Reviewerly/exHarmony huggingface dataset:
Frequency-based pseudo-domain generation for domain generalizable object detection

Domain generalizable object detection (DGOD) aims to train a detector that performs well on multiple unseen target domains, which is crucial for deploying the detector in practice. Recent methods for DGOD typically inherit the idea from domain adaptation to align or disentangle features, but these methods struggle to handle unknown target distributions. In this paper, we propose a unified framework to tackle the DGOD task from a novel pseudo-domain generation perspective. Our framework comprises two stages: distribution diversification and domain-invariant feature learning. In the distribution diversification stage, we design a Frequency-based Pseudo-domain Generator (FPG) to construct the pseudo domain via excavating latent style information and enhancing semantic information in frequency space. The generated pseudo domain can provide diverse training distributions, which enhances generalization performance. In the domain-invariant feature learning stage, we introduce Rotation Prediction and Semantic Consistency (RPSC) learning, including an auxiliary self-supervised task rotation prediction to encourage generalized feature learning and a semantic consistency loss to enforce the detector to be invariant of domain shifts. Extensive experiments are conducted on various object detection benchmarks, demonstrating the superiority of our approach over state-of-the-art methods in both single-source and multi-source settings.
Entanglement-assisted multi-aperture pulse-compression radar for angle resolving detection

Abstract Entanglement has been known to boost target detection, despite it being destroyed by lossy-noisy propagation. Recently, Zhuang and Shapiro (2022 Phys. Rev. Lett. 128 010501) proposed a quantum pulse-compression radar to extend entanglement’s benefit to target range estimation. In a radar application, many other aspects of the target are of interest, including angle, velocity and cross section. In this study, we propose a dual-receiver radar scheme that employs a high time-bandwidth product microwave pulse entangled with a pre-shared reference signal available at the receiver, to investigate the direction of a distant object and show that the direction-resolving capability is significantly improved by entanglement, compared to its classical counterpart under the same parameter settings. We identify the applicable scenario of this quantum radar to be short-range and high-frequency, which enables entanglement’s benefit in a reasonable integration time.
PCTNet: depth estimation from single structured light image with a parallel CNN-transformer network

Abstract Recent approaches based on convolutional neural networks significantly improve the performance of structured light image depth estimation in fringe projection and speckle projection 3D measurement. However, it remains challenging to simultaneously preserve the global structure and local details of objects for the structured light images in complex scenes. In this paper, we design a parallel CNN-transformer network (PCTNet), which consists of a CNN branch, a transformer branch, a bidirectional feature fusion module (BFFM), and a cross-feature multi-scale fusion module (CFMS). The BFFM and CFMS modules are proposed to fuse local and global features of the double branches in order to achieve better depth estimation. Comprehensive experiments are conducted to evaluate our model on four structured light datasets, i.e. our established simulated fringe and speckle structured light datasets, and public real fringe and speckle structured light datasets. Experiments demonstrate that the proposed PCTNet is an effective architecture, achieving state-of-the-art performance in both qualitative and quantitative evaluation.
A Quantum Algorithm for Solving Eigenproblem of the Laplacian Matrix of a Fully Connected Weighted Graph

Abstract Solving eigenproblem of the Laplacian matrix of a fully connected weighted graph has wide applications in data science, machine learning, and image processing, etc. However, this is very challenging because it involves expensive matrix operations. Here, an efficient quantum algorithm is proposed to solve it. Specifically, the optimal Hamiltonian simulation technique based on the block‐encoding framework is adopted to implement the quantum simulation of the Laplacian matrix. Then, the eigenvalues and eigenvectors of the Laplacian matrix are extracted by the quantum phase estimation algorithm. The core of this entire algorithm is to construct a block‐encoding of the Laplacian matrix. To achieve this, how to construct block‐encoding of operators containing the information of the weight matrix and the degree matrix, respectively are shown in detail, and the block‐encoding of the Laplacian matrix is further obtained. Compared with its classical counterpart, this algorithm has a polynomial speedup on the number of vertices and an exponential speedup on the dimension of each vertex. It is also shown that this algorithm can be extended to solve the eigenproblem of symmetric (non‐symmetric) normalized Laplacian matrix.
Deep Learning Enhanced Spectrum Sensing for LoRa Spreading Factor Detection

