Reviewerly/exHarmony huggingface dataset:
Contrastive Proxy Kernel Stein Path Alignment for Cross-Domain Cold-Start Recommendation

Cross-Domain Recommendation has been popularly studied to utilize different domain knowledge to solve the cold-start problem in recommender systems. In this paper, we focus on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cross-Domain Cold-Start Recommendation</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CDCSR</i> ) problem. That is, how to leverage the information from a source domain, where items are ’warm’, to improve the recommendation performance of a target domain, where items are ’cold’. It has two main challenges, i.e., (1) how to efficiently reduce the discrepancy between the latent embedding distribution across domains and (2) how to generate more robust and stable cold item embeddings. To address these two challenges, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPKSPA</monospace> , a cross-domain recommendation framework for the CDCSR problem. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPKSPA</monospace> contains three modules, i.e., rating prediction module, embedding distribution alignment module, and contrastive augmentation module. To start with, we first utilize the rating prediction module to model user-item interactions. To solve the first challenge, we propose proxy Stein path alignment with typical-subgroup discovering algorithm in the embedding distribution alignment module. To tackle the second challenge, we propose the contrastive augmentation module which adopts contrastive augmentation learning to generate more stable and robust cold item embeddings. Our empirical study on Douban and Amazon datasets demonstrates that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPKSPA</monospace> significantly outperforms the state-of-the-art models.
Prediction-Oriented Disparity Rectification Model for Geometry-Based Light Field Compression

The geometry-based light field compression approach aims to use the inherent disparity cue provided by the light field to reduce the redundancy of data and improve immersive media broadcasting. Some methods, including our previous one, transmit explicit disparity maps along with sparsely selected views and then exploit disparity-based view prediction to remove correlations in the angular domain. However, these methods neglect two important issues: the 4D correspondence between the disparity array and view array and the practical non-uniform disparity distribution in captured light fields. As a result, the prediction potentiality of disparity maps is heavily restricted, and the limited disparity-based prediction accuracy degrades the final compression performance. In this paper, we focus on these two issues of estimated disparity maps and propose a prediction-oriented disparity rectification (PoDR) model. First, based on our previous disparity estimation, we propose to utilize the 4D structural prior of light fields to refine the estimated disparity map. The 4D correspondence between a disparity array and a view array is enhanced, leading to higher prediction accuracy and lower bit costs. Second, for the refined disparity array, we propose a variable stride-based determination algorithm to obtain the practical non-uniform disparity distribution in captured light fields. Specifically, the angular distance between each pair of disparity maps is efficiently derived by the approximate solution based on the gradient variation of the prediction errors. By combining these two modules, the proposed PoDR model improves the overall compression performance compared with our previous work. Furthermore, we verify that the proposed method can obtain better light field fundamental capability (e.g., refocusing) than state-of-the-art methods.
Unsupervised Hashing Retrieval via Efficient Correlation Distillation

Deep hashing has been widely used in multimedia retrieval systems due to its storage and computation efficiency. Unsupervised hashing has received a lot of attention in recent years because it does not rely on label information. However, existing deep unsupervised hashing methods usually use rough pairwise relations to constrain the similarity between hash codes locally, which is insufficient and inefficient to reconstruct accurate correlations across samples. To address this issue, we propose a generic distillation framework for the preservation of the similarity relationship. Specifically, we design a distillation loss to reconstruct the batchwise similarity distribution between feature space and hash code space, allowing us to capture the global correlation knowledge contained in features and propagate it into hash codes efficiently. This framework can apply to both intra-modal and inter-modal scenarios. Furthermore, we design a new quantization method that quantizes the continuous values to a clipping value instead of ±1 to reduce the inconsistency between continuous features and hash codes. This method can also avoid the vanishing gradient problem during training. Finally, extensive experiments for image hashing retrieval and cross-modal hashing retrieval on public datasets demonstrate that the proposed method can yield compact hash codes and outperforms the state-of-the-art baselines.
Progressive With Purpose: Guiding Progressive Inpainting DNNs Through Context and Structure

