Reviewerly/exHarmony huggingface dataset:
Robust and reliable image copyright protection scheme using downsampling and block transform in integer wavelet domain

In this paper, a robust watermarking algorithm in integer wavelet domain using downsampling is proposed. The innovations of this paper can be summarized as follows. First, after downsampling both the host image and watermark, each sub-watermark is embedded into the corresponding sub-host image. Second, four sub-watermarks are encrypted by pixel scrambling and fractional random transform, and the right principal component of watermark is embedded in the host image to avoid false positive problem. Third, two-level singular value decomposition and block cosine transform are performed on host image and watermark respectively, and the dual embedding of watermark is realized. Fourth, guided dynamic particle swarm optimization is used to optimize the embedding factors. The simulation results show that the proposed watermarking algorithm satisfies the requirements of robust watermarking very well. It has large capacity and strong robustness to common attacks. Moreover, compared with the existing related algorithms, this algorithm has obvious advantages.
Expectation–Maximization algorithm for finite mixture of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si57.svg"><mml:mrow><mml:mi>α</mml:mi></mml:mrow></mml:math>-stable distributions

A Gaussian Mixture Model (GMM) is a parametric probability density function built as a weighted sum of Gaussian distributions. Gaussian mixtures are used for modelling the probability distribution in many fields of research nowadays. Nevertheless, in many real applications, the components are skewed or heavy tailed. For that reason, it is useful to model the mixtures as components with α-stable distribution. In this work, we present a mixture of skewed α-stable model where the parameters are estimated using the Expectation–Maximization algorithm. As the Gaussian distribution is a particular limiting case of α-stable distribution, the proposed model is a generalization of the widely used GMM. The proposed algorithm is much faster than the parameter estimation of the α-stable mixture model using a Bayesian approach and Markov chain Monte Carlo methods. Therefore, it is more suitable to be used for large vector observations.
Secure Distributed Matrix Computation With Discrete Fourier Transform

We consider the problem of secure distributed matrix computation (SDMC), where a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user</i> queries a function of data matrices generated at distributed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">source</i> nodes. We assume the availability of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> honest but curious computation servers, which are connected to the sources, the user, and each other through orthogonal and reliable communication links. Our goal is to minimize the amount of data that must be transmitted from the sources to the servers, called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">upload cost</i> , while guaranteeing that no <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> colluding servers can learn any information about the source matrices, and the user cannot learn any information beyond the computation result. We first focus on secure distributed matrix multiplication (SDMM), considering two matrices, and propose a novel polynomial coding scheme using the properties of finite field discrete Fourier transform, which achieves an upload cost significantly lower than the existing results in the literature. We then generalize the proposed scheme to include straggler mitigation, and to the multiplication of multiple matrices while keeping the input matrices, the intermediate computation results, as well as the final result secure against any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> colluding servers. We also consider a special case, called computation with own data, where the data matrices used for computation belong to the user. In this case, we drop the security requirement against the user, and show that the proposed scheme achieves the minimal upload cost. We then propose methods for performing other common matrix computations securely on distributed servers, including changing the parameters of secret sharing, matrix transpose, matrix exponentiation, solving a linear system, and matrix inversion, which are then used to show how arbitrary matrix polynomials can be computed securely on distributed servers using the proposed procedure.
Does Immersive VR Increase Learning Gain When Compared to a Non-immersive VR Learning Experience?

Currently, computer assisted learning and multimedia form a key part of teaching. Interactivity and feedback are valuable in promoting active as opposed to passive learning. The study is conducted as an assessment of the impact of immersive VR on learning gain compared with a non-immersive video capture of VR, with a primary research question focusing on exploring learning gain and a secondary question exploring user experience, whereby understanding this is paramount to recognizing how to achieve a complete and effective learning experience. The study found immersive VR to significantly increase learning gain whilst two key measures of reported experience; enjoyment and concentration, also appeared significantly higher for the immersive VR learners. The study suggests extensive avenues for further research in this growing field, recognizing the need to appeal to a variety of students’ learning preferences. For educators, the relevance of self-directed and student-centered learning to enable active learning in the immersive tool is highlighted. Findings of such VR-based studies can be applied across several disciplines, including medical education; providing opportunity for users to learn without real-world consequences of error such as in surgical intervention.
Designing Hybrid Gifts

