Reviewerly/exHarmony huggingface dataset:
Learning Logic Rules for Document-Level Relation Extraction

Document-level relation extraction aims to identify relations between entities in a whole document.Prior efforts to capture long-range dependencies have relied heavily on implicitly powerful representations learned through (graph) neural networks, which makes the model less transparent.To tackle this challenge, in this paper, we propose LogiRE, a novel probabilistic model for document-level relation extraction by learning logic rules.Lo-giRE treats logic rules as latent variables and consists of two modules: a rule generator and a relation extractor.The rule generator is to generate logic rules potentially contributing to final predictions, and the relation extractor outputs final predictions based on the generated logic rules.Those two modules can be efficiently optimized with the expectationmaximization (EM) algorithm.By introducing logic rules into neural networks, LogiRE can explicitly capture long-range dependencies as well as enjoy better interpretation.Empirical results show that LogiRE significantly outperforms several strong baselines in terms of relation performance (∼1.8 F1 score) and logical consistency (over 3.3 logic score).Our code is available at https://github.com/rudongyu/LogiRE.
Improving Abstractive Dialogue Summarization with Hierarchical Pretraining and Topic Segment

With the increasing abundance of meeting transcripts, meeting summary has attracted more and more attention from researchers.The unsupervised pre-training method based on transformer structure combined with fine-tuning of downstream tasks has achieved great success in the field of text summarization.However, the semantic structure and style of meeting transcripts are quite different from that of articles.In this work, we propose a hierarchical transformer encoder-decoder network with multi-task pre-training.Specifically, we mask key sentences at the word-level encoder and generate them at the decoder.Besides, we randomly mask some of the role alignments in the input text and force the model to recover the original role tags to complete the alignments.In addition, we introduce a topic segmentation mechanism to further improve the quality of the generated summaries.The experimental results show that our model is superior to the previous methods in meeting summary datasets AMI and ICSI.
A New Multidimensional Repulsive Potential Field to Avoid Obstacles by Nonholonomic UAVs in Dynamic Environments

The ability of autonomous flight with obstacle avoidance should be a fundamental feature of all modern unmanned aerial vehicles (UAVs). The complexity and difficulty of such a task, however, significantly increase in cases combining moving obstacles and nonholonomic UAVs. Additionally, since they assume the symmetrical distribution of repulsive forces around obstacles, traditional repulsive potential fields are not well suited for nonholonomic vehicles. The limited maneuverability of these types of UAVs, including fixed-wing aircraft, requires consideration not only of their relative position, but also their speed as well as the direction in which the obstacles are moving. To address this issue, the following work presents a novel multidimensional repulsive potential field dedicated to nonholonomic UAVs. This field generates forces that repulse the UAV not from the obstacle's geometrical center, but from areas immediately behind and in front of it located along a line defined by the obstacle's velocity vector. The strength of the repulsive force depends on the UAV's distance to the line representing the obstacle's movement direction, distance to the obstacle along that line, and the relative speed between the UAV and the obstacle projected to the line, making the proposed repulsive potential field multidimensional. Numerical simulations presented within the paper prove the effectiveness of the proposed novel repulsive potential field in controlling the flight of nonholonomic UAVs.
Experiences of undergraduates’ emergency remote education in Mexico

Emergency Remote Education started with the COVID-19 pandemic bringing significant changes to learners and teachers. Education was experienced by everyone in unprecedented ways that require research and understanding. This paper presents a quantitative and transactional study aimed at characterising student emergency e-learning experiences during COVID-19 lockdown and comparing them among the five undergraduate educational programmes offered by a public Mexican university. The participants were 969 undergraduate students, who were surveyed onlinfe. For data analysis, we calculated descriptive statistics and performed Kruskal–Wallis tests in SPSS25. Results showed that 1) mobile phones were the most used devices to connect to online classes; 2) Information Technology (IT) courses utilise specialised alternative platforms; 3) Connectivity and device problems, along with family reasons and sickness were the most common reasons for absence; 4) Eyestrain due to continuous usage of electronic devices was a common discomfort among participants; 5) Work overload, problems to understand educational materials and lack of motivation were other common problems; 6) Students had an overall good perception of the synchronous e-learning model implementation and 7) E-learning could be improved mostly through actions on the part of the instructor. For instance, focus on the most important topics and materials and provide students with more personalised attention.
Exploring the cost and performance benefits of AWS step functions using a data processing pipeline

