Miracle-dz/UniFAR-FTUs huggingface dataset:
Is telemedicine best alternative to reaching last mile: Investigation in the context of rural India

Technology has played a role in the quality and assurance of life. Application of technology in the healthcare field has increased manifolds in past decade with the development of new devices and IT. Telemedicine-a technology for remote diagnosis of diseases provides health care facilities to the remote area. In the context of India, telemedicine can be an option to provide healthcare facilities to last mile, the interior villages with the help of technology. Although it is a conceptual paper, the paper investigates the enabler of telemedicine technology adoption and implementation and ends up with the proposed model with the constructs and constructs definitions with the proposed items which are useful for telemedicine implementation. Through the systematic literature review followed by one to one interaction with the physicians, patients and telemedicine experts who all are currently using the technology the propositions are made which are likely to affect the implementation and adoption of telemedicine technology. telemedicine is a useful technology, when it comes to the affordability, quality and reach of healthcare services in India. Private hospitals as well as government both have to work together to provide the better healthcare for better India.
OpenFact: Factuality Enhanced Open Knowledge Extraction

We focus on the factuality property during the extraction of an OpenIE corpus named OpenFact, which contains more than 12 million high-quality knowledge triplets. We break down the factuality property into two important aspects—expressiveness and groundedness—and we propose a comprehensive framework to handle both aspects. To enhance expressiveness, we formulate each knowledge piece in OpenFact based on a semantic frame. We also design templates, extra constraints, and adopt human efforts so that most OpenFact triplets contain enough details. For groundedness, we require the main arguments of each triplet to contain linked Wikidata1 entities. A human evaluation suggests that the OpenFact triplets are much more accurate and contain denser information compared to OPIEC-Linked (Gashteovski et al., 2019), one recent high-quality OpenIE corpus grounded to Wikidata. Further experiments on knowledge base completion and knowledge base question answering show the effectiveness of OpenFact over OPIEC-Linked as supplementary knowledge to Wikidata as the major KG.
Before We Let Go!: Operationalizing Culturally Informed Education

Disparate educational outcomes persist for culturally and linguistically diverse learners despite numerous efforts to encourage pre-service and in-service teachers to adopt culturally relevant education. The operationalization of culturally relevant education, however, remains relatively unexplored as an explanation for teacher resistance. Culturally Responsive Teaching (CRT) and Culturally Relevant Pedagogy (CRP) are among the most empirically and practically warranted educational approaches to serve diverse learners. We posit that combining CRT and CRP to create an inclusive hybrid conceptual framework creates a stronger empirical case for utilizing culturally relevant educational practices. The proposed framework would offer new possibilities for increased quantitative research, which remains underutilized to examine culturally relevant education. Despite their uniqueness, CRT and CRP are often applied interchangeably in the literature. Therefore, we argue that a logical step forward would be establishing an approach whereby CRT and CRP are operationalized as instructional approaches and learning outcomes, respectively. We proffer culturally informed education as a conceptual framework that places CRT and CRP in tandem. To this end, we first examine notable challenges to the current implementation of CRT and CRP by examining the literature on teacher resistance to culturally relevant education using the Diffusion of Innovation (DOI) theory. Then we present a merger between CRT and CRP as a new conceptual framework that supports a shared understanding through operationalization.
Exploring Stigma and Black Mental Health on Twitter

Studies have shown that normalizing conversations about mental health on social media can influence behavior and reduce mental health disparities in minority populations. Using a computational analysis of 169,936 publicly available tweets, this study sought to examine how social media users communicate about Black mental health on Twitter, and who is adding to the conversations surrounding mental health. Findings provide evidence of mental health awareness frames being the most prevalent in the data, and although treatment frames have been shown to reduce stigma, terms related to mental health treatment were not as prevalent. Findings also suggest that mental health conversations are framed in the context of historical trauma and injustice and its impact on the mental health of Black communities. Despite celebrities, influencers, and mental health providers having a positive influence on mental health behaviors, results show that everyday individuals were the majority contributing to mental health conversations. The findings suggest the need for culturally and politically appropriate mental health interventions and assessments and discusses implications for the immersion of mental health providers in Black digital spaces.
Racist and Anti-diversity Attitudes as Predictors of Support for Political Violence among Supporters of Mainstream Political Parties

