Reviewerly/exHarmony huggingface dataset:
Analyzing Differentiable Fuzzy Logic Operators

The AI community is increasingly putting its attention towards combining symbolic and neural approaches, as it is often argued that the strengths and weaknesses of these approaches are complementary. One recent trend in the literature is weakly supervised learning techniques that employ operators from fuzzy logics. In particular, these use prior background knowledge described in such logics to help the training of a neural network from unlabeled and noisy data. By interpreting logical symbols using neural networks, this background knowledge can be added to regular loss functions, hence making reasoning a part of learning. We study, both formally and empirically, how a large collection of logical operators from the fuzzy logic literature behave in a differentiable learning setting. We find that many of these operators, including some of the most well-known, are highly unsuitable in this setting. A further finding concerns the treatment of implication in these fuzzy logics, and shows a strong imbalance between gradients driven by the antecedent and the consequent of the implication. Furthermore, we introduce a new family of fuzzy implications (called sigmoidal implications) to tackle this phenomenon. Finally, we empirically show that it is possible to use Differentiable Fuzzy Logics for semi-supervised learning, and compare how different operators behave in practice. We find that, to achieve the largest performance improvement over a supervised baseline, we have to resort to non-standard combinations of logical operators which perform well in learning, but no longer satisfy the usual logical laws.
Facing the challenges of ‘digital competence’

Digital technology has become increasingly central in curriculum plans during the last decade, but there is hardly any research about the curriculum in the digital age, nor about 'digital competence' as a new didactic area that has entered the educational field.In this article, we discuss and analyse two interrelated issues: first, on recent trends in curriculum developments in Norway, Sweden and Finland concerning digital competence; and, second, on related research initiatives of relevance for digital competence as an educational field.The article is based on a theoretically informed review of policy documents during the last decade and research initiatives in this field.The outcome of our study show that digital competence has become an increasingly complex concept, with a key emphasis in recent curricula, and is strongly linked to 21 st century education.The article contributes to the ongoing debate about digital competence as part of contemporary educational strategies.
Global refining catalysts market (2021 to 2026) – Growth, trends, COVID-19 impact and forecasts

OntoFarm is a collection of heterogeneously structured ontologies describing the same domain (conference organization). It has been repeatedly used for benchmarking purposes within the OAEI conference track, but also in several other projects. Since its inception in 2005, it has undergone several iterations of extension and improvement, especially through addition of pairwise reference alignments. Ten years after the initial launch of the collection seems to be adequate time to collect and summarize feedback on its usage, benefits and shortcomings. Besides the analysis of the involved ontologies proper and of the related literature, we performed a survey among the users of OntoFarm addressing the current role of the OntoFarm collection and its prospects for the future. The majority of respondents intends to keep using the collection in the future, ideally with additional reference alignments.
The Serverless Computing Survey: A Technical Primer for Design Architecture

The development of cloud infrastructures inspires the emergence of cloud-native computing. As the most promising architecture for deploying microservices, serverless computing has recently attracted more and more attention in both industry and academia. Due to its inherent scalability and flexibility, serverless computing becomes attractive and more pervasive for ever-growing Internet services. Despite the momentum in the cloud-native community, the existing challenges and compromises still wait for more advanced research and solutions to further explore the potential of the serverless computing model. As a contribution to this knowledge, this article surveys and elaborates the research domains in the serverless context by decoupling the architecture into four stack layers: Virtualization, Encapsule, System Orchestration, and System Coordination. Inspired by the security model, we highlight the key implications and limitations of these works in each layer, and make suggestions for potential challenges to the field of future serverless computing.
GROMACS in the Cloud: A Global Supercomputer to Speed Up Alchemical Drug Design

