Reviewerly/exHarmony huggingface dataset:
UX in AR-Supported Industrial Human–Robot Collaborative Tasks: A Systematic Review

The fourth industrial revolution is promoting the Operator 4.0 paradigm, originating from a renovated attention towards human factors, growingly involved in the design of modern, human-centered processes. New technologies, such as augmented reality or collaborative robotics are thus increasingly studied and progressively applied to solve the modern operators’ needs. Human-centered design approaches can help to identify user’s needs and functional requirements, solving usability issues, or reducing cognitive or physical stress. The paper reviews the recent literature on augmented reality-supported collaborative robotics from a human-centered perspective. To this end, the study analyzed 21 papers selected after a quality assessment procedure and remarks the poor adoption of user-centered approaches and methodologies to drive the development of human-centered augmented reality applications to promote an efficient collaboration between humans and robots. To remedy this deficiency, the paper ultimately proposes a structured framework driven by User eXperience approaches to design augmented reality interfaces by encompassing previous research works. Future developments are discussed, stimulating fruitful reflections and a decisive standardization process.
Using virtual reality learning environments to motivate and socialize undergraduates in distance learning

Interest in virtual reality technologies has increased significantly in recent years, and an effort is being made to integrate it into educational environments. This study aims to investigate the impact of synchronous distance courses in the Virtual Reality (VR) learning environment (via vAcademia) and Web-based (via Adobe Connect) learning environment on undergraduate students' learning motivation and levels of perceived sociability. The sample of the study was conducted with experimental (VR) and control group (Web-based) quasi-experimental design consists of 41 undergraduate students (experimental: 21, control: 20). As a result of the t-test and two-way ANOVA analysis, there was a significant difference between the groups in favor of the VR group regarding motivation and perceived sociability. The motivation and perceived sociability in the 3D VR environment of the students in distance learning were significantly higher than in the web-based environment. It was also found out that students' motivation and perceived sociability levels did not vary depending on gender or on the technological device (smartphone or computer) that students use when connecting to online classes. The research results indicated that the VR learning environment effectively ensured students' motivation and sociability in the distance learning process. In this context, institutions should be aware of the importance of VR technologies and plan to teach accordingly which may make the process more efficient.
Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations

Emotion Recognition in Conversation (ERC) has gained much attention from the NLP community recently.Some models concentrate on leveraging commonsense knowledge or multitask learning to help complicated emotional reasoning.However, these models neglect direct utterance-knowledge interaction.In addition, these models utilize emotion-indirect auxiliary tasks, which provide limited affective information for the ERC task.To address the above issues, we propose a Knowledge-Interactive Network with sentiment polarity intensity-aware multi-task learning, namely KI-Net, which leverages both commonsense knowledge and sentiment lexicon to augment semantic information.Specifically, we use a self-matching module for internal utterance-knowledge interaction.Considering correlations with the ERC task, a phrase-level Sentiment Polarity Intensity Prediction (SPIP) task is devised as an auxiliary task.Experiments show that all knowledge introduction, self-matching and SPIP modules improve the model performance respectively on three datasets.Moreover, our KI-Net model shows 1.04% performance improvement over the state-of-the-art model on the IEMOCAP dataset. ConceptNet & SenticnetContext-and
Dynamic posted-price mechanisms for the blockchain transaction-fee market

In recent years, prominent blockchain systems such as Bitcoin and Ethereum have experienced explosive growth in transaction volume, leading to frequent surges in demand for limited block space and causing transaction fees to fluctuate by orders of magnitude. The status quo auctions sell space using a first-price auction [27]; however, users find it difficult to estimate how much they need to bid in order to get their transactions accepted onto the chain. If they bid too low, their transactions can have long confirmation times. If they bid too high, they pay larger fees than necessary.
Novel private data access control scheme suitable for mobile edge computing

Efficient response speed and information processing speed are among the characteristics of mobile edge computing (MEC). However, MEC easily causes information leakage and loss problems because it requires frequent data exchange. This work proposes an anonymous privacy data protection and access control scheme based on elliptic curve cryptography (ECC) and bilinear pairing to protect the communication security of the MEC. In the proposed scheme, the information sender encrypts private information through the ECC algorithm, and the information receiver uses its own key information and bilinear pairing to extract and verify the identity of the information sender. During each round of communication, the proposed scheme uses timestamps and random numbers to ensure the freshness of each round of conversation. Experimental results show that the proposed scheme has good security performance and can provide data privacy protection, integrity verification, and traceability for the communication process of MEC. The proposed scheme has a lower cost than other related schemes. The communication and computational cost of the proposed scheme are reduced by 31.08% and 22.31% on average compared with those of the other related schemes.
HRel: Filter pruning based on High Relevance between activation maps and class labels

