Reviewerly/exHarmony huggingface dataset:
Student evaluation of teacher digitals skills at Granada University

The following study is a descriptive/correlational analysis of students’ perception of digital competence in teaching. It is based on the current needs of university students with regard to the digitization of teaching and learning processes. Knowing student satisfaction with teaching digital skills could be used to propose improvements in the quality of university education to the extent that it can channel the various socio-economic demands. The European Higher Education Area is not oblivious to this problem by creating a framework of ambitious objectives for member states where ICT and digital competence of teachers are a priority. The research is carried out at the University of Granada and takes as a reference the framework: Marco Común de Competencia Digital Docente. This Framework in the context of Spain is concerned with indicating which areas educators need to acquire a skill higher degree differentiating between the following levels of classification: basic, intermediate and advanced. A quantitative correlational study was carried out, and an online questionnaire/form was distributed to a random probability sample of 307 students at the University of Granada belonging to different faculties of education. According to the results obtained, the students unanimously share the opinion that, the lecturers at Granada University do not possess a sufficient level of experience or training to competently deliver course content in a purely digital format.
Development of Students’ Digital Competence When Using the “Oracle” Electronic Portal

In today's constant change in the market of educational services and opportunities to engage in various formats of education (vocational, general education, special, higher, and others), consumers are faced with the question of choosing the quality education.High-quality education is one of the key conditions of student motivation and the attractiveness of higher education.The present study explores indicators of the quality of education in the context of the development of digital competence in future specialists in economics as part of a combination of interactive digital technologies and electronic platforms for the pedagogical interaction of participants in the educational process.The purpose of the study is to design a pedagogical technology relying on the management of learning and creative activities of economics students with the use of the electronic resource "Oracle" as a means to improve the quality of professional training of specialists in modern conditions.The feasibility of using "Oracle" in the formation of the digital competence of future economists is determined through an expert survey.The conducted pedagogical experiment reveals that the effectiveness of the educational process organization, an indicator of which is the quality of professional training of economic students, is 17.5 % higher with the model of educational management using the chosen info-digital electronic resource (business intelligence platform) compared to the traditional learning system due to improved training of future specialists in all components of digital competence.
An Optimal Transport Formulation of Bayes’ Law for Nonlinear Filtering Algorithms

This paper presents a variational representation of the Bayes’ law using optimal transportation theory. The variational representation is in terms of the optimal transportation between the joint distribution of the (state, observation) and their independent coupling. By imposing certain structure on the transport map, the solution to the variational problem is used to construct a Brenier-type map that transports the prior distribution to the posterior distribution for any value of the observation signal. The new formulation is used to derive the optimal transport form of the Ensemble Kalman filter (EnKF) for the discrete-time filtering problem and propose a novel extension of EnKF to the non-Gaussian setting utilizing input convex neural networks. Finally, the proposed methodology is used to derive the optimal transport form of the feedback particle filler (FPF) in the continuous-time limit, which constitutes its first variational construction without explicitly using the nonlinear filtering equation or Bayes’ law.
(k, n) secret image sharing scheme with privileged set

In traditional (k, n) threshold secret image sharing (SIS) scheme, all participants play the same roles in the revealing process. The threshold value k is reflected by the security level, which is a fixed integer predetermined before sharing. However, in practice of SIS, some participants are more important than others. These important participants have the privilege that they can reveal the secret image with smaller threshold value. In this paper, we propose the first (k, n)-SIS with privileged set (SISPS) scheme. There is a privileged set of participants. Normally, the secret image can be revealed by any k out of n participants. Besides, it also can be revealed by kp privileged participants, where kp<k. The privileged set can be used as hidden set, authentication set in different applications. We also further discuss the applications of SISPS used in progressive mode and two SISPS with the same privileged set. Some experiments are conducted to show the effectiveness of the proposed SISPS.
Multimodal Inverse Cloze Task for Knowledge-Based Visual Question Answering

