Reviewerly/exHarmony huggingface dataset:
A Unified Spectral Rotation Framework Using a Fused Similarity Graph

Multi-view spectral clustering has recently received a lot of attention. Existing methods, however, have two problems to be addressed: 1) similarity matrices used in clustering omit the high-order neighbor information, reducing embedding accuracy; 2) two independent procedures of embedding and discretization may result in a suboptimal result, lowering the final performance. To address the abovementioned issues, we propose a unified spectral rotation framework for multi-view clustering using a fused similarity graph. The method begins with establishing similarity graphs for each view and constructing first-order and high-order Laplacian matrices for capturing the hidden similarity among different nodes. Then embedding and discretization procedures are integrated into a new framework for performing a spectral rotation to obtain a global clustering result. Finally, a three-step optimization method for obtaining the final clustering labels is proposed. We conduct extensive experiments on a variety of real-world and synthetic datasets to validate the effectiveness of the proposed algorithm. Our method outperforms state-of-the-art methods by 8.0% on average, according to experimental results. The code of the proposed method is available at https://github.com/lting0120/USRF_FSG.git .
Modeling Graphs Beyond Hyperbolic: Graph Neural Networks in Symmetric Positive Definite Matrices

Recent research has shown that alignment between the structure of graph data and the geometry of an embedding space is crucial for learning high-quality representations of the data. The uniform geometry of Euclidean and hyperbolic spaces allows for representing graphs with uniform geometric and topological features, such as grids and hierarchies, with minimal distortion. However, real-world graph data is characterized by multiple types of geometric and topological features, necessitating more sophisticated geometric embedding spaces. In this work, we utilize the Riemannian symmetric space of symmetric positive definite matrices ( $${\text {SPD}}$$ ) to construct graph neural networks that can robustly handle complex graphs. To do this, we develop an innovative library that leverages the $${\text {SPD}}$$ gyrocalculus tools [26] to implement the building blocks of five popular graph neural networks in $${\text {SPD}}$$ . Experimental results demonstrate that our graph neural networks in $${\text {SPD}}$$ substantially outperform their counterparts in Euclidean and hyperbolic spaces, as well as the Cartesian product thereof, on complex graphs for node and graph classification tasks. We release the library and datasets at https://github.com/andyweizhao/SPD4GNNs .
Psikologi Sastra dalam Novel Arafat Rahasia Besar Bocah Gaza Karya Arum Faiza

This research is focused on the field of literary psychology which discusses personality structure according to Freud, namely id, ego, and superego. The purpose of this study is to describe, analyze, interpret, and conclude the character's personality structure related to id, ego, and superego in Arafat's novel The Great Secret of the Gaza Boy by Arum Faiza. This research belongs to the type of qualitative research with descriptive methods (content analysis). The data in this study are quotes of words, phrases, clauses, and sentences that contain personality structures, namely id, ego, and superego contained in Arafat's novel The Great Secret of the Gaza Boy by Arum Faiza. From the results of research on the personality structure of the id, ego, and superego, the dominant element is the ego. Ego personality structure found as many as 20 data. The personality structure of the ego is more dominant because in Arafat's novel The Big Secret of the Gaza Boy tells of heroism. Because of the heroic theme of the novel, what appears the most is the ego personality structure. When talking about heroism, it must be real or real action. To support this, the author makes the characters appear more real in the novel. To show the heroic side can not be from id. Because to show the heroic side you have to look real, therefore you have to appear with ego.
Covid-19 contact-tracing apps and the public/private co-production of security

This article examines how the smartphone contributes to the co-production of security through an analysis of Covid-19 contact-tracing apps. Building on existing research in security studies that mobilizes the science and technology concept of co-production, the article proposes the notion of ‘appropriation’ as a concrete way of extending our understanding of the public/private co-production of security. Appropriation highlights how consumer technology may be repurposed for security and shows how private-sector actors that own consumer technology not only influence, but actively condition the co-production of security. Bringing new, typically commercial, concerns to bear on security practices, appropriation also has the effect of complicating conventional understandings of the relationship between liberty and security. Focusing on the NHS Covid-19 app and its contentious relationship with Google/Apple’s framework for digital contact-tracing, the article demonstrates how the smartphone enables private-sector actors to gain influence in the security domain. Google and Apple used their control over smartphone technology to compel the British health authorities to adopt a less effective but more privacy-preserving approach than they originally intended, and thus enforced a seemingly liberal response to an exceptional political situation.
Real‐time 3D multi‐pedestrian detection and tracking using 3D LiDAR point cloud for mobile robot