Over the last decade, there has been a significant increase in the demand for wireless sensor applications, in line with the increased connectivity of objects in the Internet of Things concept. As the number of wireless sensors continues to grow, there is a corresponding increase in the demand for spectrum resources which will eventually lead to overcrowding, packet collisions and interference ultimately resulting in a reduction of the wireless sensor network performance level. Obtaining a detailed understanding of the radio spectrum is critical in preventing collisions in wireless sensor networks. This requires knowledge of the level of spectrum occupancy and the specific radio modulation schemes employed, enabling the implementation of effective collision mitigation strategies. This paper proposes a LoRa spreading factor detection scheme based on Convolutional Neural Network (CNN) classification of spectrograms. Our results show that the developed algorithm shows a high degree of performance in detecting radio modulations, both in high SNR and low SNR conditions, resulting in a detection accuracy of over 98%.
“I would I had that corporal soundness”: Pervez Rizvi's Analysis of the Word Adjacency Network Method of Authorship Attribution

Abstract In his two-part article ‘An Analysis of the Word Adjacency Network Method—Part 1—The evidence of its unsoundness’ and ‘Part 2—A true understanding of the method’ Digital Scholarship in the Humanities, 38: 347-78 (2022), Pervez Rizvi attempts to replicate the Word Adjacency Network (WAN) method for authorship attribution and show that it does not produce the new knowledge that we, its inventors, claim for it. In the present essay, we will show that Rizvi misrepresents fundamental aspects of the WAN method, that his attempted replication fails not because the method is flawed but because he erred in replicating it, and that Rizvi misunderstands key aspects of the mathematics of Information Theory that the method uses.
Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao

We study the problem of cross-domain click-through rate (CTR) prediction for recommendation at Taobao. Cross-domain CTR prediction has been widely studied in recent years, while most attempts ignore the continual learning setting in industrial recommender systems. In light of this, we present a necessary but less-studied problem named Continual Transfer Learning (CTL), which transfers knowledge from a time-evolving source domain to a time-evolving target domain. We propose an effective and efficient model called CTNet to perform CTR prediction under the CTL setting. The core idea behind CTNet is to treat source domain representations as external knowledge for target domain CTR prediction, such that the continually well-trained source and target domain parameters can be preserved and reused during knowledge transfer. Extensive offline experiments and online A/B testing at Taobao demonstrate the efficiency and effectiveness of CTNet. CTNet is now fully deployed online at Taobao bringing significant improvements.
Fine-grained recognition: Multi-granularity labels and category similarity matrix

The core of fine-grained recognition is to distinguish different subcategories within a same broad category through subtle differences in images. Yet two important factors are less explored: Firstly, the degree of similarity between categories, with the more similar categories being more likely to be confused. Secondly, different fine-grained definitions under divergent levels of expertise, with one coarse label corresponding to multiple similar fine categories (e.g., a brand of car has multiple models). In this paper, we discover that multi-granularity label can function as an intermediary for turning inter-category similarity relations into fine-grained recognition performance. Specifically, we first explore the association between label hierarchies in multi-granularity prediction: both coarse and fine label predictions require coarse information, but fine label prediction additionally requires subtle information. Then, a multi-granularity classification framework is proposed, where the extracted feature is decoupled and re-coupled into groups for predicting labels at different granularities, respectively. The key is to involve coarse-grained features in the prediction of finer-level labels. The realization way is to train these features with a joint probability-based loss that we design to reduce inter-task interference through inter-granularity probability relations. Lastly, for tasks without coarse labels, a category similarity matrix is suggested for measuring inter-class similarity and a further non-parametric aggregation method is devised for clustering fine labels into coarse labels. The evaluation is carried out on several fine-grained classification benchmark datasets. Results show that the proposed approach achieves state-of-the-art in multi-granularity classification and exhibits the potential to enhance existing fine-grained recognition models.
Positive-weighting feature enhancement for weakly supervised object localization

