Co-Occurrence of Reading and Writing Difficulties: The Application of the Interactive Dynamic Literacy Model

This article presents the application of the interactive dynamic literacy (IDL) model (Kim, 2020a) toward understanding difficulties in learning to read and write. According to the IDL model, reading and writing are part of communicative acts that draw on largely shared processes and skills as well as unique processes and skills. As such, reading and writing are dissociable but interdependent systems that have hierarchical, interactive, and dynamic relations. These key tenets of the IDL model are applied to the disruption of reading and writing development to explain co-occurrence of reading–writing difficulties using a single framework. The following hypotheses are presented: (a) co-occurrence between word reading and spelling and handwriting difficulties; (b) co-occurrence of dyslexia with written composition difficulties; (c) co-occurrence between reading comprehension and written composition difficulties; (d) co-occurrence of language difficulties with reading difficulties and writing difficulties; (e) co-occurrence of reading, writing, and language difficulties with weak domain-general skills or executive functions such as working memory and attentional control (including attention-deficit hyperactivity disorder [ADHD]); and (f) multiple pathways for reading and writing difficulties. Implications are discussed.

COVID-19 Data Forecasting and Spread Visualization Using ML

The COVID-19 global pandemic has affected everyone's day-to-day life. The COVID spread data is increasing rapidly which needs to be visualized in some format. The statistical data includes infected, recovered, and the death count which is visualized by various tools. This project presents an interactive dynamic dashboard to display the details about the COVID-19 patient reports, scheduled reports, timely reports, geographical reports including state-wise, district wise. It should have options to display the metrics using charts, graphs, etc. Application features include registration, download report in multiple formats, email the report, schedule a report, share a report. Users can check for Epass availability; the decision will be taken by checking the covid-affected counts on the source and destination. Patient details will be stored in the cloud. The model includes a prediction of upcoming covid-affected count using ML.

Customer Value of Smart Grid Application

The authors investigate customer value of smart grid application in smart city from the perspective of main research paradigms of customer value. Data is based on questionnaire for customers (N=131), deep interviews among specialists (7=N), and two months of observation. The results show that the typical user of smart grid technology is a male aged between 30 and 69 who considers that using the application is interesting because of the perceived benefits. Developing strong customer relationship is formed through the provision of e-service quality that has a key role in maintaining customer trust, satisfaction, and loyalty. End-product and service process paradigms to measure customer value do not fully take the complex context of smart cities into consideration, and the ecosystem paradigm must be developed to analyze customer value in smart cities in interactive dynamic decentralized environment where cumulative big data is used to match the customer needs with new digital services.

Topic Extraction and Interactive Knowledge Graphs for Learning Resources

Humanity development through education is an important method of sustainable development. This guarantees community development at present time without any negative effects in the future and also provides prosperity for future generations. E-learning is a natural development of the educational tools in this era and current circumstances. Thanks to the rapid development of computer sciences and telecommunication technologies, this has evolved impressively. In spite of facilitating the educational process, this development has also provided a massive amount of learning resources, which makes the task of searching and extracting useful learning resources difficult. Therefore, new tools need to be advanced to facilitate this development. In this paper we present a new algorithm that has the ability to extract the main topics from textual learning resources, link related resources and generate interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks no matter how big or small the texts are. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm’s accuracy was evaluated against Gensim, largely improving its accuracy. This could be a step towards strengthening self-learning and supporting the sustainable development of communities, and more broadly of humanity, across different generations.

Bumbometer digital crowd game: collaboration through competition in entertainment events

The use of mobile devices, especially smartphones, is widespread across all social strata and age groups, helping to ensure faster access from anywhere, data collection, and more regular and frequent control to aid urban, environmental, and social management. In this scenario, the entertainment industry has benefited from this powerful individual technological resource in cultural and sporting events. In this way, this work presents a proposal for interaction and engagement in entertainment events in a more prosperous and more technological way, through the development of a collaborative and competitive mobile-­web crowd game, intended for enhancing interaction between the crowd and as a unified group, whether physically co-­located or online. The application, called Bumbometer, uses motion sensors during an interactive dynamic with the crowd applying concepts from Mobile Crowd Sensing and User eXperience. We conducted two experimental studies to evaluate the proposed technology, the first in a real scenario of a folk cultural festival and the second in a controlled environment, simulating an event considering a scenario in which users were geographically distant. The results indicate that people feel immersed and engaged during the interaction through the proposed game, which reinforces the statement that the game meets an increasingly growing need to use technologies to ensure more significant interaction and audience immersion at crowd entertainment events, a creative and far­-reaching form.

Stratégies conversationnelles dans le discours médical médiatisé

A través de este artículo, que se enmarca en el campo disciplinar del análisis del discurso, intentaremos identificar las principales estrategias conversacionales desplegadas en el discurso médico mediado, apoyándonos en una entrevista exclusiva emitida en el canal de televisión BFMTV. Más precisamente, destacaremos las especificidades discursivas propias de este tipo de comunicación y analizaremos de cerca la dinámica interactiva y los comportamientos lingüísticos específicos de los interlocutore Through this article, which falls within the disciplinary field of discourse analysis, we will try to identify the main conversational strategies deployed in mediated medical discourse, by relying on a corpus made up of an exclusive interview broadcast on the BFMTV television channel. More precisely, we will highlight the specific discursive features specific to this type of communication and will closely analyze the interactive dynamic and the specific language behaviors of the interlocutors.

Toward data-driven solutions to interactive dynamic influence diagrams

With the availability of significant amount of data, data-driven decision making becomes an alternative way for solving complex multiagent decision problems. Instead of using domain knowledge to explicitly build decision models, the data-driven approach learns decisions (probably optimal ones) from available data. This removes the knowledge bottleneck in the traditional knowledge-driven decision making, which requires a strong support from domain experts. In this paper, we study data-driven decision making in the context of interactive dynamic influence diagrams (I-DIDs)—a general framework for multiagent sequential decision making under uncertainty. We propose a data-driven framework to solve the I-DIDs model and focus on learning the behavior of other agents in problem domains. The challenge is on learning a complete policy tree that will be embedded in the I-DIDs models due to limited data. We propose two new methods to develop complete policy trees for the other agents in the I-DIDs. The first method uses a simple clustering process, while the second one employs sophisticated statistical checks. We analyze the proposed algorithms in a theoretical way and experiment them over two problem domains.