The advent of deep learning in the past decade has significantly helped advance image inpainting. Although achieving promising performance, deep learning-based inpainting algorithms still struggle from the distortion caused by the fusion of structural and contextual features, which are commonly obtained from, respectively, deep and shallow layers of a convolutional encoder. Motivated by this observation, we propose a novel progressive inpainting network that maintains the structural and contextual integrity of a processed image. More specifically, inspired by the Gaussian and Laplacian pyramids, the core of the proposed network is a feature extraction module named GLE. Stacking GLE modules enables the network to extract image features from different image frequency components. This ability is important to maintain structural and contextual integrity, for high frequency components correspond to structural information while low frequency components correspond to contextual information. The proposed network utilizes the GLE features to progressively fill in missing regions in a corrupted image in an iterative manner. Our benchmarking experiments demonstrate that the proposed method achieves clear improvement in performance over many state-of-the-art inpainting algorithms.
Complexity function and complexity of validity of modal and superintuitionistic propositional logics

Abstract We consider the relationship between the algorithmic properties of the validity problem for a modal or superintuitionistic propositional logic and the size of the smallest Kripke countermodels for non-theorems of the logic. We establish the existence, for every degree of unsolvability, of a propositional logic whose validity problem belongs to the degree and whose every non-theorem is refuted on a Kripke frame that validates the logic and has the size linear in the length of the non-theorem. Such logics are obtained among the normal extensions of the propositional modal logics $\textbf {KTB}$, $\textbf {GL}$ and $\textbf {Grz}$ as well as in the lattice of superintuitionistic propositional logics. This shows that the computational complexity of a modal or superintuitionistic propositional logic is, in general, not related to the size of the countermodels for its non-theorems.
A geospatial image based eye movement dataset for cartography and GIS

Eye movement is a new type of data for cartography and geographic information science (GIS) research. However, previous studies rarely built eye movement datasets with geospatial images. In this paper, we firstly proposed a geospatial image-based eye movement dataset called GeoEye, a publicly shared, widely available eye movement dataset. This dataset consists of 110 college-aged participants who freely viewed 500 images, including thematic maps, remote sensing images, and street view images. In addition, we used the dataset for geospatial image saliency prediction and map user identification. Results demonstrated the scientific benefits and applications of the proposed dataset. GeoEye dataset will not only promote the application of eye-tracking data in cartography and GIS research but also intelligence and customization of geographic information services.
Toward a blind image quality evaluator in the wild by learning beyond human opinion scores

Nowadays, most existing blind image quality assessment (BIQA) models inthewild heavily rely on human ratings, which are extraordinarily labor-expensive to collect. Here, we propose an opinion−free BIQA method that learns from multiple annotators to assess the perceptual quality of images captured in the wild. Specifically, we first synthesize distorted images based on the pristine counterparts. We then randomly assemble a set of image pairs from the synthetic images, and use a group of IQA models to assign pseudo-binary labels for each pair indicating which image has higher quality as the supervisory signal. Based on the newly established pseudo-labeled dataset, we train a deep neural network (DNN)-based BIQA model to rank the perceptual quality, optimized for consistency with the binary rank labels. Since there exists domain shift, e.g., distortion shift and content shift, between the synthetic and in-the-wild images, we leverage two ways to alleviate this issue. First, the simulated distortions should be similar to authentic distortions as much as possible. Second, an unsupervised domain adaptation (UDA) module is further applied to encourage learning domain-invariant features between two domains. Extensive experiments demonstrate the effectiveness of our proposed opinion−free BIQA model, yielding SOTA performance in terms of correlation with human opinion scores, as well as gMAD competition. Our code is available at: https://github.com/wangzhihua520/OF_BIQA.
Optimal diagonal qutrit gates for creating Wigner negativity

In the phase space formulation of quantum mechanics, the Wigner function plays an instrumental role. Unlike the joint probability distribution in classical theory, the Wigner function may take negative values. This Wigner negativity lies in the heart of various quantum features such as non-classicality and non-stabilizerness, and may be exploited as a resource for quantum information processing. In the stabilizer formalism of quantum computation, since the Wigner negativity is a necessary ingredient for promoting the stabilizer circuits to fault-tolerant quantum computation, it is desirable to investigate which gates are optimal for creating Wigner negativity from stabilizer states. In this work, we completely characterize all optimal diagonal qutrit gates for creating Wigner negativity, and demonstrate that all these optimal gates are Clifford equivalent to the remarkable qutrit T-gate, which has been widely used in the paradigm of Clifford+T set of quantum circuits.
Learning an attention-aware parallel sharing network for facial attribute recognition