In traditional cloud computing, dedicated hardware is substituted by dynamically allocated, utility-oriented resources such as virtualized servers. While cloud services are following the pay-as-you-go pricing model, resources are billed based on instance allocation and not on the actual usage, leading the customers to be charged needlessly. In serverless computing, as exemplified by the Function-as-a-Service (FaaS) model where functions are the basic resources, functions are typically not allocated or charged until invoked or triggered. Functions are not applications, however, and to build compelling serverless applications they frequently need to be orchestrated with some kind of application logic. A major issue emerging by the use of orchestration is that it complicates further the already complex billing model used by FaaS providers, which in combination with the lack of granular billing and execution details offered by the providers makes the development and evaluation of serverless applications challenging.
The First GAEN-Based COVID-19 Contact Tracing App in Norway Identifies 80% of Close Contacts in “Real Life” Scenarios

The coronavirus disease 2019 (COVID-19) response in most countries has relied on testing, isolation, contact tracing, and quarantine (TITQ), which is labor- and time-consuming. Therefore, several countries worldwide launched Bluetooth-based apps as supplementary tools. The aim of using contact tracing apps is to rapidly notify people about their possible exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and thus make the process of TITQ more efficient, especially upon exposure in public places. We evaluated the Norwegian Google Apple exposure notification (GAEN)-based contact tracing app Smittestopp v2 under relevant "real-life" test scenarios. We used a total of 40 devices, representing six different brands, and compared two different exposure configurations, experimented with different time thresholds and weights of the Bluetooth attenuation levels (buckets), and calculated the true notification rates among close contacts (≤2 m and ≥15 min) and false notification of sporadic contacts. In addition, we assessed the impact of using different operating systems and locations of the phone (hand/pocket). The best configuration tested to trigger exposure notification resulted in the correct notification of 80% of the true close contacts and incorrect notification of 34% of the sporadic contacts. Among those who incorrectly received notifications, most (67%) were within 2 m but the duration of contact was <15 min and thus they were not, per se, considered as "close contacts." Lower sensitivity was observed when using the iOS operating systems or carrying the phone in the pocket instead of in the hand. The results of this study were used to improve and evaluate the performance of the Norwegian contact-tracing app Smittestopp.
Broadband coherent perfect absorption by cavity coupled to three-level atoms in linear and nonlinear regimes

Abstract A broadband coherent perfect absorption (CPA) scheme consisting of an optical resonator coupled with three-level atoms excited by single cavity mode is proposed and analyzed. We show the output light field from the system is completely suppressed under specific conditions when the system is excited in linear and nonlinear regimes by two identical light fields from two ends of optical cavity. An analytical broadband CPA criterion for central and sideband excitations of cavity quantum electrodynamics system is derived in linear regime. Moreover, we show the resonant excitation criterion for CPA is greatly extended in nonlinear regime. A new type of bistability behavior is found. The output field intensity and the bistability curve can be well tuned by dynamically adjusting system parameters. Our results demonstrate that the CPA is quite universal, and it should be useful in a variety of applications in optical logic and optical communication devices.
Utilization of Gadget Technology as a Learning Media

The increasing demands and needs of the community, such as clothing, food, and housing, continue to soar, just like the current soaring advances in technology and information. Which is where the relationship and interaction between humans and technology have become things like basic needs and needs at any time, one example of human attachment to technology which is currently growing and becoming a competitive world for companies and technology developers, one of which is gadget technology. Gadgets are small electronic devices that have special functions in each type to make work and human needs more practical and efficient. Unlike humans who have advanced technology that is only owned by the upper strata, it is different with mini-electronic technology which is now almost owned by the middle and lower majority, namely gadget technology.
p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks

In this work, we aim to classify nodes of unstructured peer-to-peer networks with communication uncertainty, such as users of decentralized social networks. Graph Neural Networks (GNNs) are known to improve the accuracy of simple classifiers in centralized settings by leveraging naturally occurring network links, but graph convolutional layers are challenging to implement in decentralized settings when node neighbors are not constantly available. We address this problem by employing decoupled GNNs, where base classifier predictions and errors are diffused through graphs after training. For these, we deploy pre-trained and gossip-trained base classifiers and implement peer-to-peer graph diffusion under communication uncertainty. In particular, we develop an asynchronous decentralized formulation of diffusion that converges to centralized predictions in distribution and linearly with respect to communication rates. We experiment on three real-world graphs with node features and labels and simulate peer-to-peer networks with uniformly random communication frequencies; given a portion of known labels, our decentralized graph diffusion achieves comparable accuracy to centralized GNNs with minimal communication overhead (less than 3% of what gossip training already adds).
Cryptocurrency: usability perspective versus volatility threat