We present a new pre-training method, Multimodal Inverse Cloze Task, for Knowledge-based Visual Question Answering about named Entities (KVQAE). KVQAE is a recently introduced task that consists in answering questions about named entities grounded in a visual context using a Knowledge Base. Therefore, the interaction between the modalities is paramount to retrieve information and must be captured with complex fusion models. As these models require a lot of training data, we design this pre-training task from existing work in textual Question Answering. It consists in considering a sentence as a pseudo-question and its context as a pseudo-relevant passage and is extended by considering images near texts in multimodal documents. Our method is applicable to different neural network architectures and leads to a 9% relative-MRR and 15% relative-F1 gain for retrieval and reading comprehension, respectively, over a no-pre-training baseline.
An empirical evaluation of enhanced teleportation for navigating large urban immersive virtual environments

Navigation is the most prevalent interaction in large urban virtual environments (VEs). Any Metaverse application that foresees navigating or exploring virtual cities requires an effective and efficient navigation technique. These environments, however, have distinct characteristics that make the navigation more challenging and the design of the interactions more critical. We have conducted an empirical study to assess how enhancing the teleportation technique with additional capabilities affects the performance of navigating large urban VEs. For this purpose, three interactions have been developed that extend the conventional point-and-click teleportation. The first one is named Mini-Map and provides a top-down view of the user’s surroundings. Portal Preview provides a preview of the selected target location and allows users to choose their desired orientation at that location. The last technique, called X-Ray Vision, makes the buildings around the user translucent and allows teleporting to locations that would otherwise be obscured. A within-subject controlled lab study with twenty five participants has been conducted, where each extension is evaluated individually as well as in combination with others. Our results show that extending the teleportation can significantly improve its performance when navigating large urban VEs. Overall, the X-Ray Vision was the most successful extension with respect to both task metrics and usability measures. Mini-Map was able to improve some of the task metrics, but did not have a significant effect on most self-reported measures. Portal Preview was the least effective extension, however, multiple participants liked the fact that they could define their desired orientation with the controller. Combining all interactions together performed well with respect to the task metrics, but this option was not favored by the participants. Extending the teleportation with X-Ray Vision and Mini-Map was by far the most favored option.
IoTSL: Toward Efficient Distributed Learning for Resource-Constrained Internet of Things

Recently proposed split learning (SL) is a promising distributed machine learning paradigm that enables machine learning without accessing the raw data of the clients. SL can be viewed as one specific type of serial federation learning. However, deploying SL on resource-constrained Internet of Things (IoT) devices still has some limitations, including high communication costs and catastrophic forgetting problems caused by imbalanced data distribution of devices. In this article, we design and implement IoTSL, which is an efficient distributed learning framework for efficient cloud-edge collaboration in IoT systems. IoTSL combines generative adversarial networks (GANs) and differential privacy techniques to train local data-based generators on participating devices, and generate data with privacy protection. On the one hand, IoTSL pretrains the global model using the generative data, and then fine-tunes the model using the local data to lower the communication cost. On the other hand, the generated data is used to impute the missing classes of devices to alleviate the commonly seen catastrophic forgetting phenomenon. We use three common data sets to verify the proposed framework. Extensive experimental results show that compared to the conventional SL, IoTSL significantly reduces communication costs, and efficiently alleviates the catastrophic forgetting phenomenon.
Enhanced Embedded AutoEncoders: An Attribute-Preserving Face De-Identification Framework

Nowadays, face recognition technology has been dramatically boosted by the advances in deep learning and big data fields. However, this also poses grand challenges in protecting personal identity information in intelligent applications of the Internet of Things (IoT). Existing methods based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -Same algorithm have low effectiveness for protecting personal identity while preserving face attributes. In this article, we propose an attribute-preserving face de-identification framework called Enhanced Embedded AutoEncoders to address this problem. Our framework consists of three parts: 1) a privacy removal network (PRN); 2) a feature selection network; and 3) a privacy evaluation network. The main purpose of our framework is to ensure that the PRN is capable of discarding information involving identity privacy and retaining desired face attributes for certain prediction applications. In order to achieve this goal, the design of the PRN is crucial. Specifically, we employ two different autoencoders, one of which is embedded within the other. Extensive experimental results show that our framework outperforms existing methods by an average of 3.42%–26.22% in terms of data utility under comparable face de-identification performance, which indicates that the proposed framework can not only effectively retain face attributes but also protect personal identity well.
Block-Adaptive Point Cloud Attribute Coding With Region-Aware Optimized Transform

