Reviewerly/exHarmony huggingface dataset:
Detecting Rainfall Events Leveraging Climate Knowledge Graphs

Rainfall detection nowadays requires vast amounts of diverse climate data, such as temperature, precipitation, and wind speed, to be coupled with machine learning models in order to get a better understanding of meteorological effects. Meanwhile, knowledge graphs excel in integrating disparate data sources and providing an interoperable framework for web-wide data integration, referred to as Linked Data Principles. Despite this, today’s knowledge graph data is often incompatible with the input of many machine learning pipelines, such as the Python package “scikit-learn”, which takes tabular data as input and is frequently used by studies to assist in the creation of data intelligence. In this paper, we utilize our recently created knowledge graph about climate observations as a use case to create a work ow for scholars to facilitate the extraction of climate data from any knowledge graph for automated conversion from graph-form data to tabular-form data suitable for feeding into machine learning models. As a result, our process enables the integration of diverse climate data while also smoothing the introduction of diverse climate graph data into machine learning techniques for rainfall detection.
Quantum steering with Gaussian states: A tutorial

Quantum steering refers to the apparent possibility of exploiting quantum correlations to remotely influence the quantum state of a subsystem, by measuring local degrees of freedom. In continuous-variable (CV) quantum information, this notion is strongly linked to the possibility of demonstrating the EPR paradox, whence the name EPR steering. Recently, another type of steering with CV Gaussian states was proposed under the name of nonclassical steering, stemming from the idea of remotely generating Glauber P-nonclassicality by conditional Gaussian measurements on two-mode Gaussian states. In this tutorial, we thoroughly illustrate these phenomena, firstly introducing quantum steering in its most general setting, and then focusing on Gaussian states and the connection with P-nonclassicality. We discuss the strong and weak forms of nonclassical steering, their relation with entanglement and how to formulate them in an invariant form with respect to local Gaussian unitary operations. For two-mode squeezed thermal states (TMST), we show that EPR steering coincides with nonclassical steering, and in particular this implies that a single type of Gaussian measurements is sufficient to check steerability for this class, unlike for the more general situation which usually requires the choice of distinct measurements.
Reliability assessment of multi-sensor perception system in automated driving functions

Precise environment perception, which consists of multi-sensor systems, ensures the safety of the automated driving functions (ADFs). With the rapid evolution of sensor technologies, sensor data collection errors occur rarely. Nevertheless, accurate interpretation of the sensor data context, such as 3D multi-object tracking, is still full of challenges. Safety of the Intended Functionality (SOTIF) takes concern of the vulnerability of perception systems. The research of quantitative SOTIF analysis is still ongoing. In this paper, we propose a multi-sensor system model to observe both false negative and false positive errors in different field-of-views. Besides, we also extend a proven Markov-based approach, which takes dependencies between successive sensor errors and correlation between two dependent sensors into account, to model three correlated sensors sharing the same region of interest. In the end, we present a numerical example to illustrate the quantitative reliability analysis of a multi-sensor perception system according to SOTIF.
Ultra-Low Resolution Infrared Sensor-Based Wireless Sensor Network for Privacy-Preserved Recognition of Daily Activities of Living

In the last few decades, the number of elderly people living alone surge worldwide due to an increase in human life expectancy. Researchers suggest different types of methods to monitor elderly residents living alone to prevent them from being unable to get help on time after some incidents such as accidental falling or heart attack. High-resolution data generating methods in monitoring elderly people with RGB cameras or wearable devices are inconvenient for elderly residents. While the former raises a privacy concern, the latter is not practical to wear on and off or charge its battery frequently. One of the solutions that solve both aforementioned problems is ultra-low resolution infrared (IR) sensor arrays. We offer a dataset for Activities of Daily Living (ADL) collected with 8×8 IR sensor arrays from 74 volunteers. For ADL recognition, four types of deep learning models, Convolutional Neural Network (CNN), two types of Recurrent Neural Networks (RNN), and Transformer models are employed. Among them, CNN and Transformer models showed promising results. We believe the dataset is a good contribution to versatile data sources for researchers to accelerate their work on the development of privacy-preserved ADL recognition systems.
Discrete Wavelet Transform-Based Reversible Data Hiding in Encrypted Images

