Reviewerly/exHarmony huggingface dataset:
Deep panoramic depth prediction and completion for indoor scenes

Abstract We introduce a novel end-to-end deep-learning solution for rapidly estimating a dense spherical depth map of an indoor environment. Our input is a single equirectangular image registered with a sparse depth map, as provided by a variety of common capture setups. Depth is inferred by an efficient and lightweight single-branch network, which employs a dynamic gating system to process together dense visual data and sparse geometric data. We exploit the characteristics of typical man-made environments to efficiently compress multi-resolution features and find short- and long-range relations among scene parts. Furthermore, we introduce a new augmentation strategy to make the model robust to different types of sparsity, including those generated by various structured light sensors and LiDAR setups. The experimental results demonstrate that our method provides interactive performance and outperforms state-of-the-art solutions in computational efficiency, adaptivity to variable depth sparsity patterns, and prediction accuracy for challenging indoor data, even when trained solely on synthetic data without any fine tuning.
A deep learning framework for non-functional requirement classification

Abstract Analyzing, identifying, and classifying nonfunctional requirements from requirement documents is time-consuming and challenging. Machine learning-based approaches have been proposed to minimize analysts’ efforts, labor, and stress. However, the traditional approach of supervised machine learning necessitates manual feature extraction, which is time-consuming. This study presents a novel deep-learning framework for NFR classification to overcome these limitations. The framework leverages a more profound architecture that naturally captures feature structures, possesses enhanced representational power, and efficiently captures a broader context than shallower structures. To evaluate the effectiveness of the proposed method, an experiment was conducted on two widely-used datasets, encompassing 914 NFR instances. Performance analysis was performed on the applied models, and the results were evaluated using various metrics. Notably, the DReqANN model outperforms the other models in classifying NFR, achieving precision between 81 and 99.8%, recall between 74 and 89%, and F1-score between 83 and 89%. These significant results highlight the exceptional efficacy of the proposed deep learning framework in addressing NFR classification tasks, showcasing its potential for advancing the field of NFR analysis and classification.
Transformation and digital challenges in Peru during the COVID-19 pandemic, in the educational sector between 2020 and 2023: Systematic Review

Introduction: Digital transformation in the Peruvian educational sector has experienced a significant boost after facing the COVID-19 pandemic. During the period between 2020 and 2023, various innovative methods have been implemented to ensure the continuity of the academic year.Objective: Explain how the digital transformation was carried out in the Peruvian educational sector after facing the COVID-19 pandemic to the present (2020 – 2023).Method: Examples from many institutions, statistical studies and scientific and technological references were taken into account to achieve the objective. Throughout this work we are analyzing the different and innovative methods used by teachers to provide continuity to the academic year and how digital challenges were overcome.Results: 78 documents from Scopus and Scielo were reviewed, leaving 62 after filtering. These cover 8 categories on the impact of the pandemic on education, the transition to online teaching, job skills, challenges and advantages of virtual education, innovation in higher education, educational evaluation in virtual environments, educational internationalization and challenges for teachers during the COVID-19 pandemic.Conclusions: In conclusion, the digital transformation in the Peruvian educational sector after the COVID-19 pandemic has been fundamental to guarantee the continuity of the teaching-learning process.
Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network

Abstract Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the conclusion that the traditional U-Net framework appears to be deficient in certain respects, despite its overall excellence in segmenting multimodal medical images. Therefore, we propose several modifications to the existing cutting-edge U-Net model. The technical approach involves applying a Multi-Dimensional U-Convolutional Neural Network to achieve accurate segmentation of multimodal biomedical images, enhancing precision and comprehensiveness in identifying and analyzing structures across diverse imaging modalities. As a result of the enhancements, we propose a novel framework called Multi-Dimensional U-Convolutional Neural Network (MDU-CNN) as a potential successor to the U-Net framework. On a large set of multimodal medical images, we compared our proposed framework, MDU-CNN, to the classical U-Net. There have been small changes in the case of perfect images, and a huge improvement is obtained in the case of difficult images. We tested our model on five distinct datasets, each of which presented unique challenges, and found that it has obtained a better performance of 1.32%, 5.19%, 4.50%, 10.23% and 0.87%, respectively.
Interacting with Smart Virtual Assistants for Individuals with Dysarthria: A Comparative Study on Usability and User Preferences