Purpose The objective of this study is to examine the nature of cryptocurrencies, risks involved in using it due to its volatile nature, advantages, disadvantages and its functions as money. Design/methodology/approach This is an inductive approach to a descriptive analysis (Qualitative research). In order to come to an adequate conclusion, we reviewed several studies and articles previously published in this field related to our research questions, and then explored the nature of Cryptocurrencies, their advantages and disadvantages, risks associated with cryptocurrency usage and their user-friendliness in Saudi Arabia. Findings The findings of this study reveal that anonymity and concealment are important aspects of cryptocurrencies. This system does not follow a transparent process that can make it parallel to conventional fiat currency. Research limitations/implications Although this study focuses on the issue of trust, it fails to recognize more technological factors hampering its transaction mechanism instead of enhancing it, owing to a lack of facts and knowledge. Practical implications Like conventional transaction system users must sign their crypto transactions that others must duly verify easily. Once a promise is made, one will not be able to back out of it until it is protected from revocation by the signer. Originality/value In comparison with reviewed literature, this study focuses more on the issue of volatility, which accounts for the fact that cryptocurrency has not been accepted as a permanent tool of monetary policy. Additionally, the study finds that the Saudi public is largely pessimistic toward such currencies.
Determining middle school students' perceptions of the concept of artificial intelligence: A metaphor analysis

Apart from the fact that human-like robots are still one of the most interesting topics in science fiction, artificial intelligence (AI) continues to develop rapidly as a popular phenomenon for all sectors. Although the idea that this rapid rise of AI means the rise of humanity has been voiced by many, the point of how AI will affect humanity continues to raise doubts in certain parts of the society. In this study, it is aimed to determine the perceptions of middle school students, which are a part of the future of humanity, towards the concept of AI, on which many discussions have been made, through metaphors. The sample consisted of 339 seventh and eighth grade students of four secondary schools in the central districts of Afyonkarahisar and Izmir in the 2019-2020 academic year. This study used a qualitative approach utilizing metaphor analysis as a research tool to investigate phenomena. Participants were asked the complete the sentence “Artificial intelligence is like.................., because ..................” Data were analyzed using content analysis. Participants’ metaphors for AI were grouped under 11 categories: smart, brain, nature, security, humanistic, the dilemma of good and evil, service, object, technology, life, and time. The data obtained showed that the participants generally used positive metaphors to describe AI, that is, they had positive perceptions about AI. However, in this study, which focused on the AI perceptions of middle school students, all of the metaphors collected under 11 conceptual categories containing positive and negative perceptions were examined and interpreted separately. It is thought that educational computer systems can be designed to shape students' perceptions of AI. Teachers can consider students' perceptions of AI by using AI-assisted teaching and designing content to enhance students' learning skills.
Evaluate the Distribution of Heavy Elements that Dissolved in Ground Water Using IDW in AL-Wafa City, Al-Ramadi,Iraq

Groundwater can be assessed by studying water wells. This study was conducted in Al-Wafa District, Anbar Governorate, Iraq. The water samples were collected from 24 different wells in the study area, in January 2021. A laboratory examination of the samples was conducted. Geographical information systems technique was relied on to determine the values of polluting elements in the wells. The chemical elements that were measured were [cadmium, lead, cobalt and chromium]. The output of this research were planned to be spatial maps that show the distribution of the elements with respect to their concentrations. The results show a variation in the heavy elements concentrations at the studied area groundwater. The samples show different values of concentrations; for Cadmium (0.218-1.624) ppm, Lead (0.217-1.157) ppm, Cobalt (0.014-0156) ppm, and for Chromium (0.045-0263) ppm. The distribution of the materials concentrations differs for each element which is refer to the sources of pollution are not relate to industry, but it could be relate to fertilization.
Time scaling and quantum speed limit in non-Hermitian Hamiltonians

We report on a time-scaling technique to enhance the performances of quantum control protocols in non-Hermitian systems. The considered time scaling involves no extra couplings and yields a significant enhancement of the quantum fidelity for a comparable amount of resources. We discuss the application of this technique to quantum-state transfers in two- and three-level open quantum systems. We derive the quantum speed limit in a system governed by a non-Hermitian Hamiltonian. Interestingly, we show that with an appropriate driving the time-scaling technique preserves the optimality of the quantum speed with respect to the quantum speed limit whereas reducing significantly the damping of the quantum-state norm.
A deep-learning model for predictive archaeology and archaeological community detection

