Reviewerly/exHarmony huggingface dataset:
The Potential Role of an Adjunctive Real-Time Locating System in Preventing Secondary Transmission of SARS-CoV-2 in a Hospital Environment: Retrospective Case-Control Study

There has been an increasing demand for new technologies regarding infection control in hospital settings to reduce the burden of contact tracing.This study aimed to compare the validity of a real-time locating system (RTLS) with that of the conventional contact tracing method for identifying high-risk contact cases associated with the secondary transmission of SARS-CoV-2.A retrospective case-control study involving in-hospital contact cases of confirmed COVID-19 patients, who were diagnosed from January 23 to March 25, 2022, was conducted at a university hospital in South Korea. Contact cases were identified using either the conventional method or the RTLS. The primary endpoint of this study was secondary transmission of SARS-CoV-2 among contact cases. Univariate and multivariable logistic regression analysis comparing test positive and versus negative contact cases were performed.Overall, 509 and 653 cases were confirmed by the conventional method and the RTLS, respectively. Only 74 contact cases were identified by both methods, which could be attributed to the limitations of each method. Sensitivity was higher for the RTLS tracing method (653/1088, 60.0%) than the conventional tracing method (509/1088, 46.8%) considering all contact cases identified by both methods. The secondary transmission rate in the RTLS model was 8.1%, while that in the conventional model was 5.3%. The multivariable logistic regression model revealed that the RTLS was more capable of detecting secondary transmission than the conventional method (adjusted odds ratio 6.15, 95% CI 1.92-28.69; P=.007).This study showed that the RTLS is beneficial when used as an adjunctive approach to the conventional method for contact tracing associated with secondary transmission. However, the RTLS cannot completely replace traditional contact tracing.
An upper bound on the success probability of minimum-error discrimination

Minimum-error discrimination of the ensemble of pure states {|ϕj〉,wj} with given probabilities {wj} is considered by introducing an ancillary degree of freedom, and therefore relating the success probability ps of minimum-error discrimination to the l1-norm coherence in the ancillary degree of freedom. A new upper bound of the success probability ps is obtained this way.
A Multi-Pedestrian Tracking Algorithm for Dense Scenes Based on an Attention Mechanism and Dual Data Association

Aiming at the problems of frequent identity switches (IDs) and trajectory interruption of multi-pedestrian tracking algorithms in dense scenes, this paper proposes a multi-pedestrian tracking algorithm based on an attention mechanism and dual data association. First, the FairMOT algorithm is used as a baseline to introduce the feature pyramid network in the CenterNet detection network and up-sampling the output multi-scale fused feature maps, effectively reducing the rate of missed detection of small-sized and obscured pedestrians. The improved channel attention mechanism module is embedded in the CenterNet’s backbone network to improve detection accuracy. Then, a re-identification (ReID) branch is embedded in the head of the detection network, and the two sub-tasks of pedestrian detection and pedestrian apparent feature extraction are combined in a multi-task joint learning approach to output the pedestrian apparent feature vectors while detecting pedestrians, which improves the computational efficiency and localization accuracy of the algorithm. Finally, we propose a dual data association tracking model that tracks by associating almost every detection box instead of only the high-scoring ones. For low-scoring detection boxes, we utilize their similarities with trajectories to recover obscured pedestrians. The experiment using the MOT17 dataset shows that the tracking accuracy is improved by 0.6% compared with the baseline FairMOT algorithm, and the number of switches decreases from 3303 to 2056, which indicates that the proposed algorithm can effectively reduce the number of trajectory interruptions and identity switching.
Exploring the Application of Elementary Mathematics Supporting System using Artificial Intelligence in Teaching and Learning

The purpose of this study is to explore the application of the ‘elementary mathematics supporting system using artificial intelligence(AI)’ to support students' mathematics learning. To achieve this goal, literature reviews, classroom observations, and in-depth interview with teachers are applied to derive pedagogical implications. Based on the results, the following four types of teaching and learning models and scenarios were developed: guidance on functions, understanding of number concepts, exploring the principle of computation, and practicing the mathematical concepts and principles. The developed models and scenarios were applied to 43 elementary school teachers. Based on the results of the survey for teachers, we elaborated teaching and learning models and scenarios, and derived implications for the effective application of the ‘elementary mathematics supporting system using artificial intelligence(AI)’.
Sustainable Foodtures: Exploring Roles of Future Technology in Sustainable Food Shopping