This study explores the effectiveness and user experience of different interaction methods used by individuals with dysarthria when engaging with Smart Virtual Assistants (SVAs). It focuses on three primary modalities: direct speech commands through Alexa, non-verbal voice cues via the Daria system, and eye gaze control. The objective is to assess the usability, workload, and user preferences associated with each method, catering to the varying communication capabilities of individuals with dysarthria. While Alexa and Daria facilitate voice-based interactions, eye gaze control offers an alternative for those unable to use voice commands, including users with severe dysarthria. This comparative approach aims to determine how the usability of each interaction method varies, conducted with eight participants with dysarthria. The results indicated that non-verbal voice interactions, particularly with the Daria system, were favored because of their lower workload and ease of use. The eye gaze technology, while viable, presented challenges in terms of the higher workload and usability. These findings highlight the necessity of diversifying interaction methods with SVAs to accommodate the unique needs of individuals with dysarthria.
Exploring the role of blockchain technology in modern high-value food supply chains: global trends and future research directions

Abstract Trust, safety, and quality are among the most important factors in the agri-food supply chains. Traceability is a powerful tool to ensure them, but implementing a transparent and effective system is a complex operation. As a result, innovative systems, like blockchain, could be introduced. Although research on its impacts in the agri-food is recent, the literature appears fragmented. The objective is to investigate the studied aspects of the blockchain adoption in agri-food, with the purpose of retrieving meaningful considerations about the current state of the art about strategic high-value supply chains, such as wine and olive oil, particularly subjected to fraudulent behaviors. A productivity measurement was applied to retrieve the evolution of the number of documents through the years, the most productive countries, the sources, the research areas, and the most significant papers in terms of number of citations received. To understand the research trends, a co-occurrence analysis was employed. Results show that most of the existing studies focus on the role of blockchain in the resolution of some critical issues as food safety and frauds. While wine is currently an emerging sector in which this approach can be implemented, olive oil still needs more attention. In both cases, blockchain could potentially help to support the profitability and sustainability of the production. The research underlines the importance of focusing on the environmental and social dimension of the blockchain phenomenon and the use of technology to improve the efficiency of agri-food chains and reduce waste and resource use.
Hybrid structural graph attention network for POI recommendation

In the era of big data, information overload poses a challenge, complicating user decision-making. Recommendation systems aim to assist in this process. In recent years, research on point-of-interest (POI) recommendations has been gaining momentum with some studies pointing to issues that need to be resolved. Previous studies often used heterogeneous graphs to learn across different entity types, overlooking same-type entity relationships. Certain studies solely extract raw node features from a single source, thus disregarding information diversity, whereas others employ inappropriate methods that fail to preserve the inherent characteristics of the relevant information in the design of raw inputs. The integration of multiple sources of information can introduce a certain amount of noise into the data; however, the approaches used in related research may not be effective in handling this situation. To address these issues, we propose a hybrid structural graph attention network (HS-GAT) for POI recommendation. In this approach, multisource data are first preprocessed and relevant raw features are initialized. Subsequently, heterogeneous graphs are built for user-POI-POI attributes and POI-user-user attributes. These heterogeneous graphs are aggregated using a dual-attention mechanism, to create embedding matrices for users and POIs, which are then used to construct user-user and POI-POI homogeneous graphs. These graph structures are then combined with user and POI embeddings obtained from heterogeneous graphs and fed into a graph attention network (GAT) , which yields the final embedding representations for users and POIs. Finally, recommendations for POIs are made in the form of inner products. A comprehensive performance evaluation of HS-GAT on the Yelp, Boston, Chicago and London datasets demonstrated that the proposed approach outperforms other state-of-the-art methods.
Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning

For the precise measurement of complex surfaces, determining the position, direction, and path of a laser sensor probe is crucial before obtaining exact measurements. Accurate surface measurement hinges on modifying the overtures of a laser sensor and planning the scan path of the point laser displacement sensor probe to optimize the alignment of its measurement velocity and accuracy. This manuscript proposes a 3D surface laser scanning path planning technique that utilizes adaptive ant colony optimization with sub-population and fuzzy logic (SFACO), which involves the consideration of the measurement point layout, probe attitude, and path planning. Firstly, this study is based on a four-coordinate measuring machine paired with a point laser displacement sensor probe. The laser scanning four-coordinate measuring instrument is used to establish a coordinate system, and the relationship between them is transformed. The readings of each axis of the object being measured under the normal measuring attitude are then reversed through the coordinate system transformation, thus resulting in the optimal measuring attitude. The nominal distance matrix, which demonstrates the significance of the optimal measuring attitude, is then created based on the readings of all the points to be measured. Subsequently, a fuzzy ACO algorithm that integrates multiple swarm adaptive and dynamic domain structures is suggested to enhance the algorithm’s performance by refining and utilizing multiple swarm adaptive and fuzzy operators. The efficacy of the algorithm is verified through experiments with 13 popular TSP benchmark datasets, thereby demonstrating the complexity of the SFACO approach. Ultimately, the path planning problem of surface 3D laser scanning measurement is addressed by employing the proposed SFACO algorithm in conjunction with a nominal distance matrix.
Face masks and fake masks: the effect of real and superimposed masks on face matching with super-recognisers, typical observers, and algorithms

Abstract Mask wearing has been required in various settings since the outbreak of COVID-19, and research has shown that identity judgements are difficult for faces wearing masks. To date, however, the majority of experiments on face identification with masked faces tested humans and computer algorithms using images with superimposed masks rather than images of people wearing real face coverings. In three experiments we test humans (control participants and super-recognisers) and algorithms with images showing different types of face coverings. In all experiments we tested matching concealed or unconcealed faces to an unconcealed reference image, and we found a consistent decrease in face matching accuracy with masked compared to unconcealed faces. In Experiment 1, typical human observers were most accurate at face matching with unconcealed images, and poorer for three different types of superimposed mask conditions. In Experiment 2, we tested both typical observers and super-recognisers with superimposed and real face masks, and found that performance was poorer for real compared to superimposed masks. The same pattern was observed in Experiment 3 with algorithms. Our results highlight the importance of testing both humans and algorithms with real face masks, as using only superimposed masks may underestimate their detrimental effect on face identification.
Simulation in engineering education: The transition from physical experimentation to digital immersive simulated environments

Besides its use as a powerful systems analysis tool, simulation has also been used for decades in educational settings as a teaching and learning method. Simulation can replace or augment real-world inquiry-based experiences by providing learners with a low-cost and risk-free experimentation platform to develop knowledge and skills in a simulated environment. This paper presents an overview of current applications and the ongoing transition from physical experimentation to digital simulations and immersive simulated learning environments in engineering education. The paper highlights major implementation and research gaps related to simulation-based learning and immersive simulated learning environments, namely, lack of integration with learning theories and limited formal assessments of effectiveness. Potential implementation approaches and important areas for future educational research are discussed and exemplified in response to the identified gaps. The discussions presented are intended for simulationists, educational researchers, and instructors who are interested in designing and/or utilizing engineering education interventions involving simulated learning environments and immersive technologies in their teaching and educational research. In particular, the Immersive Simulation-Based Learning (ISBL) approach discussed in the paper provides a framework for simulationists to reuse the models developed as part of their simulation projects for educational purposes.
Discrete-Time Quantum Walks Community Detection in Multi-Domain Networks

Abstract The problem of detecting communities in real-world networks has been extensively studied in the past, but most of the existing approaches work on single-domain networks, i.e. they consider only one type of relationship between nodes. Single-domain networks may contain noisy edges and they may lack some important information. Thus, some authors have proposed to consider the multiple relationships that connect the nodes of a network, thus obtaining multi-domain networks. However, most community detection approaches are limited to multi-layer networks, i.e. networks generated from the superposition of several single-domain networks (called layers) that are regarded as independent of each other. In addition to being computationally expensive, multi-layer approaches might yield inaccurate results because they ignore potential dependencies between layers. This paper proposes a multi-domain discrete-time quantum walks (MDQW) model for multi-domain networks. First, the walking space of network nodes in multi-domain network is constructed. Second, the quantum permutation circuit of the coin state is designed based on the coded particle state. Then, using different coin states, the shift operator performs several quantum walks on the particles. Finally, the corresponding update rule is selected to move the node according to the measurement result of the quantum state. With continuous update iteration, the shift operator automatically optimizes the discovered community structure. We experimentally compared our MDQW method with four state-of-the-art competitors on five real datasets. We used the normalized mutual information (NMI) to compare clustering quality, and we report an increase in NMI of up to 3.51 of our MDQW method in comparison with the second-best performing competitor. The MDQW method is much faster than its competitors, allowing us to conclude that MDQW is a useful tool in the analysis of large real-life multi-domain networks. Finally, we illustrate the usefulness of our approach on two real-world case studies.
Combining Critical and Quantum Metrology