Abstract Mobile robots are used in modern life; however, object recognition is still insufficient to realize robot navigation in crowded environments. Mobile robots must rapidly and accurately recognize the movements and shapes of pedestrians to navigate safely in pedestrian‐rich spaces. This study proposes real‐time, accurate, three‐dimensional (3D) multi‐pedestrian detection and tracking using a 3D light detection and ranging (LiDAR) point cloud in crowded environments. The pedestrian detection quickly segments a sparse 3D point cloud into individual pedestrians using a lightweight convolutional autoencoder and connected‐component algorithm. The multi‐pedestrian tracking identifies the same pedestrians considering motion and appearance cues in continuing frames. In addition, it estimates pedestrians' dynamic movements with various patterns by adaptively mixing heterogeneous motion models. We evaluate the computational speed and accuracy of each module using the KITTI dataset. We demonstrate that our integrated system, which rapidly and accurately recognizes pedestrian movement and appearance using a sparse 3D LiDAR, is applicable for robot navigation in crowded spaces.
Analisis Kesalahan Berbahasa Tataran Sintaksis di Instagram Kementerian Kesehatan Republik Indonesia

This study aims to analyze language errors at the syntactic level in the Instagram status of the Ministry of Health of the Republic of Indonesia. The data source used is the Instagram status which contains grammatical errors and grammatical errors. The data collection technique used is to see, observe, and record. Data analysis was performed using qualitative description techniques to explain the forms and causes of syntax errors. The research data amounted to 40, consisting of 14 phrase errors and 26 sentence errors. Based on the results of the analysis, phrasal errors are caused by the use of wrong prepositions, inappropriate wording, and excessive use of elements, while sentence errors consist of sentences without subjects, stump sentences, between predicates and inserted objects, omissions of conjunctions, use of conjunctions that are exaggeration, the use of foreign terms, and the use of unnecessary question words.
Not All Tasks Are Equal: A Parameter-Efficient Task Reweighting Method for Few-Shot Learning

Meta-learning has emerged as an effective and popular approach for few-shot learning (FSL) due to its fast adaptation to novel tasks. However, this kind of method assumes that the meta-training and testing tasks come from the same task distribution and assigns equal weights to all tasks during meta-training. This assumption limits their ability to perform well in real-world scenarios where some meta-training tasks contribute more to the testing tasks than others. To address this issue, we propose a parameter-efficient task reweighting (PETR) method, which assigns proper weights to meta-training tasks according to their contribution to the testing tasks while using few parameters. Specifically, we formulate a bi-level optimization problem to jointly learn the few-shot learning model and the task weights. In the inner loop, the meta-parameters of the few-shot learning model are updated based on a weighted training loss. In the outer loop, the task weight parameters are updated with the implicit gradient. Additionally, to address the challenge of a large number of task weight parameters, we introduce a hypothesis that significantly reduces the required parameters by considering the factors that influence the importance of each meta-training task. Empirical evaluation results on both traditional FSL and FSL with out-of-distribution (OOD) tasks show that our PETR method outperforms state-of-the-art meta-learning-based FSL methods by assigning proper weights to different meta-training tasks.
Expectations from modern evolutionary approaches for image processing

Nature-inspired optimized algorithms such as genetic algorithms, particle swarm optimization, and ant colony optimization have proven to be effective tools for solving complex optimization problems in a variety of fields such as engineering, finance, and healthcare. These algorithms are inspired by natural phenomena and biological systems, and they use natural selection and evolution to find the best solution. Constrained and unconstrained optimization algorithms have distinct advantages and are appropriate for a variety of problems. Constrained optimization algorithms take constraints into account when determining the best solution, whereas unconstrained optimization algorithms do not. When the problem has specific constraints, constrained optimization algorithms are more appropriate, whereas unconstrained optimization algorithms are faster and more efficient. Design is one of the engineering applications of optimization algorithms.
Meta-HRNet: A High Resolution Network for Coarse-to-Fine Few-Shot Classification

Fine-grained classification has achieved success with the application of deep learning on large datasets. However, in practical scenarios, fine-grained categories often suffer from a lack of training data due to the difficulty of labeling. Leveraging accessible coarse-grained labeled data provides a promising way to alleviate this challenge, that is, the model learns from a large number of coarse-grained labeled data to perform better on fine-grained classification. In this paper, we focus on this coarse-to-fine few-shot problem and attribute the difficulty of this problem to two factors: the undistinguishable appearance of fine-grained images and the lack of fine-grained training samples. To address the first factor, we demonstrate that high-resolution features can capture more distinctive details that are useful for fine-grained classification tasks. Thus, we construct an improved high-resolution network called Meta-HRNet to capture rich details and filter the crucial detailed information for fine-grained classification. To address the second factor, we train the model by a two-step strategy that combines supervised training and episodic training. During the first training stage, the backbone of Meta-HRNet is optimized to obtain a basic ability of detailed representation. In the second stage, the attention module of the Meta-HRNet is trained to learn and sift key details given a low number of training samples. The effectiveness of our model is verified on four datasets. Experimental results demonstrate that the attention paid to the important details of images contributes to improving the performance of fine-grained classification tasks.
Duplicate Multi-modal Entities Detection with Graph Contrastive Self-training Network