Several efforts have been made to use technology to encourage people to buy more environmentally sustainable food. However, only a small amount of research has attempted to understand the roles of technology in food shopping by incorporating perspectives from various stakeholders. We aimed to explore how the design space for future technology in sustainable food shopping is negotiated among sustainability-aware individuals, sustainability professionals, and designers through co-design workshops and speculative enactments. We identify three roles of technology in sustainable food shopping: technology as guidance, technology as mediator, and technology for reflection. Additional insights are provided regarding how parts of these roles for future technology are rejected, adopted, or adjusted when enacted in everyday food shopping. We discuss design implications for the roles of sustainability technology in terms of data accuracy and transparency, as well as embodied data experiences. Furthermore, we provide methodological considerations for speculative enactments in SHCI.
All the World is our Stage

Bullying is a serious problem concerning us all. The problem has already been tackled within HCI research, including different design and technology-based interventions, some of which strongly advocate human agency, social responsibility, action taking, and ownership for children. Along these lines, we address the problem of bullying in collaboration with children through critical design and Making. Critical and empowering approaches have aroused interest in child computer interaction (CCI) research, while we particularly explore Theatre of the Oppressed as a tool for evaluating design concepts with children and for empowering children to act as agents of change. Different theatre and drama-based methods have been utilized in CCI, while their potential for empowering children remains poorly explored. This study positions Theatre of the Oppressed within critical design and Making process, examines children's experiences with it and scrutinizes in which sense it can be considered empowering, with implications for CCI research and design.
Work of the Future

Remote and hybrid work has received significant attention in the last years in both academic and industrial contexts, especially due to the COVID-19 pandemic and attendant lockdown. Some of the remaining challenges in current remote technologies include limited embodiment, hierarchy and agency issues, and significant technological limitations. In this workshop, we will, together with the participants, explore how to design socio-technical systems that connect people and artefacts during collaborative activities. The workshop will use provocations, artefacts, and group work to imagine the futures of work with a focus on hybrid work practices.
Between Egoism and Altruism: A Mixed-Methods Study of Reflections about Energy Use in the Life Cycle of High Preference Grocery Products

The global food system is a major contributor to environmental unsustainability partly due to the immense amount of energy that is consumed in product life cycles. Our endeavour with the present study was to gain insights about how people experience and reflect upon energy use embedded in the life cycle of grocery products. Based on a two-month mixed-methods study that encompassed interviews, technology probe deployment, and continuous surveys with eight participants, we identified characteristics of choices for high and low preference foods on sustainability; impacts of various abstractions of information; and challenges and opportunities for digital technology in grocery shopping. We discuss the findings in relation to the notion of value plurality and present design implications for food assistance systems in Sustainable HCI.
Introduction to Data-Enabled Design

During the Introduction to Data-Enabled Design (DED) tutorial attendees learn about designing for intelligent ecosystems while using data as a creative material. We will take the tutorial participants through both major steps, contextual and informed, of this method by means of data collected by the attendees at the conference. This tutorial offers a careful balance between hands-on work and DED theory. The learning outcomes focus on topics of (physical) prototyping, (remote) data collection and analysis, using data as a creative material, and designing remote interventions and propositions.
PregnancyCubes: A design process to cultivate tensions

This pictorial proposes a strategy to cultivate tensions in a design process inspired by Stengers' notion of problematization. This strategy is a response to the issue that a current ideal is to design technology to smooth out the curves of our everyday lives rather than acknowledging our entangled relationships with all the human and non-human actors we engage with. The argument is that if we keep striving for minimal friction, we will end up estranged and discontent. The technologies will only provide a shallow technological fix that does not fit into our entangled lives. Our case is a design process to support the relationship of expecting parents. The result is a set of nine PregnancyCubes which are meant to cultivate tensions within a relationship in different ways.
Does Anyone Dream of Invisible A.I.? A Critique of the Making Invisible of A.I. Policing

For most people, using their body to authenticate their identity is an integral part of daily life. From our fingerprints to our facial features, our physical characteristics store the information that identifies us as "us." This biometric information is becoming increasingly vital to the way we access and use technology. As more and more platform operators struggle with traffic from malicious bots on their servers, the burden of proof is on users, only this time they have to prove their very humanity and there is no court or jury to judge, but an invisible algorithmic system. In this paper, we critique the invisibilization of artificial intelligence policing. We argue that this practice obfuscates the underlying process of biometric verification. As a result, the new "invisible" tests leave no room for the user to question whether the process of questioning is even fair or ethical. We challenge this thesis by offering a juxtaposition with the science fiction imagining of the Turing test in Blade Runner to reevaluate the ethical grounds for reverse Turing tests, and we urge the research community to pursue alternative routes of bot identification that are more transparent and responsive.
XAI for learning: Narrowing down the digital divide between “new” and “old” experts