Hybrid gifting combines physical artefacts and experiences with digital interactivity to generate new kinds of gifts. Our review details how gifting is a complex social phenomenon and how digital gifting is less engaging than physical gifting for both givers and receivers. Employing a Research Through Design approach, we developed a portfolio of four hybrid gifting experiences: an augmented advent calendar; edible music tracks; personalized museum tours; and a narrated city walk. Our reflection addresses three concepts: hybrid wrapping where physical gifts become wrapped in digital media and vice versa; the importance of effortful interactions that are visible and pleasurable; and the need to consider social obligation, including opportunities for acknowledgement and reciprocation, dealing with embarrassment, and recognizing the distinction between giving and sharing. Our concepts provide guidance to practitioners who wish to design future gifting experiences while helping HCI researchers engage with the concept of gifting in a nuanced way.
Translations and Boundaries in the Gap Between HCI Theory and Design Practice

The gap between research and design practice has long been a concern for the HCI community. In this article, we explore how different translations of HCI knowledge might bridge this gap. A literature review characterizes the gap as having two key dimensions—one between general theory and particular artefacts and a second between academic HCI research and professional UX design practice. We report on a 5-year engagement between HCI researchers and a major media company to explore how a particular piece of HCI research, the trajectories conceptual framework , might be translated for and with UX practitioners. We present various translations of this framework and fit them into the gap we previously identified. This leads us to refine the idea of translations , suggesting that they may be led by researchers, by practitioners or co-produced by both as boundary objects . We consider the benefits of each approach.
The Hopeless University: Intellectual Work at the End of the End of History

The University is being explicitly restructured for the production, circulation and accumulation of value, materialised in the form of rents and surpluses on operating activities. The pace of restructuring is affected by the interplay between financial crisis and Covid-19, through which the public value of the University is continually questioned. In this conjuncture of crises that affect the body of the institution and the bodies of its labourers, the desires of Capital trump human needs. The structural adjustment of sectoral and institutional structures as forms, cultures as pathologies, and activities as methodologies enacts scarring. However, the visibility of scars has led to a reawakening of politics inside and beyond the University. The idea that History had ended because there is no alternative to capitalism or its political horizon, is in question. Instead, the political content of the University has reasserted itself at the end of The End of History. In this article, the idea that the University at The End of History has become a hopeless space, unable both to fulfil the desires of those who labour within it for a good life and to contribute solutions to socio-economic and socio-environmental ruptures, is developed dialectically. This enables us to consider the potential for reimagining intellectual work as a movement of sensuous human activity in the world, rather than being commodified for value.
Algorithm for quantum computation of particle decays

A quantum algorithm is developed to calculate decay rates and cross sections using quantum resources that scale polynomially in the system size assuming similar scaling for state preparation and time evolution. This is done by computing finite-volume one- and two-particle Green's functions on the quantum hardware. Particle decay rates and two-particle scattering cross sections are extracted from the imaginary parts of the Green's function. A $0+1$ dimensional implementation of this method is demonstrated on IBM's superconducting quantum hardware for the decay of a heavy scalar particle to a pair of light scalars.
Decision Theory Meets Explainable AI

Explainability has been a core research topic in AI for decades and therefore it is surprising that the current concept of Explainable AI (XAI) seems to have been launched as late as 2016. This is a problem with current XAI research because it tends to ignore existing knowledge and wisdom gathered over decades or even centuries by other relevant domains. This paper presents the notion of Contextual Importance and Utility (CIU), which is based on known notions and methods of Decision Theory. CIU extends the notions of importance and utility for the non-linear models of AI systems and notably those produced by Machine Learning methods. CIU provides a universal and model-agnostic foundation for XAI.
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey

With widespread applications of artificial intelligence (AI), the capabilities of the perception, understanding, decision-making, and control for autonomous systems have improved significantly in recent years. When autonomous systems consider the performance of accuracy and transferability, several AI methods, such as adversarial learning, reinforcement learning (RL), and meta-learning, show their powerful performance. Here, we review the learning-based approaches in autonomous systems from the perspectives of accuracy and transferability. Accuracy means that a well-trained model shows good results during the testing phase, in which the testing set shares a same task or a data distribution with the training set. Transferability means that when a well-trained model is transferred to other testing domains, the accuracy is still good. Firstly, we introduce some basic concepts of transfer learning and then present some preliminaries of adversarial learning, RL, and meta-learning. Secondly, we focus on reviewing the accuracy or transferability or both of these approaches to show the advantages of adversarial learning, such as generative adversarial networks, in typical computer vision tasks in autonomous systems, including image style transfer, image super-resolution, image deblurring/dehazing/rain removal, semantic segmentation, depth estimation, pedestrian detection, and person re-identification. We furthermore review the performance of RL and meta-learning from the aspects of accuracy or transferability or both of them in autonomous systems, involving pedestrian tracking, robot navigation, and robotic manipulation. Finally, we discuss several challenges and future topics for the use of adversarial learning, RL, and meta-learning in autonomous systems.
Does behavior simulation based on augmented reality improve moral imagination?