Duplicate multi-modal entities detection aims to find highly similar entities from massive entities with multi-modal information, which is a basic task in many applications and becoming more important and urgent with the development of Internet and e-commerce platforms. Traditional methods employ machine learning or deep learning on feature embedding extracted from multi-modal information, which ignores the correlation among entities and modals. Inspired by the popular Graph Neural Networks (GNNs), we can analyze the multi-relation graph of entities constructed from their multi-modal information with GNN. However, this solution still faces the extreme label sparsity challenge, particularly in industrial applications. In this work, we propose a novel graph contrastive self-training network model, named CT-GNN, for duplicate multi-modal entities detection with extreme label sparsity. With the multi-relation graph of entities constructed from multi-modal features of entities with KNN, we first learn the preliminary node embeddings with existing GNN, e.g., GCNs. To alleviate the problem of extremely sparse labels, we design a layer contrastive module to effectively exploit implicit label information, as well as a pseudo labels extension module to determine label boundary. In addition, graph structure learning is introduced to refine the structure of the multi-relation graph. A uniform optimization framework is designed to seamlessly integrate these three components. Sufficient experiments on real datasets, in comparison with SOTA baselines, well demonstrate the effectiveness of our proposed method.
Posterior Consistency for Missing Data in Variational Autoencoders

We consider the problem of learning Variational Autoencoders (VAEs), i.e., a type of deep generative model, from data with missing values. Such data is omnipresent in real-world applications of machine learning because complete data is often impossible or too costly to obtain. We particularly focus on improving a VAE’s amortized posterior inference, i.e., the encoder, which in the case of missing data can be susceptible to learning inconsistent posterior distributions regarding the missingness. To this end, we provide a formal definition of posterior consistency and propose an approach for regularizing an encoder’s posterior distribution which promotes this consistency. We observe that the proposed regularization suggests a different training objective than that typically considered in the literature when facing missing values. Furthermore, we empirically demonstrate that our regularization leads to improved performance in missing value settings in terms of reconstruction quality and downstream tasks utilizing uncertainty in the latent space. This improved performance can be observed for many classes of VAEs including VAEs equipped with normalizing flows.
Cold-Start Multi-hop Reasoning by Hierarchical Guidance and Self-verification

Multi-hop reasoning has attracted wide attention for knowledge graph (KG) completion since it can provide interpretable reasoning paths. Most prior multi-hop reasoning studies assume the KGs are static with fixed entities. However, in real applications, KGs are often dynamic since new entities will emerge continuously in the form of new fact triplets. In this paper, we are particularly interested in the cold-start scenario toward dynamic KGs to facilitate more practical multi-hop reasoning, which aims to explore the reasoning paths between emerging entities and existing entities. There are two challenging issues arising from this scenario: i) lacking precise guidance since available information for emerging entities is extremely limited in the cold-start scenario, ii) lacking explicit path since the emerging entities and existing ones are isolated. To address these issues, we propose a generation-based model, namely SelfHier, to explore the reasoning paths by hierarchical guidance and self-verification strategies. The hierarchical guidance strategy guides the reasoning process using hierarchical fine-grained sub-relations and coarse-grained clusters. The self-verification strategy constructs explicit reasoning paths by supplementing some missing fact triplets. Experimental results prove that SelfHier performs well in the cold-start scenario on dynamic KGs and also significantly outperforms existing multi-hop reasoning methods in the standard scenario on static KGs.
How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning

Do we need active learning? The rise of strong deep semi-supervised methods raises doubt about the usability of active learning in limited labeled data settings. This is caused by results showing that combining semi-supervised learning (SSL) methods with a random selection for labeling can outperform existing active learning (AL) techniques. However, these results are obtained from experiments on well-established benchmark datasets that can overestimate the external validity. However, the literature lacks sufficient research on the performance of active semi-supervised learning methods in realistic data scenarios, leaving a notable gap in our understanding. Therefore we present three data challenges common in real-world applications: between-class imbalance, within-class imbalance, and between-class similarity. These challenges can hurt SSL performance due to confirmation bias. We conduct experiments with SSL and AL on simulated data challenges and find that random sampling does not mitigate confirmation bias and, in some cases, leads to worse performance than supervised learning. In contrast, we demonstrate that AL can overcome confirmation bias in SSL in these realistic settings. Our results provide insights into the potential of combining active and semi-supervised learning in the presence of common real-world challenges, which is a promising direction for robust methods when learning with limited labeled data in real-world applications.
Learning Fast and Slow: Towards Inclusive Federated Learning

