Reviewerly/exHarmony huggingface dataset:
Photonic architecture for reinforcement learning

The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with these recent developments, this work brings together the state of the art of both fields within the framework of reinforcement learning. We present the blueprint for a photonic implementation of an active learning machine incorporating contemporary algorithms such as SARSA, Q-learning, and projective simulation. We numerically investigate its performance within typical reinforcement learning environments, showing that realistic levels of experimental noise can be tolerated or even be beneficial for the learning process. Remarkably, the architecture itself enables mechanisms of abstraction and generalization, two features which are often considered key ingredients for artificial intelligence. The proposed architecture, based on single-photon evolution on a mesh of tunable beamsplitters, is simple, scalable, and a first integration in portable systems appears to be within the reach of near-term technology.
Safe Reinforcement Learning Using Probabilistic Shields

This paper concerns the efficient construction of a safety shield for reinforcement learning. We specifically target scenarios that incorporate uncertainty and use Markov decision processes (MDPs) as the underlying model to capture such problems. Reinforcement learning (RL) is a machine learning technique that can determine near-optimal policies in MDPs that may be unknown before exploring the model. However, during exploration, RL is prone to induce behavior that is undesirable or not allowed in safety- or mission-critical contexts. We introduce the concept of a probabilistic shield that enables RL decision-making to adhere to safety constraints with high probability. We employ formal verification to efficiently compute the probabilities of critical decisions within a safety-relevant fragment of the MDP. These results help to realize a shield that, when applied to an RL algorithm, restricts the agent from taking unsafe actions, while optimizing the performance objective. We discuss tradeoffs between sufficient progress in the exploration of the environment and ensuring safety. In our experiments, we demonstrate on the arcade game PAC-MAN and on a case study involving service robots that the learning efficiency increases as the learning needs orders of magnitude fewer episodes.
Fully Automatic Assessment of Background Parenchymal Enhancement on Breast <scp>MRI</scp> Using Machine‐Learning Models

Background Automated measurement and classification models with objectivity and reproducibility are required for accurate evaluation of the breast cancer risk of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE). Purpose To develop and evaluate a machine‐learning algorithm for breast FGT segmentation and BPE classification. Study Type Retrospective. Population A total of 794 patients with breast cancer, 594 patients assigned to the development set, and 200 patients to the test set. Field Strength/Sequence 3T and 1.5T; T 2 ‐weighted, fat‐saturated T 1 ‐weighted (T 1 W) with dynamic contrast enhancement (DCE). Assessment Manual segmentation was performed for the whole breast and FGT regions in the contralateral breast. The BPE region was determined by thresholding using the subtraction of the pre‐ and postcontrast T 1 W images and the segmented FGT mask. Two radiologists independently assessed the categories of FGT and BPE. A deep‐learning‐based algorithm was designed to segment and measure the volume of whole breast and FGT and classify the grade of BPE. Statistical Tests Dice similarity coefficients (DSC) and Spearman correlation analysis were used to compare the volumes from the manual and deep‐learning‐based segmentations. Kappa statistics were used for agreement analysis. Comparison of area under the receiver operating characteristic (ROC) curves (AUC) and F1 scores were calculated to evaluate the performance of BPE classification. Results The mean (±SD) DSC for manual and deep‐learning segmentations was 0.85 ± 0.11. The correlation coefficient for FGT volume from manual‐ and deep‐learning‐based segmentations was 0.93. Overall accuracy of manual segmentation and deep‐learning segmentation in BPE classification task was 66% and 67%, respectively. For binary categorization of BPE grade (minimal/mild vs. moderate/marked), overall accuracy increased to 91.5% in manual segmentation and 90.5% in deep‐learning segmentation; the AUC was 0.93 in both methods. Data Conclusion This deep‐learning‐based algorithm can provide reliable segmentation and classification results for BPE. Level of Evidence 3 Technical Efficacy Stage 2
Generating and Understanding Personalized Explanations in Hybrid Recommender Systems