Abstract Deep learning is a powerful tool for exploring large datasets and discovering new patterns. This work presents an account of a metric learning-based deep convolutional neural network (CNN) applied to an archaeological dataset. The proposed account speaks of three stages: training, testing/validating, and community detection. Several thousand artefact images, ranging from the Lower Palaeolithic period (1.4 million years ago) to the Late Islamic period (fourteenth century AD), were used to train the model (i.e., the CNN), to discern artefacts by site and period. After training, it attained a comparable accuracy to archaeologists in various periods. In order to test the model, it was called to identify new query images according to similarities with known (training) images. Validation blinding experiments showed that while archaeologists performed as well as the model within their field of expertise, they fell behind concerning other periods. Lastly, a community detection algorithm based on the confusion matrix data was used to discern affiliations across sites. A case-study on Levantine Natufian artefacts demonstrated the algorithm’s capacity to discern meaningful connections. As such, the model has the potential to reveal yet unknown patterns in archaeological data.
Estimation of Gaussian random displacement using non-Gaussian states

In continuous-variable quantum information processing, quantum error correction of Gaussian errors requires simultaneous estimation of both quadrature components of displacements in phase space. However, quadrature operators $x$ and $p$ are noncommutative conjugate observables, whose simultaneous measurement is prohibited by the uncertainty principle. Gottesman-Kitaev-Preskill (GKP) error correction deals with this problem using complex non-Gaussian states called GKP states. On the other hand, simultaneous estimation of displacement using experimentally feasible non-Gaussian states has not been well studied. In this paper, we consider a multiparameter estimation problem of displacements assuming an isotropic Gaussian prior distribution and allowing postselection of measurement outcomes. We derive a lower bound for the estimation error when only Gaussian operations are used and show that even simple non-Gaussian states such as single-photon states can beat this bound. Based on Ghosh's bound, we also obtain a lower bound for the estimation error when the maximum photon number of the input state is given. Our results reveal the role of non-Gaussianity in the estimation of displacements and pave the way toward the error correction of Gaussian errors using experimentally feasible non-Gaussian states.
Self-pulsing in driven-dissipative photonic Bose-Hubbard dimers

We report on the observation of nonlinear dynamics in driven-dissipative Bose-Hubbard dimers using a pair of coupled fiber ring resonators coherently driven by a single laser beam. We comprehensively explore the optical switching arising when scanning the detuning of the undriven cavity and demonstrate the key role played by the driven cavity detuning in the nonlinear regime. By driving the photonic dimer out of equilibrium, we observe the occurrence of robust self-switching oscillations.Received 1 April 2021Revised 14 July 2021Accepted 14 October 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.L042031Published 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 AreasBifurcationsNonlinear opticsOptical bistabilityPhysical SystemsCoupled oscillatorsTechniquesCavity resonatorsPhase space methodsNonlinear DynamicsAtomic, Molecular & Optical
High-order autoencoder with data augmentation for collaborative filtering

Early DNN-based collaborative filtering (CF) approaches have demonstrated their superior performance than traditional CF such as Matrix Factorization. However, such approaches treat each user–item interaction as separate data and thus overlook the intrinsic relationships among data instances. Inspired by the discovery that the autoencoder architecture can force the hidden representation to capture information about the structure of the graph data, in this work, we propose a novel framework called High-order Autoencoder based Collaborative Filtering (HACF) that enhances the classic NeuMF framework with autoencoders for capturing latent high-order connectivity signals in the user–item interaction graph. Specifically, each user–item pair is augmented with higher-order neighbours and input to two sets of autoencoders, one set for the users and the other for the items. All the autoencoders in one set share parameters so increasing the number of autoencoders does not increase the model size. We have conducted extensive experiments on four popular public benchmark datasets with different sparsity. The overall comparison results demonstrate the advantages of autoencoder-based methods and show that our framework outperforms some state-of-the-art DNN-based collaborative filtering approaches.
Design of Higher Education System Based on Artificial Intelligence Technology

In order to solve the problems of low security and response efficiency and slow running speed of the current designed higher education system, a higher education system based on artificial intelligence technology is designed. Firstly, according to the characteristics of artificial intelligence technology, intelligent teaching system, agent technology, and data mining technology are introduced in detail. Then it analyzes the overall and detailed functional requirements of the system and adaptively generates knowledge content and teaching mode suitable for students’ ability and personality by using intelligent reasoning ability and the collection and analysis of students’ personality characteristics. Through data mining of intelligent teaching system, the decision tree about curriculum is obtained, and the students’ cognitive ability is calculated. Based on the theory of cognitive science, using the “double master” teaching mode, combined with agent technology and intelligent teaching system, the system function is divided into six modules. Through the design of database structure and data table, the design of higher education system based on artificial intelligence technology is realized. The experimental results show that the proposed method has high security and response efficiency, fast running speed, and good teaching effect.