Today’s deep learning systems rely on large amounts of useful data to make accurate predictions. Often such data is private and thus not readily available due to rising privacy concerns. Federated learning (FL) tackles this problem by training a shared model locally on devices to aid learning in a privacy-preserving manner. Unfortunately, FL’s effectiveness degrades when model training involves clients with heterogeneous devices; a common case especially in developing countries. Slow clients are dropped in FL, which not only limits learning but also systematically excludes slow clients thereby potentially biasing results. We propose Hasaas; a system that tackles this challenge by adapting the model size for slow clients based on their hardware resources. By doing so, Hasaas obviates the need to drop slow clients, which improves model accuracy and fairness. To improve robustness in the presence of statistical heterogeneity, Hasaas uses insights from the Central Limit Theorem to estimate model parameters in every round. Experimental evaluation involving large-scale simulations and a small-scale real testbed shows that Hasaas provides robust performance in terms of test accuracy, fairness, and convergence times compared to state-of-the-art schemes.
Boosting Generalized Few-Shot Learning by Scattering Intra-class Distribution

Generalized Few-Shot Learning (GFSL) applies the model trained with the base classes to predict the samples from both base classes and novel classes, where each novel class is only provided with a few labeled samples during testing. Limited by the severe data imbalance between base and novel classes, GFSL easily suffers from the prediction shift issue that most test samples tend to be classified into the base classes. Unlike the existing works that address this issue by either multi-stage training or complicated model design, we argue that extracting both discriminative and generalized feature representations is all GFSL needs, which could be achieved by simply scattering the intra-class distribution during training. Specifically, we introduce two self-supervised auxiliary tasks and a label permutation task to encourage the model to learn more image-level feature representations and push the decision boundary from novel towards base classes during inference. Our method is one-stage and could perform online inference. Experiments on the miniImageNet and tieredImageNet datasets show that the proposed method achieves comparable performance with the state-of-the-art multi-stage competitors under both traditional FSL and GFSL tasks, empirically proving that feature representation is the key for GFSL.
vMF Loss: Exploring a Scattered Intra-class Hypersphere for Few-Shot Learning

Few-shot learning (FSL), which aims to learn from very few labeled examples, is a challenging task but frequently appears in real-world applications. An appealing direction to tackle it is the metric-based method, which seeks to learn a transferable embedding space across different tasks from a related base dataset and generalize it for novel few-shot tasks. Recently, a large corpus of literature has been proposed to design more complicated representation learning methods to improve performance. Despite some promising results, how these methods improve the few-shot performance remains unexplored. Motivated by this question, we investigate the relationship between the performance and the structure of the learned embedding space. We find they are strongly correlated to each other. To capture more valuable features of novel classes, the intra-class distribution of base classes should be more scattered. Therefore, we introduce von Mises-Fisher (vMF) distribution and employ a vMF similarity loss function that uses a concentration parameter, $$\kappa $$ , to control the intra-class distribution on a hypersphere. By setting a smaller $$\kappa $$ , our method can learn a more transferrable embedding space with high intra-class diversity. Extensive experiments on two widely used datasets demonstrate the effectiveness of our method.
Graph Rebasing and Joint Similarity Reconstruction for Cross-Modal Hash Retrieval

Cross-modal hash retrieval methods improve retrieval speed and reduce storage space at the same time. The accuracy of intra-modal and inter-modal similarity is insufficient, and the large gap between modalities leads to semantic bias. In this paper, we propose a Graph Rebasing and Joint Similarity Reconstruction (GRJSR) method for cross-modal hash retrieval. Particularly, the graph rebasing module is used to filter out graph nodes with weak similarity and associate graph nodes with strong similarity, resulting in fine-grained intra-modal similarity relation graphs. The joint similarity reconstruction module further strengthens cross-modal correlation and implements fine-grained similarity alignment between modalities. In addition, we combine the similarity representation of real-valued and hash features to design the intra-modal and inter-modal training strategies. GRJSR conducted extensive experiments on two cross-modal retrieval datasets, and the experimental results effectively validated the superiority of the proposed method and significantly improved the retrieval performance.