Reviewerly/exHarmony huggingface dataset:
Curriculum Pre-training Heterogeneous Subgraph Transformer for Top- <i>N</i> Recommendation

To characterize complex and heterogeneous side information in recommender systems, the heterogeneous information network (HIN) has shown superior performance and attracted much research attention. In HIN, the rich entities, relations, and paths can be utilized to model the correlations of users and items; such a task setting is often called HIN-based recommendation . Although HIN provides a general approach to modeling rich side information, it lacks special consideration on the goal of the recommendation task. The aggregated context from the heterogeneous graph is likely to incorporate irrelevant information, and the learned representations are not specifically optimized according to the recommendation task. Therefore, there is a need to rethink how to leverage the useful information from HIN to accomplish the recommendation task. To address the above issues, we propose a Curriculum pre-training based HEterogeneous Subgraph Transformer (called CHEST ) with new data characterization , representation model, and learning algorithm . Specifically, we consider extracting useful information from HIN to compose the interaction-specific heterogeneous subgraph, containing highly relevant context information for recommendation. Then, we capture the rich semantics (e.g., graph structure and path semantics) within the subgraph via a heterogeneous subgraph Transformer, where we encode the subgraph into multi-slot sequence representations. Besides, we design a curriculum pre-training strategy to provide an elementary-to-advanced learning process. The elementary course focuses on capturing local context information within the subgraph, and the advanced course aims to learn global context information. In this way, we gradually capture useful semantic information from HIN for modeling user-item interactions. Extensive experiments conducted on four real-world datasets demonstrate the superiority of our proposed method over a number of competitive baselines, especially when only limited training data is available.
NoisyOTNet: A Robust Real-Time Vehicle Tracking Model for Traffic Surveillance

With the rapid development of intelligent transportation, automated traffic surveillance is considered as an important component. In the field of traffic surveillance, it is particularly important to achieve robust and real-time tracking of vehicles in complex scenes. In this paper, a robust real-time vehicle tracking model named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NoisyOTNet</i> is proposed, which formulates tracking as reinforcement learning with parameter space noise. In this formulation, the exploration ability of the model is enhanced to improve the robustness of tracking. Specifically, we develop a new implementation for noisy network based on deep deterministic policy gradients (DDPGs) with parameter noise, which can better cope with the tracking task and directly predict the tracking result. To improve the tracking accuracy in complex conditions, e.g. fast motion and large deformation, this paper presents an adaptive update strategy that can exploit the vehicle spatial-temporal information based on Upper Confidence Bound (UCB) algorithm by exploiting. Moreover, as for the recovery of the lost target, a relocation algorithm based on incremental learning is developed. The results of extensive experiments demonstrate that the proposed NoisyOTNet can effectively track vehicles in complex scenes and achieve competitive performance compared to the state-of-the-art methods.
How to handle noisy labels for robust learning from uncertainty

Most deep neural networks (DNNs) are trained with large amounts of noisy labels when they are applied. As DNNs have the high capacity to fit any noisy labels, it is known to be difficult to train DNNs robustly with noisy labels. These noisy labels cause the performance degradation of DNNs due to the memorization effect by over-fitting. Earlier state-of-the-art methods used small loss tricks to efficiently resolve the robust training problem with noisy labels. In this paper, relationship between the uncertainties and the clean labels is analyzed. We present novel training method to use not only small loss trick but also labels that are likely to be clean labels selected from uncertainty called “Uncertain Aware Co-Training (UACT)”. Our robust learning techniques (UACT) avoid over-fitting the DNNs by extremely noisy labels. By making better use of the uncertainty acquired from the network itself, we achieve good generalization performance. We compare the proposed method to the current state-of-the-art algorithms for noisy versions of MNIST, CIFAR-10, CIFAR-100, T-ImageNet and News to demonstrate its excellence.
A Disparity Feature Alignment Module for Stereo Image Super-Resolution