This paper proposes an Information Bottleneck theory based filter pruning method that uses a statistical measure called Mutual Information (MI). The MI between filters and class labels, also called Relevance, is computed using the filter's activation maps and the annotations. The filters having High Relevance (HRel) are considered to be more important. Consequently, the least important filters, which have lower Mutual Information with the class labels, are pruned. Unlike the existing MI based pruning methods, the proposed method determines the significance of the filters purely based on their corresponding activation map's relationship with the class labels. Architectures such as LeNet-5, VGG-16, ResNet-56, ResNet-110 and ResNet-50 are utilized to demonstrate the efficacy of the proposed pruning method over MNIST, CIFAR-10 and ImageNet datasets. The proposed method shows the state-of-the-art pruning results for LeNet-5, VGG-16, ResNet-56, ResNet-110 and ResNet-50 architectures. In the experiments, we prune 97.98%, 84.85%, 76.89%, 76.95%, and 63.99% of Floating Point Operation (FLOP)s from LeNet-5, VGG-16, ResNet-56, ResNet-110, and ResNet-50 respectively. The proposed HRel pruning method outperforms recent state-of-the-art filter pruning methods. Even after pruning the filters from convolutional layers of LeNet-5 drastically (i.e., from 20, 50 to 2, 3, respectively), only a small accuracy drop of 0.52% is observed. Notably, for VGG-16, 94.98% parameters are reduced, only with a drop of 0.36% in top-1 accuracy. ResNet-50 has shown a 1.17% drop in the top-5 accuracy after pruning 66.42% of the FLOPs. In addition to pruning, the Information Plane dynamics of Information Bottleneck theory is analyzed for various Convolutional Neural Network architectures with the effect of pruning. The code is available at https://github.com/sarvanichinthapalli/HRel.
Optimal quantum annealing: A variational shortcut-to-adiabaticity approach

Suppressing unwanted transitions out of the instantaneous ground state is a major challenge in unitary adiabatic quantum computation. A recent approach consists in building counterdiabatic potentials approximated using variational strategies. In this contribution, we extend this variational approach to Lindbladian dynamics, having as a goal the suppression of diabatic transitions between pairs of Jordan blocks in quantum annealing. We show that, surprisingly, unitary counterdiabatic ans\"atze are successful for dissipative dynamics as well, allowing for easier experimental implementations compared to Lindbladian ans\"atze involving dissipation. Our approach not only guarantees improvements of open-system adiabaticity but also enhances the success probability of quantum annealing.
Models and Datasets for Cross-Lingual Summarisation

We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language.The corpus covers twelve language pairs and directions for four European languages, namely Czech, English, French and German, and the methodology for its creation can be applied to several other languages.We derive cross-lingual document-summary instances from Wikipedia by combining lead paragraphs and articles' bodies from language aligned Wikipedia titles.We analyse the proposed cross-lingual summarisation task with automatic metrics and validate it with a human study.To illustrate the utility of our dataset we report experiments with multi-lingual pretrained models in supervised, zero-and fewshot, and out-of-domain scenarios.
Diverging Quantum Speed Limits: A Herald of Classicality

When is the quantum speed limit (QSL) really quantum? While vanishing QSL times often indicate emergent classical behavior, it is still not entirely understood what precise aspects of classicality are at the origin of this dynamical feature. Here, we show that vanishing QSL times (or, equivalently, diverging quantum speeds) can be traced back to reduced uncertainty in quantum observables and can thus be understood as a consequence of emerging classicality for these particular observables. We illustrate this mechanism by developing a QSL formalism for continuous-variable quantum systems undergoing general Gaussian dynamics. For these systems, we show that three typical scenarios leading to vanishing QSL times, namely large squeezing, small effective Planck’s constant, and large particle number, can be fundamentally connected to each other. In contrast, by studying the dynamics of open quantum systems and mixed states, we show that the classicality that emerges due to incoherent mixing of states from the addition of classical noise typically increases the QSL time.Received 21 July 2021Accepted 10 November 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040349Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum controlQuantum information processingQuantum information processing with continuous variablesQuantum Information
Research on Face Recognition Classification Based on Improved GoogleNet