Recommender systems are ubiquitous and shape the way users access information and make decisions. As these systems become more complex, there is a growing need for transparency and interpretability. In this article, we study the problem of generating and visualizing personalized explanations for recommender systems that incorporate signals from many different data sources. We use a flexible, extendable probabilistic programming approach and show how we can generate real-time personalized recommendations. We then turn these personalized recommendations into explanations. We perform an extensive user study to evaluate the benefits of explanations for hybrid recommender systems. We conduct a crowd-sourced user study where our system generates personalized recommendations and explanations for real users of the last.fm music platform. First, we evaluate the performance of the recommendations in terms of perceived accuracy and novelty. Next, we experiment with (1) different explanation styles (e.g., user-based, item-based), (2) manipulating the number of explanation styles presented, and (3) manipulating the presentation format (e.g., textual vs. visual). We also apply a mixed-model statistical analysis to consider user personality traits as a control variable and demonstrate the usefulness of our approach in creating personalized hybrid explanations with different style, number, and format. Finally, we perform a post analysis that shows different preferences for explanation styles between experienced and novice last.fm users.
Multi-view Action Recognition Using Cross-View Video Prediction

In this work, we address the problem of action recognition in a multi-view environment. Most of the existing approaches utilize pose information for multi-view action recognition. We focus on RGB modality instead and propose an unsupervised representation learning framework, which encodes the scene dynamics in videos captured from multiple viewpoints via predicting actions from unseen views. The framework takes multiple short video clips from different viewpoints and time as input and learns an holistic internal representation which is used to predict a video clip from an unseen viewpoint and time. The ability of the proposed network to render unseen video frames enables it to learn a meaningful and robust representation of the scene dynamics. We evaluate the effectiveness of the learned representation for multi-view video action recognition in a supervised approach. We observe a significant improvement in the performance with RGB modality on NTU-RGB+D dataset, which is the largest dataset for multi-view action recognition. The proposed framework also achieves state-of-the-art results with depth modality, which validates the generalization capability of the approach to other data modalities. The code is publicly available at https://github.com/svyas23/cross-view-action .
The Impact of Covid-19 on International Higher Education: New Models for the New Normal

COVID-19 has had a major impact on international higher education with border closures, cancelled flights, and a shift to online teaching and learning. As a result, many international students have decided to either abandon or defer their plans to study abroad. If students stay in their home countries, many institutions that rely heavily on foreign students’ fees will suffer, with potential impacts on national economies. Beyond the economic implications, it is also important to consider the personal impact of COVID-19 on international students, who may face delays or obstacles to program completion, employment and/or immigration. Though there are certainly risks and losses in the short term, the demand for international education, and the benefits it offers, are expected to grow. This presents an opportunity for higher education institutions (HEIs) and governments, not just to lessen the impact of COVID-19 on their current business models, but to explore new models and opportunities. HEIs and governments must look at redefining international higher education for the new normal, which will entail a shift in policies and programmes. This paper outlines the implications of the COVID-19 crisis for international higher education and presents potential opportunities for governments and higher education institutions to refresh and redefine their approaches for the new normal.
Deep convolutional neural network based autonomous drone navigation

This paper presents a novel approach for aerial drone autonomous navigation along predetermined paths using only visual input form an onboard camera and without reliance on a Global Positioning System (GPS). It is based on using a deep Convolutional Neural Network (CNN) combined with a regressor to output the drone steering commands. Furthermore, multiple auxiliary navigation paths that form a ‘navigation envelope’ are used for data augmentation to make the system adaptable to real-life deployment scenarios. The approach is suitable for automating drone navigation in applications that exhibit regular trips or visits to same locations such as environmental and desertification monitoring, parcel/aid delivery and drone-based wireless internet delivery. In this case, the proposed algorithm replaces human operators, enhances accuracy of GPS-based map navigation, alleviates problems related to GPS-spoofing and enables navigation in GPS-denied environments. Our system is tested in two scenarios using the Unreal Engine-based AirSim [32] plugin for drone simulation with promising results of average cross track distance less than 1.4 meters and mean waypoints minimum distance of less than 1 meter.
Multi-Access Edge Computing and Blockchain-based Secure Telehealth System Connected with 5G and IoT

