Reviewerly/exHarmony huggingface dataset:
University Teachers’ Digital Competence: A Case Study from Portugal

It has become increasingly important to train for an educational culture that makes use of digital technology to enhance its practices. Frameworks such as DigCompEdu enable identification of weaknesses and areas which require more training. This paper presents the results of a case study with 249 participants from a Portuguese University examining differences in the digital competence of teachers according to gender, age, faculty and experience. Based on the data obtained, it was found that there were no significant differences and that it was, therefore, more important to consider training for digital empowerment according to the different areas of the DigCompEdu framework and to address weaknesses found in each of these areas.
Measurement and control of a superconducting quantum processor with a fully integrated radio-frequency system on a chip

We describe a digital microwave platform called Presto, designed for measurement and control of multiple quantum bits (qubits) and based on the third-generation radio-frequency system on a chip. Presto uses direct digital synthesis to create signals up to 9 GHz on 16 synchronous output ports, while synchronously analyzing responses on 16 input ports. Presto has 16 DC-bias outputs, four inputs and four outputs for digital triggers or markers, and two continuous-wave outputs for synthesizing frequencies up to 15 GHz. Scaling to a large number of qubits is enabled through deterministic synchronization of multiple Presto units. A Python application programming interface configures a firmware for synthesis and analysis of pulses, coordinated by an event sequencer. The analysis integrates template matching (matched filtering) and low-latency (184-254 ns) feedback to enable a wide range of multi-qubit experiments. We demonstrate Presto's capabilities with experiments on a sample consisting of two superconducting qubits connected via a flux-tunable coupler. We show single-shot readout and active reset of a single qubit; randomized benchmarking of single-qubit gates showing 99.972% fidelity, limited by the coherence time of the qubit; and calibration of a two-qubit iSWAP gate.
Segmentation with mixed supervision: Confidence maximization helps knowledge distillation

Despite achieving promising results in a breadth of medical image segmentation tasks, deep neural networks (DNNs) require large training datasets with pixel-wise annotations. Obtaining these curated datasets is a cumbersome process which limits the applicability of DNNs in scenarios where annotated images are scarce. Mixed supervision is an appealing alternative for mitigating this obstacle. In this setting, only a small fraction of the data contains complete pixel-wise annotations and other images have a weaker form of supervision, e.g., only a handful of pixels are labeled. In this work, we propose a dual-branch architecture, where the upper branch (teacher) receives strong annotations, while the bottom one (student) is driven by limited supervision and guided by the upper branch. Combined with a standard cross-entropy loss over the labeled pixels, our novel formulation integrates two important terms: (i) a Shannon entropy loss defined over the less-supervised images, which encourages confident student predictions in the bottom branch; and (ii) a Kullback–Leibler (KL) divergence term, which transfers the knowledge (i.e., predictions) of the strongly supervised branch to the less-supervised branch and guides the entropy (student-confidence) term to avoid trivial solutions. We show that the synergy between the entropy and KL divergence yields substantial improvements in performance. We also discuss an interesting link between Shannon-entropy minimization and standard pseudo-mask generation, and argue that the former should be preferred over the latter for leveraging information from unlabeled pixels. We evaluate the effectiveness of the proposed formulation through a series of quantitative and qualitative experiments using two publicly available datasets. Results demonstrate that our method significantly outperforms other strategies for semantic segmentation within a mixed-supervision framework, as well as recent semi-supervised approaches. Moreover, in line with recent observations in classification, we show that the branch trained with reduced supervision and guided by the top branch largely outperforms the latter. Our code is publicly available: https://github.com/by-liu/ConfKD.
Adaptive fairness improvement based on causality analysis

Given a discriminating neural network, the problem of fairness improvement is to systematically reduce discrimination without significantly scarifies its performance (i.e., accuracy). Multiple categories of fairness improving methods have been proposed for neural networks, including pre-processing, in-processing and post-processing. Our empirical study however shows that these methods are not always effective (e.g., they may improve fairness by paying the price of huge accuracy drop) or even not helpful (e.g., they may even worsen both fairness and accuracy). In this work, we propose an approach which adaptively chooses the fairness improving method based on causality analysis. That is, we choose the method based on how the neurons and attributes responsible for unfairness are distributed among the input attributes and the hidden neurons. Our experimental evaluation shows that our approach is effective (i.e., always identify the best fairness improving method) and efficient (i.e., with an average time overhead of 5 minutes).
SLISEMAP: supervised dimensionality reduction through local explanations