We assess costs and efficiency of state-of-the-art high-performance cloud computing and compare the results to traditional on-premises compute clusters. Our use case is atomistic simulations carried out with the GROMACS molecular dynamics (MD) toolkit with a particular focus on alchemical protein-ligand binding free energy calculations. We set up a compute cluster in the Amazon Web Services (AWS) cloud that incorporates various different instances with Intel, AMD, and ARM CPUs, some with GPU acceleration. Using representative biomolecular simulation systems, we benchmark how GROMACS performs on individual instances and across multiple instances. Thereby we assess which instances deliver the highest performance and which are the most cost-efficient ones for our use case. We find that, in terms of total costs, including hardware, personnel, room, energy, and cooling, producing MD trajectories in the cloud can be about as cost-efficient as an on-premises cluster given that optimal cloud instances are chosen. Further, we find that high-throughput ligand-screening can be accelerated dramatically by using global cloud resources. For a ligand screening study consisting of 19 872 independent simulations or ∼200 μs of combined simulation trajectory, we made use of diverse hardware available in the cloud at the time of the study. The computations scaled-up to reach peak performance using more than 4 000 instances, 140 000 cores, and 3 000 GPUs simultaneously. Our simulation ensemble finished in about 2 days in the cloud, while weeks would be required to complete the task on a typical on-premises cluster consisting of several hundred nodes.
Deep learning for recommender systems: A Netflix case study

Abstract Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service. We found that different model architectures excel at different tasks. Even though many deep‐learning models can be understood as extensions of existing (simple) recommendation algorithms, we initially did not observe significant improvements in performance over well‐tuned non‐deep‐learning approaches. Only when we added numerous features of heterogeneous types to the input data, deep‐learning models did start to shine in our setting. We also observed that deep‐learning methods can exacerbate the problem of offline–online metric (mis‐)alignment. After addressing these challenges, deep learning has ultimately resulted in large improvements to our recommendations as measured by both offline and online metrics. On the practical side, integrating deep‐learning toolboxes in our system has made it faster and easier to implement and experiment with both deep‐learning and non‐deep‐learning approaches for various recommendation tasks. We conclude this article by summarizing our take‐aways that may generalize to other applications beyond Netflix.
Fault Sneaking Attack

Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring high reliability. We propose the fault sneaking attack on DNNs, where the adversary aims to misclassify certain input images into any target labels by modifying the DNN parameters. We apply ADMM (alternating direction method of multipliers) for solving the optimization problem of the fault sneaking attack with two constraints: 1) the classification of the other images should be unchanged and 2) the parameter modifications should be minimized. Specifically, the first constraint requires us not only to inject designated faults (misclassifications), but also to hide the faults for stealthy or sneaking considerations by maintaining model accuracy. The second constraint requires us to minimize the parameter modifications (using ℓ0 norm to measure the number of modifications and ℓ2 norm to measure the magnitude of modifications). Comprehensive experimental evaluation demonstrates that the proposed framework can inject multiple sneaking faults without losing the overall test accuracy performance.
The environmental impact of cryptocurrencies using proof of work and proof of stake consensus algorithms: A systematic review

A systematic literature review is conducted to investigate the environmental impact of Proof of Work (PoW) and Proof of Stake (PoS) cryptocurrencies. Due to the focus of recent research on Bitcoin, an inductive approach has been applied to analyze and cluster the findings of PoW cryptocurrencies into seven aspects that effect the environmental impact of cryptocurrencies: resources, energy consumption, carbon footprint, environmental-related social and economic aspects, policy regulation and subsidization, and electronic waste. Subsequently, interconnections and rebound effects are presented and discussed by synthesizing results from each of the seven aspects into one scenario analysis inspired by the crackdown on cryptocurrency miners in China, 2021. Furthermore, it was observed that proposed policy regulation in literature is strongly focusing on miners. As the profitability of miners globally depends on the price of the PoW cryptocurrency, researchers and policymakers are advised to focus more on investors and third-party services such as regulated exchanges. Thus, identifying and implementing policies that demotivate investment in PoW cryptocurrencies could reduce their prices and the incentive to mine. Ultimately, it was assessed that PoW cryptocurrencies, especially Bitcoin, are historically associated with an ever-increasing environmental impact. In contrary, researchers address PoS as a sustainable alternative that poses a solution to the environmental issues related to PoW.
High-fidelity qutrit entangling gates for superconducting circuits

Ternary quantum information processing in superconducting devices poses a promising alternative to its more popular binary counterpart through larger, more connected computational spaces and proposed advantages in quantum simulation and error correction. Although generally operated as qubits, transmons have readily addressable higher levels, making them natural candidates for operation as quantum three-level systems (qutrits). Recent works in transmon devices have realized high fidelity single qutrit operation. Nonetheless, effectively engineering a high-fidelity two-qutrit entanglement remains a central challenge for realizing qutrit processing in a transmon device. In this work, we apply the differential AC Stark shift to implement a flexible, microwave-activated, and dynamic cross-Kerr entanglement between two fixed-frequency transmon qutrits, expanding on work performed for the ZZ interaction with transmon qubits. We then use this interaction to engineer efficient, high-fidelity qutrit CZ† and CZ gates, with estimated process fidelities of 97.3(1)% and 95.2(3)% respectively, a significant step forward for operating qutrits on a multi-transmon device.
SEM-machine learning-based model for perusing the adoption of metaverse in higher education in UAE