Face recognition is a relatively mature technology, which has some applications in many aspects, and now there are many networks studying it, which has indeed brought a lot of convenience to mankind in all aspects. This paper proposes a new face recognition technology. First, a new GoogLeNet-M network is proposed, which improves network performance on the basis of streamlining the network. Secondly, regularization and migration learning methods are added to improve accuracy. The experimental results show that the GoogLeNet-M network with regularization using migration learning technology has the best performance, with a recall rate of 0.97 and an accuracy of 0.98. Finally, it is concluded that the performance of the GoogLeNet-M network is better than other networks on the dataset, and the migration learning method and regularization help to improve the network performance.
Impact and dynamics of hate and counter speech online

Abstract Citizen-generated counter speech is a promising way to fight hate speech and promote peaceful, non-polarized discourse. However, there is a lack of large-scale longitudinal studies of its effectiveness for reducing hate speech. To this end, we perform an exploratory analysis of the effectiveness of counter speech using several different macro- and micro-level measures to analyze 131,366 political conversations that took place on German Twitter over four years. We report on the dynamic interactions of hate and counter speech over time and provide insights into whether, as in ‘classic’ bullying situations, organized efforts are more effective than independent individuals in steering online discourse. Taken together, our results build a multifaceted picture of the dynamics of hate and counter speech online. While we make no causal claims due to the complexity of discourse dynamics, our findings suggest that organized hate speech is associated with changes in public discourse and that counter speech—especially when organized—may help curb hateful rhetoric in online discourse.
AI-assisted knowledge assessment techniques for adaptive learning environments

The growth of online learning, enabled by the availability on the Internet of different forms of didactic materials such as MOOCs and Intelligent Tutoring Systems (ITS), in turn, increases the relevance of personalized instructions for students in an adaptive learning environment. There are increasing interests as well as many challenges in the application of Artificial Intelligence (AI) techniques in educational settings to provide adaptive learning content to learners. Knowledge assessment is necessary for providing an adaptive learning environment. A student model serves as a fundamental building block of knowledge assessment in an adaptive learning environment. This paper intends to review the development of dominant families of student models with psychometric theory in early educational research, recent adaptations, and advances with machine learning and deep learning techniques. Our review covers not only the important families of student models but also why they were invented from both theoretical and practical viewpoints with AI and educational perspectives. We believe that the discussion covered in this review will be a valuable reference of introductory insights to AI for educational researchers, as well as an endeavor of introducing basic psychometric perspectives to AI experts for knowledge assessment in the field of learning science. Finally, we provide recent challenges and some potential directions for developing efficient knowledge assessment techniques in future adaptive learning ecosystems.
Decentralized probabilistic multi-robot collision avoidance using buffered uncertainty-aware Voronoi cells

In this paper, we present a decentralized and communication-free collision avoidance approach for multi-robot systems that accounts for both robot localization and sensing uncertainties. The approach relies on the computation of an uncertainty-aware safe region for each robot to navigate among other robots and static obstacles in the environment, under the assumption of Gaussian-distributed uncertainty. In particular, at each time step, we construct a chance-constrained buffered uncertainty-aware Voronoi cell (B-UAVC) for each robot given a specified collision probability threshold. Probabilistic collision avoidance is achieved by constraining the motion of each robot to be within its corresponding B-UAVC, i.e. the collision probability between the robots and obstacles remains below the specified threshold. The proposed approach is decentralized, communication-free, scalable with the number of robots and robust to robots' localization and sensing uncertainties. We applied the approach to single-integrator, double-integrator, differential-drive robots, and robots with general nonlinear dynamics. Extensive simulations and experiments with a team of ground vehicles, quadrotors, and heterogeneous robot teams are performed to analyze and validate the proposed approach.
Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network

