Reviewerly/exHarmony huggingface dataset:
Zero-order fuzzy neural network with adaptive fuzzy partition and its applications on high-dimensional problems

The number of fuzzy rules in a fuzzy neural network usually depends on the grid-type partition or clustering-based partition. However, the number of grids or clusters needs to be set in advance and the initial number of fuzzy sets on each dimension is usually the same. This usually does not fit the real situation (for example, when forecasting the weather, various features such as temperature, humidity, wind direction, wind speed, etc., may have different numbers of fuzzy sets). In this paper, a novel zero-order fuzzy neural network with an adaptive fuzzy partition (AdFPFNN) is proposed, which does not need to preset the number of fuzzy sets and owns various numbers of fuzzy sets on different dimensions. The trick is to use the mean shift algorithm to perform adaptive clustering for each feature individually. For common fuzzy neural networks, dealing with high-dimensional problems is challenging work on account of the “fuzzy rule curse” and “computation underflow”. To overcome these issues, an improved version of AdFPFNN is devised to tackle high-dimensional problems, called PaCoAMF-based AdFPFNN. It adopts two techniques to overcome the “fuzzy rule curse” and “computation underflow”. One is the construction of a partially combined fuzzy rule base, which can generate rules that are adequate but do not grow exponentially with the number of features. Another is the proposal of an adaptive membership function that can guarantee the elimination of numerical underflow due to the product T-norm. Without feature selection or other dimensional reduction methods, the proposed PaCoAMF-based AdFPFNN can be directly used to solve high-dimensional problems. Simulation results on one constructed dataset and 13 real-world datasets (including 5 high-dimensional problems) confirm the effectiveness of the proposed models.
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing

There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL). In particular, much attention is being turned to FL scenarios where the network is strongly heterogeneous in terms of communication resources (e.g., bandwidth) and data distribution. In these cases, communication between local machines (agents) and the central server (Master) is a main consideration. In this work, we present SHED, an original communication-constrained Newton-type (NT) algorithm designed to accelerate FL in such scenarios. SHED is by design robust to non independent identically distributed (non i.i.d.) data distributions, handles heterogeneity of agents’ communication resources (CRs), only requires sporadic Hessian computations, and achieves global asymptotic super-linear convergence. This is possible thanks to an incremental strategy, based on eigendecomposition of the local Hessian matrices, which exploits (possibly) outdated second-order information. SHED is thoroughly validated on real datasets by assessing (i) the number of communication rounds required for convergence, (ii) the overall amount of data transmitted and (iii) the number of local Hessian computations. For all these metrics, SHED shows superior performance against state-of-the art techniques like BFGS, GIANT and FedNL.
Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems Using Lifelong Self-Adaptation

Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this article, we focus on a particularly important challenge for learning-based self-adaptive systems: drift in adaptation spaces. With adaptation space, we refer to the set of adaptation options a self-adaptive system can select from to adapt at a given time based on the estimated quality properties of the adaptation options. A drift of adaptation spaces originates from uncertainties, affecting the quality properties of the adaptation options. Such drift may imply that the quality of the system may deteriorate, eventually, no adaptation option may satisfy the initial set of adaptation goals, or adaptation options may emerge that allow enhancing the adaptation goals. In ML, such a shift corresponds to a novel class appearance, a type of concept drift in target data that common ML techniques have problems dealing with. To tackle this problem, we present a novel approach to self-adaptation that enhances learning-based self-adaptive systems with a lifelong ML layer. We refer to this approach as lifelong self-adaptation . The lifelong ML layer tracks the system and its environment, associates this knowledge with the current learning tasks, identifies new tasks based on differences, and updates the learning models of the self-adaptive system accordingly. A human stakeholder may be involved to support the learning process and adjust the learning and goal models. We present a general architecture for lifelong self-adaptation and apply it to the case of drift of adaptation spaces that affects the decision-making in self-adaptation. We validate the approach for a series of scenarios with a drift of adaptation spaces using the DeltaIoT exemplar.
Two-stage GNN-based fraud detection with camouflage identification and enhanced semantics aggregation

