Reviewerly/exHarmony huggingface dataset:
Supervised adaptive similarity consistent latent representation hashing

Authors:
raw_author_name: Hongbin Wang
raw_affiliation_string: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
raw_author_name: Rui Chen
raw_affiliation_string: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
raw_author_name: Zhenqiu Shu
raw_affiliation_string: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
raw_author_name: Yafei Zhang
raw_affiliation_string: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
raw_author_name: Huafeng Li
raw_affiliation_string: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

Abstract: Cross–modal hashing has attracted significant attention in multimedia data similarity given its appealing computational cost and retrieval performance. Supervised hashing benefits from the auxiliary learning of a similarity matrix, which is usually predefined by inner product features or category labels. However, a predefined similarity matrix fails to reflect the real similarity relationship between image–text pairs. In addition, existing methods fix the weights to a value or update them by introducing sensitive dataset–related hyper–parameters. To overcome these problems, we propose a method to perform supervised adaptive similarity consistent latent representation hashing (SCLRH) that adaptively learns the similarity matrix during hashing learning. In SCLRH, we assume that multimodal data are observed and reconstructed from different perspectives of a common consistent latent representation. Instead of using a predefined similarity matrix, SCLRH adaptively learns this matrix to reflect the underlying manifold structure and describes the fine–grained similarity between consistent latent representations. In addition, SCLRH introduces a self–weighted learning strategy to update the weights based on the contributions of different modalities without involving additional hyper–parameters. Experimental results on three benchmark datasets demonstrate the superiority of the proposed SCLRH for cross–modal retrieval.
Surface Noise

Authors:
raw_author_name: Richard Beaudoin

Abstract: Abstract Chapter 5 analyzes the cyclic surface noise patterns heard on early recording formats including wax cylinders and gramophone discs. The sound of recording and playback machines is theorized alongside the composition being recording, treating the resultant totality as one music. An analogy is made between surface noise and a Renaissance illustration of a sighting grid by Fludd. Six modes of interaction are defined between perceived meter and the pulse patterns of surface noise: (1) performance-centricity; (2) narrative asynchrony; (3) ekphrastic (non)coincidence; (4) expressive synchrony; (5) metaphoric development; and (6) surface noise-centricity. The modes are illustrated in recordings by Simpson singing Bonds, Caruso singing Donizetti, Granados and Arensky recording their own music, Krushelnytska singing Lysenko, and a re-recording of the historic Brahms cylinder. The deterioration of the cylinder is related to a letter in which Rilke voiced his concerns about a tarnished daguerreotype of his father.
Figleaves, Dogwhistles, and Falsehood

Authors:
raw_author_name: Jennifer Mather Saul

Abstract: Abstract In this chapter, I explore the role that figleaves and dogwhistles play in the spread of falsehood and wildly implausible conspiracy theories. I begin from the Norm of Truthfulness, a woolly norm that is widely accepted, which functions in a manner similar to the Norm of Racial Equality. I show how dogwhistles and figleaves are used to provide deniability for violations of this norm. This allows blatantly false claims to remain circulation, and provides cover for political figures who might otherwise see their careers ended by their support for wild falsehoods.
Introduction

Authors:
raw_author_name: Jennifer Mather Saul

Abstract: Abstract This chapter introduces the main research question of the book: how have blatant racism and wildly implausible conspiracy theories been mainstreamed in our political culture? The chapter acknowledges that racism and political lies are far from new. Yet it argues that there is something new in the prevalence of blatant racism and obvious lies in our current political discourse. Moreover, it argues that this is dangerous in ways that should motivate us to seek a fuller understanding. This introduction sets out the basic elements of the response that the book will make, including (1) a focus on divisions among the public; (2) particular attention to manipulable groups; (3) the linguistic devices of dogwhistles and figleaves; (4) and the rise of authoritarianism. It also offers a chapter-by-chapter overview of the book.
A closer look at branch classifiers of multi-exit architectures

Authors:
raw_author_name: Shaohui Lin
raw_affiliation_string: Key Laboratory of Advanced Theory and Application in Statistics and Data Science, Ministry of Education, China; School of Computer Science and Technology, East China Normal University, 200062, China
raw_author_name: Bo Ji
raw_affiliation_string: School of Computing, National University of Singapore, 117417, Singapore
raw_author_name: Rongrong Ji
raw_affiliation_string: Lab of Media Analytics and Computing, School of Informatics, Xiamen University, 361005, China
raw_author_name: Angela Yao
raw_affiliation_string: School of Computing, National University of Singapore, 117417, Singapore

