Reviewerly/exHarmony huggingface dataset:
Orchestrating ubiquitous learning situations about Cultural Heritage with Casual Learn mobile application

Cultural Heritage learners can highly benefit from ubiquitous learning approaches that connect in-classroom activities with active on-site learning opportunities.However, teachers face the problem that the current landscape of technological support for learning Cultural Heritage mostly consists of mobile applications for informal contexts, completely decoupled from curricular activities.This paper presents the results of an exploratory-sequential mixed-methods study that focuses on the feasibility, for non-technical expert teachers, of repurposing an existing mobile application in formal Cultural Heritage ubiquitous learning situations.Such feasibility is explored from the perspective of the orchestration metaphor.More specifically, we used the "5+3 aspects orchestration framework" to understand the orchestration challenges of four ubiquitous learning situations about Cultural Heritage carried out in two secondary schools.These situations involved five teachers and 139 students who used a mobile application called Casual Learn for several weeks.The results of the study suggest that, after a brief training, teachers can design, enact, and orchestrate ubiquitous learning situations that involve both in-classroom and out-classroom activities.The teachers were able to transfer to the students most of the orchestration load of out-classroom activities.The results also showed that the flow of learning artifacts and information between in-and out-classroom activities is a critical issue that needs to be addressed by developers of ubiquitous learning technological solutions.
Mining context-aware resource profiles in the presence of multitasking

Healthcare organisations are becoming increasingly aware of the need to improve their care processes and to manage their scarce resources efficiently to secure high-quality care standards. As these processes are knowledge-intensive and heavily depend on human resources, a comprehensive understanding of the complex relationship between processes and resources is indispensable for efficient resource management. Organisational mining, a subfield of Process Mining, reveals insights into how (human) resources organise their work based on analysing process execution data recorded in Health Information Systems (HIS). This can be used to, e.g., discover resource profiles which are groups of resources performing similar activity instances, providing an extensive overview of resource behaviour within healthcare organisations. Healthcare managers can employ these insights to allocate their resources efficiently, e.g., by improving the scheduling and staffing of nurses. Existing resource profiling algorithms are limited in their ability to apprehend the complex relationship between processes and resources because they do not take into account the context in which activities were executed, particularly in the context of multitasking. Therefore, this paper introduces ResProMin-MT to discover context-aware resource profiles in the presence of multitasking. In contrast to the state-of-the-art, ResProMin-MT is capable of taking into account more complex contextual activity dimensions, such as activity durations and the degree of multitasking by resources. We demonstrate the feasibility of our method within a real-life healthcare context, validated by medical domain experts.
Two More Attacks on Proof-of-Stake GHOST/Ethereum

Ethereum, the world's second largest cryptocurrency with a market capitalization exceeding 120 billion USD as of this writing, aims to switch from Proof-of-Work (PoW) to Proof-of-Stake (PoS) based consensus later in the year 2022 (`the Merge'). Yet, so far, the proposed PoS consensus protocol lacks in rigorous security analysis. We present two new attack strategies targeting the PoS Ethereum consensus protocol. The first attack suggests a fundamental conceptual incompatibility between PoS and the Greedy Heaviest-Observed Sub-Tree (GHOST) fork choice paradigm employed by PoS Ethereum. In a nutshell, PoS allows an adversary with a vanishing amount of stake to produce an unlimited number of equivocating blocks. While most equivocating blocks will be orphaned, such orphaned 'uncle blocks' still influence fork choice under the GHOST paradigm, bestowing upon the adversary devastating control over the canonical chain. While the Latest Message Driven (LMD) aspect of current PoS Ethereum prevents a straightforward application of this attack, our second attack shows how LMD specifically can be exploited to obtain a new variant of the balancing attack that overcomes 'proposer boosting', a recent protocol addition that was intended to mitigate balancing-type attacks. Thus, in its current form, PoS Ethereum without and with LMD is vulnerable to our first and second attack, respectively.
European mineral intelligence – collecting, harmonizing and sharing data on European raw materials

Abstract The major share of raw materials needed to sustain our present lifestyle and even more importantly, required for the crucial green transition, are sourced outside Europe. The European Commission aims to enhance Europe's resilience and strengthen domestic sourcing. Although Europe has a long tradition of mining and extractive activities, it is acknowledged that there are several challenges to achieve European sourcing of certain raw materials such as the critical raw materials. A basic prerequisite to enable access to domestic raw materials is information on raw material occurrences, current and past mining activities, resources and reserves. The Geological Survey organizations (GSOs) of Europe play a key role in generating, compiling, gathering and storing the most up-to-date information as well as long-term data series on raw materials at national and regional levels. Over the last decade, the GSOs have joined forces and taken essential steps to harmonize and share data on raw materials. The results of this co-operation are illustrated as interactive maps on the European Geological Data Infrastructure (EGDI). This paper describes the data compiled in co-operation between the GSOs, and analyses the strengths and weaknesses of, as well as opportunities for and threats towards, the data.
Modified Neural Network-based Object Classification in Video Surveillance System