There is a global hype in the development of digital healthcare infrastructure to cater the massive elderly population and infectious diseases. The digital facilitation is expected to ensure the patient privacy, scalability, and data integrity on the sensitive life critical healthcare data, while aligning to the global healthcare data protection standards. The patient data sharing to third parties such as research institutions and universities is also concerned as a significant contribution to the society to sharpen the research and investigations. The emergence of 5G communication technologies eradicates the borders between patients, hospital and other institutions with high end service standards. In patients' perspective, healthcare service delivery through the digital medium is beneficial in terms of time, costs, and risks. In this paper, we propose a novel Multi-access Edge Computing(MEC) and blockchain based service architecture utilizing the lightweight ECQV (Elliptic Curve Qu-Vanstone) certificates for the realtime data privacy, integrity, and authentication between IoT, MEC, and cloud. We further attached storage offloading capability to the blockchain to ensure scalability with a massive number of connected medical devices to the cloud. We introduced a rewarding scheme to the patients and hospitals through the blockchain to encourage data sharing. The access control is handled through the smart contracts. We evaluated the proposed system in a near realistic implementation using Hyperledger Fabric blockchain platform with Raspberry Pi devices to simulate the activity of the medical sensors.
Variable Magnitude and Intensity of Strombolian Explosions: Focus on the Eruptive Processes for a First Classification Scheme for Stromboli Volcano (Italy)

Strombolian activity varies in magnitude and intensity and may evolve into a threat for the local populations living on volcanoes with persistent or semi-persistent activity. A key example comes from the activity of Stromboli volcano (Italy). The “ordinary” Strombolian activity, consisting in intermittent ejection of bombs and lapilli around the eruptive vents, is sometimes interrupted by high-energy explosive events (locally called major or paroxysmal explosions), which can affect very large areas. Recently, the 3 July 2019 explosive paroxysm at Stromboli volcano caused serious concerns in the local population and media, having killed one tourist while hiking on the volcano. Major explosions, albeit not endangering inhabited areas, often produce a fallout of bombs and lapilli in zones frequented by tourists. Despite this, the classification of Strombolian explosions on the basis of their intensity derives from measurements that are not always replicable (i.e., field surveys). Hence the need for a fast, objective and quantitative classification of explosive activity. Here, we use images of the monitoring camera network, seismicity and ground deformation data, to characterize and distinguish paroxysms, impacting the whole island, from major explosions, that affect the summit of the volcano above 500 m elevation, and from the persistent, mild explosive activity that normally has no impact on the local population. This analysis comprises 12 explosive events occurring at Stromboli after 25 June 2019 and is updated to 6 December 2020.
A Novel Structure of Blockchain Applied in Vaccine Quality Control: Double-Chain Structured Blockchain System for Vaccine Anticounterfeiting and Traceability

Vaccine, as an irreplaceable means in herd immunization, is widely applied in prevention for communicable diseases. However, adverse impacts were frequently incurred by fake or expired vaccines in China. Given the necessity of vaccine anticounterfeiting, blockchain-based transaction platform could be practiced as a solution in addressing the issue; however, most of the available experiments focused on single-chain structured design with inventible limitations. Accordingly, exploration for the effectiveness and feasibility of mixed-chains structured platform for vaccine anticounterfeiting and tracing is essentially required.Both public chain and private chain were inserted in anticounterfeiting and tracing platform designing process, which were subsequently simulated in Ethereum environment.By recording different information in public chain and private chain, partial information privacy protection requirements are realized. The transfer identification module realized the function of vaccine quality supervision and solves the problem of EPC label replication. Discussion. Compared with the traditional single-structured design, completeness information could be visited by all stakeholders in double-chain structure, including vaccine suppliers, National Medical Products Administration (NMPA), vaccine purchasers, and the vaccinated.Double-chain structured system for vaccine anticounterfeiting and tracing is more effective.
Analysis and Improvement of Steganography Protocol Based on Bell States in Noise Environment

In the field of quantum communication, quantum steganography is an important branch of quantum information hiding. In a realistic quantum communication system, quantum noises are unavoidable and will seriously impact the safe... | Find, read and cite all the research you need on Tech Science Press
Administrative risks challenging the adoption of smart contracts in construction projects