Regular eXplainable AI (XAI) approaches are often ineffective in supporting decision-makers across domains. In some instances, it can even lead to automation bias or algorithmic aversion or would simply be ignored as a redundant feature. Based on cognitive psychology literature we outline a strategy for how XAI interface design could be tailored to have a long-lasting educational value. We suggest the features that could support domain-related and technical skills development this way narrowing the digital divide between "new" and "old" experts. Lastly, we suggest an intermitted explainability approach that could help to find a balance between seamless and cognitively engaging explanations.
Sustainable Technological Futures

In this critique, we problematize the framing of technological futures in design and computing discourse through rhetorical devices such as the Futures Cone which we contend promotes a one-world-world perspective, in that it assumes a collective (western) acceptance of a particular historicity and notions of time and progress. This perspective currently dominates the practical design and implementation of many new technological devices and systems. To counter such orthodoxies, we instead adopt an alternate perspective which primarily draws from the work of Brazilian philosopher Alvaro Vieira Pinto who considered the past and the future as shaped by the present – a present that is open and creative due to constant change. Our perspective also draws upon Object-Oriented Ontology, Alien Phenomenology and Defuturing to reconsider the privileging of 'human' as part of complex assemblages of human and non-human actants which have interdependent relationships but operate within independent perspectives. Our novel framing enables us to develop and promote design practices for More-than-Human sustainable technological futures that go beyond purely human considerations and begin to accommodate futures for non-human entities, both technological and ecological (flora, fauna and climate). To concretise our argument, we present a series of Internet of Things related examples that apply Speculative Design techniques to illustrate how More-than-Human sustainable technological futures may be put into actionable design practice.
Environmental sustainability in Sub-Saharan Africa: Does information and communication technology (ICT) matter?

This study investigates the effect of information and communication technology (ICT) on environmental sustainability in 38 Sub-Saharan African (SSA) countries over a period 2000–2016. ICT is measured by internet penetration and mobile phone penetration whereas environmental sustainability is measured by CO2 emissions. The empirical evidence is based on the extended stochastic impact by regression on population, affluence and technology model. As estimation techniques, pooled ordinary least squares (OLS), fixed effect (FE), random effect (RE), panel correlated standard error (PCSE) and feasible generalised least squares (FGLS) are employed. The finding broadly shows that investment in ICT infrastructure enhances environmental sustainability. In addition, the effect of ICT is uniform across different income levels in SSA. As a policy implication, universal ICT access that encourages low pricing and broad coverage of equipment should be considered.
Applying Marketing Intelligence System in Improving Marketing Performance for Jordanian Corporations During COVID19 Pandemic

The research looks at how a marketing intelligence system might help Jordanian businesses improve their marketing effectiveness in the wake of the COVID19 Pandemic. The study’s sample size includes 234 Jordanian marketing managers. A stratified random sample of 146 Jordanian firms was chosen, and 118 valid questionnaires were obtained from marketing managers for statistical analysis, yielding an 80.8% response rate. According to the findings, the marketing intelligence system is crucial in influencing the company’s marketing performance, and its simple and practical design increases the possibility of benefiting from it in improving the company’s marketing performance. The researchers recommend allocating a separate department for marketing intelligence systems in order to maximize the system’s benefit in terms of improving Jordanian corporations’ marketing performance, as well as involving the department’s director in various strategic decisions affecting the company.
Robust back-scattered light estimation for underwater image enhancement with polarization

The scattering of particles in the turbid underwater environment reduces the contrast of the image, which can be alleviated by underwater image enhancement. However, the noise in the imaging process often interferes with the enhancement. In this paper, we propose a novel polarization-based approach that robustly estimates the background light to reduce the impact of noise. Specifically, we present a polarization parameter estimation method that adopts both spatial and frequency-domain operations. With these polarization parameters, the background light is estimated by combining the image information before and after filtering in the frequency-domain. For accurately estimating the back-scattered light at the infinity point, we exploit the characteristics of the polarization difference image and use a quad-tree method to determine it. The proposed approach can reduce the influence of the biased estimated parameters, boosting the image quality effectively. Experiments demonstrate that the our method produces better visual effect and robustness than the classic polarization-based and traditional underwater enhancement algorithms for different concentrations.
Paleoprecipitation mapping of the Marília Formation, Late Cretaceous of Brazil

