Reviewerly/exHarmony huggingface dataset:
SRflow: Deep learning based super-resolution of 4D-flow MRI data

Exploiting 4D-flow magnetic resonance imaging (MRI) data to quantify hemodynamics requires an adequate spatio-temporal vector field resolution at a low noise level. To address this challenge, we provide a learned solution to super-resolve in vivo 4D-flow MRI data at a post-processing level. We propose a deep convolutional neural network (CNN) that learns the inter-scale relationship of the velocity vector map and leverages an efficient residual learning scheme to make it computationally feasible. A novel, direction-sensitive, and robust loss function is crucial to learning vector-field data. We present a detailed comparative study between the proposed super-resolution and the conventional cubic B-spline based vector-field super-resolution. Our method improves the peak-velocity to noise ratio of the flow field by 10 and 30% for in vivo cardiovascular and cerebrovascular data, respectively, for 4 × super-resolution over the state-of-the-art cubic B-spline. Significantly, our method offers 10x faster inference over the cubic B-spline. The proposed approach for super-resolution of 4D-flow data would potentially improve the subsequent calculation of hemodynamic quantities.
Multi-task learning for toxic comment classification and rationale extraction

Social media content moderation is the standard practice as on today to promote healthy discussion forums. Toxic span prediction is helpful for explaining the toxic comment classification labels, thus is an important step towards building automated moderation systems. The relation between toxic comment classification and toxic span prediction makes joint learning objective meaningful. We propose a multi-task learning model using ToxicXLMR for bidirectional contextual embeddings of input text for toxic comment classification, and a Bi-LSTM CRF layer for toxic span or rationale identification. To enable multi-task learning in this domain, we have curated a dataset from Jigsaw and Toxic span prediction datasets. The proposed model outperformed the single task models on the curated and toxic span prediction datasets with 4% and 2% improvement for classification and rationale identification, respectively. We investigated the domain adaptation ability of the proposed MTL model on HASOC and OLID datasets that contain the out of domain text from Twitter and found a 3% improvement in the F1 score over single task models.
Adaptive behavioral model learning for software product lines

Behavioral models enable the analysis of the functionality of software product lines (SPL), e.g., model checking and model-based testing. Model learning aims to construct behavioral models. Due to the commonalities among the products of an SPL, it is possible to reuse the previously-learned models during the model learning process. In this paper, an adaptive approach, called PL*, for learning the product models of an SPL is presented based on the well-known L* algorithm. In this method, after learning each product, the sequences in the final observation table are stored in a repository which is used to initialize the observation table of the remaining products. The proposed algorithm is evaluated on two open-source SPLs and the learning cost is measured in terms of the number of rounds, resets, and input symbols. The results show that for complex SPLs, the total learning cost of PL* is significantly lower than that of the non-adaptive method in terms of all three metrics. Furthermore, it is observed that the order of learning products affects the efficiency of PL*. We introduce a heuristic to determine an ordering which reduces the total cost of adaptive learning.
Some experience in maintenance of an academic cloud

The article is devoted to the systematization of experience in the deployment, maintenance and servicing of the private academic cloud. The article contains model of the authors’ cloud infrastructure. It was developed at Ternopil Volodymyr Hnatiuk National Pedagogical University (Ukraine) on the basis of the Apache CloudStack platform. The authors identify the main tasks for maintaining a private academic cloud. Here they are making changes to the cloud infrastructure; maintenance of virtual machines (VM) to determine the performance and migration of VM instances; work with VMs; backup of all cloud infrastructure. The analysis of productivity and providing students with computing resources is carried out. The main types of VM used in training are given. The number and characteristics of VM that can be served by a private academic cloud are calculated. Approaches and schemes for performing backup are analysed. Some theoretical and practical experience of using cloud services to perform backup has been studied. Several scripts have been developed for archiving the platform database and its repositories. They allow you to upload backups to the Google Drive cloud service. The performance of these scripts for the author’s deployment of private cloud infrastructure was evaluated.
Methodology of using mobile Internet devices in the process of biology school course studying

This paper considers the problem of using mobile Internet devices in the process of biology studying in secondary schools. It has been examined how well the scientific problem is developed in pedagogical theory and educational practice. The methodology of using mobile Internet devices in the process of biology studying in a basic school, which involves the use of the Play Market server applications, Smart technologies and a website, has been created. After the analyses of the Play Market server content, there have been found several free of charge applications, which can be used while studying biology in a basic school. Among them are the following: Anatomy 4D, Animal 4D+, Augmented Reality Dinosaurs – my ARgalaxy, BioInc – Biomedical Plague, Plan+Net. Their choice is caused by the specifics of the object of biological cognition (life in all its manifestations) and the concept of bio(eco)centrism, which recognizes the life of any living system as the highest value. The paper suggests the original approach for homework checking, which involves besides computer control of students’ learning outcomes, the use of Miracast wireless technology. This demands the owning of a smartphone, a multimedia projector, and a Google Chromecast type adapter. The methodology of conducting a mobile front-line survey at the lesson on the learned or current material in biology in the test form, with the help of the free Plickers application, has been presented. The expediency of using the website builder Ucoz.ua for creation of a training website in biology has been substantiated. The methodology of organizing the educational process in biology in a basic school using the training website has been developed. Recommendations for using a biology training website have been summarized. According to the results of the forming experiment, the effectiveness of the proposed methodology of using mobile Internet devices in the process of biology studying in a basic school has been substantiated.
Auto Checker of Higher Mathematics – an element of mobile cloud education

We analyzed the main cloud services in the article. We also described the main contribution of mobile cloud technology to education. The article presents the author’s development from the field of mobile cloud education in higher mathematics. The design architecture of this application is described in detail: QR generator and scanner, authorization, sending tasks. Block diagrams and images are presented that clearly demonstrate the operation of the application. We showed an example of solving the integral from the section of integral calculus for higher mathematics and showed how to download the answer in the form of a QR code and find out whether it is correct or incorrect (this can be seen by the color on the smart phone screen). It is shown how this technology helps the teacher save time for checking assignments completed by students. This confirms its effectiveness. Such an application provides students and teachers with the ability to store and process data on a cloud computing platform.
A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting

The randomness and volatility of solar irradiance pose a challenge to efficient solar energy development and utilization across the world, which increases the necessity of developing an efficient solar irradiance forecasting model. However, previous studies rarely emphasized the importance of transfer learning and nonlinear feature selection, especially for the studies associated with newly-built photovoltaic plants. Moreover, multi-step ahead forecasting research is limited, despite its significance to dispatching efficiency improvement of photovoltaic system. Aiming to address the research gaps, a novel hybrid deep learning framework (HDLF), consisting of the modules of feature selection, feature convolution, forecasting, self-attention, transfer learning, performance evaluation, and performance analysis, is newly proposed in this paper to perform multi-step ahead solar irradiation forecasting. Specifically, the HDLF is pre-trained based on the studied datasets of global horizontal irradiation in the source domains, and the abstract information obtained from pre-training is transferred to the HDLF of the target domain to enhance its performance. To validate the effectiveness of the proposed HDLF, two simulation experiments are carried out based on the datasets from California, USA, with a resolution of 1 h. The corresponding experimental results from the modules of performance evaluation and performance analysis indicate that the maximum improvements in mean absolute error reach 80.19% and 35.67% in two experiments, respectively, thereby confirming the superiority and feasibility of the HDLF.
A plain-image correlative semi-selective medical image encryption algorithm using enhanced 2D-logistic map

Modern medical examinations have produced a large number of medical images. It is a great challenge to transmit and store them quickly and securely. Existing solutions mainly use medical image encryption algorithms, but these encryption algorithms, which were developed for ordinary images, are time-consuming and must cope with insufficient security considerations when encrypting medical images. Compared with ordinary images, medical images can be divided into the region of interest and the region of background. In this paper, based on this characteristic, a plain-image correlative semi-selective medical image encryption algorithm using the enhanced two dimensional Logistic map was proposed. First, the region of interest of a plain medical image is permuted at the pixel level, then for the whole medical image, substitution is performed pixel by pixel. An ideal compromise between encryption speed and security can be achieved by full-encrypting the region of interest and semi-encrypting the region of background. Several main types of medical images and some normal images were selected as the samples for simulation, and main image cryptanalysis methods were used to analyze the results. The results showed that the cipher-images have a good visual quality, high information entropy, low correlation between adjacent pixels, as well as uniformly distribute histogram. The algorithm is sensitive to the initial key and plain-image, and has a large keyspace and low time complexity. The time complexity is lower when compared with the current medical image full encryption algorithm, and the security performance is better when compared with the current medical image selective encryption algorithm.
SSDeN: Framework for Screen-Shooting Resilient Watermarking via Deep Networks in the Frequency Domain

Mobile devices have been increasingly used to take pictures without leaving a trace. However, the application system can lead to confidential information leaks. A framework for screen-shooting-resilient watermarking via deep networks (SSDeN) in the frequency domain is put forward in this study to solve this problem. The proposed framework can extract the watermark from the leaked photo for copyright protection. SSDeN is an end-to-end process that combines convolutional neural network (CNN) with residual block to embed and extract watermarks in the DCT domain. We simulate some screen-shooting attacks to ensure the networks embed the watermark robustly. Our framework achieves the state-of-the-art performance on existing learning architectures for screen-shooting-resilient watermarking.
Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms

Blind watermarking provides powerful evidence for copyright protection, image authentication, and tampering identification.However, it remains a challenge to design a watermarking model with high imperceptibility and robustness against strong noise attacks. To resolve this issue, we present a framework Combining the Invertible and Non-invertible (CIN) mechanisms. The CIN is composed of the invertible part to achieve high imperceptibility and the non-invertible part to strengthen the robustness against strong noise attacks. For the invertible part, we develop a diffusion and extraction module (DEM) and a fusion and split module (FSM) to embed and extract watermarks symmetrically in an invertible way. For the non-invertible part, we introduce a non-invertible attention-based module (NIAM) and the noise-specific selection module (NSM) to solve the asymmetric extraction under a strong noise attack. Extensive experiments demonstrate that our framework outperforms the current state-of-the-art methods of imperceptibility and robustness significantly. Our framework can achieve an average of 99.99% accuracy and 67.66 dB PSNR under noise-free conditions, while 96.64% and 39.28 dB combined strong noise attacks. The code will be available in https://github.com/RM1110/CIN.
Programmable low-power consumption all-optical nonlinear activation functions using a micro-ring resonator with phase-change materials

A programmable hardware implementation of all-optical nonlinear activation functions for different scenarios and applications in all-optical neural networks is essential. We demonstrate a programmable, low-loss all-optical activation function device based on a silicon micro-ring resonator loaded with phase change materials. Four different nonlinear activation functions of Relu, ELU, Softplus and radial basis functions are implemented for incident signal light of the same wavelength. The maximum power consumption required to switch between the four different nonlinear activation functions in calculation is only 1.748 nJ. The simulation of classification of hand-written digit images also shows that they can perform well as alternative nonlinear activation functions. The device we design can serve as nonlinear units in photonic neural networks, while its nonlinear transfer function can be flexibly programmed to optimize the performance of different neuromorphic tasks.
Continuous-Mixture Autoregressive Networks Learning the Kosterlitz-Thouless Transition

We develop deep autoregressive networks with multi channels to compute many-body systems with continuous spin degrees of freedom directly. As a concrete example, we demonstrate the two-dimensional XY model with the continuous-mixture networks and rediscover the Kosterlitz–Thouless (KT) phase transition on a periodic square lattice. Vortices characterizing the quasi-long range order are accurately detected by the generative model. By learning the microscopic probability distributions from the macroscopic thermal distribution, the networks are trained as an efficient physical sampler which can approximate the free energy and estimate thermodynamic observables unbiasedly with importance sampling. As a more precise evaluation, we compute the helicity modulus to determine the KT transition temperature. Although the training process becomes more time-consuming with larger lattice sizes, the training time remains unchanged around the KT transition temperature. The continuous-mixture autoregressive networks we developed thus can be potentially used to study other many-body systems with continuous degrees of freedom.
Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers

Classification of different cancer types is an essential step in designing a decision support model for early cancer predictions. Using various machine learning (ML) techniques with ensemble learning is one such method used for classifications. In the present study, various ML algorithms were explored on twenty exome datasets, belonging to 5 cancer types. Initially, a data clean-up was carried out on 4181 variants of cancer with 88 features, and a derivative dataset was obtained using natural language processing and probabilistic distribution. An exploratory dataset analysis using principal component analysis was then performed in 1 and 2D axes to reduce the high-dimensionality of the data. To significantly reduce the imbalance in the derivative dataset, oversampling was carried out using SMOTE. Further, classification algorithms such as K-nearest neighbour and support vector machine were used initially on the oversampled dataset. A 4-layer artificial neural network model with 1D batch normalization was also designed to improve the model accuracy. Ensemble ML techniques such as bagging along with using KNN, SVM and MLPs as base classifiers to improve the weighted average performance metrics of the model. However, due to small sample size, model improvement was challenging. Therefore, a novel method to augment the sample size using generative adversarial network (GAN) and triplet based variational auto encoder (TVAE) was employed that reconstructed the features and labels generating the data. The results showed that from initial scrutiny, KNN showed a weighted average of 0.74 and SVM 0.76. Oversampling ensured that the accuracy of the derivative dataset improved significantly and the ensemble classifier augmented the accuracy to 82.91%, when the data was divided into 70:15:15 ratio (training, test and holdout datasets). The overall evaluation metric value when GAN and TVAE increased the sample size was found to be 0.92 with an overall comparison model of 0.66. Therefore, the present study designed an effective model for classifying cancers which when implemented to real world samples, will play a major role in early cancer diagnosis.
Semiquantum secret sharing by using χ-type states

In this paper, a semiquantum secret sharing (SQSS) protocol based on x-type states is proposed, which can accomplish the goal that only when two classical communicants cooperate together can they extract the shared secret key of a quantum communicant. Detailed security analysis turns out that this protocol is completely robust against an eavesdropper. This protocol has some merits: (1) it only requires one kind of quantum entangled state as the initial quantum resource; (2) it doesn't employ quantum entanglement swapping or unitary operations; and (3) it needn't share private keys among different participants beforehand.
An Algebraic Framework for Silent Preprocessing with Trustless Setup and Active Security

Recently, number-theoretic assumptions including DDH, DCR and QR have been used to build powerful tools for secure computation, in the form of homomorphic secret-sharing (HSS), which leads to secure two-party computation protocols with succinct communication, and pseudorandom correlation functions (PCFs), which allow non-interactive generation of a large quantity of correlated randomness. In this work, we present a group-theoretic framework for these classes of constructions, which unifies their approach to computing distributed discrete logarithms in various groups. We cast existing constructions in our framework, and also present new constructions, including one based on class groups of imaginary quadratic fields. This leads to the first construction of two-party homomorphic secret sharing for branching programs from class group assumptions. Using our framework, we also obtain pseudorandom correlation functions for generating oblivious transfer and vector-OLE correlations from number-theoretic assumptions. These have a trustless, public-key setup when instantiating our framework using class groups. Previously, such constructions either needed a trusted setup in the form of an RSA modulus with unknown factorisation, or relied on multi-key fully homomorphic encryption from the learning with errors assumption. We also show how to upgrade our constructions to achieve active security using appropriate zero-knowledge proofs. In the random oracle model, this leads to a one-round, actively secure protocol for setting up the PCF, as well as a 3-round, actively secure HSS-based protocol for secure two-party computation of branching programs with succinct communication.
TIDEE: Tidying Up Novel Rooms Using Visuo-Semantic Commonsense Priors

We introduce $$\text {TIDEE}$$ , an embodied agent that tidies up a disordered scene based on learned commonsense object placement and room arrangement priors. $$\text {TIDEE}$$ explores a home environment, detects objects that are out of their natural place, infers plausible object contexts for them, localizes such contexts in the current scene, and repositions the objects. Commonsense priors are encoded in three modules: i) visuo-semantic detectors that detect out-of-place objects, ii) an associative neural graph memory of objects and spatial relations that proposes plausible semantic receptacles and surfaces for object repositions, and iii) a visual search network that guides the agent’s exploration for efficiently localizing the receptacle-of-interest in the current scene to reposition the object. We test $$\text {TIDEE}$$ on tidying up disorganized scenes in the AI2THOR simulation environment. $$\text {TIDEE}$$ carries out the task directly from pixel and raw depth input without ever having observed the same room beforehand, relying only on priors learned from a separate set of training houses. Human evaluations on the resulting room reorganizations show $$\text {TIDEE}$$ outperforms ablative versions of the model that do not use one or more of the commonsense priors. On a related room rearrangement benchmark that allows the agent to view the goal state prior to rearrangement, a simplified version of our model significantly outperforms a top-performing method by a large margin. Code and data are available at the project website: https://tidee-agent.github.io/ .
The Metaverse From a Multimedia Communications Perspective

eXtended reality (XR) technologies such as virtual reality and 360{\deg} stereoscopic streaming enable the concept of the Metaverse, an immersive virtual space for collaboration and interaction. To ensure high fidelity display of immersive media, the bandwidth, latency and network traffic patterns will need to be considered to ensure a user's Quality of Experience (QoE). In this article, examples and calculations are explored to demonstrate the requirements of the abovementioned parameters. Additionally, future methods such as network-awareness using reinforcement learning (RL) and XR content awareness using spatial or temporal difference in the frames could be explored from a multimedia communications perspective.
Implementing clinical trial data sharing requires training a new generation of biomedical researchers

Data sharing enhances the value of medical research and builds trust in clinical trials, but more biomedical researchers need to be trained in these approaches, which include meta-research, data science and ethical, legal and social issues.