Abstract: Multi-exit architectures consist of a backbone and branch classifiers that offer shortened inference pathways to reduce the run-time of deep neural networks. In this paper, we analyze different branching patterns that vary in their allocation of computational complexity for the branch classifiers. Constant-complexity branching keeps all branches the same, while complexity-increasing and complexity-decreasing branching place more complex branches later or earlier in the backbone respectively. Through extensive experimentation on multiple backbones and datasets, we find that complexity-decreasing branches are more effective than constant-complexity or complexity-increasing branches, which achieve the best accuracy-cost trade-off. We investigate a cause by using knowledge consistency to probe the effect of adding branches onto a backbone. Our findings show that complexity-decreasing branching yields the least disruption to the feature abstraction hierarchy of the backbone, which explains the effectiveness of the branching patterns.
Synchronization of fuzzy-chaotic systems with Z-controller in secure communication

Authors:
raw_author_name: Kanan M. Babanli
raw_affiliation_string: Istanbul University Cerrahpasa, 34320 Avcılar, Istanbul, Turkey
raw_author_name: Rana Ortac Kabaoglu
raw_affiliation_string: Istanbul University Cerrahpasa, 34320 Avcılar, Istanbul, Turkey

Abstract: Soft Computing approaches may be effectively used for the aim to improve security of communication systems. Recently, we proposed a technique for fuzzy-chaotic masking signals generation. In this paper we developed this approach further. It is proposed a procedure for receiver and transmitter synchronization by using fuzzy chaotic systems (fuzzy Lorentz systems). The receiver system utilizes a fuzzy controller based on the use of Z-numbers in If-Then rules. The transmitter system is designed in form of a classical fuzzy system. The fuzzy controller is used to synchronize the fuzzy Lorentz system-based receiver and transmitter at a minimal error.
Online controls and the protest- repression nexus in Iran

Authors:
raw_author_name: Anita R. Gohdes

Abstract: Abstract This chapters seeks to understand the strategy and impact of Internet shutdowns in an authoritarian context outside of armed conflict. It studies the mass protests that spread across Iran in November 2019 to investigate how Iranian security forces engaged in violent repression during the regime-ordered nationwide shutdown of the Internet. It combines insights from NGO and news reports, network measurement data on the Internet shutdown, and data on censorship circumvention to show how the Internet shutdown was accompanied by mass repression intended to cover up violence by security forces and motivate witnesses and bystanders to self-censor, even once the Internet was turned back on.
Deep learning for nano-photonic materials – The solution to everything!?

Authors:
raw_author_name: Peter R. Wiecha
raw_affiliation_string: LAAS, Université de Toulouse, CNRS, Toulouse, France

Abstract: Deep learning is currently being hyped as an almost magical tool for solving all kinds of difficult problems that computers have not been able to solve in the past. Particularly in the fields of computer vision and natural language processing, spectacular results have been achieved. The hype has now infiltrated several scientific communities. In (nano-) photonics, researchers are trying to apply deep learning to all kinds of forward and inverse problems. A particularly challenging problem is for instance the rational design of nanophotonic materials and devices. In this opinion article, I will first discuss the public expectations of deep learning and give an overview of the quite different scales at which actors from industry and research are operating their deep learning models. I then examine the weaknesses and dangers associated with deep learning. Finally, I’ll discuss the key strengths that make this new set of statistical methods so attractive, and review a personal selection of opportunities that shouldn’t be missed in the current developments.
Curricular-balanced long-tailed learning

Authors:
raw_author_name: Xiang Xiang
raw_affiliation_string: National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology (HUST), Wuhan, China
raw_author_name: Zihan Zhang
raw_affiliation_string: National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology (HUST), Wuhan, China
raw_author_name: Xilin Chen
raw_affiliation_string: Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

Abstract: The real-world data distribution is essentially long-tailed, which poses a significant challenge to the deep model. Classification models minimizing cross-entropy loss struggle to classify the tail classes, although cross-entropy training is successful on balanced data. We reveal that minimizing cross-entropy loss under long-tailed distribution leads to the Tail Collapse phenomenon, which fundamentally limits the performance of neural networks. To correct the optimization behavior of cross-entropy training, we propose a new Curricular Balanced Loss (CurB Loss) to alleviate the imbalance. The CurB loss has two factors: the re-weighting factor and the curriculum learning factor. We design the re-weighting factor based on the margin-based training that can theoretically reach the optimums of networks. Then, we incorporate the idea of Curriculum Learning into the re-weighting loss in an adaptive manner. We design the curriculum learning factor to make the model gradually emphasize the hard classes. The empirical results demonstrate the complementary of the two factors. Our method outperforms previous state-of-the-art methods by 0.9%, 2.7%, 1.2% on CIFAR10-LT, CIFAR-100-LT and ImageNet-LT, demonstrating the effectiveness of CurB Loss for long-tailed visual recognition.
Soft Hybrid Knowledge Distillation against deep neural networks