Existing multi-task learning based facial attribute recognition (FAR) methods usually employ the serial sharing network, where the high-level global features are used for attribute prediction. However, the shared low-level features with valuable spatial information are not well exploited for multiple tasks. This paper proposes a novel Attention-aware Parallel Sharing network termed APS for effective FAR. To make full use of the shared low-level features, the task-specific sub-networks can adaptively extract important features from each block of the shared sub-network. Furthermore, an effective attention mechanism with multi-feature soft-alignment modules is employed to evaluate the compatibility of the local and global features from the different network levels for discriminating attributes. In addition, an adaptive Focal loss penalty scheme is developed to automatically assign weights to handle the problems of class imbalance and hard example mining for FAR. Experiments demonstrate that the proposed method achieves better performance than the state-of-the-art FAR methods.
Several classes of exponential sums and three-valued Walsh spectrums over finite fields

Let r≡1(mod4) be a prime, m a positive integer, ϕ(rm)2 the multiplicative order of 2 modulo rm, and q=2ϕ(rm)2, where ϕ(⋅) is the Euler's function. Let γ be a primitive element of the finite field Fq and α=γq−1rm. In this paper, we shall explicitly calculate the exponential sums S(a)=∑i=0rm−1χ(aαi), a∈Fq, where χ is the canonical additive character of Fq. As applications, using elliptic curves and cyclotomic numbers over Fr, the three-valued Walsh spectrums of four classes of monomial functions fa(x)=Trq/2(axq−1rm) are determined.
Robustness of rank aggregation methods for malicious disturbance

Rank aggregation has widespread applications in social choice, information retrieval, and bioinformatics. Given the enormous social and economic implications of these applications and the consequent incentives for malicious users to disturb the aggregated ranking, evaluating the robustness of rank aggregation methods for malicious disturbance is a key challenge. In this article, we introduce an analytical framework for the robustness of rank aggregation for malicious disturbances, in which we generate rankings with imperfect information and observe the response of rank aggregation methods. Along with the analytical framework, we propose an experimental data generation method and a new robustness measure. Then we investigate the robustness of five common rank aggregation methods using our proposed method. Extensive experiments demonstrate that our method can effectively compare the robustness of different rank aggregation methods for malicious disturbance. Moreover, the experimental results show that the performance of rank aggregation methods for malicious disturbance may be very different from that for random error.
What we see is what we do: a practical Peripheral Vision-Based HMM framework for gaze-enhanced recognition of actions in a medical procedural task

Abstract Deep learning models have shown remarkable performances in egocentric video-based action recognition (EAR), but rely heavily on a large quantity of training data. In specific applications with only limited data available, eye movement data may provide additional valuable sensory information to achieve accurate classification performances. However, little is known about the effectiveness of gaze data as a modality for egocentric action recognition. We, therefore, propose the new Peripheral Vision-Based HMM (PVHMM) classification framework, which utilizes context-rich and object-related gaze features for the detection of human action sequences. Gaze information is quantified using two features, the object-of-interest hit and the object–gaze distance, and human action recognition is achieved by employing a hidden Markov model. The classification performance of the framework is tested and validated on a safety-critical medical device handling task sequence involving seven distinct action classes, using 43 mobile eye tracking recordings. The robustness of the approach is evaluated using the addition of Gaussian noise. Finally, the results are then compared to the performance of a VGG-16 model. The gaze-enhanced PVHMM achieves high classification performances in the investigated medical procedure task, surpassing the purely image-based classification model. Consequently, this gaze-enhanced EAR approach shows the potential for the implementation in action sequence-dependent real-world applications, such as surgical training, performance assessment, or medical procedural tasks.
Truncated Poisson–Dirichlet approximation for Dirichlet process hierarchical models

Abstract The Dirichlet process was introduced by Ferguson in 1973 to use with Bayesian nonparametric inference problems. A lot of work has been done based on the Dirichlet process, making it the most fundamental prior in Bayesian nonparametric statistics. Since the construction of Dirichlet process involves an infinite number of random variables, simulation-based methods are hard to implement, and various finite approximations for the Dirichlet process have been proposed to solve this problem. In this paper, we construct a new random probability measure called the truncated Poisson–Dirichlet process. It sorts the components of a Dirichlet process in descending order according to their random weights, then makes a truncation to obtain a finite approximation for the distribution of the Dirichlet process. Since the approximation is based on a decreasing sequence of random weights, it has a lower truncation error comparing to the existing methods using stick-breaking process. Then we develop a blocked Gibbs sampler based on Hamiltonian Monte Carlo method to explore the posterior of the truncated Poisson–Dirichlet process. This method is illustrated by the normal mean mixture model and Caron–Fox network model. Numerical implementations are provided to demonstrate the effectiveness and performance of our algorithm.
Regularization-based pruning of irrelevant weights in deep neural architectures