Weakly Supervised Object Localization (WSOL) techniques identify the object location by only using image-level labels, without any bonding box annotations. A restriction of these techniques is that traditionally a well-trained model only focuses on the most discriminative parts of an image, instead of the whole object. To overcome this problem, most of the WSOL methods dropped some or all crucial discriminative parts to force the model to learn from the secondary discriminative region as well. However, only a small number of methods are dedicated to improving the utilization of the weights within the deep neural network model. In this paper, we propose a plug-in module, Positive-weighting Feature Enhancement (PFE), to correctly utilize existing information embedded in positive weights. The proposed method is made up of two major components: 1) a complementary Noisy Feature Elimination loss (NFE) to minimize noisy features, and 2) a Dynamic Concealment Mask (DCM) to prevent secondary discriminative features from being eliminated while minimizing noisy features. Based on the experiments, our solution outperforms previous method without modifying the architecture on two single-object benchmark datasets, CUB-200-2011 and ILSVRC 2012, and also on one multi-objects dataset, VOC 2007.
Disentangled Causal Embedding With Contrastive Learning For Recommender System

Recommender systems usually rely on observed user interaction data to build personalized recommendation models, assuming that the observed data reflect user interest. However, user interacting with an item may also due to conformity, the need to follow popular items. Most previous studies neglect user’s conformity and entangle interest with it, which may cause the recommender systems fail to provide satisfying results. Therefore, from the cause-effect view, disentangling these interaction causes is a crucial issue. It also contributes to OOD problems, where training and test data are out-of-distribution. Nevertheless, it is quite challenging as we lack the signal to differentiate interest and conformity. The data sparsity of pure cause and the items’ long-tail problem hinder disentangled causal embedding. In this paper, we propose DCCL, a framework that adopts contrastive learning to disentangle these two causes by sample augmentation for interest and conformity respectively. Futhermore, DCCL is model-agnostic, which can be easily deployed in any industrial online system. Extensive experiments are conducted over two real-world datasets and DCCL outperforms state-of-the-art baselines on top of various backbone models in various OOD environments. We also demonstrate the performance improvements by online A/B testing on Kuaishou, a billion-user scale short-video recommender system.
AiTLAS: Artificial Intelligence Toolbox for Earth Observation

We propose AiTLAS—an open-source, state-of-the-art toolbox for exploratory and predictive analysis of satellite imagery. It implements a range of deep-learning architectures and models tailored for the EO tasks illustrated in this case. The versatility and applicability of the toolbox are showcased in a variety of EO tasks, including image scene classification, semantic image segmentation, object detection, and crop type prediction. These use cases demonstrate the potential of the toolbox to support the complete data analysis pipeline starting from data preparation and understanding, through learning novel models or fine-tuning existing ones, using models for making predictions on unseen images, and up to analysis and understanding of the predictions and the predictive performance yielded by the models. AiTLAS brings the AI and EO communities together by facilitating the use of EO data in the AI community and accelerating the uptake of (advanced) machine-learning methods and approaches by EO experts. It achieves this by providing: (1) user-friendly, accessible, and interoperable resources for data analysis through easily configurable and readily usable pipelines; (2) standardized, verifiable, and reusable data handling, wrangling, and pre-processing approaches for constructing AI-ready data; (3) modular and configurable modeling approaches and (pre-trained) models; and (4) standardized and reproducible benchmark protocols including data and models.
Towards Multimodal Knowledge Graphs for Data Spaces

Multimodal knowledge graphs have the potential to enhance data spaces by providing a unified and semantically grounded structured representation of multimodal data produced by multiple sources. With the ability to integrate and analyze data in real-time, multimodal knowledge graphs offer a wealth of insights for smart city applications, such as monitoring traffic flow, air quality, public safety, and identifying potential hazards. Knowledge enrichment can enable a more comprehensive representation of multimodal data and intuitive decision-making with improved expressiveness and generalizability. However, challenges remain in effectively modelling the complex relationships between and within different types of modalities in data spaces and infusing common sense knowledge from external sources. This paper reviews the related literature and identifies major challenges and key requirements for effectively developing multimodal knowledge graphs for data spaces, and proposes an ontology for their construction.
A comprehensive evaluation of SZZ Variants through a developer-informed oracle

