Reviewerly/exHarmony huggingface dataset:
Particularities of students’ perceptions of the digitalization of education: comprehending the experience of online learning in a pandemic environment

Introduction. The COVID-19 pandemic has initiated the rapid introduction of digital technology into the educational process. The purpose of the article is to analyze the particularities of the perception of the processes of digitalization of education by Russian students, to identify factors for its success, to comprehend the experience of online learning in the context of the pandemic. Methods. The leading research method was a questionnaire survey of Russian university students. The sample was 1,553 people and was spontaneous in nature. Due to quarantine restrictions at the time of the survey (February-April 2020), the survey was conducted online: the questionnaire was posted on the Google platform, the link to which was sent to the student virtual communities. To clarify the data obtained, two focus groups were organized in January-February 2021 with the participation of full-time and part-time students. Each focus group was represented by 12 participants. Results. The majority of respondents (83.8%) have a positive attitude to the digitalization of education, highly evaluating the importance of digital literacy of young people (86.9%). Students associate the advantages and disadvantages of digitalization of education with their own experience of online learning during the pandemic. The following negative effects of digitalization of education were identified: deterioration of interpersonal communication skills (58.3%), reduction of classroom time, live communication with the teacher (49.3%), reduction of students’ knowledge (37.3%), reduced concentration, a distraction from educational goals (33.4%). The majority of respondents believe that the success of learning in a digital environment depends on the competence of the teacher (67.1%) and the material and technical equipment of the university (69%). Conclusions. With the rapid introduction of digital technologies into the educational space, there is an increasing need to develop interactive teaching methods, increase students’ motivation, and preserve the principles of traditional pedagogy. Digitalization of education implies modernization of pedagogical practices, where a special role is given to the formation of media competence of young people, their analytical skills, abilities for critical thinking and effective communication.
Shortcomings in cybersecurity education for seafarers

Ships, ports, terminals and offshore facilities are increasingly becoming dependent on networked information and communication technology (ICT). Seafarers must be ready to cope with a growing number of cyber threats onboard ships with cybersecurity awareness playing an important role in emergency and crisis management. Unfortunately, current maritime education and training (MET) programmes do not provide enough information on cybersecurity to seafarers to be able to identify and mitigate the prevailing cyber threat landscape. This paper provides a structured survey of published maritime cybersecurity research and gives an overview of the role of the cybersecurity component in MET for seafarers. The results show that currently there are no requirements for MET institutions to include cybersecurity awareness or cyber hygiene practice in the curricula. Some areas for future research are also proposed.
Behavior Estimation Based on Multiple Vibration Sensors for Elderly Monitoring Systems

Aging has become a global social issue nowadays. We want to provide an elderly care system for older people who live alone. Based on the perspective of an informationally structured space (ISS), we have developed a monitoring system by using high-precision vibration sensors. In preliminary experiments, we observed that the autocorrelation coefficient reflected periodic human activities to a certain extent. Therefore, we propose a time delay neural network (TDNN) with autocorrelation as the input to analyze the vibration data. The system can estimate the current state of the elderly. When the system observes any abnormal situation of the elderly, the system can confirm by voice or notify the caregiver, if necessary. In the experiments, we compared the proposed method with traditional TDNNs using raw data as the input. The results demonstrated that proposed methods had performed well when using vibration sensors to measure user behaviors in the bathroom and living room.
Recursive distributed fusion estimation for nonlinear stochastic systems with event-triggered feedback

This paper focus on the distributed fusion estimation problem for a multi-sensor nonlinear stochastic system by considering feedback fusion estimation with its variance. For any of the feedback channels, an event-triggered scheduling mechanism is developed to decide whether the fusion estimation is needed to broadcast to local sensors. Then event-triggered unscented Kalman filters are designed to provide local estimations for fusion. Further, a recursive distributed fusion estimation algorithm related with the trigger threshold is proposed, and sufficient conditions are builded for boundedness of the fusion estimation error covariance. Moreover, an ideal compromise between fusion center-to-sensors communication rate and estimation performance is achieved. Finally, validity of the proposed method is confirmed by a numerical simulation.
Hamming-shifting graph of genomic short reads: Efficient construction and its application for compression

Graphs such as de Bruijn graphs and OLC (overlap-layout-consensus) graphs have been widely adopted for the de novo assembly of genomic short reads. This work studies another important problem in the field: how graphs can be used for high-performance compression of the large-scale sequencing data. We present a novel graph definition named Hamming-Shifting graph to address this problem. The definition originates from the technological characteristics of next-generation sequencing machines, aiming to link all pairs of distinct reads that have a small Hamming distance or a small shifting offset or both. We compute multiple lexicographically minimal k-mers to index the reads for an efficient search of the weight-lightest edges, and we prove a very high probability of successfully detecting these edges. The resulted graph creates a full mutual reference of the reads to cascade a code-minimized transfer of every child-read for an optimal compression. We conducted compression experiments on the minimum spanning forest of this extremely sparse graph, and achieved a 10 - 30% more file size reduction compared to the best compression results using existing algorithms. As future work, the separation and connectivity degrees of these giant graphs can be used as economical measurements or protocols for quick quality assessment of wet-lab machines, for sufficiency control of genomic library preparation, and for accurate de novo genome assembly.
A Hybrid Recommendation Approach for Viral Food Based on Online Reviews

Nowadays, there are many types of viral foods and consumers expect to be able to quickly find foods that meet their own tastes. Traditional recommendation systems make recommendations based on the popularity of viral foods or user ratings. However, because of the different sentimental levels of users, deviations occur and it is difficult to meet the user’s specific needs. Based on the characteristics of viral food, this paper constructs a hybrid recommendation approach based on viral food reviews and label attribute data. A user-based recommendation approach is combined with a content-based recommendation approach in a weighted combination. Compared with the traditional recommendation approaches, it is found that the hybrid recommendation approach performs more accurately in identifying the sentiments of user evaluations, and takes into account the similarities between users and foods. We can conclude that the proposed hybrid recommendation approach combined with the sentimental value of food reviews provides novel insights into improving the existing recommendation system used by e-commerce platforms.
FEATURE EXTRACTION OF DESIS AND PRISMA HYPERSPECTRAL REMOTE SENSING DATASETS FOR GEOLOGICAL APPLICATIONS

Abstract. With the recent launch of advanced hyperspectral satellites with global coverage, including DESIS and PRISMA, a massive volume of high spectral resolution data is available. This work is focused on the spectral analysis and implementation of feature extraction or data dimensionality reduction techniques on both newly available datasets for geological interpretation. Three of the best feature extraction algorithms, Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA), were tested for lithological mapping for the Rajasthan state of India. The present work demonstrates the advantage of the feature extraction algorithm in geological mapping and interpretability as it shows the excellent performance for these datasets. The narrowband ratios for the clay minerals, dolomite, kaolinite, amphiboles, and Al-OH are generated using the PCA and MNF components. All of these band ratios were compared with the Lithological Map available. It is concluded that PCA is the first choice for feature-based lithological classification of hyperspectral remote sensing data. ICA is giving some impressive results which can be explored further. DESIS and PRISMA have a 30 km swath, finer spectral resolution, and high signal-to-noise ratio, which shows much potential in lithological mapping over the parts of northern India. It is suggested to use advanced feature extraction algorithms with recently launched hyperspectral data for accurate and updated mineral mapping over a sizeable geological importance area.
Network representation learning systematic review: Ancestors and current development state

Real-world information networks are increasingly occurring across various disciplines including online social networks and citation networks. These network data are generally characterized by sparseness, nonlinearity and heterogeneity bringing different challenges to the network analytics task to capture inherent properties from network data. Artificial intelligence and machine learning have been recently leveraged as powerful systems to learn insights from network data and deal with presented challenges. As part of machine learning techniques, graph embedding approaches are originally conceived for graphs constructed from feature represented datasets, like image dataset, in which links between nodes are explicitly defined. These traditional approaches cannot cope with network data challenges. As a new learning paradigm, network representation learning has been proposed to map a real-world information network into a low-dimensional space while preserving inherent properties of the network. In this paper, we present a systematic comprehensive survey of network representation learning, known also as network embedding, from birth to the current development state. Through the undertaken survey, we provide a comprehensive view of reasons behind the emergence of network embedding and, types of settings and models used in the network embedding pipeline. Thus, we introduce a brief history of representation learning and word representation learning ancestor of network embedding. We provide also formal definitions of basic concepts required to understand network representation learning followed by a description of network embedding pipeline. Most commonly used downstream tasks to evaluate embeddings, their evaluation metrics and popular datasets are highlighted. Finally, we present the open-source libraries for network embedding.
GAVIN

Annotation is an effective reading strategy people often undertake while interacting with digital text. It involves highlighting pieces of text and making notes about them. Annotating while reading in a desktop environment is considered trivial but, in a mobile setting where people read while hand-holding devices, the task of highlighting and typing notes on a mobile display is challenging. In this paper, we introduce GAVIN, a gaze-assisted voice note-taking application, which enables readers to seamlessly take voice notes on digital documents by implicitly anchoring them to text passages. We first conducted a contextual enquiry focusing on participants' note-taking practices on digital documents. Using these findings, we propose a method which leverages eye-tracking and machine learning techniques to annotate voice notes with reference text passages. To evaluate our approach, we recruited 32 participants performing voice note-taking. Following, we trained a classifier on the data collected to predict text passage where participants made voice notes. Lastly, we employed the classifier to built GAVIN and conducted a user study to demonstrate the feasibility of the system. This research demonstrates the feasibility of using gaze as a resource for implicit anchoring of voice notes, enabling the design of systems that allow users to record voice notes with minimal effort and high accuracy.
Artificial Intelligence in Pathology

About two and a half years ago, The American Journal of Pathology (AJP) introduced a new publication topic category, "Machine Learning, Computational Pathology, and Biophysical Imaging."1 Since then, over a dozen articles have been published in this topic category in the AJP and there has been a steady influx of submissions in this topic category.
A stereo matching algorithm based on the improved PSMNet

Deep learning based on a convolutional neural network (CNN) has been successfully applied to stereo matching. Compared with the traditional method, the speed and accuracy of this method have been greatly improved. However, the existing stereo matching framework based on a CNN often encounters two problems. First, the existing stereo matching network has many parameters, which leads to the matching running time being too long. Second, the disparity estimation is inadequate in some regions where reflections, repeated textures, and fine structures may lead to ill-posed problems. Through the lightweight improvement of the PSMNet (Pyramid Stereo Matching Network) model, the common matching effect of ill-conditioned areas such as repeated texture areas and weak texture areas is solved. In the feature extraction part, ResNeXt is introduced to learn unitary feature extraction, and the ASPP (Atrous Spatial Pyramid Pooling) module is trained to extract multiscale spatial feature information. The feature fusion module is designed to effectively fuse the feature information of different scales to construct the matching cost volume. The improved 3D CNN uses the stacked encoding and decoding structure to further regularize the matching cost volume and obtain the corresponding relationship between feature points under different parallax conditions. Finally, the disparity map is obtained by a regression. We evaluate our method on the Scene Flow, KITTI 2012, and KITTI 2015 stereo datasets. The experiments show that the proposed stereo matching network achieves a comparable prediction accuracy and much faster running speed compared with PSMNet.
Spatio-Temporal Self-Attention Network for Video Saliency Prediction

3D convolutional neural networks have achieved promising results for video tasks in computer vision, including video saliency prediction that is explored in this paper.However, 3D convolution encodes visual representation merely on fixed local spacetime according to its kernel size, while human attention is always attracted by relational visual features at different time.To overcome this limitation, we propose a novel Spatio-Temporal Self-Attention 3D Network (STSANet) for video saliency prediction, in which multiple Spatio-Temporal Self-Attention (STSA) modules are employed at different levels of 3D convolutional backbone to directly capture long-range relations between spatiotemporal features of different time steps.Besides, we propose an Attentional Multi-Scale Fusion (AMSF) module to integrate multi-level features with the perception of context in semantic and spatio-temporal subspaces.Extensive experiments demonstrate the contributions of key components of our method, and the results on DHF1K, Hollywood-2, UCF, and DIEM benchmark datasets clearly prove the superiority of the proposed model compared with all state-of-the-art models.
Implementation of a 3 × 3 directionally-unbiased linear optical multiport

Linear optical multiports are widely used in photonic quantum information processing. Naturally, these devices are directionally-biased since photons always propagate from the input ports toward the output ports. Recently, the concept of directionally-unbiased linear optical multiports was proposed. These directionally-unbiased multiports allow photons to propagate along a reverse direction, which can greatly reduce the number of required linear optical elements for complicated linear optical quantum networks. Here, we report an experimental demonstration of a 3 × 3 directionally-unbiased linear optical fiber multiport using an optical tritter and mirrors. Compared to the previous demonstration using bulk optical elements which works only with light sources with a long coherence length, our experimental directionally-unbiased 3 × 3 optical multiport does not require a long coherence length since it provides negligible optical path length differences among all possible optical trajectories. It can be a useful building block for implementing large-scale quantum walks on complex graph networks.
LightBGM for Human Activity Recognition Using Wearable Sensors

Human activity recognition (HAR) has become a hot research topic, especially in the fields of vital records, adaptive tracking and health monitoring. In order to overcome the problem of low accuracy of commonly used algorithms for human recognition activities, this paper proposes a LightBGM classification method based on the UCI dataset. This method combines the user and the surrounding environment with the computer, and uses the smart phone to perceive people’s actions. It does not need to use special sensors to collect the action information in different positions of the body. It only uses the smart phone with inertial sensors to collect the action information, and carries out classification and recognition, and tests on UCI data sets. The LightGBM algorithm shows a higher accuracy rate than other algorithms and can more accurately identify various actions.
Taxonomy of Saliency Metrics for Channel Pruning

Pruning unimportant parameters can allow deep neural networks (DNNs) to reduce their heavy computation and memory requirements. A saliency metric estimates which parameters can be safely pruned with little impact on the classification performance of the DNN. Many saliency metrics have been proposed, each within the context of a wider pruning algorithm. The result is that it is difficult to separate the effectiveness of the saliency metric from the wider pruning algorithm that surrounds it. Similar-looking saliency metrics can yield very different results because of apparently minor design choices. We propose a novel taxonomy of saliency metrics based on four mostly-orthogonal principal components. We show that a broad range of metrics from the pruning literature can be grouped according to these components. Our taxonomy serves as a guide to prior work, and allows us to construct new saliency metrics by exploring novel combinations of our taxonomic components. We perform the first in-depth experimental investigation of more than 300 saliency metrics made up of existing techniques and new combinations of components. Our results provide decisive answers to open research questions. In particular, we demonstrate the importance of reduction and scaling when pruning groups of weights. We also propose a novel scaling method based on the number of weights transitively removed. We find that some of our constructed metrics can outperform the best existing state-of-the-art metrics for convolutional neural network channel pruning. We find further that our novel scaling method improves existing saliency metrics.
Approximation properties of deep ReLU CNNs

This paper focuses on establishing $L^2$ approximation properties for deep ReLU convolutional neural networks (CNNs) in two-dimensional space. The analysis is based on a decomposition theorem for convolutional kernels with a large spatial size and multi-channels. Given the decomposition result, the property of the ReLU activation function, and a specific structure for channels, a universal approximation theorem of deep ReLU CNNs with classic structure is obtained by showing its connection with one-hidden-layer ReLU neural networks (NNs). Furthermore, approximation properties are obtained for one version of neural networks with ResNet, pre-act ResNet, and MgNet architecture based on connections between these networks.
Fast and differentiable simulation of driven quantum systems

The controls enacting logical operations on quantum systems are described by time-dependent Hamiltonians that often include rapid oscillations. In order to accurately capture the resulting time dynamics in numerical simulations, a very small integration time step is required, which can severely impact the simulation run time. Here, we introduce a semianalytic method based on the Dyson expansion that allows us to time-evolve driven quantum systems much faster than standard numerical integrators. This solver, which we name Dysolve, efficiently captures the effect of the highly oscillatory terms in the system Hamiltonian, significantly reducing the simulation's run time as well as its sensitivity to the time-step size. Furthermore, this solver provides the exact derivative of the time-evolution operator with respect to the drive amplitudes. This key feature allows for optimal control in the limit of strong drives and goes beyond common pulse-optimization approaches that rely on rotating-wave approximations. As an illustration of our method, we show results of the optimization of a two-qubit gate using transmon qubits in the circuit QED architecture.
Synth-by-Reg (SbR): Contrastive Learning for Synthesis-Based Registration of Paired Images

Nonlinear inter-modality registration is often challenging due to the lack of objective functions that are good proxies for alignment. Here we propose a synthesis-by-registration method to convert this problem into an easier intra-modality task. We introduce a registration loss for weakly supervised image translation between domains that does not require perfectly aligned training data. This loss capitalises on a registration U-Net with frozen weights, to drive a synthesis CNN towards the desired translation. We complement this loss with a structure preserving constraint based on contrastive learning, which prevents blurring and content shifts due to overfitting. We apply this method to the registration of histological sections to MRI slices, a key step in 3D histology reconstruction. Results on two public datasets show improvements over registration based on mutual information (13% reduction in landmark error) and synthesis-based algorithms such as CycleGAN (11% reduction), and are comparable to registration with label supervision. Code and data are publicly available at https://github.com/acasamitjana/SynthByReg.