Visual surveillance emerged as an active automated research area of Computer Vision from the traditional mathematical approach to neural networks. A novel modified neural network technique for object detection and classification for input images and video feed from many cameras overlapping target areas is presented in this research.Modified Neural Network methodology represents layered architecture as the input, preprocessing and Operation layer, to simplify the processing needed to prepare for training neural networks. This strategy aids in delegating the tasks to layers with predefined tasks thus simplifying training, reducing computational requirements, and delivering performance. Two modules of the Neural Network will process the input. The first module is a modified Neural Network and will differ from traditional Neural Network in respect of connectivity between Neurons and their operations. This will still be Neural Network for data shared and threshold followed for marking differences – Markers, between the two inputs and simplified training. The second Module will be a traditional Neural Network for detection and classification that will track the detected objects. This paper proposed a system that provides the combined image as an output from multiple cameras feed using an untraditional Mathematical and Algorithmic Approach.
Robust Vehicle Detection Based on Improved You Look Only Once

Vehicle detection is still challenging for intelligent transportation systems (ITS) to achieve satisfactory performance.The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance.Due to advancements in detection technology, deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms.This paper presents a robust vehicle detection technique based on Improved You Look Only Once (RVD-YOLOv5) to enhance vehicle detection accuracy.The proposed method works in three phases; in the first phase, the K-means algorithm performs data clustering on datasets to generate the classes of the objects.Subsequently, in the second phase, the YOLOv5 is applied to create the bounding box, and the Non-Maximum Suppression (NMS) technique is used to eliminate the overlapping of the bounding boxes of the vehicle.Then, the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third phase.The simulation results show that the proposed method achieves better results when compared with other state-of-art techniques, namely Lightweight Dilated Convolutional Neural Network (LD-CNN), Single Shot Detector (SSD), YOLOv3 and YOLOv4 on the performance metric like precision, recall, mAP and F1-Score.The simulation and analysis are carried out on PASCAL VOC 2007, 2012 and MS COCO 2017 datasets to obtain better performance for vehicle detection.Finally, the RVD-YOLOv5 obtains the results with an mAP of 98.6% and Precision, Recall, and F1-Score are 98%, 96.2% and 97.09%, respectively.
Data-Driven Method for Detecting Flight Trajectory Deviation Anomaly

With the rapid growth of air traffic, it often happens that aircraft deviate from the original flight plan during actual flight. This paper proposes an anomaly detection method for aircraft trajectory deviation to realize single-point and successive multipoint anomaly detection from a data-driven perspective. Given the one-to-many relationship between reporting points of planned and real trajectories, a matching algorithm is used to match these points. Four trajectory deviation features (which are the position deviation, distance deviation, altitude deviation, and flight stage) are defined. On this basis, a one-class support vector machine is trained to detect single-point anomalies using the deviation features as input. Furthermore, successive multipoint anomaly detection of the aircraft is realized by considering the deviation of successive segments of the trajectory. Taking the flights taking off and landing at four Chinese hub airports as examples, the proposed method obtained an F score, which is a balance of the precision and recall, over 0.92, indicating it achieves high accuracy for anomaly detection.
Sentiment analysis using lexico-semantic features

Sentiment analysis of the text deals with the mining of the opinions of people from their written communication. With the increasing usage of online social media platforms for user interactions, abundant opinionated textual data emerges. Therefore, it leads to increased mining of opinions and sentiments and hence greater interest in sentiment analysis. The article introduces the novel Lexico-Semantic features and their use in the sentiment polarity task of English language text. These features are derived using the semantic extension of the lexicons by employing sentiment lexicons and semantic models. These features make data sample size consistent when used in deep learning settings, thereby eliminating the zero padding. For evaluation, we use different semantic models and lexicons to determine the role and impact of Lexico-Semantic features in classification performance. These features, along with the other features, are used to train the different classifiers. Our experimental evaluation shows that introducing Lexico-Semantic features to various state-of-the-art methods of both machine and deep learning improves the overall performance of classifiers.
Parsing randomness