Critical metrology relies on the precise preparation of a system in its ground state near a quantum phase transition point where quantum correlations get very strong. Typically, this increases the quantum Fisher information with respect to changes in system parameters and thus improves the optimally possible measurement precision limited by the Cram\'er-Rao bound. Hence critical metrology involves encoding information about the unknown parameter in changes of the system's ground state. Conversely, in conventional metrology methods like Ramsey interferometry, the eigenstates of the system remain unchanged, and information about the unknown parameter is encoded in the relative phases that excited system states accumulate during their time evolution. Here we introduce an approach combining these two methodologies into a unified protocol applicable to closed and driven-dissipative systems. We show that the quantum Fisher information in this case exhibits an additional interference term originating from the interplay between eigenstate and relative phase changes. We provide analytical expressions for the quantum and classical Fisher information in such a setup, elucidating as well a straightforward measurement approach that nearly attains the maximum precision permissible under the Cram\'er-Rao bound. We showcase these results by focusing on the squeezing Hamiltonian, which characterizes the thermodynamic limit of Dicke and Lipkin-Meshkov-Glick Hamiltonians.
Physical Reservoir Computing Enabled by Solitary Waves and Biologically Inspired Nonlinear Transformation of Input Data

Reservoir computing (RC) systems can efficiently forecast chaotic time series using the nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more-efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper, we experimentally validated a physical RC system that substitutes the effect of randomness that underpins the operation of the traditional RC algorithm for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the ‘next-generation’ improvement of the traditional RC algorithm.
Entanglement and entropy in multipartite systems: a useful approach

Abstract Quantum entanglement and quantum entropy are crucial concepts in the study of multipartite quantum systems. In this work, we show how the notion of concurrence vector, re-expressed in a particularly useful form, provides new insights and computational tools for the analysis of both. In particular, using this approach for a general multipartite pure state, one can easily prove known relations in an easy way and to build up new relations between the concurrences associated with the different bipartitions. The approach is also useful to derive sufficient conditions for genuine entanglement in generic multipartite systems that are computable in polynomial time. From an entropy-of-entanglement perspective, the approach is powerful to prove properties of the Tsallis-2 entropy, such as the subadditivity, and to derive new ones, e.g., a modified version of the strong subadditivity which is always fulfilled; thanks to the purification theorem these results hold for any multipartite state, whether pure or mixed.
Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions

The development of applications based on artificial intelligence and implemented over wireless networks is increasingly rapidly and is expected to grow dramatically in the future. The resulting demand for the aggregation of large amounts of data has caused serious communication bottlenecks in wireless networks and particularly at the network edge. Over-the-air federated learning (OTA-FL), leveraging the superposition feature of multi-access channels (MACs), enables users at the network edge to share spectrum resources and achieves efficient and low-latency global model aggregation. This paper provides a holistic review of progress in OTA-FL and points to potential future research directions. Specifically, we classify OTA-FL from the perspective of system settings, including single-antenna OTA-FL, multi-antenna OTA-FL, and OTA-FL with the aid of the emerging reconfigurable intelligent surface (RIS) technology, and the contributions of existing works in these areas are summarized. Moreover, we discuss the trust, security and privacy aspects of OTA-FL, and highlight concerns arising from security and privacy. Finally, challenges and potential research directions are discussed to promote the future development of OTA-FL in terms of improving system performance, reliability, and trustworthiness. Specifical challenges to be addressed include model distortion under channel fading, the ineffective OTA aggregation of local models trained on substantially unbalanced data, and the limited accessibility and verifiability of individual local models.