Reviewerly/exHarmony huggingface dataset:
Interactions between healthcare robots and older people in Japan: A qualitative descriptive analysis study

Abstract Aim This study aimed to describe the interactions between healthcare robots and older persons in two selected institutions. Methods A qualitative descriptive analysis study using simultaneous observations of healthcare robot and older person interactions was conducted at two healthcare institutions in Japan. The Pepper robot equipped with Care Prevention Gymnastics Exercise programs was used in two situations: (a) exercise regimen led by Pepper; and (b) interactive dialogue between Pepper and older persons during the exercise process. These interactions were recorded using audiovisual equipment and analyzed using content analysis thereby revealing four thematic categories. Interpretation and discussion were structured based on assumptions of the Transactive Relationship Theory of Nursing. Results The analysis revealed four thematic categories: (a) intermediary participation of healthcare practitioners; (b) technologies of care for practice; (c) human‐robot interactions involving artificial intelligence; and (d) valuing safety in nursing through technology. Conclusion Interactions between healthcare robots and older persons may improve quality of care. However, issues regarding fidelity and functionality of dialogic conversations, and ensuring safety and security of using healthcare robots are recognized as critical nursing practice concerns in healthcare robot use in institutional settings.
An adaptive multi-agent system for task reallocation in a MapReduce job

We study the problem of task reallocation for load-balancing of MapReduce jobs in applications that process large datasets. In this context, we propose a novel strategy based on cooperative agents used to optimize the task scheduling in a single MapReduce job. The novelty of our strategy lies in the ability of agents to identify opportunities within a current unbalanced allocation, which in turn triggers concurrent and one-to-many negotiations amongst agents to locally reallocate some of the tasks within a job. Our contribution is that tasks are reallocated according to the proximity of the resources and they are performed in accordance to the capabilities of the nodes in which agents are situated. To evaluate the adaptivity and responsiveness of our approach, we implement a prototype test-bed and conduct a vast panel of experiments in a heterogeneous environment and by exploring varying hardware configurations. This extensive experimentation reveals that our strategy significantly improves the overall runtime over the classical Hadoop data processing.
Combating Adversaries with Anti-adversaries

Deep neural networks are vulnerable to small input perturbations known as adversarial attacks. Inspired by the fact that these adversaries are constructed by iteratively minimizing the confidence of a network for the true class label, we propose the anti-adversary layer, aimed at countering this effect. In particular, our layer generates an input perturbation in the opposite direction of the adversarial one and feeds the classifier a perturbed version of the input. Our approach is training-free and theoretically supported. We verify the effectiveness of our approach by combining our layer with both nominally and robustly trained models and conduct large-scale experiments from black-box to adaptive attacks on CIFAR10, CIFAR100, and ImageNet. Our layer significantly enhances model robustness while coming at no cost on clean accuracy.
Law, innovation and technology: fast forward to 2021

This article, introducing a new extended form of the journal, offers some reflections on the changing context in which we now research law, innovation, and technology. Three major changes are highlighted: the evolving landscape of Law 3.0, potentially de-centring both rules and humans from the legal enterprise; the new ‘normal’ of life with pandemics, underlining the vulnerability of humans; and, threading through all of this, the Anthropocene, destabilising a host of baseline distinctions, and a constant warning about the fragility of the global commons and the human condition. In this changing context, the question is whether technology can provide the solutions to our global challenges without involving an irreversible erosion of human agency. With this, we open the floor to our contributors.
Technology against COVID-19 A Blockchain-based framework for Data Quality

general Blockchain-based framework for data governance that aims to ensure a high level of data trust, security, and privacy.
A Stochastic Local Search Algorithm for the Partial Max-SAT Problem Based on Adaptive Tuning and Variable Depth Neighborhood Search

The Partial Max-SAT (PMSAT) problem is an optimization variant of the well-known Propositional Boolean Satisfiability (SAT) problem.It holds an important place in theory and practice, because a huge number of real-world problems, such as timetabling, planning, routing, bioinformatics, fault diagnosis, etc., could be encoded into it.Stochastic local search (SLS) methods can solve many realworld problems that often involve large-scale instances at reasonable computation costs while delivering good-quality solutions.In this work, we propose a novel SLS algorithm called adaptive variable depth SLS for PMSAT problem solving based on a dynamic local search framework.Our algorithm exploits two algorithmic components of an SLS method: parameter tuning and neighborhood search.Our first contribution is the design of an adaptive parameter tuner that searches for the best parameter setting for each instance by considering its features.The second contribution is a variable depth neighborhood search (VDS) algorithm adopted for PMSAT problem, which our empirical evaluation proves is a more efficient w.r.t.single neighborhood search.We conducted our experiments on the PMSAT benchmarks from MaxSAT Evaluation 2014 to 2019, including more than 3600 instances which have been encoded from a broad range of domains such as verification, optimization, graph theory, automated-reasoning, pseudo Boolean, etc.Our experimental evaluation results show that AVD-SLS solver, which is implemented based on our algorithm, outperforms state-of-the-art PMSAT SLS solvers in most benchmark classes, including random, crafted, and industrial instances.Furthermore, AVD-SLS reports remarkably better results on weighted benchmark, and shows competitive results with several well-known hybrid PMSAT solvers.
Experimental function estimation from quantum phase measurements