Random data generators can be thought of as parsers of streams of randomness. This perspective on generators for random data structures is established folklore in the programming languages community, but it has never been formalized, nor have its consequences been deeply explored. We build on the idea of freer monads to develop free generators, which unify parsing and generation using a common structure that makes the relationship between the two concepts precise. Free generators lead naturally to a proof that a monadic generator can be factored into a parser plus a distribution over choice sequences. Free generators also support a notion of derivative, analogous to the familiar Brzozowski derivatives of formal languages, allowing analysis tools to "preview" the effect of a particular generator choice. This gives rise to a novel algorithm for generating data structures satisfying user-specified preconditions.
Exploring the Player Experiences of Wearable Gaming Interfaces: A User Elicitation Study

The design and development of playful wearable devices is a challenging and complicated problem. It entails not only multidisciplinary expertise but also a comprehensive understanding of player experience. There is a scarcity of evidence-based studies in current state-of-art literature that investigate general design practices and provide pragmatic design implications and suggestions based on solid user-centered research. To bridge the gap, we developed five experience prototypes based on the speculative design concepts from previous studies, and a Wizard of Oz experiment was conducted to elicit end users' feedback regarding general gaming experience as well as specific design themes in different gaming scenarios. The user experiment results were analyzed qualitatively following a rigorous thematic analysis, generating five major design implications as output. We believe this study will offer forward-looking insights to designers, developers and the research community, facilitating future work in this field.
Multi-agent actor-critic with time dynamical opponent model

In multi-agent reinforcement learning, multiple agents learn simultaneously while interacting with a common environment and each other. Since the agents adapt their policies during learning, not only the behavior of a single agent becomes non-stationary, but also the environment as perceived by the agent. This renders it particularly challenging to perform policy improvement. In this paper, we propose to exploit the fact that the agents seek to improve their expected cumulative reward and introduce a novel Time Dynamical Opponent Model (TDOM) to encode the knowledge that the opponent policies tend to improve over time. We motivate TDOM theoretically by deriving a lower bound of the log objective of an individual agent and further propose Multi-Agent Actor-Critic with Time Dynamical Opponent Model (TDOM-AC). We evaluate the proposed TDOM-AC on a differential game and the Multi-agent Particle Environment. We show empirically that TDOM achieves superior opponent behavior prediction during test time. The proposed TDOM-AC methodology outperforms state-of-the-art Actor-Critic methods on the performed tasks in cooperative and especially in mixed cooperative-competitive environments. TDOM-AC results in a more stable training and a faster convergence. Our code is available at https://github.com/Yuantian013/TDOM-AC.
Multi-level mutual supervision for cross-domain Person Re-identification

The challenges of cross-domain person re-identification mainly derive from two aspects: (1) The missing of target data labels. (2) The bias between source domain and target domain. Most of existing works focus on only one problem in the above two or deal with them separately. In this paper, we propose a new approach referred as to multi-level mutual supervision to achieve full utilization of labeled source data and unlabeled target data. Along this approach, we construct a dual-branch framework of which the upper branch is trained with original source data and target data while the lower branch is trained with augmented source data and target data. By applying common-pseudo-label and Maximum Mean Discrepancy (MMD) loss in our framework, the mutual supervision in multi levels is achieved. The results show that our model achieves SOTA performance on multiple popular benchmark datasets.
The development, evaluation and application of an aviation radiotelephony specialised technical vocabulary list

International Civil Aviation Organisation (ICAO) standard phraseology is an integral element of aviation radiotelephony (two-way radio) communication between air traffic controllers and other aviation personnel. A key issue with standard phraseology in this context is uncertainty as to its role. This is important in the aviation setting as miscommunication can lead to accidents. This paper presents a corpus-based Tower Aviation Radiotelephony Technical Vocabulary List (TARTVL) that is derived from ICAO standard phraseology. The list includes: technical words e.g. taxiway , proper noun classifications e.g. aircraft type , number classifications e.g. headings , acronyms e.g. FOD (Foreign Object Debris) and multiword units e.g. lineup and wait . Evaluation of the list shows that it is a promising training tool. The article discusses how the TARTVL can be used by English for specific purposes (ESP) and content instructors to provide aviation radiotelephony language training for ab initio, first language and non-native English speaking aviation personnel. Such training could help reduce miscommunication in multilingual workplaces, thus enhancing safety in aviation. • Technical vocabulary is critical in aviation radiotelephony communication. • The pedagogically oriented aviation technical vocabulary list contains 274 items. • The technical vocabulary list helps meet training needs of aviation personnel. • Technical vocabulary training could reduce miscommunication in aviation workplaces.
Data augmentation based estimation for the censored quantile regression neural network model