The aim of this study was to develop and test a post-processing technique for detection and classification of lesions according to the BI-RADS atlas in automated breast ultrasound (ABUS) based on deep convolutional neural networks (dCNNs).In this retrospective study, 645 ABUS datasets from 113 patients were included; 55 patients had lesions classified as high malignancy probability. Lesions were categorized in BI-RADS 2 (no suspicion of malignancy), BI-RADS 3 (probability of malignancy < 3%), and BI-RADS 4/5 (probability of malignancy > 3%). A deep convolutional neural network was trained after data augmentation with images of lesions and normal breast tissue, and a sliding-window approach for lesion detection was implemented. The algorithm was applied to a test dataset containing 128 images and performance was compared with readings of 2 experienced radiologists.Results of calculations performed on single images showed accuracy of 79.7% and AUC of 0.91 [95% CI: 0.85-0.96] in categorization according to BI-RADS. Moderate agreement between dCNN and ground truth has been achieved (κ: 0.57 [95% CI: 0.50-0.64]) what is comparable with human readers. Analysis of whole dataset improved categorization accuracy to 90.9% and AUC of 0.91 [95% CI: 0.77-1.00], while achieving almost perfect agreement with ground truth (κ: 0.82 [95% CI: 0.69-0.95]), performing on par with human readers. Furthermore, the object localization technique allowed the detection of lesion position slice-wise.Our results show that a dCNN can be trained to detect and distinguish lesions in ABUS according to the BI-RADS classification with similar accuracy as experienced radiologists.• A deep convolutional neural network (dCNN) was trained for classification of ABUS lesions according to the BI-RADS atlas. • A sliding-window approach allows accurate automatic detection and classification of lesions in ABUS examinations.
Mobile augmented reality adapted to the ARCS model of motivation: a case study during the COVID-19 pandemic

The rushed introduction of online education for universities because of the current covid-19 health crisis, has started to affect the quality of education for millions of students around the world. This pandemic has emphasized the need to improve the teaching process through the use of innovating educational tools, such as mobile augmented reality (mAR). This pilot study intends to evaluate the relationship between motivation and meaningful learning for university students through mAR, as well as the effects and implications of its use how supporting teaching activities in an Industrial Design and Technical Drawing course. A quantitative method research approach was applied to collect, process, and analyze the research data of the students' perception under the health restrictions caused by the current pandemic, using the Instructional Materials Motivation Survey (IMMS). The total number of questionnaires collected was 96, applied to students of the Industrial Engineering major at Universidad Católica del Maule in Chile. The instrument's reliability was measured using Cronbach's alpha, giving an overall value of 0.89. The implementation of an instructional model called ARCS (Attention-Relevance-Confidence-Satisfaction) was achieved. The implementation of mRA was positively valued by most of the students surveyed. An increase the percentage of students who achieved the expected learning objectives was found, in compared to previous versions of the course (without mAR). Finally, the results demonstrate a positive relation between mAR and the learning level achieved by students. There was no evidence of negative effects under the particular imposed conditions, because of sanitary restrictions when implementing the mAR.
Facilitating adoption of AI in natural disaster management through collaboration

Artificial intelligence can enhance our ability to manage natural disasters. However, understanding and addressing its limitations is required to realize its benefits. Here, we argue that interdisciplinary, multistakeholder, and international collaboration is needed for developing standards that facilitate its implementation.
AALpy: an active automata learning library

Abstract AALpy is an extensible open-source Python library providing efficient implementations of active automata learning algorithms for deterministic, non-deterministic, and stochastic systems. We put a special focus on the conformance testing aspect in active automata learning, as well as on an intuitive and seamlessly integrated interface for learning automata characterizing real-world reactive systems. In this article, we present AALpy ’s core functionalities, illustrate its usage via examples, and evaluate its learning performance. Finally, we present selected case studies on learning models of various types of systems with AALpy .
Ontologies4Chem: the landscape of ontologies in chemistry

Abstract For a long time, databases such as CAS, Reaxys, PubChem or ChemSpider mostly rely on unique numerical identifiers or chemical structure identifiers like InChI, SMILES or others to link data across heterogeneous data sources. The retrospective processing of information and fragmented data from text publications to maintain these databases is a cumbersome process. Ontologies are a holistic approach to semantically describe data, information and knowledge of a domain. They provide terms, relations and logic to semantically annotate and link data building knowledge graphs. The application of standard taxonomies and vocabularies from the very beginning of data generation and along research workflows in electronic lab notebooks (ELNs), software tools, and their final publication in data repositories create FAIR data straightforwardly. Thus a proper semantic description of an investigation and the why, how, where, when, and by whom data was produced in conjunction with the description and representation of research data is a natural outcome in contrast to the retrospective processing of research publications as we know it. In this work we provide an overview of ontologies in chemistry suitable to represent concepts of research and research data. These ontologies are evaluated against several criteria derived from the FAIR data principles and their possible application in the digitisation of research data management workflows.