ABSTRACT Although there are widespread concerns about support for political violence among people affiliated with mainstream political parties, this topic remains largely under-researched. This article examines the relationship between the respondents’ support for political violence and their endorsement of social and political positions that are highly divisive between the left and the right. We collected survey data from a sample of 4,019 respondents from Victoria (Australia). Our analyses distinguished between people affiliated with left-wing parties, right-wing parties, and people with no party affiliation, and found that support for racist and anti-diversity positions is significantly associated with support for political violence across the three groups. Specifically, having negative attitudes to ethnic and religious minorities and having negative attitudes to diversity are significant predictors of support for political violence in the right-wing group. Having negative attitudes to diversity is a predictor of support for political violence in the left-wing group. Having anti-Muslim prejudice is a predictor of support for political violence among people with no party affiliation. Other significant predictors are having anti-democratic views (across the three groups) being male and being young (in the left-wing and right-wing groups) and having experienced discrimination (in the the left-wing group).
Improving the eST-Miner Models by Replacing Imprecise Structures Using Place Projection

In process discovery, the goal is to find, for a given event log, the model describing the underlying process. While process models can be represented in a variety of ways, Petri nets form a theoretically well-explored description language. A Petri net describes the underlying process well if it allows for all relevant behavior (fitness) and, at the same time, restricts other process executions not contained in the event log (precision). A process discovery algorithm aiming to balance these often conflicting requirements is the eST-Miner. To this end, the approach employs a user-definable parameter which indicates a lower bound for the fraction of behavior in the event log that each place in the Petri net must be able to replay. As the eST-Miner is restricted to uniquely labeled transitions, i.e., it does not return silent or duplicate transitions, there are inputs where precision is decreased for the purpose of guaranteeing minimal fitness. An example is the situation where part of the process is optional and can be skipped, which is usually modeled by using a silent transition. Being constraint to uniquely labeled transitions, the eST-Miner models such behavior using a place with a loop. On the one hand this correctly allows to skip the looping behavior (maintains fitness), on the other hand precision is decreased drastically by allowing the behavior to be repeated arbitrarily often. Thus, the models returned by the eST-Miner often contain imprecise substructures, most prominently, ”flower-like” places (places with one-loops). In this paper, we aim to replace such places by more meaningful and precise submodels that include silent transitions, while preserving desirable structures existing in the input uniquely labeled Petri net. We describe an approach to distill the behavior related to a specific, imprecise place from the event log such that the resulting log projection can be used to discover and insert a more precise Petri net. Furthermore, extensions of the approach to more complex imprecise structures are discussed.
Domain-agnostic Procedural Content Generation Can Be Done Declaratively

Procedural content generation is applied in the development process of many commercial games: automatically generated contents are delivered to players in order to offer a constantly changing user experience and enrich the game itself. Designing algorithms for content generators can be a tedious job. The product of this work is often too domain specific and offers limited reusability and customizability. Declarative approaches to content generation, more properly defined as Declarative Content Specification (DCS) techniques, like the ones based on Answer Set Programming (ASP), promise to overcome some of these drawbacks, since they allow focusing on describing content requirements rather than programming ad-hoc generation engines. Also, DCS speed up the prototype generation techniques themselves. In turn, DCS techniques struggle to gain momentum mainly because of lack of integration with game engines. Furthermore, the promise of generality and reusability is neutralized by the burden of wiring and adapting declarative content specifications to the context of the game at hand. In this work, we report about our progress toward a general DCS module working in the Unity game engine, and integrated in an existing asset for declaratively defining AI modules. We illustrate both the design and runtime workflow of this module, and how game content developers could use it for devising their own content generation schemes. For this purpose, an example highlighting the advantages of this approach is described.
Nondeterministic Interactive Refutations for Nearest Boolean Vector

Most n -dimensional subspaces A of R m are Ω( √ m )-far from the Boolean cube {− 1 , 1 } m when n < cm for some constant c > 0. How hard is it to certify that the Nearest Boolean Vector (NBV) is at least γ √ m far from a given random A ? Certifying NBV instances is relevant to the computational complexity of approximating the Sherrington-Kirkpatrick Hamiltonian, i.e. maximizing x T Ax over the Boolean cube for a matrix A sampled from the Gaussian Orthogonal Ensemble. The connection was discovered by Mohanty, Raghavendra, and Xu (STOC 2020). Improving on their work, Ghosh, Jeronimo, Jones, Potechin, and Rajendran (FOCS 2020) showed that certification is not possible in the sum-of-squares framework when m ≪ n 1 . 5 , even with distance γ = 0. We present a non-deterministic interactive certification algorithm for NBV when m ≫ n log n and γ ≪ 1 /mn 1 . 5 . The algorithm is obtained by adapting a public-key encryption scheme of Ajtai and Dwork. 2012
Graph-Theoretical Arguments in Support of a Quantum Declarative Manifesto

The encoding of graphs and the development of efficient algorithms over graphs in the Quantum framework is still a challenging problem. More in general, the understanding of whether a problem can benefit of the Quantum speed-up or not is far from being complete. In this paper we analyse, compare, and generalize some proposals for the encoding of graphs in Quantum computing. A question peeping out on the horizon of our analysis concern the shift from a procedural way of thinking to a declarative one in the context of Quantum computing.
A novel method for estimating the distribution of convective heat flux in ducts: Gaussian filtered singular value decomposition