In the era of multimedia technology, not only reinstatement of the original media without any loss of information is required but also the security of the content is of utmost importance. In this paper, a novel discrete wavelet transforms (DWT)-based RDH-EI scheme is proposed. The approximation band of DWT has been utilized for data embedding. Also, disarrangement of DWT sub-bands is practiced for enhancing the security. Proposed scheme is simple to implement and takes less time to execute. Standard grayscale images are tested and compared with existing techniques. The results reflect supremacy of proposed scheme in terms of correlation, PSNR, and computational efficiency.
Local Global Relational Network for Facial Action Units Recognition

Many existing facial action units (AUs) recognition approaches often enhance the AU representation by combining local features from multiple independent branches, each corresponding to a different AU. However, such multi-branch combination-based methods usually neglect potential mutual assistance and exclusion relationship between AU branches or simply employ a pre-defined and fixed knowledge-graph as a prior. In addition, extracting features from pre-defined AU regions of regular shapes limits the representation ability. In this paper, we propose a novel Local Global Relational Network (LGRNet) for facial AU recognition. LGRNet mainly consists of two novel structures, i.e., a skip-BiLSTM module which models the latent mutual assistance and exclusion relationship among local AU features from multiple branches to enhance the feature robustness, and a feature fusion&refining module which explores the complementarity between local AUs and the whole face in order to refine the local AU features to improve the discriminability. Experiments on the BP4D and DISFA AU datasets show that the proposed approach outperforms the state-of-the-art methods by a large margin.
Compact and Flexible KEM From Ideal Lattice

A remarkable breakthrough in mathematics in recent years is the proof of the long-standing conjecture: sphere packing in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E_{8}$ </tex-math></inline-formula> lattice is optimal in the sense of the best density for sphere packing in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {R}^{8}$ </tex-math></inline-formula> . In this work, we design a mechanism for asymmetric key consensus from noise (AKCN), referred to as AKCN-E8, for error correction and key consensus. As a direct application, we present a practical key encapsulation mechanism (KEM) from the ideal lattice based on the ring learning with errors (RLWE) problem. Compared with NewHope-KEM that was the second round candidate of the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standardization, our AKCN-E8 KEM scheme overcomes some limitations and shortcomings of NewHope-KEM. Compared with some other dominating KEM schemes based on the variants of LWE, specifically Kyber and Saber, AKCN-E8 has a comparable performance but enjoys much flexible shared-key sizes. Specifically, the key encapsulated by AKCN-E8-512 (resp., 768, 1024) has the size of 256 (resp., 384, 512) bits. Flexible key size renders us stronger security against quantum attacks, more powerful and economic ability of key transportation, and better matches the demand in interactive protocols like TLS where parties need to negotiate the security parameters including the shared key length.
Accelerated sparse nonnegative matrix factorization for unsupervised feature learning

Sparse Nonnegative Matrix Factorization (SNMF) is a fundamental unsupervised representation learning technique, and it represents low-dimensional features of a data set and lends itself to a clustering interpretation. However, the model and algorithm of SNMF have some shortcomings. In this work, we created a clustering method by improving the SNMF model and its Alternating Direction Multiplier Method acceleration algorithm. A novel, fast and closed-form iterative solution is proposed for SNMF with implicit sparse constraints which are L1 and L2 norms of the coefficient and basis matrixes, respectively. A low-dimensional feature space is also proposed as result of the closed-form iteration formats of each sub-problem obtained by variable splitting. In addition, the convergence points of the presented iterative algorithms are stationary points of the model. Finally, numerical experiments show that the improved algorithm is comparable to the sate-of-the-art methods in data clustering.
A Research of object detection on UAVs aerial images

Object detection has a key application in the field of UAV aerial photography, and its research is of great significance for the expansion of unmanned aerial vehicles (UAVs) application scenarios. In view of the characteristics of UAVs aerial images, the commonly used algorithms in this field are mainly single-stage object detection, which have good performance in balancing detection speed and detection accuracy. In recent years, a series of progress has been made in object detection, and research in the field of UAVs has gradually become hot. This paper describes the research progress of object detection, analyzes the advantages and disadvantages of related algorithms, and lists representative UAVs aerial photography object detection algorithms for introduction and analysis. It is of great significance to the development of object detection in the field of UAVs.
Mixing Catalan, English and Spanish on WhatsApp

Abstract The purpose of this study is to examine language choice, language alternation and code-switching practices in Instant Messaging (IM). Specifically, this article presents the results from an analysis of the written conversations of a specific community formed by Valencian adults fluent in English. Taking a computer-mediated, discourse-centred ethnographic approach to online discourse, the study has shown that, in this specific trilingual online community of language teachers, language choice and the choice of a specific written variety is intimately related to audience. The group members mix Catalan, English and Spanish regularly, their language choice and code-switching strategies serving to establish in-group solidarity, familiarity and lessen face-threatening acts. Switches to English, sometimes followed by a switch to Catalan, are usually employed for humorous word play. Interestingly, the case study exemplifies how the use of a certain language may be highly correlated to ideological and political considerations.
Effects of Dynamic Visual Stimuli on the Development of Carsickness in Real Driving

Whereas autonomous vehicles are expected to provide several advantages, the current scenarios envisioned for self-driving vehicles are expected to increase the incidence of motion sickness. This study investigates the effects of dynamic visual stimuli on the development of carsickness under two different view conditions. A prototypical light-emitting diode (LED) feedback system visualizing longitudinal driving dynamics in the passenger’s peripheral visual field was installed in the rear of a modified serial vehicle. A real driving experiment was conducted on the test track of a major car manufacturer. Subjective motion sickness ratings were recorded. It was hypothesized that carsickness can be mitigated with the information from the visual feedback system. Subjective motion sickness scores tended to be lower with the LED feedback system while there was no substantial interaction effect with the view condition. Although the results indicate potential benefits of the LED feedback system for the mitigation of motion sickness, further development of the system and its functionalities and the inclusion of psychophysiological measures to objectively quantify motion sickness is necessary to confirm these findings.
A systematic comparison of generative models for medical images

This work aims for a systematic comparison of popular shape and appearance models. Here, two statistical and four deep-learning-based shape and appearance models are compared and evaluated in terms of their expressiveness described by their generalization ability and specificity as well as further properties like input data format, interpretability and latent space distribution and dimension.Classical shape models and their locality-based extension are considered next to autoencoders, variational autoencoders, diffeomorphic autoencoders and generative adversarial networks. The approaches are evaluated in terms of generalization ability, specificity and likeness depending on the amount of training data. Furthermore, various latent space metrics are presented in order to capture further major characteristics of the models.The experimental setup showed that locality statistical shape models yield best results in terms of generalization ability for 2D and 3D shape modeling. However, the deep learning approaches show strongly improved specificity. In the case of simultaneous shape and appearance modeling, the neural networks are able to generate more realistic and diverse appearances. A major drawback of the deep-learning models is, however, their impaired interpretability and ambiguity of the latent space.It can be concluded that for applications not requiring particularly good specificity, shape modeling can be reliably established with locality-based statistical shape models, especially when it comes to 3D shapes. However, deep learning approaches are more worthwhile in terms of appearance modeling.
Sharding for Blockchain based Mobile Edge Computing System: A Deep Reinforcement Learning Approach

With the growth of data scale in the mobile edge computing (MEC) network, data security of the MEC network has become a burning concern. The application of blockchain technology in MEC enhances data security and privacy protection. However, throughput becomes the bottleneck of the blockchain-enabled MEC system. Hence, this paper proposes a novel hierarchical and partitioned blockchain framework to improve scalability while guaranteeing the security of partitions. Next, we model the joint optimization of throughput and security as a Markov decision process (MDP). After that, we adopt deep reinforcement learning (DRL) based algorithms to obtain the number of partitions, the size of micro blocks and the large block generation interval. Finally, we analyze the security and throughput performance of proposed schemes. Simulation results demonstrate that proposed schemes can improve throughput while ensuring the security of partitions.
Ontological user profile for E-orientation platforms

In recent years, E-orientation systems have played an increasingly significant role in the proposal of an academic and professional orientation to students. Research efforts have grown to provide more useful and effective E-orientation systems for research or other purposes. The implementation of E-orientation systems resulting from these efforts utilizes several techniques including Artificial Intelligence (AI) methodologies. This study proposes a personalised approach to support an E-orientation system that is tailored to the student’s characteristics. A key component of this system comprises an ontological model of the user profile. The objective of this research was to propose an ontology that is able to collect and analyze the user related information as well as customize the profiles with the most appropriate recommendation or orientation. The ontology employed in this study was developed using the OWL (Ontology Web Language), a knowledge representation language for authoring ontologies. In this paper we will present a definition for the user profile, and then we present our methodology of ontological modeling of the user profile, and finally the conceptual model of the user model for e-orientation systems.
Using Business Games to Build Engineering Competencies in Technological University Students