The paleosols , due to their potential as paleoenvironmental and stratigraphic proxies, are widely used in studies of paleolandscape and paleoclimate reconstructions. As it constitutes one of the main climatic elements, the mapping of precipitation represents an indispensable factor in the determination of climatic conditions. Thus, the article aimed to map the distribution patterns of Maastrichtian paleoprecipitation from the Bauru Basin, Upper Cretaceous of Brazil, through the use of paleosols as proxies, climofunctions, and geostatistical interpolation of data. The results revealed an average of 683 mm for the Brazilian Maastrichtian, confirming the predominance of the semi-arid climate for the Marília Formation. Maastrichtian paleoprecipitation mapping of the Bauru Basin, using paleosols, climatic functions, and environmental modeling by geostatistical interpolation, showed a scenario of favorable rainfall for the development of paleosols with Bk, Bg, Bss, and Bt horizons, reaching consensus with most previous studies on the climate scenario of the Marília Formation, Late Cretaceous of Brazil. • The estimates revealed an average rainfall of 683 mm for the Marília Formation, confirming the predominance of a semi-arid climate. • The Maastrichtian paleoprecipitation map of the Bauru Basin showed a scenario of favorable rainfall for the development of paleosols with Bk, Bg, Bss, and Bt horizons. • The use of paleosols, climofunctions, and environmental modeling by geostatistical interpolation constitute important tools for the paleoclimatic mapping of the Upper Cretaceous of Brazil.
Development Trend of Digital Physical Education Teaching by Integrating Intelligent Sensor Technology

Digital teaching has gradually banned traditional offline teaching, and its advantages are self-evident, but the current digital teaching has certain limitations. Especially in physical education, the key content of physical education is the changes in physical movements; the process is not only complex; the differences between movements are also extremely small; and ordinary digital cannot accurately teach these complex and subtle movements. In this context, this paper mainly studies the application of digitalization of fusion intelligent sensor technology in physical education teaching and combines the characteristics of high precision and recognizable actions of intelligent sensors with digitalization to improve physical education teaching. This paper mainly selects the students majoring in physical education in the second year of school A as the research object to test the effect of digitalization of fusion intelligent sensor technology in physical education teaching. Before the experiment, the research subjects were divided into groups and the T-test was performed, and it was concluded that there was no significant difference between the experimental group and the control group. On this basis, the experimental group and the control group were respectively taught table tennis, gymnastics, martial arts, football, and 100 meters and then tested and tested. The results showed that the experimental group was higher than the control group in table tennis, gymnastics, martial arts, football, and 100 meters test. In the 100 meter test, the boys in the experimental group were 7.13, 6.94, and 6.93 points higher than the control group, and the girls in the experimental group were 2, 1, and 2 points higher than the control group. Overall, after the improved digital teaching, both boys and girls in the experimental group performed better than the control group in table tennis, gymnastics, martial arts, football, and 100 meters. It shows that the effect of the improved digital physical education is remarkable, and it also provides a certain theoretical basis and guidance for the teaching improvement of other courses.
Analysis of the Factors Influencing the Adaptability of College English Learning Based on Artificial Intelligence Teaching Assistance

Good learning adaptability is the key to ensure students’ learning quality. Learning maladjustment not only affects students’ learning effect but also affects the effectiveness of AI-enabled English learning. Although some studies have found that there is a certain degree of poor learning adaptability in the practice of artificial intelligence (AI) supporting English teaching, most studies only describe the phenomenon and do not further explore the causes of poor learning adaptability. Therefore, the research intends to understand the current situation of college students’ English learning adaptability under the support of AI through the investigation and analysis of college students’ English learning adaptability under the support of AI and clarifies the impact of various influencing factors on learning adaptability as well as the relationship between learning adaptability and various factors, and put forward strategies to improve students’ learning adaptability. This research is a new exploration of learning adaptability in the field of AI English learning. At the same time, it is also an extension of the research environment of learning adaptability in information-based learning and a supplement to the research results. This paper combs the composition structure of college students’ English learning adaptability under the support of AI and constructs the influencing factor model of college students’ English learning adaptability under the support of AI. The research has certain theoretical significance and practical value.