Raif-Semantics: A Robust Automated Interlinking Framework for Semantic Web Using MapReduce and Multi-node Data Processing

The era of the web has evolved and the industry strives to work better every day, the constant need for data to be accessible at a random moment is expanding, and with this expansion, the need to create a meaningful query technique in the web is a major concerns. To transmit meaningful data or rich semantics, machines/projects need to have the ability to reach the correct information and make adequate connections, this problem is addressed after the emergence of Web 3.0, the semantic web is developing and being collected an immense. Information to prepare, this passes the giant data management test, to provide an ideal result at any time needed. Accordingly, in this article, we present an ideal system for managing huge information using MapReduce structures that internally help an engine bring information using the strength of fair preparation using smaller map occupations and connection disclosure measures. Calculations for similarity can be challenging, this work performs five similarity detection algorithms and determines the time it takes to address the patterns that has to be a better choice in the calculation decision. The proposed framework is created using the most recent and widespread information design, that is, the JSON design, the HIVE query language to obtain and process the information planned according to the customer’s needs and calculations for the disclosure of the interface. Finally, the results on a web page is made available that helps a user stack json information and make connections somewhere in the range of dataset 1 and dataset 2. The results are examined in 2 different sets, the results show that the proposed approach helps to interconnect significantly faster; Regardless of how large the information is, the time it takes is not radically extended. The results demonstrate the interlinking of the dataset 1 and dataset 2 is most notable using LD and JW, the time required is ideal in both calculations, this paper has mechanized the method involved with interconnecting via a web page, where customers can merge two sets of data that should be associated and used.

Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education

This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers’ roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models.

LSTM-Based Path Prediction for Effective Sensor Filtering in Sensor Registry System

The Internet of Things (IoT) is expected to provide intelligent services by receiving heterogeneous data from ambient sensors. A mobile device employs a sensor registry system (SRS) to present metadata from ambient sensors, then connects directly for meaningful data. The SRS should provide metadata for sensors that may be successfully connected. This process is location-based and is also known as sensor filtering. In reality, GPS sometimes shows the wrong position and thus leads to a failed connection. We propose a dual collaboration strategy that simultaneously collects GPS readings and predictions from historical trajectories to improve the probability of successful requests between mobile devices and ambient sensors. We also update the evaluation approach of sensor filtering in SRS by introducing a Monte Carlo-based simulation flow to measure the service provision rate. The empirical study shows that the LSTM-based path prediction can compensate for the loss of location abnormalities and is an effective sensor filtering model.

Effective Market Segmentation for Open Distance Learning Institutions’ Marketing Strategy

This study aims to examine prospective learners’ perceptions toward effective market segmentation strategies for Open Distance Learning (ODL) advertisements. A descriptive study was conducted to assess preferences based on the generational cohort. Self-administered questionnaires were used to obtain meaningful data from 282 potential learners in Malaysia. The potential learners were working adults based on generational cohorts, namely, Gen Z, Gen Y, Gen X and Baby Boomers. The study was conducted from October to December 2020. Based on the analysis, Substantiality and Accessibility between ODL institutions and the market were viewed by the majority of the respondents as more effective than Responsiveness and Identifiability. However, the standardised path coefficient shows that all variables are significant predictors of the effective market segmentation. Therefore, it is noteworthy to develop creative marketing strategies to attract the attention of a wider audience. The insights obtained can potentially assist ODL institutions to better understand the characteristics of effective market segmentation. The study adds to the existing marketing literature and has strong implications in both academic and ODL institutions, particularly regarding the aspect of prospective learners’ opinions pertaining to effective market segmentation.

Participatory photography and undocumented migration through Mexico at the intersection of social work and anthropology

This article reflects on participatory photography in the context of ethnographic fieldwork at a humanitarian migrant shelter in Central Mexico to consider broader intersections between social work and anthropology. I describe how shifting immigration enforcement trends across Mexico reconfigured my original plan for integrating participatory photography into my work as a researcher and shelter worker. Tracing these changes through three cases examples, I highlight tensions between photography as a reflection of social experience and photography as a mechanism for enacting social change. In light of recent critiques surrounding formulaic and cursory discussions of empowerment in participatory photography, I argue that unexpected shifts in research and practice strategies are themselves meaningful data, especially in uncertain policy environments.

Face Mask Face-Offs

In the United States, the COVID-19 pandemic has proved to be especially destructive and divisive. One of the few things that has united Americans during the pandemic, however, is the experience of watching a new genre of viral videos—face mask face-offs—that showcase citizens going toe-to-toe in public places because someone refuses to wear a mask. These videos are not mere political theater; they are replete with sociologically meaningful data about the nature of Americans’ cultural divisions. By closely analyzing recorded conflicts over collective coronavirus risks and individual freedoms in public settings, the authors identify six justifications for not wearing a mask. These justifications point to emerging cultural discourses and practices organized around phones that not only point to new ways for us to observe social life but participate in the reconfiguration of social life—and social conflict—itself.

Personal Narratives in Technology Design: The Value of Sharing Older Adults’ Stories in the Design of Social Robots

The storytelling lens in human-computer interaction has primarily focused on personas, design fiction, and other stories crafted by designers, yet informal personal narratives from everyday people have not been considered meaningful data, such as storytelling from older adults. Storytelling may provide a clear path to conceptualize how technologies such as social robots can support the lives of older or disabled individuals. To explore this, we engaged 28 older adults in a year-long co-design process, examining informal stories told by older adults as a means of generating and expressing technology ideas and needs. This paper presents an analysis of participants’ stories around their prior experience with technology, stories shaped by social context, and speculative scenarios for the future of social robots. From this analysis, we present suggestions for social robot design, considerations of older adults’ values around technology design, and promotion of participant stories as sources for design knowledge and shifting perspectives of older adults and technology.

Assay Harmonization and Use of Biological Standards To Improve the Reproducibility of the Hemagglutination Inhibition Assay: a FLUCOP Collaborative Study

The hemagglutination inhibition (HAI) assay is the most commonly used serology assay to detect antibodies from influenza vaccination or influenza virus infection. This assay has been used for decades but requires improved standardization of procedures to provide meaningful data.