Abstract Existing methods for explaining black box learning models often focus on building local explanations of the models’ behaviour for particular data items. It is possible to create global explanations for all data items, but these explanations generally have low fidelity for complex black box models. We propose a new supervised manifold visualisation method, slisemap , that simultaneously finds local explanations for all data items and builds a (typically) two-dimensional global visualisation of the black box model such that data items with similar local explanations are projected nearby. We provide a mathematical derivation of our problem and an open source implementation implemented using the GPU-optimised PyTorch library. We compare slisemap to multiple popular dimensionality reduction methods and find that slisemap is able to utilise labelled data to create embeddings with consistent local white box models. We also compare slisemap to other model-agnostic local explanation methods and show that slisemap provides comparable explanations and that the visualisations can give a broader understanding of black box regression and classification models.
Containerization for High Performance Computing Systems: Survey and Prospects

Containers improve the efficiency in application deployment and thus have been widely utilised on Cloud and lately in High Performance Computing (HPC) environments.Containers encapsulate complex programs with their dependencies in isolated environments making applications more compatible and portable.Often HPC systems have higher security levels compared to Cloud systems, which restrict users' ability to customise environments.Therefore, containers on HPC need to include a heavy package of libraries making their size relatively large.These libraries usually are specifically optimised for the hardware, which compromises portability of containers.Per contra, a Cloud container has smaller volume and is more portable.Furthermore, containers would benefit from orchestrators that facilitate deployment and management of containers at a large scale.Cloud systems in practice usually incorporate sophisticated container orchestration mechanisms as opposed to HPC systems.Nevertheless, some solutions to enable container orchestration on HPC systems have been proposed in state of the art.This paper gives a survey and taxonomy of efforts in both containerisation and its orchestration strategies on HPC systems.It highlights differences thereof between Cloud and HPC.Lastly, challenges are discussed and the potentials for research and engineering are envisioned.
Robust neural dynamics with adaptive coefficient applied to solve the dynamic matrix square root

Abstract Zeroing neural networks (ZNN) have shown their state-of-the-art performance on dynamic problems. However, ZNNs are vulnerable to perturbations, which causes reliability concerns in these models owing to the potentially severe consequences. Although it has been reported that some models possess enhanced robustness but cost worse convergence speed. In order to address these problems, a robust neural dynamic with an adaptive coefficient (RNDAC) model is proposed, aided by the novel adaptive activation function and robust evolution formula to boost convergence speed and preserve robustness accuracy. In order to validate and analyze the performance of the RNDAC model, it is applied to solve the dynamic matrix square root (DMSR) problem. Related experiment results show that the RNDAC model reliably solves the DMSR question perturbed by various noises. Using the RNDAC model, we are able to reduce the residual error from 10 $$^1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>1</mml:mn> </mml:msup> </mml:math> to 10 $$^{-4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> </mml:math> with noise perturbed and reached a satisfying and competitive convergence speed, which converges within 3 s.
Alternative approach to quantum imaginary time evolution

There is increasing interest in quantum algorithms (QAs) that are based on the imaginary time evolution (ITE), a successful classical numerical approach to obtain ground states. However, most of the proposals so far require heavy postprocessing computational steps on a classical computer, such as solving linear equations. Here we provide an alternative approach to implement ITE. A key feature in our approach is the use of an orthogonal basis set: the propagated state is efficiently expressed in terms of orthogonal basis states at every step of the evolution. We argue that the number of basis states needed at those steps to achieve an accurate solution can be kept on the order of $n$, the number of qubits, by controlling the precision (number of significant digits) and the imaginary time increment. The number of quantum gates per imaginary time step is estimated to be polynomial in $n$. Additionally, while in many QAs the locality of the Hamiltonian is a key assumption, in our algorithm this restriction is not required. This characteristic of our algorithm renders it useful for studying highly nonlocal systems, such as the occupation-representation nuclear shell model. We illustrate our algorithm through numerical implementation on an IBM quantum simulator.
Real-Time Neural Character Rendering with Pose-Guided Multiplane Images

We propose pose-guided multiplane image (MPI) synthesis which can render an animatable character in real scenes with photorealistic quality. We use a portable camera rig to capture the multi-view images along with the driving signal for the moving subject. Our method generalizes the image-to-image translation paradigm, which translates the human pose to a 3D scene representation—MPIs that can be rendered in free viewpoints, using the multi-views captures as supervision. To fully cultivate the potential of MPI, we propose depth-adaptive MPI which can be learned using variable exposure images while being robust to inaccurate camera registration. Our method demonstrates advantageous novel-view synthesis quality over the state-of-the-art approaches for characters with challenging motions. Moreover, the proposed method is generalizable to novel combinations of training poses and can be explicitly controlled. Our method achieves such expressive and animatable character rendering all in real time, serving as a promising solution for practical applications.
VR Facial Animation for Immersive Telepresence Avatars

VR Facial Animation is necessary in applications requiring clear view of the face, even though a VR headset is worn. In our case, we aim to animate the face of an operator who is controlling our robotic avatar system. We propose a real-time capable pipeline with very fast adaptation for specific operators. In a quick enrollment step, we capture a sequence of source images from the operator without the VR headset which contain all the important operator-specific appearance information. During inference, we then use the operator keypoint information extracted from a mouth camera and two eye cameras to estimate the target expression and head pose, to which we map the appearance of a source still image. In order to enhance the mouth expression accuracy, we dynamically select an auxiliary expression frame from the captured sequence. This selection is done by learning to transform the current mouth keypoints into the source camera space, where the alignment can be determined accurately. We, furthermore, demonstrate an eye tracking pipeline that can be trained in less than a minute, a time efficient way to train the whole pipeline given a dataset that includes only complete faces, show exemplary results generated by our method, and discuss performance at the ANA Avatar XPRIZE semifinals.
Enhancing Steganography by Image Segmentation and Multi-level Deep Hiding

In this paper, we present Modify Deep Hiding Extraction Algorithm (MDHEA) that is a steganography algorithm with Multi-Level Steganography (MLS) and color image segmentation. Through experimental results, MDHEA shows improvement in the results of previous works by securing encrypted secret data against attacks. We use segmentation to choose the appropriate segment, pass it on the cover image, calculate the value of the change at the pixel of the segment and select the best segment and its location in the cover image based on the least effect. MDHEA applies multi-level steganography to hide the confidential data in color images to ensure the integrity of the hidden data and obtain the largest volume of hidden data without distorting the image of the stego image. To reduce distortion in the cover image due to hiding a large amount of secret data and obtaining a high-quality stego image after hiding the secret data, we implement the Blue Smoothing Algorithm (BSA) to achieve smoothing the largest possible number of pixels in the image.
Multi-view subspace clustering network with block diagonal and diverse representation

Learning multi-view data, and especially multi-view data clustering, is a popular area in computer vision and pattern recognition. The multi-view subspace clustering has achieved a better clustering quality than the single-view subspace clustering, mainly because of the complementarity of multi-view information. First, for not directly pursuing a block diagonal representation matrix of previous ℓ1 or ℓ2 regularizers in a deep subspace clustering network, a k-block diagonal regularizer is proposed to replace traditional regularizers. This block diagonal representation module is integrated into this multi-view subspace clustering network, and it can improve the clustering quality. Secondly, there exists some redundancy among the representation matrices, and a diverse representation module can be introduced into this network. This can boost the diversity of representation matrices, and make learned representation matrices more discriminative and help improve the clustering performance. In this paper, based on the deep subspace clustering network, we integrate the block diagonal and diverse representation into the network, and a multi-view subspace clustering network with the block diagonal and the diverse representation is proposed. The experimental results on the UCI Digit, Caltech101-20, COIL100 and Caltech101-7 datasets have demonstrated the superior performance of the proposed algorithm over other popular multi-view algorithms.
DomainATM: Domain adaptation toolbox for medical data analysis

Domain adaptation (DA) is an important technique for modern machine learning-based medical data analysis, which aims at reducing distribution differences between different medical datasets. A proper domain adaptation method can significantly enhance the statistical power by pooling data acquired from multiple sites/centers. To this end, we have developed the Domain Adaptation Toolbox for Medical data analysis (DomainATM) – an open-source software package designed for fast facilitation and easy customization of domain adaptation methods for medical data analysis. The DomainATM is implemented in MATLAB with a user-friendly graphical interface, and it consists of a collection of popular data adaptation algorithms that have been extensively applied to medical image analysis and computer vision. With DomainATM, researchers are able to facilitate fast feature-level and image-level adaptation, visualization and performance evaluation of different adaptation methods for medical data analysis. More importantly, the DomainATM enables the users to develop and test their own adaptation methods through scripting, greatly enhancing its utility and extensibility. An overview characteristic and usage of DomainATM is presented and illustrated with three example experiments, demonstrating its effectiveness, simplicity, and flexibility. The software, source code, and manual are available online.
A Multi-Strategy Adaptive Particle Swarm Optimization Algorithm for Solving Optimization Problem

In solving the portfolio optimization problem, the mean-semivariance (MSV) model is more complicated and time-consuming, and their relations are unbalanced because they conflict with each other due to return and risk. Therefore, in order to solve these existing problems, multi-strategy adaptive particle swarm optimization, namely APSO/DU, has been developed to solve the portfolio optimization problem. In the present study, a constraint factor is introduced to control velocity weight to reduce blindness in the search process. A dual-update (DU) strategy is based on new speed, and position update strategies are designed. In order to test and prove the effectiveness of the APSO/DU algorithm, test functions and a realistic MSV portfolio optimization problem are selected here. The results demonstrate that the APSO/DU algorithm has better convergence accuracy and speed and finds the least risky stock portfolio for the same level of return. Additionally, the results are closer to the global Pareto front (PF). The algorithm can provide valuable advice to investors and has good practical applications.
Constructing Multiple CISTs on BCube-Based Data Center Networks in the Occurrence of Switch Failures

The scale of data center networks (DCNs) has grown rapidly with the increasing popularity of cloud computing, data explosion, and the dramatic drop in setup costs. Thus, inevitable component failures (including switches and servers) will become more frequent. A DCN requires maintaining regular and reliable operation and providing efficient routing algorithms for transmitting data between servers. Particularly, fault-tolerant routing is necessary. Recently, constructing completely independent spanning trees (CISTs) on DCNs has received much attention as a dual-CIST (i.e., two CISTs) suffices to configure protection routing, which is a fault-tolerant routing. Moreover, the protection routing can additionally realize a secure mechanism if it is configured by more CISTs. BCube is a server-centric DCN with many advantages, and many variations were deformed from BCube with application requirements, such as RCube and RRect. In this paper, we provide a unified framework called BCube-based DCN (BDCN) that integrates the representation of the logic graphs of DCNs mentioned above, facilitating consistent algorithms’ design. Then, we develop efficient algorithms to construct multiple CISTs on BDCN under the consideration of switch failures. Note that this is the first study that constructs multiple CISTs in DCNs with switch failures. Finally, using standard metrics, such as average path length (APL) and transmission failure rate (TFR), we evaluate the performance of the fault-tolerant routing through experiments.
AQRS: Anti-quantum ring signature scheme for secure epidemic control with blockchain

Epidemics, such as Corona Virus Disease 2019 (COVID-19), have serious consequences globally, of which the most effective way to control the infection is contact tracing. Nowadays, research related to privacy-preserving epidemic infection control has been conducted, nevertheless, current researchers do not regard the authenticity of records and infection facts as well as poor traceability. Moreover, with the emergence of quantum computing, there is a bottleneck in upholding privacy, security and efficiency. Our paper proposes a privacy-preserving epidemic infection control scheme through lattice-based linkable ring signature in blockchain, called AQRS. Firstly, our scheme adopts a blockchain with three ledgers to store information in a distributed manner, which offers transparency and immunity from the Single Point of Failure (SPoF) and Denial of Service (DoS) attacks. Moreover, we design a lattice-based linkable ring signature scheme to secure privacy-preserving of epidemic infection control. Significantly, we are the first to introduce the lattice-based linkable ring signature into privacy preserving in epidemic control scenario. Security analysis indicates that our scheme ensures unconditional users anonymity, record unforgeability, signature linkability, link non-slanderability and contact traceability. Finally, the comprehensive performance evaluation demonstrates that our scheme has an efficient time-consuming, storage consumption and system communication overhead and is practical for epidemic and future pandemic privacy-preserving.
Generating Privacy Preserving Synthetic Medical Data

Due to the recent development in the deep learning community and the availability of state-of-the-art models, medical practitioners are getting more interested in computer vision and deep learning for diagnosis tasks. Moreover, those medical diagnostic models can also increase the reliability of conventional findings. As radiology images can convey a lot of information for a patient’s diagnosis task, the problem is that such medical data may contain sensitive private information in their content header. De-anonymization (i.e., removal of sensitive header information) does not work well due to the re-identification risk, which may link those images to essential details (e.g., birth date, SSN, institution name, etc.), and such an approach can also reduce utility. In the medical domain, utility is significant because a less accurate diagnosis may lead to the wrong course of treatment and/or loss of life. In this paper, we developed a differentially private approach that can generate high-quality and high dimensional synthetic medical image data with guaranteed differential privacy. It can be used to create sufficient quality data to train a deep model. Moreover, we used W-GAN for bounded gradient guarantee, which eliminates the need for an extensive clipping hyperparameter search. We also added noise selectively to the generator to maintain the privacy-utility trade-off. Due to a noise-free discriminator and such selective noise addition to the generator, high-quality and reliable generated radiology images can be utilized for diagnosis tasks. Moreover, our approach can work in a distributed system where different hospitals can contain their private images in the local server and use a central server to generate synthetic radiology images without storing patient data.