ABSTRACT A new regularization approach for the unconstrained linear least square problem is proposed and assessed. This approach improves the classical methods based on the singular value decomposition approach by employing the Gaussian filter, a kind of filter which has been proved to be effective in several applications related to noise suppression. In particular, as a benchmark, it is hereby considered the estimation of the heat flux density at the internal wall surface in a forced convection problem in ducts, by solving the inverse heat conduction problem in the solid wall only using temperature distribution available at the exterior boundary. Results on both synthetic and experimental data are reported with the aim of discussing the effectiveness of the proposed method in comparison to other similar solution approaches.
Scheduling Periodic Treatments Via Answer Set Programming

The scheduling of periodic treatments consists of planning a care path over a period of several weeks, in which patients have to perform different treatments respecting a certain periodicity. Treatments must be assigned to a day taking into account patients’ preferences, operators’ availability, the possibility of needing instruments/machinery, and of having different facilities in which operators can work and move around during the working time. This problem has been formulated to have a certain degree of flexibility and to be suitable for different contexts where periodicity is required, e.g. rehabilitation or planning a cycle of drug therapies. In this paper, we present a solution to this problem based on Answer Set Programming (ASP). We consider two different possible scenarios: scheduling one patient at a time, and scheduling blocks of patients. We have also conducted an experimental analysis showing that ASP is a suitable solution to this problem.
Frameworks for Nonclairvoyant Network Design with Deadlines or Delay

Clairvoyant network design with deadlines or delay has been studied extensively, culminating in an 𝑂 ( log 𝑛 ) -competitive general framework, where 𝑛 is the number of possible request types (Azar and Touitou, FOCS 2020). In the nonclairvoyant setting, the problem becomes much harder, as Ω (√ 𝑛 ) lower bounds are known for certain problems (Azar et al., STOC 2017). However, no frameworks are known for the nonclairvoyant setting, and previous work focuses only on specific problems, e.g., multilevel aggregation (Le et al., SODA 2023). In this paper, we present the first nonclairvoyant frameworks for network design with deadlines or delay. These frameworks are nearly optimal: their competitive ratio is ˜ 𝑂 (√ 𝑛 ) , which matches known lower bounds up to logarithmic factors. 2012
Narrative Trends over the COVID-19 Pandemic: Digital Social Listening to Inform WHO Infodemic Management

The COVID-19 infodemic is an overwhelming amount of information that has challenged pandemic communication and epidemic response. WHO has produced weekly infodemic insights reports to identify questions, concerns, information voids expressed and experienced by people online. Publicly available data was collected and categorized to a public health taxonomy to enable thematic analysis. Analysis showed three key periods of narrative volume peaks. Understanding how conversations change over time can help inform future infodemic preparedness and prevention planning.
The Effect of Employee Well-Being on Customer Service Job Performance The Mediation Role of the Promotive Voice

The Covid-19 pandemic has altered the way the industry operates, including customer service. As one of the determinants of customer satisfaction, customer service also became a critical key indicator for Indonesian online marketplaces. The purpose of the study is to investigate the impact of employee well-being on job performance and the mediation role of the promotive voice. Data gathered from 256 employees from various Indonesian online marketplaces was tested using structural equation modelling (SEM). The results indicate psychological well-being signiﬁcantly affect job performance, while physical well-being and social well-being do not signiﬁcantly affect job performance. In addition, complementary mediation found out from the promotive voice on the effect of psychological well-being on job performance.
PADLL: Taming Metadata Burstiness of HPC Jobs Through Application-level QoS Control

1 PROBLEM STATEMENT Contrary to long-lived assumptions about high-performance computing (HPC) where applications were predominately compute-bound, modern applications (e.g., deep learning training) are data-intensive and generate massive bursts of metadata operations [2]. While these workloads demand scalable, high throughput, and low latency storage, having multiple concurrent jobs submitting large amounts of metadata operations can easily saturate the shared Parallel File System (PFS) metadata resources, leading to I/O contention, overall performance degradation, and I/O unfairness. However, efficiently controlling I/O workflows of large-scale HPC storage systems poses unique challenges of which existing approaches have been unable to address. Intrusiveness to I/O layers.While existing solutions aim at mitigating I/O contention and variability of shared storage, these are tightly coupled to the implementation of core layers of the HPC I/O stack, including the shared file system and job scheduler. Such an approach requires deep understanding of the system’s internal operation model and profound code refactoring, limiting overall maintainability and portability. Partial visibility and I/O control. Existing solutions that enable QoS control at the compute node level and do not require changes to core I/O layers, actuate in isolation (i.e., agnostic of other jobs in execution), being unable to holistically coordinate the I/O generated from multiple jobs that compete for shared storage, leading to I/O contention. Metadata remains overlooked.While several works have focused on achieving QoS over data workflows (I/O bandwidth), the metadata counterpart has not received the same level of attention. This is problematic given that several HPC centers are observing a surge of metadata operations in their clusters and expect this to become more severe over time.
Ta2O5/ Al2O3/ SiO2 - antireflective coating for non-planar optical surfaces by atomic layer deposition