Purpose As a remedy to usually voluminous, complicated and not easily readable construction contracts, smart contracts can be considered as an effective and alternative solution. However, the construction industry is merely known as a frontrunner for fast adoption of recent technological advancements. Numerous administrative risks challenge construction companies to implement smart contracts. To highlight this issue, this study aims to assess the administrative risks of smart contract adoption in construction projects. Design/methodology/approach A literature survey is conducted to specify administrative risks of smart contracts followed by a pilot study to ensure that the framework is suitable to the research question. The criteria weights are calculated through the fuzzy analytical hierarchy process method, followed by a sensitivity analysis based on degree of fuzziness, which supports the robustness of the developed hierarchy and stability of the results. Then, a focus group discussion (FGD) is performed to discuss the mitigation strategies for the top-level risks in each risk category. Findings The final framework consists of 27 sub-criteria, which are categorized under five main criteria, namely, contractual, cultural, managerial, planning and relational. The findings show that (1) regulation change, (2) lack of a driving force, (3) works not accounted in planning, (4) shortcomings of current legal arrangements and (5) lack of dispute resolution mechanism are the top five risks challenging the adoption of smart contracts in construction projects. Risk mitigation strategies based on FGD show that improvements for the semi-automated smart contract drafting are considered more practicable compared to full automation. Originality/value The literature is limited in terms of the adoption of smart contracts, while the topic is receiving more attention recently. To support easy prevalence of smart contracts, this study attempts the most challenging aspects of smart contract adoption.
TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos

We consider predicting the user's head motion in 360 ° videos, with 2 modalities only: the past user's positions and the video content (not knowing other users' traces). We make two main contributions. First, we re-examine existing deep-learning approaches for this problem and identify hidden flaws from a thorough root-cause analysis. Second, from the results of this analysis, we design a new proposal establishing state-of-the-art performance. First, re-assessing the existing methods that use both modalities, we obtain the surprising result that they all perform worse than baselines using the user's trajectory only. A root-cause analysis of the metrics, datasets and neural architectures shows in particular that (i) the content can inform the prediction for horizons longer than 2 to 3 sec. (existing methods consider shorter horizons), and that (ii) to compete with the baselines, it is necessary to have a recurrent unit dedicated to process the positions, but this is not sufficient. Second, from a re-examination of the problem supported with the concept of Structural-RNN, we design a new deep neural architecture, named TRACK. TRACK achieves state-of-the-art performance on all considered datasets and prediction horizons, outperforming competitors by up to 20 percent on focus-type videos and horizons 2-5 seconds. The entire framework (codes and datasets) is online and received an ACM reproducibility badge https://gitlab.com/miguelfromeror/head-motion-prediction.
COVID-19 Digital Vaccination Certificates and Digital Technologies: Lessons from Digital Contact Tracing Apps

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Effects of Linear Visual-Vestibular Conflict on Presence, Perceived Scene Stability and Cybersickness in the Oculus Go and Oculus Quest

Humans rely on multiple senses to perceive their self-motion in the real world. For example, a sideways linear head translation can be sensed either by lamellar optic flow of the visual scene projected on the retina of the eye or by stimulation of vestibular hair cell receptors found in the otolith macula of the inner ear. Mismatches in visual and vestibular information can induce cybersickness during head-mounted display (HMD) based virtual reality (VR). In this pilot study, participants were immersed in a virtual environment using two recent consumer-grade HMDs: the Oculus Go (3DOF angular only head tracking) and the Oculus Quest (6DOF angular and linear head tracking). On each trial they generated horizontal linear head oscillations along the interaural axis at a rate of 0.5 Hz. This head movement should generate greater sensory conflict when viewing the virtual environment on the Oculus Go (compared to the Quest) due to the absence of linear tracking. We found that perceived scene instability always increased with the degree of linear visual-vestibular conflict. However, cybersickness was not experienced by 7/14 participants, but was experienced by the remaining participants in at least one of the stereoscopic viewing conditions (six of whom also reported cybersickness in monoscopic viewing conditions). No statistical difference in spatial presence was found across conditions, suggesting that participants could tolerate considerable scene instability while retaining the feeling of being there in the virtual environment. Levels of perceived scene instability, spatial presence and cybersickness were found to be similar between the Oculus Go and the Oculus Quest with linear tracking disabled. The limited effect of linear coupling on cybersickness, compared with its strong effect on perceived scene instability, suggests that perceived scene instability may not always be associated with cybersickness. However, perceived scene instability does appear to provide explanatory power over the cybersickness observed in stereoscopic viewing conditions.