Authors:
raw_author_name: Jian Zhang
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China
raw_author_name: Shichao Zhang
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China
raw_author_name: Kehua Guo
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China

Abstract: Traditional knowledge distillation approaches are typically designed for specific tasks, as they primarily distilling deep features from intermediate layers of a neural network, generally with ingeniously designed knowledge representations, which increase the difficulty of model development and interpretation. In contrast, we empirically show that a soft logits knowledge distillation is enough to significantly narrow the teacher–student model performance gap. In this paper, the Soft Hybrid Knowledge Distillation (SHKD) is proposed to generate representative features from the input and output location of models, and applicable to various teacher and student architectures. Specifically, the additional mixup task is first applied to the same batch of training images, which enriches the high-order feature information transferred by the model. And then, a top-k guided specification filter is added to the teacher model, to reduce the bias induced throughout the knowledge transfer process. Finally, the SHKD utilizes the same and different teacher–student model pairs of the deep neural network, which can be easily adjusted to the computational resources available in the current world. Extensive experiments are conducted to examine the efficiency, and show that the SHKD outperforms state-of-the-art knowledge distillation methods. With the SHKD, the high performance of models will be maintained, and the gap between the teacher model and the student model can be reduced, making the construction of deep neural networks more convenient to the unknown scenarios. The source code is available at https://github.com/lambett/SHKD.
Hi-PART: Going Beyond Graph Pooling with Hierarchical Partition Tree for Graph-Level Representation Learning

Authors:
raw_author_name: Yuyang Ren
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Haonan Zhang
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Luoyi Fu
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Shiyu Liang
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Lei Zhou
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Xinbing Wang
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Xinde Cao
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Fei Long
raw_affiliation_string: State Key Laboratory of Media Convergence Production Technology and Systems, Xinhua News Agency, China
raw_author_name: Chenghu Zhou
raw_affiliation_string: Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China

Abstract: Graph pooling refers to the operation that maps a set of node representations into a compact form for graph-level representation learning. However, existing graph pooling methods are limited by the power of the Weisfeiler–Lehman (WL) test in the performance of graph discrimination. In addition, these methods often suffer from hard adaptability to hyper-parameters and training instability. To address these issues, we propose Hi-PART, a simple yet effective graph neural network (GNN) framework with Hi erarchical Par tition T ree (HPT). In HPT, each layer is a partition of the graph with different levels of granularities that are going toward a finer grain from top to bottom. Such an exquisite structure allows us to quantify the graph structure information contained in HPT with the aid of structural information theory. Algorithmically, by employing GNNs to summarize node features into the graph feature based on HPT’s hierarchical structure, Hi-PART is able to adequately leverage the graph structure information and provably goes beyond the power of the WL test. Due to the separation of HPT optimization from graph representation learning, Hi-PART involves the height of HPT as the only extra hyper-parameter and enjoys higher training stability. Empirical results on graph classification benchmarks validate the superior expressive power and generalization ability of Hi-PART compared with state-of-the-art graph pooling approaches.
Analysis of tabular data based on graph neural network using supervised contrastive loss

Authors:
raw_author_name: Seungyeon Lee
raw_affiliation_string: Department of Computer Science and Engineering, The Ohio State University, 43210, USA; Department of Industrial and Management Engineering, Myongji University, 17058, Republic of Korea
raw_author_name: Younggeun Ahn
raw_affiliation_string: Department of Industrial and Management Engineering, Myongji University, 17058, Republic of Korea
raw_author_name: Gyeong Bok Jung
raw_affiliation_string: Department of Physics Education, Chosun University, 61452, Republic of Korea
raw_author_name: Dohyun Kim
raw_affiliation_string: Department of Industrial and Management Engineering, Myongji University, 17058, Republic of Korea

Abstract: In many applications in the industry, tabular data are the most commonly used data type. Tabular data have the advantage of being easy to understand and interpret. Machine learning algorithms are mainly used to deduce a mapping function that maps input data to output in tabular data. Machine learning methods for dealing with tabular data are classified into two categories: similarity-based approach and feature-based approach. The feature-based approach explores a mapping function by clarifying the relationships between features, while the similarity-based approach uses similarities of observations to explore a mapping function. Both aspects have their pros and cons. Feature-based models are easy to understand and intuitive to use and deploy but generally cannot utilize the relationships between observations. Similarity-based models are most suited for exploiting the relationships among observations, but their availability is usually limited. In order to take advantage of both aspects, we propose an algorithm to combine feature-based and similarity-based approaches using a graph neural network. Additionally, to complement the shortcomings of cross-entropy loss, the proposed method applies supervised contrastive learning. Through supervised contrastive learning, the proposed method boosts the efficiency of learning the generalized representation for each class. Experimental results show that the proposed method provides more precise results for classification tasks, implying that it may improve the generalization capability.
Is This a Violation? Learning and Understanding Norm Violations in Online Communities