Antireflective coatings are essential to improve transmittance of optical elements. Most research and development of AR coatings has been reported on a wide variety of plane optical surfaces; however, antireflection is also necessary on nonplanar optical surfaces. Physical vapor deposition (PVD), a common method for optical coatings, often results in thickness gradients on strongly curved surfaces, leading to a failure of the desired optical function. In this work, optical thin films of tantalum pentoxide, aluminum oxide and silicon dioxide were prepared by atomic layer deposition (ALD), which is based on self-limiting surface reactions. The results demonstrate that ALD optical layers can be deposited on both vertical and horizontal substrate surfaces with uniform thicknesses and the same optical properties. A Ta2O5/Al2O3/ SiO2 multilayer AR coating (400-700 nm) was successfully applied to a curved aspheric glass lens with a diameter of 50 mm and a center thickness of 25 mm.
A collaborative assembly for low-voltage electrical apparatuses

Low-voltage electrical apparatuses (LVEAs) have many workpieces and intricate geometric structures, and the assembly process is rigid and labor-intensive, and has little balance. The assembly process cannot readily adapt to changes in assembly situations. To address these issues, a collaborative assembly is proposed. Based on the requirements of collaborative assembly, a colored Petri net (CPN) model is proposed to analyze the performance of the interaction and self-government of robots in collaborative assembly. Also, an artificial potential field based planning algorithm (AFPA) is presented to realize the assembly planning and dynamic interaction of robots in the collaborative assembly of LVEAs. Then an adaptive quantum genetic algorithm (AQGA) is developed to optimize the assembly process. Lastly, taking a two-pole circuit-breaker controller with leakage protection (TPCLP) as an assembly instance, comparative results show that the collaborative assembly is cost-effective and flexible in LVEA assembly. The distribution of resources can also be optimized in the assembly. The assembly robots can interact dynamically with each other to accommodate changes that may occur in the LVEA assembly.
Numerical Model for Atmospheric Diffusion Analysis and Evaluation of Effective Dose for Safety Analysis Effective Stack Height and Effective Dose Estimated by Wind Tunnel and Numerical Model

A numerical simulation method has been developed to predict atmospheric flow and stack gas diffusion, considering the buildings and complex terrain located near and relatively far from a stack, respectively. The turbulence closure technique was used for flow calculation, some calculation grids on the ground near a stack were treated as buildings, and stack gas diffusion was predicted using the Lagrangian particle model. The calculated flow and stack gas diffusion results were compared with those obtained by wind tunnel experiments under actual terrain containing buildings. Effective stack height was estimated by comparing the surface concentration along the plume axis with those under a flat-plate condition, and it was apparent that the effective stack heights estimated by calculations were almost the same as those obtained by the wind tunnel experiment. Then, the effective dose and relative concentration of stack gas were calculated using the effective stack heights obtained by a numerical model. Almost the same effective dose and relative concentration were obtained when compared with those using the effective stack height obtained by wind tunnel experiment.
From Private Set Intersection to Secure Database Joins using Cross-Layer Design

1 MOTIVATION AND PROBLEM STATEMENT The adoption of the General Data Protection Regulation (GDPR) and other similar frameworks protect end-users against indiscriminate data collection and processing, but they make data sharing across different data owners challenging. Shared data, however, can have great utility – as shown by applications in machine learning, finance, and big data analysis. One approach to achieve sharing while protecting the privacy of end-users is to use Secure Multi-Party Computation (SMPC), a technique that enables several parties to jointly compute a function without disclosing private information. Recent related works show that SMPC can be succesfully applied to the domain of databases but they have also highlighted that SMPC operations are typically orders of magnitude more computeintensive than their plain-text counterparts: for instance, the Secure Join in SMCQL [1], although it has been already improved by secure query optimization, still requires almost two minutes to handle 15GB of data, which is unpractical in many real-world scenarios. Finding a balance between privacy guarantees, runtime scalability, and absolute performance is the main challenge when developing a database with a privacy-protected data sharing features. Our work is motivated by this challenge and proposes one way to address it – we consider this an important goal, given that in the future, the significance of privacy-preserving databases will only grow.