Graph neural network (GNN) has got lots of attention recently in the fraud detection task due to its message-passing mechanism, which can aggregate neighbors feature in the information network. While promising, currently most GNN-based fraud detectors fail to identify camouflage caused by fraud with graph structure information and embed entity in deep layer with rich semantics effectively. To solve these problems, we propose a two-stage GNN-based approach with camouflage identification and enhanced semantics aggregation (CIES-GNN) for fraud detection. In the proposed approach, camouflage is identified by reconstructing subgraphs with both node feature and structure information. Detailedly, hidden edges between fraudsters are found by reconstructing a dense subgraph of fraudster nodes, and redundant connections between benign nodes and fraudster nodes are eliminated by reconstructing subgraphs in a label-balance distribution. Moreover, to embed entity in deep layer without semantics obfuscation, the node information is embedded in an enhanced semantics aggregation module, which fuses higher-order information in intra-relation and aggregates semantics in inter-relation respectively. Experiments on two real-world datasets demonstrate that the proposed CIES-GNN outperforms state-of-the-art baselines.
Entity recognition method for airborne products metrological traceability knowledge graph construction

The airborne system, as one of the complex and extensive subsystems of an aircraft, primarily performs critical flight assurance functions. The quality of its components has a direct impact on the aircraft's safety and reliability. Metrology documents comprehensively document the performance parameters throughout the entire product life cycle. The Metrological Traceability Knowledge Graph (MTKG) for airborne products offers decision support to engineers engaged in metrological tasks, ensuring the continuous high quality of the products. This paper introduces an entity recognition method for airborne product metrological traceability knowledge graph construction. First, the ontology for MTKG is developed. Next, a fine-tuned multi-network model is proposed. Named entities in the field of metrology are recognized through three stages: word vector representation, sentence feature extraction, and optimal label assignment. Meanwhile, active learning methods are incorporated to reduce the expense of data annotation. The proposed model is validated using an actual metrology corpus, and the experimental results demonstrate its superior performance compared to the other four baseline methods. Finally, the MTKG is developed using this approach, offering engineers intelligent applications, including metrological traceability analysis and traceability path reasoning within the process of product metrology. This enhances the metrology capabilities of airborne products and demonstrates the extensive potential of knowledge graphs in metrology.
Safe multi-agent drone control using control barrier functions and acceleration fields

The objective of this article is to develop real-time controllers for positioning multi-agent autonomous platforms (specifically drones) without any collisions. The framework is divided in two controllers. The first, based on control barrier functions and quadratic programming, is an acceleration field, that computes an acceleration for each position and velocity of the system that guides the agents towards the goal while avoiding obstacles and inter-collisions. The second controls the agents’ total thrusts and angular velocities to track the previously-defined acceleration profiles. We show formal guarantees for both controllers, and simulation and experimental studies are used to show the efficacy of the proposed approach.
Interpretable hierarchical belief rule base expert system for complex system modeling

Belief rule base (BRB) expert system has been widely used in complex system modeling. Readability and consistency are the primary characteristics and advantages of BRB. However, these two characteristics may be lost in BRB establishment and optimization. Aiming at this problem, a novel hierarchical BRB (H-BRB) expert system is proposed. To quantify the interpretability of BRB, the definition of interpretability of BRB is proposed from the readability and the consistency. Two strategies are proposed to construct the H-BRB expert system with good readability. A new optimization method is proposed to guarantee the consistency of H-BRB, including a new optimization mechanism, a new objective function with interpretability constraints, and a modified optimization algorithm. With all the above, an H-BRB expert system with good readability and consistency can be established. A case study of the health state assessment of the aerospace relay is implemented to verify the effectiveness of the proposed method.
Supervised adaptive similarity consistent latent representation hashing

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

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

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

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

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

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

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!?

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

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

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

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

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.