Characterizing and analyzing a system often requires learning an unknown function, such as the response of a system or the profile of a field. The standard approach is to opportunely sample the function at fiducial points and then interpolate. When the quantity of interest is embodied in physical objects accessible with quantum-enhanced measurements, it becomes relevant to investigate how to transfer this advantage from the individual sampled points to the estimation of the whole function. In this article we report the experimental quantum-enhanced function estimation of the optical response of a liquid crystal. Our results illustrate that optimizing the employment of the resources is not as straightforward as it may appear at a first glance: Quantum advantage becomes substantial only past a sampling density that depends on the interpolation method, and on the function at hand. Our results show how quantum resources should successfully be employed to access the rich information contained in continuous signals.
WDPMA

The internet is very huge in size and increasing exponentially. Finding any relevant information from such a huge information source is now becoming very difficult. Millions of web pages are returned in response to a user's ordinary query. Displaying these web pages without ranking makes it very challenging for the user to find the relevant results of a query. This paper has proposed a novel approach that utilizes web content, usage, and structure data to prioritize web documents. The proposed approach has applications in several major areas like web personalization, adaptive website development, recommendation systems, search engine optimization, business intelligence solutions, etc. Further, the proposed approach has been compared experimentally by other approaches, WDPGA, WDPSA, and WDPII, and it has been observed that with a little trade off time, it has an edge over these approaches.
Simple Efficient Decoders for Quantum Key Distribution Over Quantum Repeaters with Encoding

We study the implementation of quantum key distribution (QKD) systems over quantum repeater infrastructures. We particularly consider quantum repeaters with encoding and compare them with probabilistic quantum repeaters. To that end, we propose two decoder structures for encoded repeaters that not only improve system performance but also make the implementation aspects easier by removing two-qubit gates from the QKD decoder. By developing several scalable numerical and analytical techniques, we then identify the resilience of the setup to various sources of error in gates, measurement modules, and initialization of the setup. We apply our techniques to three- and five-qubit repetition codes and obtain the normalized secret key generation rate per memory per second for encoded and probabilistic quantum repeaters. We quantify the regimes of operation, where one class of repeater outperforms the other, and find that there are feasible regimes of operation where encoded repeaters -- based on simple three-qubit repetition codes -- could offer practical advantages.
tsrobprep — an R package for robust preprocessing of time series data

Data cleaning is a crucial part of every data analysis exercise. Yet, the currently available R packages do not provide fast and robust methods for cleaning and preparation of time series data. The open source package tsrobprep introduces efficient methods for handling missing values and outliers using model based approaches. For data imputation a probabilistic replacement model is proposed, which may consist of autoregressive components and external inputs. For outlier detection a clustering algorithm based on finite mixture modelling is introduced, which considers time series properties in terms of the gradient and the underlying seasonality as features. The procedure allows to return a probability for each observation being outlying data as well as a specific cause for an outlier assignment in terms of the provided feature space. The methods work robust and are fully tunable. Moreover, by providing the auto_data_cleaning function the data preprocessing can be carried out in one cast, without comprehensive tuning and providing suitable results. The primary motivation of the package is the preprocessing of energy system data. We present application for electricity load, wind and solar power data.
Tracklet Pair Proposal and Context Reasoning for Video Scene Graph Generation

Video scene graph generation (ViDSGG), the creation of video scene graphs that helps in deeper and better visual scene understanding, is a challenging task. Segment-based and sliding-window based methods have been proposed to perform this task. However, they all have certain limitations. This study proposes a novel deep neural network model called VSGG-Net for video scene graph generation. The model uses a sliding window scheme to detect object tracklets of various lengths throughout the entire video. In particular, the proposed model presents a new tracklet pair proposal method that evaluates the relatedness of object tracklet pairs using a pretrained neural network and statistical information. To effectively utilize the spatio-temporal context, low-level visual context reasoning is performed using a spatio-temporal context graph and a graph neural network as well as high-level semantic context reasoning. To improve the detection performance for sparse relationships, the proposed model applies a class weighting technique that adjusts the weight of sparse relationships to a higher level. This study demonstrates the positive effect and high performance of the proposed model through experiments using the benchmark dataset VidOR and VidVRD.
Addition to “A Hybrid Board Game to Engage Students in Reviewing Organic Acids and Bases Concepts”: Using the Game Remotely during the COVID-19 Pandemic

ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionAddition to "A Hybrid Board Game to Engage Students in Reviewing Organic Acids and Bases Concepts": Using the Game Remotely during the COVID-19 PandemicJosé Nunes da Silva Júnior*José Nunes da Silva JúniorMore by José Nunes da Silva Júniorhttp://orcid.org/0000-0002-6631-4382, Dávila ZampieriDávila ZampieriMore by Dávila Zampierihttp://orcid.org/0000-0002-2702-7268, Marcos Carlos de MattosMarcos Carlos de MattosMore by Marcos Carlos de Mattos, Bruna Ribeiro DuqueBruna Ribeiro DuqueMore by Bruna Ribeiro Duque, Antonio José Melo Leite JuniorAntonio José Melo Leite JuniorMore by Antonio José Melo Leite Juniorhttp://orcid.org/0000-0002-5061-1489, Ulisses Silva de SousaUlisses Silva de SousaMore by Ulisses Silva de Sousahttp://orcid.org/0000-0002-5665-4383, David Macedo do NascimentoDavid Macedo do NascimentoMore by David Macedo do Nascimentohttp://orcid.org/0000-0001-8425-1680, Mary Anne Sousa LimaMary Anne Sousa LimaMore by Mary Anne Sousa Lima, and André Jalles MonteiroAndré Jalles MonteiroMore by André Jalles MonteiroCite this: J. Chem. Educ. 2021, 98, 6, 2138–2140Publication Date (Web):April 30, 2021Publication History Published online30 April 2021Published inissue 8 June 2021https://doi.org/10.1021/acs.jchemed.1c00240Copyright © 2021 American Chemical Society and Division of Chemical Education, Inc. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views1575Altmetric-Citations4LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (2 MB) Get e-Alertsclose Get e-Alerts
Calculating the Green’s function of two-site fermionic Hubbard model in a photonic system

The Green's function has been an indispensable tool to study many-body systems that remain one of the biggest challenges in modern quantum physics for decades. The complicated calculation of Green's function impedes the research of many-body systems. The appearance of the noisy intermediate-scale quantum devices and quantum-classical hybrid algorithm inspire a new method to calculate Green's function. Here we design a programmable quantum circuit for photons with utilizing the polarization and the path degrees of freedom to construct a highly-precise variational quantum state of a photon, and first report the experimental realization for calculating the Green's function of the two-site Fermionic Hubbard model, a prototypical model for strongly-correlated materials, in photonic systems. We run the variational quantum eigensolver to obtain the ground state and excited states of the model, and then evaluate the transition amplitudes among the eigenstates. The experimental results present the spectral function of Green's function, which agrees well with the exact results. Our demonstration provides the further possibility of the photonic system in quantum simulation and applications in solving complicated problems in many-body systems, biological science, and so on.
Detecting Intra-Conflicts in Non-Functional Requirements

When specifying user requirements, not only is it critical to ensure correct and unambiguous specification of functional requirements, but also that of Non-Functional Requirements (NFRs). A critical success factor in Requirements Engineering (RE) involves recognizing conflicts among NFRs specified by multiple stakeholders having differing concerns, priorities, and responsibilities. There indeed are numerous attempts made in the literature to resolve the conflicts between two NFRs, with the traditional view of considering the two NFRs different from each other e.g. security conflicting with the availability. In this paper, however, we propose that to introduce fine-grained conflict resolution – by also focusing on those situations where one NFR conflicts with another NFR of the same type. For ease of understanding, we propose to differentiate such conflicts by coining the term intra-conflicts. Thus, we propose fine-grained conflict resolution – by focusing on the notion of resolving conflicts between two NFRs of the same type. Needless to say, non-detection of any conflict between two NFRs – whether the NFRs are of the same type or not – at an early stage of RE, leads to higher costs for changes. The process of conflict resolution is essentially intuitive and hence is iterative. Our proposal hence is motivated by a view that differentiating the inter-conflicts with intra-conflicts, helps one in better focusing on the conflict resolution. We also propose an approach that allows a requirements analyst to semi-automatically identify intra-conflicts among NFRs at an early stage of RE using natural language processing, machine learning, and ontology-based semantic analysis. The controlled experiments, conducted on five publicly available datasets, achieve an average recall, precision, and F-measure of 0.57, 0.77, and 0.65 respectively.
What Makes Mental Modeling Difficult? Normative Data for the Multidimensional Relational Reasoning Task