The metaverse is an imaginary network of parallel universes. Using this technology might liven up dull lecture halls. By expanding synchronous communication into the "metaverse," many individuals may have meaningful conversations and exchange perspectives. This research focuses on finding out how medical students in the UAE feel about the metaverse system. The conceptual model incorporates elements from the Technology Acceptance Model (TAM), including perceived value and perceived ubiquity as adoption determinants. To test the validity of the suggested framework, a survey was developed and distributed to 369 full-time students at one of the universities in the United Arab Emirates (UAE). Machine learning (ML) and structural equation modeling using partial least squares (PLS-SEM) are used for data analysis. According to the results, the extent to which users saw value in and adoption of the metaverse system was a significant factor in whether or not they intended to participate. This study was helpful since it elucidated the relative significance of various healthcare components, allowing professionals to prioritize their efforts better.
Sentiment analysis based on rhetorical structure theory:Learning deep neural networks from discourse trees

Prominent applications of sentiment analysis are countless, covering areas such as marketing, customer service and communication. The conventional bag-of-words approach for measuring sentiment merely counts term frequencies; however, it neglects the position of the terms within the discourse. As a remedy, we develop a discourse-aware method that builds upon the discourse structure of documents. For this purpose, we utilize rhetorical structure theory to label (sub-)clauses according to their hierarchical relationships and then assign polarity scores to individual leaves. To learn from the resulting rhetorical structure, we propose a tensor-based, tree-structured deep neural network (named Discourse-LSTM) in order to process the complete discourse tree. The underlying tensors infer the salient passages of narrative materials. In addition, we suggest two algorithms for data augmentation (node reordering and artificial leaf insertion) that increase our training set and reduce overfitting. Our benchmarks demonstrate the superior performance of our approach. Moreover, our tensor structure reveals the salient text passages and thereby provides explanatory insights.
Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Existing methodologies for tool condition monitoring (TCM) still rely on batch approaches which cannot cope with a fast sampling rate of a metal cutting process. Furthermore, they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online TCM approach based on Parsimonious Ensemble+ (pENsemble+). The unique feature of pENsemble+ lies in its highly flexible principle where both the ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. This paper presents advancement of a newly developed ensemble learning algorithm, pENsemble, where the online active learning scenario is incorporated to reduce the operator's labeling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilizing two real-world manufacturing data streams: 1) metal turning and 2) 3-D-printing processes and comparisons with well-known algorithms were carried out. Furthermore, the efficacy of pENsemble+ was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of the operator's labeling effort.
Fast Covariance Matrix Adaptation for Large-Scale Black-Box Optimization

Covariance matrix adaptation evolution strategy (CMA-ES) is a successful gradient-free optimization algorithm. Yet, it can hardly scale to handle high-dimensional problems. In this paper, we propose a fast variant of CMA-ES (Fast CMA-ES) to handle large-scale black-box optimization problems. We approximate the covariance matrix by a low-rank matrix with a few vectors and use two of them to generate each new solution. The algorithm achieves linear internal complexity on the dimension of search space. We illustrate that the covariance matrix of the underlying distribution can be considered as an ensemble of simple models constructed by two vectors. We experimentally investigate the algorithm's behaviors and performances. It is more efficient than the CMA-ES in terms of running time. It outperforms or performs comparatively to the variant limited memory CMA-ES on large-scale problems. Finally, we evaluate the algorithm's performance with a restart strategy on the CEC'2010 large-scale global optimization benchmarks, and it shows remarkable performance and outperforms the large-scale variants of the CMA-ES.
Smart learning environment: Teacher’s role in assessing classroom attention

