used and the total WDM channels power is varied. The Secure Key Rate (SKR) and the Quantum Bit Error Ratio (QBER) are recorded showing that up to ~17 dBm total power of 30 or 60 channels at 100 Gb/s can coexist with the quantum channel. A metric to evaluate the co-propagation efficiency is also proposed to better evaluate the ability of a QKD system to provide secure keys in a co-propagation regime. The second experiment is a three spans link with a cascade of three QKD systems and two trusted nodes in a 184 km total link length. We report the transmission of a coherent 400 Gb/s Dual Polarization DP-16QAM (Quadrature Amplitude Modulation) channel that transports a QKD secured 100 GbE data stream, with other fifty-four 100 Gb/s WDM channels. Encryption is demonstrated at the same time as co-propagation.
Gaussian Adapted Markov Model with Overhauled Fluctuation Analysis-Based Big Data Streaming Model in Cloud

Authors:
raw_author_name: M. Ananthi
raw_affiliation_string: Department of Computer Science and Business Systems, Sri Sairam Engineering College, Chennai, India.
raw_author_name: K. Ramalakshmi
raw_affiliation_string: Department of Computer Science and Engineering, Alliance University, Bengaluru, India.

Abstract: An accurate resource usage prediction in the big data streaming applications still remains as one of the complex processes. In the existing works, various resource scaling techniques are developed for forecasting the resource usage in the big data streaming systems. However, the baseline streaming mechanisms limit with the issues of inefficient resource scaling, inaccurate forecasting, high latency, and running time. Therefore, the proposed work motivates to develop a new framework, named as Gaussian adapted Markov model (GAMM)—overhauled fluctuation analysis (OFA), for an efficient big data streaming in the cloud systems. The purpose of this work is to efficiently manage the time-bounded big data streaming applications with reduced error rate. In this study, the gating strategy is also used to extract the set of features for obtaining nonlinear distribution of data and fat convergence solution, used to perform the fluctuation analysis. Moreover, the layered architecture is developed for simplifying the process of resource forecasting in the streaming applications. During experimentation, the results of the proposed stream model GAMM-OFA are validated and compared by using different measures.