Innovative learning techniques are needed, to match the expectations of the current audience to improve business ethics education for the twenty-first century. One of the innovative technologies which is believed to have a big effect on a bachelor's degree is augmented reality (AR). Incorporating AR into the modern education system is expected to produce optimal augmentation in the learning and teaching environment. This will have a large impact on increasing students' moral imagination. This study examines the use of AR-based behavior simulation, as an innovative technique for learning ethics, to improve moral imagination. This study employs a 3 × 2 experiment method, three training modes (AR-based behavioral simulation, paper-based-behavioral simulation, and no training) and two times (time one and time two) between- and within-subject factorial design. The subjects are 147 students on a business ethics course. The result of this study reveals that the use of AR-based behavior simulation can improve moral imagination.
Joint multi-domain feature learning for image steganalysis based on CNN

In recent years, researchers have been making great progress in the steganalysis technology based on convolution neural networks (CNN). However, experts ignore the contribution of nonlinear residual and joint domain detection to steganalysis, and how to detect the adaptive steganographic algorithms with low embedding rates is still challenging. In this paper, we propose a CNN steganalysis model that uses a joint domain detection mechanism and a nonlinear detection mechanism. For the nonlinear detection mechanism, based on the spatial rich model (SRM), we introduce the maximum and minimum nonlinear residual feature acquisition method into the model to adapt to the nonlinear distribution of steganography information. For the joint domain detection mechanism, we not only apply the high-pass filters from the SRM for spatial residuals, but also apply the patterns from the discrete cosine transform residual (DCTR) for transformation steganographic impacts, so as to fully capture the interference trace of spatial steganography to transform domain. We also apply a new transfer learning method to improve the model’s performance. That is, we apply the low embedding rate steganography samples to initialize the model, because we think that the method makes the network more sensitive than applying high embedding rate steganography samples to initialize the model. The simulation results also confirm this assumption. Combined with the above improved methods, the detection accuracy of the model for WOW and S-UNIWARD is higher than that of SRM+EC, Ye-Net, Xu-Net, Yedroudj-Net and Zhu-Net, which is about 4 ∼6% higher than that of the optimal Zhu-Net. The results can provide a certain reference for steganalysis and image forensics tasks.
An Improved Text Feature Selection for Clustering Using Binary Grey Wolf Optimizer

Text Feature Selection (FS) is a significant step in text clustering (TC). Machine learning applications eliminate unnecessary features in order to enhance learning effectiveness. This work proposes a binary grey wolf optimizer (BGWO) algorithm to tackle the text FS problem. This method introduces a new implementation of the GWO algorithm by selecting informative features from the text. These informative features are evaluated using the clustering technique (i.e., k-means) so that time complexity is reduced, and the clustering algorithm’s efficiency is improved. The performance of BGWO is examined on six published datasets, including Tr41, Tr12, Wap, Classic4, 20Newsgroups, and CSTR. The results showed that the BGWO output outperformed the rest of the compared algorithms such as GA and BPSO based on the measurements of the evaluation. The experiments also showed that the BGWO method could achieve an average purity of 46.29%, F-measure of 42.23%.
Diverse training dataset generation based on a multi-objective optimization for semi-Supervised classification

The self-labeled technique is a type of semi-supervised classification that can be used when labeled data are lacking. Although existing self-labeled techniques show promise in many areas of classification and pattern recognition, they commonly incorrectly label data. The reasons for this problem are the shortage of labeled data and the inappropriate distribution of data in problem space. To deal with this problem, we propose in this paper a synthetic, labeled data generation method based on accuracy and density. Positions of generated data are improved through a multi-objective evolutionary algorithm with two objectives – accuracy and density. The density function generates data with an appropriate distribution and diversity in feature space, whereas the accuracy function eliminates outlier data. The advantage of the proposed method over existing ones is that it simultaneously considers accuracy and distribution of generated data in feature space. We have applied the new proposed method on four self-labeled techniques with different features, i.e., Democratic-co, Tri-training, Co-forest, and Co-bagging. The results show that the proposed method is superior to existing methods in terms of classification accuracy. Also, the superiority of the proposed method is confirmed over other data generation methods such as SMOTE, Borderline SMOTE, Safe-level SMOTE and SMOTE-RSB.
A Generic IoT Architecture for Ubiquitous Context-Aware Learning

Ubiquitous learning environments move learners out of a classroom and into the real world where learners engage in experiential and tangible learning involving instrumented physical things. Learners use peer-to-peer networks connecting learners, teachers, and a host of learning “things,” such as instrumented pieces of art, flower pots, and even buildings. A key component of such systems is wireless-enabled edge devices augmented with various types of sensors to represent the state of physical things and their context. This article presents a novel generic architecture for context-aware ubiquitous learning systems based on the Internet of Things (IoT) computing paradigm. A commonly used IoT edge device was used to implement four variants of the proposed architecture. The variants were based on advanced message queuing protocol, constrained application protocol (CoAP), message queue telemetry transport, and extensible messaging and presence protocol. All variants provided more than adequate performance for implementing ubiquitous learning scenarios. As expected, CoAP was generally more resource-efficient. However, practical differences in performance were so small that the choice of which architectural variant to use may depend on nonfunctional requirements such as reliability, security, maximum payload size, and ease of implementation.
Single-shot phase extraction for fringe projection profilometry using deep convolutional generative adversarial network

Abstract This paper presents a single-shot phase extraction approach based on a deep convolutional generative adversarial network that generates a phase map and a quality mask from an input fringe pattern image. A novel loss function is proposed, and a large-scale (28 800 samples) real fringe pattern dataset is collected to train the network. The experiments demonstrate that the proposed method achieves significantly improved phase extraction accuracy and overcomes the main limitations of Fourier transform profilometry. In addition, the proposed method presents excellent performance for real-time computing, reaching approximately 100 f s −1 with a single GPU. Moreover, the proposed learning-based approach can automatically perform denoising and phase extraction, without any manually set parameters.
Towards a novel biometric system for forensic document examination

Biometric systems have demonstrated an important enhancement in writer identification from online handwriting. Indeed, writer identification is still a challenging task in the definition of a set of features able to characterize the different samples of handwriting documents. These samples are not usually stable and show a vast variability from the same writer over time, or from different writers. The ability to identify the documents’ authors provides more chances for using these documents for various purposes and especially in forensic analysis. In this paper, we present a biometric based recognition system for forensic document examination capable of identifying a document's author. This system consists of the preprocessing and the segmentation of online handwriting into a sequence of strokes in a first step. Then, from each stroke, we extract a set of static and dynamic features. Next, all the segments which are composed of two consecutive strokes are categorized into groups and subgroups according to their position and their geometric characteristics. Finally, Deep Neural Network is used as a classifier. Experiments which are conducted on IBM_UB_1, IAM OnDB, NLPR Handwriting and ADAB databases, demonstrate that the proposed system outperforms the existing writer identification systems on Latin and Arabic scripts. This justifies its usefulness in forensic examination of handwriting.
A Korean named entity recognition method using Bi-LSTM-CRF and masked self-attention

Named entity recognition (NER) is a fundamental task in natural language processing. The existing Korean NER methods use the Korean morpheme, syllable sequence, and part-of-speech as features, and use a sequence labeling model to tackle this problem. In Korean, on one hand, morpheme itself contains strong indicative information of named entity (especially for time and person). On the other hand, the context of the target morpheme plays an important role in recognizing the named entity(NE) tag of the target morpheme. To make full use of these two features, we propose two auxiliary tasks. One of them is the morpheme-level NE tagging task which will capture the NE feature of syllable sequence composing morpheme. The other one is the context-based NE tagging task which aims to capture the context feature of target morpheme through the masked self-attention network. These two tasks are jointly trained with Bi-LSTM-CRF NER Tagger. The experimental results on Klpexpo 2016 corpus and Naver NLP Challenge 2018 corpus show that our model outperforms the strong baseline systems and achieves the state of the art.