Recently, the performance of super-resolution has been improved by the stereo images since the additional information could be obtained from another view. However, it is a challenge to interact the cross-view information since disparities between left and right images are variable. To address this issue, we propose a disparity feature alignment module (DFAM) to exploit the disparity information for feature alignment and fusion. Specifically, we design a modified atrous spatial pyramid pooling module to estimate disparities and warp stereo features. Then we use spatial and channel attention for feature fusion. In addition, DFAM can be plugged into an arbitrary SISR network to super-resolve a stereo image pair. Extensive experiments demonstrate that DFAM incorporates stereo information with less inference time and memory cost. Moreover, RCAN equipped with DFAMs achieves better performance against state-of-the-art methods. The code can be obtained at https://github.com/JiawangDan/DFAM.
Self-Supervised Learning of Domain Invariant Features for Depth Estimation

We tackle the problem of unsupervised synthetic-to-real domain adaptation for single image depth estimation. An essential building block of single image depth estimation is an encoder-decoder task network that takes RGB images as input and produces depth maps as output. In this paper, we propose a novel training strategy to force the task network to learn domain invariant representations in a self-supervised manner. Specifically, we extend self-supervised learning from traditional representation learning, which works on images from a single domain, to domain invariant representation learning, which works on images from two different domains by utilizing an image-to-image translation network. Firstly, we use an image-to-image translation network to transfer domain-specific styles between synthetic and real domains. This style transfer operation allows us to obtain similar images from the different domains. Secondly, we jointly train our task network and Siamese network with the same images from the different domains to obtain domain invariance for the task network. Finally, we fine-tune the task network using labeled synthetic and unlabeled real-world data. Our training strategy yields improved generalization capability in the real-world domain. We carry out an extensive evaluation on two popular datasets for depth estimation, KITTI and Make3D. The results demonstrate that our proposed method outperforms the state-of-the-art on all metrics, e.g. by 14.7% on Sq Rel on KITTI. The source code and model weights will be made available.
3D Film Animation Image Acquisition and Feature Processing Based on the Latest Virtual Reconstruction Technology

In this paper, the latest virtual reconstruction technology is used to conduct in-depth research on 3D movie animation image acquisition and feature processing. This paper firstly proposes a time-division multiplexing method based on subpixel multiplexing technology to improve the resolution of integrated imaging reconstruction images. By studying the degradation effect of the reconstruction process of the 3D integrated imaging system, it is proposed to improve the display resolution by increasing the pixel point information of fixed display array units. According to the subpixel multiplexing, an algorithm to realize the reuse of pixel point information of 3D scene element image gets the element image array with new information; then, through the high frame rate light emitting diode (LED) large screen fast output of the element image array, the human eye temporary retention effect is used, so that this group of element image array information go through a plane display, to increase the limited display array information capacity thus improving the reconstructed image. In this way, the information capacity of the finite display array is increased and the display resolution of the reconstructed image is improved. In this paper, we first use the classification algorithm to determine the gender and expression attributes of the face in the input image and filter the corresponding 3D face data subset in the database according to the gender and expression attributes, then use the sparse representation theory to filter the prototype face like the target face in the data subset, then use the filtered prototype face samples to construct the sparse deformation model, and finally use the target faces. Finally, the target 3D face is reconstructed using the feature points of the target face for model matching. The experimental results show that the algorithm reconstructs faces with high realism and accuracy, and the algorithm can reconstruct expression faces.
Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three data sets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Further, the method is computationally efficient and allows real-time assurance monitoring of CPS.
Collaborative filtering via heterogeneous neural networks

Over the last few years, the deep neural network is utilized to solve the collaborative filtering problem, a method of which has achieved immense success on computer vision, speech recognition as well as natural language processing. On one hand, the deep neural network can be used to capture the side information of users and items. On the other hand, it is also capable of modeling interactions between users and items. Most of existing approaches exploit the neural network with solo structure to model user–item interactions such that the learning representation may be insufficient over the extremely sparse rating data. Recently, a large number of neural networks with mixed structures are devised for learning better representations. A carefully designed hybrid network is able to achieve considerable accuracy but only requires a small amount of extra computation. In order to model user–item interactions, we elaborate a hybrid neural network consisting of the global neural network and several local neural blocks. The multi-layer perceptron is adopted to build the global neural network and the residual network is used to form the local neural block which is inserted into two adjacent global layers. The hybrid network is further combined with the generalized matrix factorization to capture both the linear and nonlinear relationships between users and items. It is verified by experimental results on benchmark datasets that our method is superior to certain state-of-the-art approaches in terms of top-n item recommendation. • Devise a hybrid neural network consisting of a global network and local blocks. • Apply the hybrid neural network to model user-item interactions. • Verify the hybrid network superiority in solving CF by comparing with rivals.
Variational autoencoders for anomaly detection in the behaviour of the elderly using electricity consumption data