Automatically linking bug-fixing changes to bug-inducing ones (BICs) is one of the key data-extraction steps behind several empirical studies in software engineering. The SZZ algorithm is the de facto standard to achieve this goal, with several improvements proposed over time. Evaluating the performance of SZZ implementations is, however, far from trivial. In previous works, researchers (i) manually assessed whether the BICs identified by the SZZ implementation were correct or not, or (ii) defined oracles in which they manually determined BICs from bug-fixing commits. However, ideally, the original developers should be involved in defining a labeled dataset to evaluate SZZ implementations. We propose a methodology to define a "developer-informed" oracle for evaluating SZZ implementations, without requiring a manual inspection from the original developers. We use Natural Language Processing (NLP) to identify bug-fixing commits in which developers explicitly reference the commit(s) that introduced the fixed bug. We use the built oracle to extensively evaluate existing SZZ variants defined in the literature. We also introduce and evaluate two new variants aimed at addressing two weaknesses we observed in state-of-the-art implementations (i.e., processing added lines and handling of revert commits).
Development of a Machine Learning Forecast Model for Global Horizontal Irradiation Adapted to Tibet Based on Visible All-Sky Imaging

The Qinghai-Tibet Plateau is rich in renewable solar energy resources. Under the background of China’s “dual-carbon” strategy, it is of great significance to develop a global horizontal irradiation (GHI) prediction model suitable for Tibet. In the radiation balance budget process of the Earth-atmosphere system, clouds, aerosols, air molecules, water vapor, ozone, CO2 and other components have a direct influence on the solar radiation flux received at the surface. For the descending solar shortwave radiation flux in Tibet, the attenuation effect of clouds is the key variable of the first order. Previous studies have shown that using Artificial intelligence (AI) models to build GHI prediction models is an advanced and effective research method. However, regional localization optimization of model parameters is required according to radiation characteristics in different regions. This study established a set of AI prediction models suitable for Tibet based on ground-based solar shortwave radiation flux observation and cloud cover observation data of whole sky imaging in the Yangbajing area, with the key parameters sensitively tested and optimized. The results show that using the cloud cover as a model input variable can significantly improve the prediction accuracy, and the RMSE of the prediction accuracy is reduced by more than 20% when the forecast horizon is 1 h compared with a model without the cloud cover input. This conclusion is applicable to a scenario with a forecast horizon of less than 4 h. In addition, when the forecast horizon is 1 h, the RMSE of the random forest and long short-term memory models with a 10-min step decreases by 46.1% and 55.8%, respectively, compared with a 1-h step. These conclusions provide a reference for studying GHI prediction models based on ground-based cloud images and machine learning.
“A tour guide losing her cool”

Abstract This article seeks to explore the mechanisms of holding others accountable for a perceived deviation from moral order through public complaints on Chinese social media as well as the influences of emotional stance and social positioning when people perceive a breach of the moral order and try to restore it. Our data consists of a transcribed complaint narrative (CN) widely deemed morally transgressive, and a corpus of web-based news comments (WNC), displaying public counter-offensive actions to the CN. A contextualized discourse analysis reveals that abundant context-spanning impoliteness formulae in WNC are strategies for constructing collective identities and magnifying the condemnation of immorality. In the process of moral order appeal, using various emotive impoliteness formulae appear to be a situated norm.
A Generative Model for the Mycenaean Linear B Script and Its Application in Infilling Text from Ancient Tablets

We present a generative neural language model for the most ancient proven stage of the Greek language, the Mycenaean Greek attributed by the Linear B script. To capture the statistical structure of the Mycenaean documents, we present a Bidirectional Recurrent Neural Network and compare it to the traditionally used n -grams. The model is used to supplement the damaged parts of the Mycenaean texts, namely the incomplete, to a greater or lesser extent, words, which are typically discovered on partially damaged clay tablets. We verify our method experimentally using ground-truth, then we demonstrate our results on real cases and compare with experts’ opinions. We also present a methodology to augment our dataset, which turns out to improve our results.
The fear of missing out on cryptocurrency and stock investments: Direct and indirect effects of financial literacy and risk tolerance

Abstract The “Fear of Missing Out” or FoMO has become an accepted motivator of behaviours extending from the purchase of limited-edition sneaker brands to social media use and cryptocurrency investment. As a motivator of individual financial behaviours, such as cryptocurrency and stock investment, it is unclear how FoMO relates to consumer financial literacy and other consumer traits, including risk tolerance and personality. We propose, and assess, a model of reported investment behaviour and investment behaviour intention. We find a larger association between FoMO and crypto ownership, both current and intended, compared with stocks. FoMO has a small association with current stock ownership, relative to the association of financial literacy and risk tolerance. Context matters when measuring FoMO with the more context-specific measures having the largest associations with investment behaviour and investment intentions. Finally, our results suggest financial literacy is an antecedent of FoMO, more so for stocks.
Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm.