Reviewerly/exHarmony huggingface dataset:
CenterNet3D: An Anchor Free Object Detector for Point Cloud

Accurate and fast 3D object detection from point clouds is a key task in autonomous driving. Existing one-stage 3D object detection methods can achieve real-time performance, however, they are dominated by anchor-based detectors which are inefficient and require additional post-processing. In this paper, we eliminate anchors and model an object as a single point—the center point of its bounding box. Based on the center point, we propose an anchor-free CenterNet3D network that performs 3D object detection without anchors. Our CenterNet3D uses keypoint estimation to find center points and directly regresses 3D bounding boxes. However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundaries. To solve this issue, we propose an extra corner attention module to enforce the CNN backbone to pay more attention to object boundaries. Besides, considering that one-stage detectors suffer from the discordance between the predicted bounding boxes and corresponding classification confidences, we develop an efficient keypoint-sensitive warping operation to align the confidences to the predicted bounding boxes. Our proposed CenterNet3D is non-maximum suppression free which makes it more efficient and simpler. We evaluate CenterNet3D on the widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art anchor-based one-stage methods and has comparable performance to two-stage methods as well. It has an inference speed of 20 FPS and achieves the best speed and accuracy trade-off. Our source code will be released at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/wangguojun2018/CenterNet3d</uri></i> .
Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder

Detecting anomalies in the Brake Operating Unit (BOU) braking system of metro trains is very important for trains’ reliability and safety. However, current periodic maintenance and inspection cannot detect anomalies at an early stage. In addition, constructing a stable and accurate anomaly detection system is a very challenging task. Hence, in this work, we propose a method for detecting anomalies of BOU on metro vehicles using a one-class long short-term memory (LSTM) autoencoder. First, we extracted brake cylinder (BC) pressure data from the BOU data since one of the anomaly cases of metro trains is that BC pressure relief time is delayed by 4 s. After that, extracted BC pressure data is split into subsequences which are fed into our proposed one-class LSTM autoencoder which consists of two LSTM blocks (encoder and decoder). The one-class LSTM autoencoder is trained using training data which only consists of normal subsequences. To detect anomalies from test data that contain abnormal subsequences, the mean absolute error (MAE) for each subsequence is calculated. When the error is larger than a predefined threshold which was set to the maximum value of MAE in the training (normal) dataset, we can declare that example an anomaly. We conducted the experiments with the BOU data of metro trains in Korea. Experimental results show that our proposed method can detect anomalies of the BOU data well.
Using different approaches to organizing distance learning during the COVID-19 pandemic: opportunities and disadvantages

In recent years, distance learning has become widespread in both secondary and higher educational institutions around the world. However, the pace of the spread of remote technologies has significantly increased with the introduction of physical distancing and quarantine against the background of the development of the COVID-19 pandemic, since most educational institutions in various countries of the world were closed to hold the spread of the virus. Many educational institutions were not fully ready for the mass introduction of distance learning, and if in developed countries schools and universities had the opportunity to use developments in the educational process of various programs and technologies for conducting online learning, then in countries and regions where there were serious problems with the implementation of distance learning, attempts were made to use the possibilities of social networks and messengers for organizing the educational process. However, since these resources were not fully intended for the implementation of full-fledged pedagogical activities, their use in the educational process, along with the advantages, had several disadvantages.
User incentives for blockchain-based data sharing platforms

Data sharing is very important for accelerating scientific research, business innovations, and for informing individuals. Yet, concerns over data privacy, cost, and lack of secure data-sharing solutions have prevented data owners from sharing data. To overcome these issues, several research works have proposed blockchain-based data-sharing solutions for their ability to add transparency and control to the data-sharing process. Yet, while models for decentralized data sharing exist, how to incentivize these structures to enable data sharing at scale remains largely unexplored. In this paper, we study different incentive mechanisms for decentralized data-sharing platforms. Smart contracts are used to automate different payment options between data owners and data requesters. We evaluate multiple cost pricing scenarios for data monetization by simulating incentive mechanisms on a blockchain-based data-sharing platform. We show that a cost compensation model for the data owner rapidly cover the cost of data sharing and balance the overall incentives for all the actors in the platform.
Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval

Learning the similarity between remote sensing (RS) images forms the foundation for content-based RS image retrieval (CBIR). Recently, deep metric learning approaches that map the semantic similarity of images into an embedding (metric) space have been found very popular in RS. A common approach for learning the metric space relies on the selection of triplets of similar (positive) and dissimilar (negative) images to a reference image called an anchor. Choosing triplets is a difficult task particularly for multilabel RS CBIR, where each training image is annotated by multiple class labels. To address this problem, in this article, we propose a novel triplet sampling method in the framework of deep neural networks (DNNs) defined for multilabel RS CBIR problems. The proposed method selects a small set of the most representative and informative triplets based on two main steps. In the first step, a set of anchors that are diverse to each other in the embedding space is selected from the current minibatch using an iterative algorithm. In the second step, different sets of positive and negative images are chosen for each anchor by evaluating the relevancy, hardness, and diversity of the images among each other based on a novel strategy. Experimental results obtained on two multilabel benchmark archives show that the selection of the most informative and representative triplets in the context of DNNs results in: 1) reducing the computational complexity of the training phase of the DNNs without any significant loss on the performance and 2) an increase in learning speed since informative triplets allow fast convergence. The code of the proposed method is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://git.tu-berlin.de/rsim/image-retrieval-from-triplets</uri> .
A look into the future of blockchain technology

In this paper, we use a Delphi approach to investigate whether, and to what extent, blockchain-based applications might affect firms' organizations, innovations, and strategies by 2030, and, consequently, which societal areas may be mainly affected. We provide a deep understanding of how the adoption of this technology could lead to changes in Europe over multiple dimensions, ranging from business to culture and society, policy and regulation, economy, and technology. From the projections that reached a significant consensus and were given a high probability of occurrence by the experts, we derive four scenarios built around two main dimensions: the digitization of assets and the change in business models.
Synthetizing Published Evidence on Survival by Reconstruction of Patient-Level Data and Generation of a Multi-Trial Kaplan-Meier Curve

Introduction In conducting a survival meta-analysis, the typical methodological approach analyses the hazard ratios (HRs) of individual trials and then combines them into a pooled meta-analytical estimate. The length of follow-up of individual trials is not generally accounted for. Recent techniques aimed at individual patient-data reconstruction from Kaplan-Meier graphs represent an important methodological innovation. These techniques permit the combination of the survival curves published in a single clinical trial but are also applicable to more than one trial. In the case of multiple trials, a meta-analysis can be conducted without using any statistical model of meta-analysis. Methods As an example of this new approach, we applied a technique of individual patient data reconstruction to the Kaplan-Meier graphs of overall survival reported in two phase-III trials, which were conducted on patients with locally advanced/advanced non-small cell lung cancer selected according to their PD-L1 expression status, not previously treated for their metastatic disease. Only subjects with PD-L1 ≥50% were considered for our analysis. The experimental arms received pembrolizumab monotherapy while the control arms were given platinum-based chemotherapy. The survival graphs were obtained for both trials. For each Kaplan-Meier curve, the graph was firstly digitalized. Then, the Shiny package was used to reconstruct patient-level data. Finally, the pooled survival curves were generated from the reconstructed patient-level data along with the relevant Cox statistics; for this purpose, we used three packages ("coxph", "survfit", and "ggsurvplot") under the R-platform. Results In our pooled analysis based on this procedure, we compared 453 patients given pembrolizumab vs. 451 controls given chemotherapy. The HR estimated from reconstructed patient-level data was 0.670 (95% confidence interval [CI], 0.566 to 0.793). Conclusion The analysis described herein demonstrates the easy applicability of the Shiny technique. This technique was successful in generating a pooled survival graph for the experimental treatment groups vs. controls and efficiently estimated the pooled HR in which the results of the two trials were combined.
Blockchain for Smart Mobility—Literature Review and Future Research Agenda