Abstract According to the World Health Organization, between and , the proportion of the world's population over will double, from to . In absolute numbers, this age group will increase from million to billion in the course of half a century. It is a reality that most of them prefer to live alone, so it is necessary to look for mechanisms and tools that will help them to improve their autonomy. Although in recent years, we have been living in a veritable explosion of domotic systems that facilitate people's daily lives, it is also true that there are not many tools specifically aimed at this sector of the population. The aim of this paper is to present a potential solution to the monitoring of activity of daily living in the least intrusive way for people. In this case, anomalous patterns of daily activities will be detected by analysing the daily consumption of household appliances. People who live alone usually have a pattern of daily behaviour in the use of household appliances (coffee machine, microwave, television, etc.). A neuronal model is proposed for the detection of abnormal behaviour based on an autoencoder architecture. This solution will be compared with a variational autoencoder to analyse the improvements that can be obtained. The well‐known dataset called UK‐DALE will be used to validate the proposal.
Expectations Vs. Reality: Unreliability and Transparency in a Treasure Hunt Game With Icub

Trust is essential in human-robot interactions, and in times where machines are yet to be fully reliable, it is important to study how robotic hardware faults can affect the human counterpart.This experiment builds on a previous research that studied trust changes in a game-like scenario with the humanoid robot iCub.Several robot hardware failures (validated in another online study) were introduced in order to measure changes in trust due to the unreliability of the iCub.A total of 68 participants took part in this study.For half of them, the robot adopted a transparent approach, explaining each failure after it happened.Participants' behaviour was also compared to the 61 participants that played the same game with a fully reliable robot in the previous study.Against all expectations, introducing manifest hardware failures does not seem to significantly affect trust, while transparency mainly deteriorates the quality of interaction with the robot.
Cryptanalysis of phase information based on a double random-phase encryption method

Phase-only information derived using a double random-phase encryption (DRPE) algorithm is not visually recognizable but can be authenticated using a nonlinear cross-correlation method. This can be considered to enhance DRPE security (which is vulnerable to a chosen-plaintext attack). Here, we show that phase-only images derived using the DRPE method remains vulnerable to such an attack; the at-risk images include full- and sparse-phase images and phase information with noise. We develop an encoder–decoder deep-learning method to decrypt phase-only images encrypted using the DRPE method. The deep-learning structure can be trained using only spare phase information from the encrypted image; experimentally, the trained model readily decrypted phase-only or partial-phase-encrypted images based on the DRPE algorithm. • We have developed an encoder–decoder deep-learning model to simulate a chosen-ciphertext attack on phase-only information created by the DRPE. • The method robustly extracted plaintext when the phase images were sheared or polluted with additive or multiplicative noise. Image authentication via nonlinear cross-correlation was possible. • We have shown that phase-only images derived using the DRPE method remains vulnerable to such an attack; the at-risk images include full- and sparse-phase images and phase information with noise.
What determines interest rates for bitcoin lending?

This study analyses the determinants of interest rates in the cryptocurrency lending market using a unique database from the Decentralised Finance platform. We confirm the existence of both mediation and moderation effects in the cryptocurrency lending market by employing a moderated mediation model. First, the empirical results show that the interest rate is closely related to the loan-to-value ratio, which works as the mediation variable in lending. Second, the interest rate reveals a clear connection with price fluctuations of Bitcoin. This brings up the momentum phenomenon in the lending process and incentives borrowers to acquire more money, leading to pro-cyclical speculation. Third, the lending amount reflects a moderation effect in the lending market, and the net effect of the currency price on the interest rate turns negative when the loan amount exceeds a threshold, resulting in the 'seesaw' effect in cryptocurrency lending. The above findings confirm that cryptocurrency lending reflects a certain degree of option characteristics and complies with the risk-debt model, which provides more evidence for understanding the momentum phenomenon and investor behaviour in the cryptocurrency lending market.
Can adults discriminate between fraudulent and legitimate e-mails? Examining the role of age and prior fraud experience