Authors:
raw_author_name: Thiago Freitas dos Santos
raw_affiliation_string: Artificial Intelligence Research Institute (IIIA), CSIC, Barcelona, Catalonia, Spain; Universitat Autonoma de Barcelona, Catalonia, Spain
raw_author_name: Nardine Osman
raw_affiliation_string: Artificial Intelligence Research Institute (IIIA), CSIC, Barcelona, Catalonia, Spain
raw_author_name: Marco Schorlemmer
raw_affiliation_string: Artificial Intelligence Research Institute (IIIA), CSIC, Barcelona, Catalonia, Spain

Abstract: Using norms to guide and coordinate interactions has gained tremendous attention in the multi-agent community. However, new challenges arise as the interest moves towards dynamic socio-technical systems, where human and software agents interact, and interactions are required to adapt to human's changing needs. For instance, different agents (human or software) might not have the same understanding of what it means to violate a norm (e.g., what characterizes hate speech), or their understanding of a norm might change over time (e.g., what constitutes an acceptable response time). The challenge is to address these issues by learning the meaning of a norm violation from limited interaction data. For this, we use batch and incremental learning to train an ensemble of classifiers. Ensemble learning and data-sampling handle the imbalanced class distribution of the interaction stream. At the same time, the training approaches use different strategies to ensure that the ensemble models reflect the latest community view on the meaning of norm violation. Batch learning uses weight assignment, while incremental learning continuously updates the ensemble models as community members interact. Here, we extend our previous work by creating a different balance strategy for online learning and integrating interpretability to understand norm violations. Additionally, we evaluate the proposed approaches in the context of Wikipedia article edits, where interactions revolve around editing articles, and the norm in question is prohibiting vandalism. Lastly, we conduct ablation studies to compare the ensemble's performance against a single model approach and to examine the behavior of two data sampling techniques. Results indicate that the different machine learning frameworks can learn the meaning of a norm violation in a setting with data imbalance and concept drift, although with significant differences.
Multi-modal anchor adaptation learning for multi-modal summarization

Authors:
raw_author_name: Zhongfeng Chen
raw_affiliation_string: School of Artificial Intelligence, Nanjing University of information science & Technology, Jiangsu, Nanjing 210044, China
raw_author_name: Zhenyu Lu
raw_affiliation_string: School of Artificial Intelligence, Nanjing University of information science & Technology, Jiangsu, Nanjing 210044, China
raw_author_name: Huan Rong
raw_affiliation_string: School of Artificial Intelligence, Nanjing University of information science & Technology, Jiangsu, Nanjing 210044, China
raw_author_name: Chuanjun Zhao
raw_affiliation_string: School of Information, Shanxi University of Finance and Economics, Shanxi, Taiyuan, 030006, China

Abstract: In this paper, we focus on analyzing the relationship between the input of source text and source image, and then through the integration and generalization of the multi-modal information (e.g., texts and images), outputs the multi-modal summarization including text and image. However, existing multi-modal summarization methods face several challenges: (1) Different modalities exist in different semantic spaces, and expressing the multi-modal information of source modalities in a similar semantic representation space is crucial. (2) For the result of text summarization, it is crucial to explore how to capture both the differences and similarities within the source modalities, which can reduce redundancy to improve the quality of text summarization. In order to overcome the challenge above, Multi-Modal Anchor Adaptation Learning for Multi-Modal Summarization (MA-Sum) has been proposed. Specifically, MA-Sum employs a novel and highly efficient image anchor selection method, which selects the object sample containing the richest image information as the image anchor. Simultaneously, it carefully selects the text sentence closely intertwined with the image semantics to serve as the language anchor. Therefore, the multi-modal anchor can be seen as a bridge for multi-modal alignment to alleviate the semantic gap between textual and visual. Moreover, based on the distance between the anchors and the semantic information in the respective modal, the positive and negative semantic information of each modal will be distinguished. Based on negative semantic information, the counterfactual learning mechanism is constructed to optimize the result of multi-modal summarization. Finally, the process of multi-modal features interaction is optimized by image summary which is chosen by using multi-modal anchors. According to the experimental results, compared with the state-of-art multi-modal summarization, our proposed MA-Sum can be optimized in terms of summarization consistency and completeness, so as to obtain the optimal multi-modal summarization metric.