Today’s cities face numerous challenges due to climate change and urbanization. The concept of a smart city aims to help cities to address these challenges by adapting modern information and communication technology. Smart mobility and transportation form one important aspect of smart cities. Inefficient mobility in cities can lead to problems such as traffic congestion, which results in frustration for residents and a decrease in the quality of life. Against the backdrop of global warming, cities also strive to reduce CO2 emissions, an attempt which requires sustainable and novel mobility concepts. Blockchain is a current technology, said to have huge potential, that is being investigated for application in many facets of smart cities. In the context of smart mobility, blockchain can be used for transactions relating to ridesharing and electric charging, handling of interactions of platoon members, or serving as a foundation for communication between vehicles. Although initial research about this topic exists, it is distributed among different use-cases and applications. This article conducts a systematic literature review to analyze blockchain’s role in mobility and transportation in smart cities, and its potential to increase efficiency in these areas. With this review, we aim to consolidate and summarize the current knowledge about this topic. As a first result, we present the findings from our literature review, which can be divided into five categories of use-cases. We also present a platform for further research about this emerging topic by identifying promising future research avenues. For this purpose, we derive a future research agenda based on our findings.
Human-Aware Navigation Planner for Diverse Human-Robot Interaction Contexts

As more robots are being deployed into human environments, a human-aware navigation planner needs to handle multiple contexts that occur in indoor and outdoor environments. In this paper, we propose a tunable human-aware robot navigation planner that can handle a variety of human-robot contexts. We present the architecture of the system and discuss the features along with some implementation details. Then we present a detailed analysis of various simulated human-robot contexts using the proposed planner. Further, we show that our system performs better when compared with an exiting human-aware planner in various contexts. Finally, we show the results in a real-world scenario after deploying our system on a real robot.
A systematic literature review about the level of digital competences defined by DigCompEdu in higher education

Nowadays, we are facing a historical moment in which education practices are being transformed mainly due to the increase of technologies and their massive use at all levels of society, thus it is necessary to integrate them in educational contexts. In this context, the aim of this paper is to: analyse the level of technological competences of university professors. To this end, a systematic literature review based on the PRISMA methodology is carried out. The search was focused on WoS and SCOPUS databases. Initially, 815 documents have been retrieved and after applying the exclusion criteria 30 papers have been selected. The selected papers have been analysed in detail and the final conclusions have been structured according to DigCompEdu digital competence framework. The results show that the level of Digital Competences of University Professors (DCUP) is moderate and highlight aspects to improve: Reflective Practice is not cited in any article and Learner´s Empowerment and Facilitating Students' Digital Competence are the least referred. These aspects should be considered for future research. For this reason, it is recommended to carry out continuous training for university professors supported by the DigCompEdu framework.
Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human-computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.
AMLBID: An auto-explained Automated Machine Learning tool for Big Industrial Data

The Machine Learning (ML) based solutions in manufacturing industrial contexts often require skilled resources. More practical non-expert software solutions are then desired to enhance the usability of ML algorithms. The algorithm selection and configuration is one of the most difficult tasks for users like manufacturing specialists. The identification of the most appropriate algorithm in an automatic manner is among the major research challenges to achieve optimal performance of ML tools. In this paper, we present an auto-explained Automated Machine Learning tool for Big Industrial Data (AMLBID) to better cope with the prominent challenges posed by the evolution of Big Industrial Data. It is a meta-learning based decision support system for the automated selection and tuning of implied hyperparameters for ML algorithms. Moreover, the framework is equipped with an explainer module that makes the outcomes transparent and interpretable for well-performing ML systems.
An Immersive Virtual Reality Training Game for Power Substations Evaluated in Terms of Usability and Engagement

Safety-focused training is essential for the operation and maintenance concentrated on the reliability of critical infrastructures, such as power grids. This paper introduces and evaluates a system for power substation operational training by exploring and interacting with realistic models in virtual worlds using serious games. The virtual reality (VR) simulator used building information modelling (BIM) from a 115 kV substation to develop a scenario with high technical detail suitable for professional training. This system created interactive models that could be explored using a first-person-perspective serious game in a cave automatic virtual environment (CAVE). Different operational missions could be carried out in the serious game, allowing several skills to be coached. The suitability for vocational training carried out by utility companies was evaluated in terms of usability and engagement. The evaluation used a System Usability Scale (SUS) and a Game Engagement Questionnaire (GEQ) filled by 16 power substation operators demonstrating marginally acceptable usability, with improvement opportunities and high acceptance (by utility technicians) of this system for operation training focused on safety in such hazardous tasks.
Provably Secure Lattice-Based Self-Certified Signature Scheme