Deep neural networks exploiting million parameters are currently the norm. This is a potential issue because of the great number of computations needed for training, and the possible loss of generalization performance of overparameterized networks. We propose in this paper a method for learning sparse neural topologies via a regularization approach that identifies nonrelevant weights in any type of layer (i.e., convolutional, fully connected, attention and embedding ones) and selectively shrinks their norm while performing a standard back-propagation update for relevant layers. This technique, which is an improvement of classical weight decay, is based on the definition of a regularization term that can be added to any loss function regardless of its form, resulting in a unified general framework exploitable in many different contexts. The actual elimination of parameters identified as irrelevant is handled by an iterative pruning algorithm. To explore the possibility of an interdisciplinary use of our proposed technique, we test it on six different image classification and natural language generation tasks, among which four are based on real datasets. We reach state-of-the-art performance in one out of four imaging tasks while obtaining results better than competitors for the others and one out of two of the considered language generation tasks, both in terms of compression and metrics.
Indicators of employee phishing email behaviours: Intuition, elaboration, attention, and email typology

Employees’ behaviour to phishing emails can strengthen or undermine business organisations’ cyber security. This phishing simulation and survey study explored the relationship between sociodemographic, cyber security training, phishing email typology and information processing factors and risky and secure email response behaviours. Participants (N = 590) were employees of a large financial institution who received one of four types of phishing emails. Participants who engaged in risky cyber email behaviour clicked on the link in the phishing email whereas those who engaged in secure cyber email behaviour reported the email to the institutions cyber security team. Our findings show that the likelihood of clicking on a link in a phishing email was lower for participants who had greater faith in their intuition and paid more attention to the sender's email address. The likelihood of clicking on a link in a phishing email was greater for participants who received the ‘Undelivered package’ email relative to the ‘Received PDF’. The likelihood of reporting a phishing email was greater for participants who engaged in greater elaborative processing to evaluate the email than those who used less elaboration. Theoretical and practical implications as well as future directions are discussed.
Evaluation metrics for galaxy image generators

A major problem with deep generative models is verifying that the generated distribution resembles the target distribution while the individual generated sample is indistinguishable from the original data. In particular, for application in astrophysics we need to be sure that the generated data matches our prior knowledge and that the generated samples entail all object types with the correct frequency and diversity. We currently lack objective ways to systematically assess these quality aspects, where human inspection reaches its limits, as this requires detailed analysis of a large data volume. In this work, we identify reasonable metrics for the quality of galaxy image generators. To this end, we compare a small set of conditional image generators, trained on galaxy images with classification labels for visual morphology features. Our main contribution is a new set of cluster-based metrics for matching the generated distribution to the target distribution. Furthermore, we use the Wasserstein distance on proxies for galaxy morphology as well as a number of other metrics commonly used for image generators. The newly introduced cluster-based metrics are good proxies for the quality of the generated distribution and are suited for automatized identification of mode collapse. Furthermore, the cluster metrics allow for a qualitative interpretation of the generated distribution. The metrics based on morphological statistics provide a useful tool to probe the physical soundness of generated samples. Finally, we find that kernel inception distance used with an InceptionV3 model pre-trained on ImageNet is a good proxy for the overall quality of galaxy image generators, although it cannot be interpreted that easily.
The Cybercrime Victim-Offender Overlap: Evaluating Predictors for Victims, Offenders, Victim-Offenders, and Those Who are Neither

Few studies have examined the victim-offender overlap in cybercrimes, especially using mutually exclusive groups: victims-only, offenders-only, victim-offenders, and those who are neither. The current study uses a sample of adults (N = 837) to evaluate the predictors of cybercrime victimization and offending generally, as well as the group-specific differences and similarities. Cybercrime victimization significantly predicted offending and offending predicted victimization. Level of self-control, time spent participating in routine online activities, and demographic characteristics were significant predictors for both victimization and offending. Results showed that 40.5% of participants were victims-only, 20% offenders-only, and 16.6% were victim-offenders. Multinomial regression suggests there are significant differences and similarities between these groups.