Block-based compression scheme shows remarkable success in image and video coding. However, existing tree-type block partition methods usually divide point clouds into clusters with few or disjoint points due to the irregular sampling, which is adverse for the subsequent transform to exploit the local region correlation. Moreover, the widely-used optimal transform, e.g., Discrete Cosine Transform (DCT), is deduced with the assumption of the specified probability model. Thus these transforms cannot adequately conform to diverse statistical characteristics of point cloud attributes. To address the above two problems, we propose a block-adaptive codec with the optimized transform for point cloud attribute compression, including Progressive Clustering (PC) for block partition and Region-Aware Signal Modeling (RASM) for transform. PC is designed via the split-and-merge strategy. In the splitting phase, over-segmented clusters are obtained by iteratively searching the nearest neighbors to maintain the spatial continuity of points in one cluster. In the merging step, blocks are determined by merging over-segmented clusters under the guidance of minimizing texture complexity, which adjusts block sizes to adapt to texture variation. RASM adaptively captures the color correlations to respond to diverse statistical characteristics by optimizing the overall Rate-Distortion (RD) cost. Then, the optimal transform bases are obtained via the eigen-decomposition of the color correlation representation with respect to the Gaussian Markov Random Field, where input colors are obtained considering the linear relationship between geometry and color variations. The linear coefficients also need to be encoded to combine geometry to reconstruct the transform bases at the decoder in the same way as the encoder. Particularly, we accelerate the RASM by constraining the independent relationship between signals in the local region, which hardly influences RD performance. Extensive experiments not only indicate the effectiveness of PC and RASM but also show the significant RD gains achieved by our approach compared with several state-of-the-art platforms.
Dynamics in Deep Classifiers Trained with the Square Loss: Normalization, Low Rank, Neural Collapse, and Generalization Bounds

We overview several properties-old and new-of training overparameterized deep networks under the square loss. We first consider a model of the dynamics of gradient flow under the square loss in deep homogeneous rectified linear unit networks. We study the convergence to a solution with the absolute minimum ρ, which is the product of the Frobenius norms of each layer weight matrix, when normalization by Lagrange multipliers is used together with weight decay under different forms of gradient descent. A main property of the minimizers that bound their expected error for a specific network architecture is ρ. In particular, we derive novel norm-based bounds for convolutional layers that are orders of magnitude better than classical bounds for dense networks. Next, we prove that quasi-interpolating solutions obtained by stochastic gradient descent in the presence of weight decay have a bias toward low-rank weight matrices, which should improve generalization. The same analysis predicts the existence of an inherent stochastic gradient descent noise for deep networks. In both cases, we verify our predictions experimentally. We then predict neural collapse and its properties without any specific assumption-unlike other published proofs. Our analysis supports the idea that the advantage of deep networks relative to other classifiers is greater for problems that are appropriate for sparse deep architectures such as convolutional neural networks. The reason is that compositionally sparse target functions can be approximated well by "sparse" deep networks without incurring in the curse of dimensionality.
Multi-objective optimal scheduling of laminar cooling water supply system for hot rolling mills driven by digital twin for energy-saving

As a result of the complex structure of laminar cooling water supply systems, both facets of strict process requirements and various intermittent running conditions could cause challenges of energy-saving and equipment upkeep. Given the energy wastage issue of the laminar cooling system of hot rolling, this paper develops an optimal scheduling system according to digital twin using Online Sequential Extreme Learning Machine (OS-ELM) and multi-objective evolutionary optimization using Improved Sparrow Search Algorithm (ISSA). The optimal scheduling system according to digital twins can accurately predict the water consumption trend of the water supply system and optimize the scheduling instructions and operation scheme through dynamic information interaction and mapping of process constraints, intermittent operating conditions, rolling rhythm, measured data, etc. between physical space and virtual space. Experimental results reveal that the proposed method can lessen power usage by 13.60% and water consumption by 10.54% regarding the premise of ensuring what is needed of cooling procedures. In addition, the water pump can maintain high effectiveness during operation to guarantee the security and stability of laminar cooling water supply systems.
Contrastive pre-training and linear interaction attention-based transformer for universal medical reports generation