Digital signatures are crucial network security technologies. However, in traditional public key signature schemes, the certificate management is complicated and the schemes are vulnerable to public key replacement attacks. In order to solve the problems, in this paper, we propose a self-certified signature scheme over lattice. Using the self-certified public key, our scheme allows a user to certify the public key without an extra certificate. It can reduce the communication overhead and computational cost of the signature scheme. Moreover, the lattice helps prevent quantum computing attacks. Then, based on the small integer solution problem, our scheme is provable secure in the random oracle model. Furthermore, compared with the previous self-certified signature schemes, our scheme is more secure.
A Deep Learning Model Based on BERT and Sentence Transformer for Semantic Keyphrase Extraction on Big Social Data

In the evolution of the Internet, social media platform like Twitter has permitted the public user to share information such as famous current affairs, events, opinions, news, and experiences. Extracting and analyzing keyphrases in Twitter content is an essential and challenging task. Keyphrases can become precise the main contribution of Twitter content as well as it is a vital issue in vast Natural Language Processing (NLP) application. Extracting keyphrases is not only a time-consuming process but also requires much effort. The current works are on graph-based models or machine learning models. The performance of these models relies on feature extraction or statistical measures. In recent year, the application of deep learning algorithms to Twitter data have more insight due to automatic feature extraction can improve the performance of several tasks. This work aims to extract the keyphrase from Big social data using a sentence transformer with Bidirectional Encoder Representation Transformers (BERT) deep learning model. This BERT representation retains semantic and syntactic connectivity between tweets, enhancing performance in every NLP task on large data sets. It can automatically extract the most typical phrases in the Tweets. The proposed Semkey-BERT model shows that BERT with sentence transformer accuracy of 86% is higher than the other existing models.
Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning

Machine Learning (ML)-based methods have been identified as capable of providing up to one day ahead Photovoltaic (PV) power forecasts. In this research, we introduce a generic physical model of a PV system into ML predictors to forecast from one to three days ahead. The only requirement is a basic dataset including power, wind speed and air temperature measurements. Then, these are recombined into physics informed metrics able to capture the operational point of the PV. In this way, the models learn about the physical relationships of the different features, effectively easing training. In order to generalise the results, we also present a methodology evaluating this physics informed approach. We present a study-case of a PV system in Denmark to validate our claims by extensively evaluating five different ML methods: Random Forest, Support Vector Machine, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and a hybrid CNN-LSTM. The results show consistently how the best predictors use the proposed physics-informed features disregarding the particular ML-method, and forecasting horizon. However, also, how there is a threshold regarding the number of previous samples to be included that appears as a convex function.
Cybersecurity Enterprises Policies: A Comparative Study

Cybersecurity is a critical issue that must be prioritized not just by enterprises of all kinds, but also by national security. To safeguard an organization's cyberenvironments, information, and communication technologies, many enterprises are investing substantially in cybersecurity these days. One part of the cyberdefense mechanism is building an enterprises' security policies library, for consistent implementation of security controls. Significant and common cybersecurity policies of various enterprises are compared and explored in this study to provide robust and comprehensive cybersecurity knowledge that can be used in various enterprises. Several significant common security policies were identified and discussed in this comprehensive study. This study identified 10 common cybersecurity policy aspects in five enterprises: healthcare, finance, education, aviation, and e-commerce. We aimed to build a strong infrastructure in each business, and investigate the security laws and policies that apply to all businesses in each sector. Furthermore, the findings of this study reveal that the importance of cybersecurity requirements differ across multiple organizations. The choice and applicability of cybersecurity policies are determined by the type of information under control and the security requirements of organizations in relation to these policies.