Input Keywords Research Articles

빅 히스토리를 활용한 AI 기반 융합질문 생성 교육용 서비스 ‘OASIS’ 개발

This study aims to design an AI-based educational service using Big History to enhance convergence competency in secondary school students and to evaluate the effectiveness and satisfaction of the designed service, OASIS(Online AI-based Service for Inquiry Studies). Through three stages of model improvement, the core model OASIS was developed. Convergence competency tests were conducted on 128 first-year high school students before and after using OASIS. Additionally, the effectiveness and user satisfaction were evaluated by a user satisfaction survey conducted with the same students and 15 educational experts. OASIS utilizes ChatGPT to provide multidisciplinary information on input keywords, thereby enhancing interdisciplinary understanding. The service is structured to foster convergence competency through the creation of convergence inquiries and interactive activities. The results of the convergence competency tendency test and user satisfaction survey indicated that the use of OASIS helped enhance students' convergence competency and showed high satisfaction regarding the usefulness of information. However, limitations such as the inaccuracy of information and slow loading speeds were noted. Addressing these limitations could improve the service’s applicability in secondary education for fostering convergence.

DEVELOPING A COMPREHENSIVE LIVE NEWS AGGREGATOR: INTEGRATING REAL-TIME DATA SOURCES FOR ENHANCED INFORMATION ACCESSIBILITY

Abstract—News Aggregator is simply an online software which collects new stories and events around the world from various sources all in one place. News aggregator plays a very important role in reducing time consumption, as all of the news that would be explored through more than one website will be placed only in a single location. Also, summarizing this aggregated content absolutely will save reader’s time. A proposed technique used called the TextRank algorithm that showed promising results for summarization. This paper presents the main goal of this project which is developing a news aggregator able to aggregate relevant articles of a certain input keyword or key-phrase. Summarizing the relevant articles after enhancing the text to give the reader understandable and efficient summary. Index Term : News aggregator; text summarization; text enhancement; TextRank algorithm

Research on the Impact of Social Media Keyword-based Recommendation Methods on Users

The purpose of this study is to investigate the keyword-based recommendation strategy of social media and the impact of this recommendation strategy on user attitudes. In this study, three representative content creation social media platforms, Weibo, TikTok, and Bilibili, were selected to investigate their recommendation strategies, and a questionnaire was designed to simulate the content recommendation strategies of social media platforms after users input keywords to investigate users' attitudes towards keyword-based content recommendation strategies in social media. Through investigation, the keyword-based recommendation strategy of social media is mainly to recommend content explicitly associated with keywords. After the user returns to the home page, the system occasionally recommends a small amount of content implicitly associated with keywords. After a given keyword, social media users are more willing to click on content closely related to themselves when facing multiple recommended content. After a series of user choices, a series of entries recommended by the system to most users are explicitly associated with a given keyword. The content recommendation strategy with explicit association is still dominant in social media.

System Search Service Implementation Based on a Custom Lexical Search

The search bar on any system is a highly employed and advantageous feature that helps make lives easier every day. Users see a tremendous improvement in their efficiencies as the search bar retrieves applications, and documents in a specific folder with just the file name. It helps avoid manual searching throughout the system. However, the existing technology does not help in the retrieval of documents where the search query is generic and about the document, and not just its name. This study presents a developed application that could be used to retrieve documents from anywhere on the system with user input keywords. These keywords are present inside the document and cannot be read by the existing technology of the search bar on the systems. The user can enter vernacular text as the search query on this application’s search bar. The application would accurately retrieve the files, order them according to relevancy, and list them as ready-to-open files with just a click. The program works with the elimination of the redundancy of reading all documents for each search query and using parallel processing to divide the files into batches. The application runs 303418.495ms, approximately 5 minutes, for the first run where it reads all the documents on the system. From the second run onwards, it takes only 20.080ms, approximately 0.02008 seconds.

Service Recommendation Method based on Multi Model Fusion

<p>In recent years, the rapid development of service-oriented computing technology has increased the burden of choice for software developers when developing service-based applications. Existing Web service recommendation systems often face two challenges. First, developers are required to input keywords for service search, but due to their lack of knowledge in the relevant field, the keywords entered by the developers are usually freestyle, causing an inability to accurately locate services. Second, it is exceedingly difficult to extract services that meet the requirements due to the 99.8% sparseness of the application service interaction records. To address the above challenges, a framework for service recommendation through multi-model fusion (SRM) is proposed in this paper. Firstly, we employ graph neural network algorithms to deeply mine historical records, extract the features of applications and services, and calculate their preferences. Secondly, we use the BERT model to analyze text information and use the attention mechanism and fully connected neural networks to deeply mine the matching degree between candidate services and development requirements. The two models mentioned above are further merged to obtain the final service recommendation list. Extensive experiments on datasets demonstrate that SRM can significantly enhance the effectiveness of recommendations in service recommendation scenarios.</p> <p> </p>

A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse

The profusion of existing ontologies in different domains has made reusing ontologies a best practice when developing new ontologies. The ontology reuse process reduces the expensive cost of developing a new ontology, in terms of time and effort, and supports semantic interoperability. Existing ontology development tools do not assist in the recommendation of ontologies or their concepts to be reused. Also, existing ontology recommendation tools could suggest whole ontologies covering a set of input keywords without referring to which parts of them (e.g., concepts) can be reused. In this paper, we propose an effective ontology recommendation system that helps the user in the iterative development and reuse of ontologies. The system allows the user to provide explicit preferences about the new ontology, and iteratively guides the user to parts from existing ontologies which match his preferences for reuse. Finally, we developed a prototype of our ontology recommendation system and conducted a user-based evaluation to assess the effectiveness of our approach.

A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies

In large social media knowledge retrieval systems, employing a keyword-based fuzzy matching method to obtain knowledge presents several challenges, such as irrelevant, inaccurate, disorganized, or non-systematic knowledge results. Therefore, this paper proposes a knowledge retrieval method capable of returning hierarchical, systematized knowledge results. The method can match the knowledge demands according to the keyword input by users and then present the knowledge supplies corresponding to the knowledge demands as results to the users. Firstly, a knowledge structure named Knowledge Demand is designed to represent the genuine needs of social media users. This knowledge structure measures the popularity of topic combinations in the Topic Map, so the topic combinations with high popularity are regarded as the main content of the Knowledge Demands. Secondly, the proposed method designs a hierarchical and systematic knowledge structure, named Knowledge Supply, which provides Knowledge Solutions matched with the Knowledge Demands. The Knowledge Supply is generated based on the Knowledge Element Repository, using the BLEU similarity matrix to retrieve Knowledge Elements with high similarity, and then clustering these Knowledge Elements into several knowledge schemes to extract the Knowledge Solutions. The organized Knowledge Elements and Knowledge Solutions are the presentation of each Knowledge Supply. Finally, this research crawls posts in the “Autohome Forum” and conducts an experiment by simulating the user’s actual knowledge search process. The experiment shows that the proposed method is an effective knowledge retrieval method, which can provide users with hierarchical and systematized knowledge.

Task: An Efficient Framework for Instant Error-Tolerant Spatial Keyword Queries on Road Networks

Instant spatial keyword queries return the results as soon as users type in some characters instead of a complete keyword, which allow users to query the geo-textual data in a type-as-you-search manner. However, the existing methods of instant spatial keyword queries suffer from several limitations. For example, the existing methods do not consider the typographical errors of input keywords, and cannot be applied to the road networks. To overcome these limitations, in this paper, we propose a new query type, i.e., instant error-tolerant spatial keyword queries on road networks. To answer the queries efficiently, we present a framework, termed as Task, which consists of index component, query component, and update component. In the index component, we design a novel index called reverse 2-hop label based trie, which seamlessly integrates spatial and textual information for each vertex of the road network. Based on our proposed index, we devise efficient algorithms to progressively return and update the query results in the query component and update component, respectively. Finally, we conduct extensive experiments on real-world road networks to evaluate the performance of our presented Task. