Reviewerly/exHarmony huggingface dataset:
Marginalized Augmented Few-Shot Domain Adaptation

Domain adaptation (DA) has recently drawn a lot of attention, as it facilitates unlabeled target learning by borrowing knowledge from an external source domain. Most existing DA solutions seek to align feature representations between the labeled source and unlabeled target data. However, the scarcity of target data easily results in negative transfer, as it misleads the cross DA to the dominance of the source. To address the challenging few-shot domain adaptation (FSDA) problem, in this article, we propose a novel marginalized augmented FSDA (MAF) approach to address the cross-domain distribution disparity and insufficiency of target data simultaneously. On the one hand, cross-domain continuity augmentation (CCA) synthesizes abundant intermediate patterns across domains leading to a continuous domain-invariant latent space. On the other hand, sufficient source-supervised semantic augmentation (SSA) is explored to progressively diversify the conditional distribution within and across domains. Moreover, the proposed augmentation strategies are implemented efficiently via an expected transferable cross-entropy (CE) loss over the augmented distribution instead of explicit data synthesis, and minimizing the upper bound of the expected loss introduces negligible extra computing cost. Experimentally, our method outperforms the state of the art in various FSDA benchmarks, which demonstrates the effectiveness and contribution of our work. Our source code is provided at https://github.com/scottjingtt/MAF.git.
A systematic literature review of blockchain-based e-KYC systems

The know your customer (KYC) guidelines in financial services require that institutions make an effort to verify the identity, suitability, and assess risks involved while maintaining a business relationship. The procedures fit within the broader scope of any financial institution’s Anti-Money Laundering (AML) policy. Governments around the world have digitalised this process to make it more convenient and transparent by introducing the notion of e-KYC (Electronic KYC). e-KYC provides a flexibility for the users as they might even quickly complete the on-boarding process from the comfort of their homes. However, there are a few outstanding issues, such as the lack of any global standardisation, possible fraudulent activities during the e-KYC process and other privacy concerns, that must be addressed before its full benefits can be achieved. Recently, blockchain technology (or blockchain in short) has emerged as a foundational technology with possibilities to disrupt a wide range of application domains. Understandably, it is increasingly being investigated how blockchain can be a useful tool to address these outstanding issues. Towards this aim, a number of research works have emerged in the recent years. In this article, we present a systematic literature review (SLR) of these works using the PRISMA model in order to identify and critically analyse the existing researches at the intersection of e-KYC and blockchain. Based on our study we have also identified the limitations of the existing solutions and provided future directions.
Multi-Branch and Progressive Network for Low-Light Image Enhancement

Low-light images incur several complicated degradation factors such as poor brightness, low contrast, color degradation, and noise. Most previous deep learning-based approaches, however, only learn the mapping relationship of single channel between the input low-light images and the expected normal-light images, which is insufficient enough to deal with low-light images captured under uncertain imaging environment. Moreover, too deeper network architecture is not conducive to recover low-light images due to extremely low values in pixels. To surmount aforementioned issues, in this paper we propose a novel multi-branch and progressive network (MBPNet) for low-light image enhancement. To be more specific, the proposed MBPNet is comprised of four different branches which build the mapping relationship at different scales. The followed fusion is performed on the outputs obtained from four different branches for the final enhanced image. Furthermore, to better handle the difficulty of delivering structural information of low-light images with low values in pixels, a progressive enhancement strategy is applied in the proposed method, where four convolutional long short-term memory networks (LSTM) are embedded in four branches and an recurrent network architecture is developed to iteratively perform the enhancement process. In addition, a joint loss function consisting of the pixel loss, the multi-scale perceptual loss, the adversarial loss, the gradient loss, and the color loss is framed to optimize the model parameters. To evaluate the effectiveness of proposed MBPNet, three popularly used benchmark databases are used for both quantitative and qualitative assessments. The experimental results confirm that the proposed MBPNet obviously outperforms other state-of-the-art approaches in terms of quantitative and qualitative results. The code will be available at https://github.com/kbzhang0505/MBPNet.
A southeast Asian perspective on hotel service robots: Trans diagnostic mechanics and conditional indirect effects

Previous studies have demonstrated how Europeans perceive service robots, but there is a lack of empirical evidence for the same in Asians, particularly from cross-national studies with a substantial sample size. In this study, we adopt a transdiagnostic approach and analyse the disorder caused by hotel service robots (HSR) in the hotel industry using a sample of 1311 respondents from an Asian context. This approach has traditionally been used in psychiatry, and we provide a significant discovery from a Southeast Asian perspective by employing the same methodology.Through the adoption of the methodology used in this study, we recognise that prior research on hotel services has produced uncertain findings, likely due to the presence of multiple service situations. Furthermore, this study is notable because it introduces a methodology that has traditionally been utilized in psychiatry to the field of human-robot management for the first time. Two situational experiments have shown that customers have a preference for highly human-like hotel service robots (HSR), and they believe that these robots perform better in situations where they perceive a high level of control. The effects are only significant in social situations and are reversed when there is a lower perceived level of control. The study's high level of novelty arises from the fact that the effect is absent in luxury hotels within the geographic boundaries of the investigation. These results provide a new outlook on how humans and robots perceive the acceptance of HSR in the hotel industry. Professionals, academics, and hoteliers who are evaluating the decision to implement robots that could either enhance or diminish their service standards may find this study to be valuable.
Understanding Version Control as Material Interaction with Quickpose

Whether a programmer with code or a potter with clay, practitioners engage in an ongoing process of working and reasoning with materials. Existing discussions in HCI have provided rich accounts of these practices and processes, which we synthesize into three themes: (1) reciprocal discovery of goals and materials, (2) local knowledge of materials, and (3) annotation for holistic interpretation. We then apply these design principles generatively to the domain of version control to present Quickpose: a version control system for creative coding. In an in-situ, longitudinal study of Quickpose guided by our themes, we collected usage data, version history, and interviews. Our study explored our participants' material interaction behaviors and the initial promise of our proposed measures for recognizing these behaviors. Quickpose is an exploration of version control as material interaction, using existing discussions to inform domain-specific concepts, measures, and designs for version control systems.
Adaptive HEVC Video Steganography With High Performance Based on Attention-Net and PU Partition Modes

With the increasing popularity of digital video, video steganography has become a hot research topic in the field of covert communication and privacy protection. The existing prediction unit (PU) based video steganography often tends to result in large bit rate increase, which is also easily noticeable to the steganography analyst. To solve this problem, an adaptive steganography for HEVC video based on attention-net and PU partition modes is proposed. First, the distortion of modified PUs is analyzed from the perspective of rate distortion optimization at the group of pictures (GOP) level, and we find that modifying PU will lead to distortion accumulation and abnormal bitrate increase. Therefore, an adaptive distortion function based on the improved rate distortion cost is designed, and the embedding distortion is minimized by using Syndrome-Trellis Code (STC) steganography coding. Meanwhile, a super-resolution convolutional neural network with non-local sparse attention-net filter is proposed to replace the in-loop filter in HEVC to reconstruct the reference frame, thereby reducing the bitrate cost and improving the visual quality of stego-video. Experimental results show that the proposed algorithm can achieve superior perceptual quality and bitrate performance comparing with the sate-of-the-art works.
CSAST: Content self-supervised and style contrastive learning for arbitrary style transfer

Arbitrary artistic style transfer has achieved great success with deep neural networks, but it is still difficult for existing methods to tackle the dilemma of content preservation and style translation due to the inherent content-and-style conflict. In this paper, we introduce content self-supervised learning and style contrastive learning to arbitrary style transfer for improved content preservation and style translation, respectively. The former one is based on the assumption that stylization of a geometrically transformed image is perceptually similar to applying the same transformation to the stylized result of the original image. This content self-supervised constraint noticeably improves content consistency before and after style translation, and contributes to reducing noises and artifacts as well. Furthermore, it is especially suitable to video style transfer, due to its ability to promote inter-frame continuity, which is of crucial importance to visual stability of video sequences. For the latter one, we construct a contrastive learning that pull close style representations (Gram matrices) of the same style and push away that of different styles. This brings more accurate style translation and more appealing visual effect. A large number of qualitative and quantitative experiments demonstrate superiority of our method in improving arbitrary style transfer quality, both for images and videos.
Topology-preserving transfer learning for weakly-supervised anomaly detection and segmentation

Models pre-trained on the ImageNet dataset are introduced to be exploited for knowledge transfer in numerous downstream computer vision tasks, including the weakly-supervised anomaly detection and segmentation area. Specifically, in anomaly segmentation, the former study shows that representing images with feature maps extracted by pre-trained models significantly improves over previous techniques. This kind of representation method requires both high-quality and task-specific features, but feature extractors obtained from ImageNet directly are very general. One intuition for obtaining stronger features is by transferring a pre-trained model to the target dataset. However, in this paper, we show that under weakly-supervised settings, naïve fine-tune techniques that typically work for supervised learning can lead to catastrophic feature space collapse and reduce performance greatly. Thus, we propose to apply a topology-preserving constraint during transferring. Our method preserves the topology graph to keep the feature space from collapsing under weakly-supervised settings. And then we combine the transferred model with a simple anomaly detection and segmentation baseline for performance evaluation. The experiments show that our method achieves competitive accuracy on several benchmarks meanwhile setting a new state-of-the-art for anomaly detection on CIFAR100/10 and BTAD datasets.
Multistage Spatial Context Models for Learned Image Compression

Recent state-of-the-art Learned Image Compression methods feature spatial context models, achieving great rate-distortion improvements over hyperprior methods. However, the autoregressive context model requires serial decoding, limiting run-time performance. The Checkerboard context model allows parallel decoding at a cost of reduced RD performance. We present a series of multistage spatial context models allowing both fast decoding and better RD performance. We split the latent space into square patches and decode serially within each patch while different patches are decoded in parallel. The proposed method features a comparable decoding speed to Checkerboard while reaching the RD performance of Autoregressive and even also outperforming Autoregressive. Inside each patch, the decoding order must be carefully decided as a bad order negatively impacts performance; therefore, we also propose a decoding order optimization algorithm.
Uncertainty-Aware Multi-Dimensional Mutual Learning for Brain and Brain Tumor Segmentation

Existing segmentation methods for brain MRI data usually leverage 3D CNNs on 3D volumes or employ 2D CNNs on 2D image slices. We discovered that while volume-based approaches well respect spatial relationships across slices, slice-based methods typically excel at capturing fine local features. Furthermore, there is a wealth of complementary information between their segmentation predictions. Inspired by this observation, we develop an Uncertainty-aware Multi-dimensional Mutual learning framework to learn different dimensional networks simultaneously, each of which provides useful soft labels as supervision to the others, thus effectively improving the generalization ability. Specifically, our framework builds upon a 2D-CNN, a 2.5D-CNN, and a 3D-CNN, while an uncertainty gating mechanism is leveraged to facilitate the selection of qualified soft labels, so as to ensure the reliability of shared information. The proposed method is a general framework and can be applied to varying backbones. The experimental results on three datasets demonstrate that our method can significantly enhance the performance of the backbone network by notable margins, achieving a Dice metric improvement of 2.8% on MeniSeg, 1.4% on IBSR, and 1.3% on BraTS2020.
METHODS AND ALGORITHMS FOR THE FORMATION OF DISTANCE EDUCATION SYSTEMS BASED ON BLOCKCHAIN AND ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN THE DIGITAL ECONOMY

Currently, artificial intelligence technologies are rapidly entering many areas of our society. It should be said that currently several reforms are being implemented in the Republic of Uzbekistan for the development of artificial intelligence technologies. In particular, the decision of the President of the Republic of Uzbekistan "On measures to create conditions for the rapid introduction of artificial intelligence technologies" dated February 17, 2021, No. PQ-4996 defines the main tasks in this regard. One of the most pressing issues studied by modern researchers is the issues related to the topic of digitalization of the educational process. In this article, the stages of development of artificial intelligence, and its application in the field of modern education are presented. The potential of artificial intelligence in education is also considered. At the same time, the processes of intellectual education are modeled mathematically. Algorithms of intellectual educational processes are proposed using CCN, and SHAP algorithms for organizing intellectual educational processes.Also, issues of ensuring the immutability and confidentiality of student evaluation results on distance education platforms, a strategy for ensuring the security of student test and control data using blockchain technology are presented.
Universal crossover in quantum circuits governed by a proximate classical error correction transition

We formulate a semiclassical circuit model to clarify the role of quantum entanglement in the recently discovered encoding phase transitions in quantum circuits with measurements. As a starting point, we define a random circuit model with nearest neighbor classical gates interrupted by erasure errors. In analogy with the quantum setting, this system undergoes a purification transition at a critical error rate above which the classical information entropy in the output state vanishes. We show that this phase transition is in the directed percolation universality class, consistent with the fact that having zero entropy is an absorbing state of the dynamics; this classical circuit cannot generate entropy. Adding an arbitrarily small density of quantum gates in the presence of errors eliminates the transition by destroying the absorbing state: the quantum gates generate internal entanglement, which can be effectively converted to classical entropy by the errors. We describe the universal properties of this instability in an effective model of the semiclassical circuit. Our model highlights the crucial differences between information dynamics in classical and quantum circuits.
Linear Optical Quantum Computation with Frequency-Comb Qubits and Passive Devices

We propose a linear optical quantum computation scheme using time-frequency degree of freedom. In this scheme, a qubit is encoded in single-photon frequency combs, and manipulation of the qubits is performed using time-resolving detectors, beam splitters, and optical interleavers. This scheme does not require active devices such as high-speed switches and electro-optic modulators and is robust against temporal and spectral errors, which are mainly caused by the detectors' finite resolution. We show that current technologies almost meet the requirements for fault-tolerant quantum computation.
Expert elicitation and data noise learning for material flow analysis using Bayesian inference

Abstract Bayesian inference allows the transparent communication and systematic updating of model uncertainty as new data become available. When applied to material flow analysis (MFA), however, Bayesian inference is undermined by the difficulty of defining proper priors for the MFA parameters and quantifying the noise in the collected data. We start to address these issues by first deriving and implementing an expert elicitation procedure suitable for generating MFA parameter priors. Second, we propose to learn the data noise concurrent with the parametric uncertainty. These methods are demonstrated using a case study on the 2012 US steel flow. Eight experts are interviewed to elicit distributions on steel flow uncertainty from raw materials to intermediate goods. The experts' distributions are combined and weighted according to the expertise demonstrated in response to seeding questions. These aggregated distributions form our model parameters' informative priors. Sensible, weakly informative priors are adopted for learning the data noise. Bayesian inference is then performed to update the parametric and data noise uncertainty given MFA data collected from the United States Geological Survey and the World Steel Association. The results show a reduction in MFA parametric uncertainty when incorporating the collected data. Only a modest reduction in data noise uncertainty was observed using 2012 data; however, greater reductions were achieved when using data from multiple years in the inference. These methods generate transparent MFA and data noise uncertainties learned from data rather than pre‐assumed data noise levels, providing a more robust basis for decision‐making that affects the system.
Research data services (RDS) in Spanish academic libraries

Research data services (RDS) are actions undertaken to provide researchers with support, advising, training, information, a technological infrastructure and a regulatory framework with respect to research data management (RDM) throughout the life cycle of the data. The implementation of RDS is an activity that is becoming increasingly important within the functions carried out by academic and research libraries. The aim of this study is to provide a snapshot of the research data services implemented in academic libraries in public universities in Spain, identifying the number and types of services deployed. We conducted a Website Content Analysis of the libraries of the 48 Spanish public universities to discover which RDS they are implementing and promoting. The results show that, despite a lack of institutional policies regarding research data, libraries are deploying these new services, albeit unevenly in terms of both the number of services and the type of support provided. Services related to development of the data management plan (DMP) and preservation of the data in repositories predominate. This inventory is presented as a first step towards learning the level of RDS implementation in Spanish academic libraries, providing a partial perspective and leading the way to exploring other aspects in greater depth in future studies.
Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy

A large amount of personal health data that is highly valuable to the scientific community is still not accessible or requires a lengthy request process due to privacy concerns and legal restrictions. As a solution, synthetic data has been studied and proposed to be a promising alternative to this issue. However, generating realistic and privacy-preserving synthetic personal health data retains challenges such as simulating the characteristics of the patients’ data that are in the minority classes, capturing the relations among variables in imbalanced data and transferring them to the synthetic data, and preserving individual patients’ privacy. In this paper, we propose a differentially private conditional Generative Adversarial Network model (DP-CGANS) consisting of data transformation, sampling, conditioning, and network training to generate realistic and privacy-preserving personal data. Our model distinguishes categorical and continuous variables and transforms them into latent space separately for better training performance. We tackle the unique challenges of generating synthetic patient data due to the special data characteristics of personal health data. For example, patients with a certain disease are typically the minority in the dataset and the relations among variables are crucial to be observed. Our model is structured with a conditional vector as an additional input to present the minority class in the imbalanced data and maximally capture the dependency between variables. Moreover, we inject statistical noise into the gradients in the networking training process of DP-CGANS to provide a differential privacy guarantee. We extensively evaluate our model with state-of-the-art generative models on personal socio-economic datasets and real-world personal health datasets in terms of statistical similarity, machine learning performance, and privacy measurement. We demonstrate that our model outperforms other comparable models, especially in capturing the dependence between variables. Finally, we present the balance between data utility and privacy in synthetic data generation considering the different data structures and characteristics of real-world personal health data such as imbalanced classes, abnormal distributions, and data sparsity.