The main purpose of this article is to investigate the impact of teacher's position on students' performance in higher education.A new pedagogical approach based on collaborative learning is used due to the design of a smart learning environment (SLE).This workspace uses, respectively, information and communication technologies (ICT) and radio frequency identification (RFID)-based indoor positioning system in order to examine students' perceptions and the involvement of groups into this smart classroom.The merge of interactive multimedia system, ubiquitous computing and several handheld devices should lead to a successful active learning process.Firstly, we provide a detailed description of the proposed collaborative environment using mainly new technologies and indoor location system serving as a platform for evaluating attention.The research provides an obvious consensus on the teacher's role in assessing classroom attention.We discuss our preliminary results on how teacher's position influences essentially students' participation.Our first experiments show that the integration of novel technologies in the area of higher education is extremely promoting the traditional way of teaching.The smart classroom model has been recommended to support this evolution.As a result, the found results indicate that the teacher's position increases the learner's motivation, engagement and effective learning.
An improved lightweight certificateless generalized signcryption scheme for mobile-health system

The mobile-health system, also known as the wireless body area network for remote patient monitoring, is a system used to remotely monitor the human body’s health status parameters in real time. The generalized signcryption can realize encryption, signature, and signcryption with only one key pair and one algorithm. To address the communication security requirement for the mobile-health system, Zhang et al. recently proposed a lightweight secure data transmission protocol for the mobile-health system, which uses a certificateless generalized signcryption scheme. However, Zhang et al.’s certificateless generalized signcryption scheme is insecure. In this article, we propose an improved certificateless generalized signcryption scheme and then give a rigorous security proof of it. The confidentiality of our improved scheme can be reduced to the computational Diffie–Hellman problem, and the unforgeability, the Elliptic Curve Discrete Logarithm problem. Performance evaluation shows that our scheme has only slightly increased computational and communicational costs compared with the original scheme, but it is more efficient than other certificateless generalized signcryption schemes existing at present. What is more, it is also an efficient scheme compared with those ones protecting the mobile-health system. Based on our scheme, the same lightweight secure data transmission protocol for the mobile-health system can also be constructed, just like the one based on the original scheme.
Asymmetric monetary policy effects on cryptocurrency markets

Cryptocurrencies have recently attracted numerous interests from investors as potential investable assets. However, research on their responses to macroeconomic policies remains insufficient. This study examines the asymmetric impacts of monetary policies on cryptocurrency returns during monetary tightening versus monetary easing regimes. Interestingly, we find significant responses of four major cryptocurrencies including Bitcoin to Chinese tightening monetary policies; however, U.S. monetary policies do not significantly affect cryptocurrency returns.
A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance

The existence of adversarial examples in which an imperceptible change in the input can fool well trained neural networks was experimentally discovered by Szegedy et al in 2013, who called them "Intriguing properties of neural networks". Since then, this topic had become one of the hottest research areas within machine learning, but the ease with which we can switch between any two decisions in targeted attacks is still far from being understood, and in particular it is not clear which parameters determine the number of input coordinates we have to change in order to mislead the network. In this paper we develop a simple mathematical framework which enables us to think about this baffling phenomenon from a fresh perspective, turning it into a natural consequence of the geometry of $\mathbb{R}^n$ with the $L_0$ (Hamming) metric, which can be quantitatively analyzed. In particular, we explain why we should expect to find targeted adversarial examples with Hamming distance of roughly $m$ in arbitrarily deep neural networks which are designed to distinguish between $m$ input classes.
Generating Logical Forms from Graph Representations of Text and Entities

Structured information about entities is critical for many semantic parsing tasks.We present an approach that uses a Graph Neural Network (GNN) architecture to incorporate information about relevant entities and their relations during parsing.Combined with a decoder copy mechanism, this approach provides a conceptually simple mechanism to generate logical forms with entities.We demonstrate that this approach is competitive with the stateof-the-art across several tasks without pretraining, and outperforms existing approaches when combined with BERT pre-training.
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models

Variational autoencoders (VAEs) have received much attention recently as an end-toend architecture for text generation with latent variables.However, previous works typically focus on synthesizing relatively short sentences (up to 20 words), and the posterior collapse issue has been widely identified in text-VAEs.In this paper, we propose to leverage several multi-level structures to learn a VAE model for generating long, and coherent text.In particular, a hierarchy of stochastic layers between the encoder and decoder networks is employed to abstract more informative and semantic-rich latent codes.Besides, we utilize a multi-level decoder structure to capture the coherent long-term structure inherent in long-form texts, by generating intermediate sentence representations as highlevel plan vectors.Extensive experimental results demonstrate that the proposed multi-level VAE model produces more coherent and less repetitive long text compared to baselines as well as can mitigate the posterior-collapse issue.