Interpreting medical images such as chest X-ray images and retina images is an essential step for diagnosing and treating relevant diseases. Proposing automatic and reliable medical report generation systems can reduce the time-consuming workload, improve efficiencies of clinical workflows, and decrease practical variations between different clinical professionals. Many recent approaches based on image-encoder and language-decoder structure have been proposed to tackle this task. However, some technical challenges remain to be solved, including the fusion efficacy between the language and visual cues and the difficulty of obtaining an effective pre-trained image feature extractor for medical-specific tasks. In this work, we proposed the weighted query-key interacting attention module, including both the second-order and first-order interactions. Compared with the conventional scaled dot-product attention, this design generates a strong fusion mechanism between language and visual signals. In addition, we also proposed the contrastive pre-training step to reduce the domain gap between the image encoder and the target dataset. To test the generalizability of our learning scheme, we collected and verified our model on the world-first multi-modality retina report generation dataset referred to as Retina ImBank and another large-scale retina Chinese-based report dataset referred to as Retina Chinese. These two datasets will be made publicly available and serve as benchmarks to encourage further research exploration in this field. From our experimental results, we demonstrate that our proposed method has outperformed multiple state-of-the-art image captioning and medical report generation methods on IU X-RAY, MIMIC-CXR, Retina ImBank, and Retina Chinese datasets.
Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions

Deep learning-based algorithms have led to tremendous progress over the last years, but they face a bottleneck as their optimal development highly relies on access to large datasets. To mitigate this limitation, cross-silo federated learning has emerged as a way to train collaborative models among multiple institutions without having to share the raw data used for model training. However, although artificial intelligence experts have the expertise to develop state-of-the-art models and actively share their code through notebook environments, implementing a federated learning system in real-world applications entails significant engineering and deployment efforts. To reduce the complexity of federation setups and bridge the gap between federated learning and notebook users, this paper introduces a solution that leverages the Jupyter environment as part of the federated learning pipeline and simplifies its automation, the Notebook Federator. The feasibility of this approach is then demonstrated with a collaborative model solving a digital pathology image analysis task in which the federated model reaches an accuracy of 0.8633 on the test set, as compared to the centralized configurations for each institution obtaining 0.7881, 0.6514, and 0.8096, respectively. As a fast and reproducible tool, the proposed solution enables the deployment of a cross-country federated environment in only a few minutes.
On the Optimization Landscape of Dynamic Output Feedback: A Case Study for Linear Quadratic Regulator

The convergence of policy gradient algorithms in reinforcement learning hinges on the optimization landscape of the underlying optimal control problem.Theoretical insights into these algorithms can often be acquired from analyzing those of linear quadratic control.However, most of the existing literature only considers the optimization landscape for static full-state or output feedback policies (controllers).We investigate the more challenging case of dynamic outputfeedback policies for linear quadratic regulation (abbreviated as dLQR), which is prevalent in practice but has a rather complicated optimization landscape.We first show how the dLQR cost varies with the coordinate transformation of the dynamic controller and then derive the optimal transformation for a given observable stabilizing controller.At the core of our results is the uniqueness of the stationary point of dLQR when it is observable, which is in a concise form of an observer-based controller with the optimal similarity transformation.These results shed light on designing efficient algorithms for general decision-making problems with partially observed information.
Characterizing Superradiant Phase of the Quantum Rabi Model

Recently, a superradiant phase transition first predicted theoretically in the quantum Rabi model (QRM) has been verified experimentally. This further stimulates the interest in the study of the process of phase transition and the nature of the superradiant phase since the fundamental role of the QRM in describing the interaction of light and matter, and more importantly, the QRM contains rich physics deserving further exploration despite its simplicity. Here we propose a scheme consisting of two successive diagonalization to accurately obtain the ground-state and excited states wavefunctions of the QRM in full parameter regime ranging from weak to deep-strong couplings. Thus one is able to see how the phase transition happens and how the photons populate in Fock space of the superradiant phase. We characterize the photon populations by borrowing the distribution concept in random matrix theory and find that the photon population follows a Poissonian-like distribution once the phase transition happens and further exhibits the statistics of Gaussian unitary ensemble as increasing coupling strength. More interestingly, the photons in the excited states behave even like the statistics of Gaussian orthogonal ensemble. Our results not only deepen understanding on the superradiant phase transition but also provide an insight on the nature of the superradiant phase of the QRM and related models.