Quantile regression neural network (QRNN) model has received wide attentions in recent years to explore complex nonlinear problems. However, when the responses yi are subject to censoring (left censoring, right censoring and interval censoring might occur), predictions by using observed data, will lead to unbelievable results. Thus, new method for QRNN model with censored data is appealing. In this paper, we propose an iterative approach based on the data augmentation method for censored QRNN model estimation. Firstly the censored data are imputed through a data augmentation process, then the QRNN model is updated with the imputed data, finally we make predictions through the updated QRNN model. It is worth mentioning that simulation studies and real data illustrations show the superiority of our proposed method. Using the results based on full uncensored data as the benchmark, we compare the estimation efficiency of the proposed method with the existing ones. Our method outperforms others in terms of quantile loss and prediction interval width, yielding prediction results that are much closer to the benchmark. The proposed estimation method for censored QRNN model can be easily adapted to deal with different censoring types including left censoring, right censoring and interval censoring, remedying the defect that existing method is only suitable for right censoring type.
Improving Path Accuracy of Mobile Robots in Uncertain Environments by Adapted Bézier Curves

An algorithm that presents the best possible approximation for the theoretical Bézier curve and the real path on which a mobile robot moves in a dynamic environment with mobile obstacles and boundaries is introduced in this paper. The algorithm is tested on a set of scenarios that comprehensively cover critical situations of obstacle avoidance. The selection of scenarios is made by deploying robot navigation performances into constraints and further into descriptive characteristics of the scenarios. Computer-simulated environments are created with dedicated tools (i.e., Gazebo) and modeling and programming technologies (i.e., Robot Operating System (ROS) and Python). It is shown that the proposed algorithm improves the performance of the path for robot navigation in a highly dynamic environment, with dense mobile obstacles.
fastball: a fast algorithm to randomly sample bipartite graphs with fixed degree sequences

Many applications require randomly sampling bipartite graphs with fixed degrees, or randomly sampling incidence matrices with fixed row and column sums. Although several sampling algorithms exist, the ``curveball'' algorithm is the most efficient with an asymptotic time complexity of O(n log n), and has been proven to sample uniformly at random. In this paper, we introduce the ``fastball'' algorithm, which adopts a similar approach but has an asymptotic time complexity of O(n). We show that a C++ implementation of fastball randomly samples large bipartite graphs with fixed degrees faster than curveball, and illustrate the value of this faster algorithm in the context of the fixed degree sequence model for backbone extraction.
Dynamic hierarchical quantum secret sharing based on the multiscale entanglement renormalization ansatz

Tensor networks offer a novel and powerful tool for solving a variety of problems in mathematics, data science, and engineering. One such network is the multiscale entanglement renormalization ansatz (MERA). The MERA exhibits a hierarchical structure of layers, where each layer corresponds to a particular length (or energy) scale. The structure can be easily constructed using isometric and disentangler transformations. The following question arises: Is it possible to use the MERA to build hierarchical quantum secret sharing (HQSS)? The paper answers the question in the affirmative. In particular, it shows how a hierarchy of participant trust and authority relates to a MERA structure. The structure consists of binary and ternary MERA modules, which generate secret shares for participants. Because a binary MERA can be replaced by its ternary sibling and vice versa, our HQSS scheme is dynamic, allowing promotion and demotion of participants from the different layers but also enrollment of new ones and disenrollment of old ones from the same layer. The correctness and security of our dynamic hierarchical quantum secret sharing scheme are discussed.
Augmented Reality Based Interactive Cooking Guide

Cooking at home is a critical survival skill. We propose a new cooking assistance system in which a user only needs to wear an all-in-one augmented reality (AR) headset without having to install any external sensors or devices in the kitchen. Utilizing the built-in camera and cutting-edge computer vision (CV) technology, the user can direct the AR headset to recognize available food ingredients by simply looking at them. Based on the types of the recognized food ingredients, suitable recipes are suggested accordingly. A step-by-step video tutorial providing details of the selected recipe is then displayed with the AR glasses. The user can conveniently interact with the proposed system using eight kinds of natural hand gestures without needing to touch any devices throughout the entire cooking process. Compared with the deep learning models ResNet and ResNeXt, experimental results show that the YOLOv5 achieves lower accuracy for ingredient recognition, but it can locate and classify multiple ingredients in one shot and make the scanning process easier for users. Twenty participants test the prototype system and provide feedback via two questionnaires. Based on the analysis results, 19 of the 20 participants would recommend others to use the proposed system, and all participants are overall satisfied with the prototype system.