The paper deals with the experience of using business games for the purpose of complex formation of engineering competencies when teaching students of the Mechanical Department of the Kazan National Research Technological University. Business games are used as a simulation model for the actualization and development of engineering competencies of students in educational, professional, and quasi-professional forms of activity. As an example, in this paper we present the business game “Startup idea” which form a set of knowledge, skills and traits that contribute to the successful implementation of engineering tasks described in terms of the observed behavior. We consider that in modern conditions, business games serve as a tool of revising, applying and consolidating knowledge, developing practical thinking, since they model the goals and competencies of engineering activities. This method of training contributes to the development of communication and interaction skills in a small group, fixing the rules of joint activity, developing analysis and self-analysis skills in the process of group reflection.
Spacing learning units affects both learning and forgetting

Spaced learning produces better learning performance than extended learning periods without or with little interruptions. This "spacing effect" exists on different time scales, ranging from seconds to months. We recently found large spacing effects with a hitherto rarely investigated 12-hours spacing interval. The present study tested for potentially larger learning effects in the temporal vicinity of 12 h and analyzed spacing effects separately for learning and forgetting. 102 participants learned 40 German-Japanese vocabulary pairs in separate conditions with 7.5 min and 4-, 8-, 12-, and 24-hours spacing intervals. Two final tests were executed after retention intervals of 24 h and 7 days. The 7.5-min spacing interval produced a steeper initial learning curve than all other spacing intervals. 24 h after the last learning unit, we found almost no forgetting in the 4-, 8- and 12-hours spacing conditions, but about 9.3% and 3.6% forgetting in the 7.5 min and 24 h spacing conditions. After 7 days, forgetting was in the range of 13% for all conditions between 4 and 24 h. The 7.5 min condition produced 34% forgetting. Our results indicate that spacing intervals in the range of 8 h ± 4 h provide high learning performance and can be easily integrated in our daily schedules.
Conformal Prediction in Clinical Medical Sciences

The use of machine learning (ML) and artificial intelligence (AI) applications in medicine has attracted a great deal of attention in the medical literature, but little is known about how to use Conformal Predictions (CP) to assess the accuracy of individual predictions in clinical applications. We performed a comprehensive search in SCOPUS® to find papers reporting the use of CP in clinical applications. We identified 14 papers reporting the use of CP for clinical applications, and we briefly describe the methods and results reported in these papers. The literature reviewed shows that CP methods can be used in clinical applications to provide important insight into the accuracy of individual predictions. Unfortunately, the review also shows that most of the studies have been performed in isolation, without input from practicing clinicians, not providing comparisons among different approaches and not considering important socio-technical considerations leading to clinical adoption.
Blockchain-enabled Access Control with Fog Nodes for Independent IoTs

Internet of Things (IoT) devices is being conveyed at an enormous scope, with many experts predicting the deployment of 500 billion devices by 2030. Because the IoT devices have limited capabilities and are being deployed in a large amount shortly, current traditional IoT platforms may not be able to handle access control effectively in terms of scalability, reliability, and real-time response. On the other hand, with the advancement of blockchain technology, edge computing, and role-based IoT networks, we can produce a secure access control scheme in a distributed manner with higher reliability, scalability, and real-time response. Therefore, we proposed a decentralized IoT access control system based on blockchain, fog nodes, and the role of things utilizing Ethereum smart contracts for secure device-to-device access. The paper presents overall platform architecture, use case scenarios, crucial evaluation results identified with security analyses, and implementation of the proposed work.
Digital Literacy of Teachers, Families, and Students for Virtual Participation in School: A Multiple Comparison Analysis

This research studies the issue of family involvement in educational centres using digital media, as evidenced to a large extent in the last months of 2019/20 due to the COVID-19 lockdown. As a result of this situation, teachers, students and their families had to use digital resources to ensure continuity of learning, and the only way to involve families was through virtual participation. This paper studies digital literacy of in-service and pre-service teachers, families and pupils, in order to encourage the virtual participation of families in schools. To do so, a non-experimental ex-post facto survey design was used, using statistical analysis of variance (univariate ANOVA). The results suggest that teachers in training and in service demonstrate highest digital literacy levels in particular in informational competence on the Internet and computational competence on office software. Digital competence presents less rating than other dimensions for all profiles, especially for families and pupils, in view of significant differences in the comparative analysis which has been carried out.