Relational reasoning is a complex form of human cognition involving the evaluation of relations between mental representations of information. Prior studies have modified stimulus properties of relational reasoning problems and examined differences in difficulty between different problem types. While subsets of these stimulus properties have been addressed in separate studies, there has not been a comprehensive study, to our knowledge, which investigates all of these properties in the same set of stimuli. This investigative gap has resulted in different findings across studies which vary in task design, making it challenging to determine what stimulus properties make relational reasoning-and the putative formation of mental models underlying reasoning-difficult. In this article, we present the Multidimensional Relational Reasoning Task (MRRT), a task which systematically varied an array of stimulus properties within a single set of relational reasoning problems. Using a mixed-effects framework, we demonstrate that reasoning problems containing a greater number of the premises as well as multidimensional relations led to greater task difficulty. The MRRT has been made publicly available for use in future research, along with normative data regarding the relative difficulty of each problem.
When robots contribute to eradicate the COVID-19 spread in a context of containment

In the era of autonomous robots, multi-targets search methods inspired researchers to develop adapted algorithms to robot constraints, and with the rising of Swarm Intelligence approaches, Swarm Robotics became a very popular topic. In this paper, the problem of searching for an exponentially increasing number of targets in a complex and unknown environment is addressed. Our main objective is to propose a Robotic target search strategy based on the Elephants Herding Optimization (EHO) algorithm, namely Robotic-EHO (REHO). The main additions were the collision-free path planning strategy, the velocity limitation, and the extension to the multi-target version in discrete environments. The proposed method has been the subject of many experiments, emulating the search of infected individuals by COVID-19 in a context of containment within complex and unknown random environments, as well as in the real case study of the USA. The particularity of these environments is their increasing targets' number and the dynamic Containment Rate (CR) that we propose. The experimental results show that REHO reacts much better in high CR, early start search mission, and where the robots' speed is higher than the virus spread speed.
Quantumness Measures for a System of Two Qubits Interacting with a Field in the Presence of the Time-Dependent Interaction and Kerr Medium

In this work, we introduce the standard Tavis-Cummings model to describe two-qubit system interacting with a single-mode field associated to power-law (PL) potentials. We explore the effect of the time-dependent interaction and the Kerr-like medium. We solve the Schrödinger equation to obtain the density operator that allows us to investigate the dynamical behaviour of some quantumness measures, such as von Neumann entropy, negativity and Mandel's parameter. We provide how these entanglement measures depend on the system parameters, which paves the way towards better control of entanglement generation in two-qubit systems. We find that the enhancement and preservation of the atoms-field entanglement and atom-atom entanglement can be achieved by a proper choice of the initial parameters of the field in the absence and presence of the time-dependent interaction and Kerr medium. We examine the photons distribution of the field and determine the situations for which the field exhibits super-poissonian, poissonian or sub-poissonian distribution.
Hybrid Entanglement between Optical Discrete Polarizations and Continuous Quadrature Variables

By coherently combining advantages while largely avoiding limitations of two mainstream platforms, optical hybrid entanglement involving both discrete and continuous variables has recently garnered widespread attention and emerged as a promising idea for building heterogenous quantum networks. In contrast to previous results, here we propose a new scheme to remotely generate hybrid entanglement between discrete polarization and continuous quadrature optical qubits heralded by two-photon Bell-state measurement. As a novel nonclassical light resource, we further use it to discuss two examples of ways—entanglement swapping and quantum teleportation—in which quantum information processing and communications could make use of this hybrid technique.
A Blockchain-Based Sealed-Bid e-Auction Scheme with Smart Contract and Zero-Knowledge Proof

e-Auction improves the efficiency of bid transaction. However, the protection of bidders’ privacy, transaction fairness and verifiability, transaction data security, high cost of third-party auction center, and other issues have attracted more attention. According to the transaction process and basic principles of the sealed auction, we explored the problems existing in the current sealed-bid e-auction schemes. Based on the blockchain technology, we proposed a sealed-bid e-auction scheme with smart contract technology, Bulletproofs zero-knowledge proof protocols, and Pedersen commitment algorithm. The proposed scheme constructed an auction mechanism without the third-party auctioneer so as to restrict the behaviors of auction parties for the sake of auction security, reliability, fairness, and privacy protection. Compared with the related sealed e-auction schemes based on blockchain technologies in six metrics, we conducted the experiment to show that the proposed scheme protected the bid information from leakage well and successfully verified the winning bid price and the related bidder by all transaction participants without the third-party auctioneer.