The present study assessed how accurate adults are at detecting fraudulent e-mail activity. A total of 100 younger (18–26 years) and 96 older adults (60–90 years) categorized a series of e-mails as legitimate or fraudulent phishing schemes and self-reported their fraud experiences. Younger and older adults did not differ in accuracy rates when categorizing the e-mails (72%), but older adults used a "high-suspicion" strategy where they were more likely to mislabel a legitimate e-mail as fraudulent compared to younger adults. Younger adults were less likely to be targeted by fraud than older adults, but the groups were victimized at similar rates. Being a prior fraud victim negatively related to e-mail detection performance, but this differed across age groups and the extent of fraud experience. Together, these results provide insight into the relation between fraud experience and the ability to detect e-mail scams and can inform fraud prevention and education initiatives.
STALP: Style Transfer with Auxiliary Limited Pairing

Abstract We present an approach to example‐based stylization of images that uses a single pair of a source image and its stylized counterpart. We demonstrate how to train an image translation network that can perform real‐time semantically meaningful style transfer to a set of target images with similar content as the source image. A key added value of our approach is that it considers also consistency of target images during training. Although those have no stylized counterparts, we constrain the translation to keep the statistics of neural responses compatible with those extracted from the stylized source. In contrast to concurrent techniques that use a similar input, our approach better preserves important visual characteristics of the source style and can deliver temporally stable results without the need to explicitly handle temporal consistency. We demonstrate its practical utility on various applications including video stylization, style transfer to panoramas, faces, and 3D models.
Robustness certification with generative models

Generative neural networks are powerful models capable of learning a wide range of rich semantic image transformations such as altering person's age, head orientation, adding mustache, changing the hair color and many more. At a high level, a generative model effectively produces new and previously unseen images with the desired properties, which can then be used to improve the accuracy of existing models. In this work, we advance the state-of-the-art in verification by bridging the gap between (i) the well studied but limited norm-based and geometric transformations, and (ii) the rich set of semantic transformations used in practice. This problem is especially hard since the images are generated from a highly non-convex image manifold, preventing the use of most existing verifiers, which often rely on convex relaxations. We present a new verifier, called GenProve, which is capable of certifying the rich set of semantic transformations of generative models. GenProve can provide both sound deterministic and probabilistic guarantees, by capturing infinite non-convex sets of activation vectors and distributions over them, while scaling to realistic networks.
On the Definition of Quantum Programming Modules

There are no doubts that quantum programming and, in general, quantum computing, is one of the most promising areas within computer science and one of the areas where most expectations are being placed in recent years. Although the days when reliable and affordable quantum computers will be available is still a long way off, the explosion of programming languages for quantum programming has grown exponentially in recent years. The software engineering community has been quick to react to the need to adopt and adapt well-known tools and methods for software development, and for the design of new ones tailored to this new programming paradigm. However, many key aspects for its success depend on the establishment of an appropriate conceptual framework for the conception and design of quantum programs. This article discusses the concept of module, key in the software engineering discipline, and establishes initial criteria for determining the cohesion and coupling levels of a module in the field of quantum programming as a first step towards a sound quantum software engineering. As detailed in the article, the conceptual differences between classical and quantum computing are so pronounced that the translation of classical concepts to the new programming approach is not straightforward.
Evaluating the robustness of source code plagiarism detection tools to pervasive plagiarism-hiding modifications

Source code plagiarism is a common occurrence in undergraduate computer science education. In order to identify such cases, many source code plagiarism detection tools have been proposed. A source code plagiarism detection tool evaluates pairs of assignment submissions to detect indications of plagiarism. However, a plagiarising student will commonly apply plagiarism-hiding modifications to source code in an attempt to evade detection. Subsequently, prior work has implied that currently available source code plagiarism detection tools are not robust to the application of pervasive plagiarism-hiding modifications. In this article, 11 source code plagiarism detection tools are evaluated for robustness against plagiarism-hiding modifications. The tools are evaluated with data sets of simulated undergraduate plagiarism, constructed with source code modifications representative of undergraduate students. The results of the performed evaluations indicate that currently available source code plagiarism detection tools are not robust against modifications which apply fine-grained transformations to the source code structure. Of the evaluated tools, JPlag and Plaggie demonstrates the greatest robustness to different types of plagiarism-hiding modifications. However, the results also indicate that graph-based tools, specifically those that compare programs as program dependence graphs, show potentially greater robustness to pervasive plagiarism-hiding modifications.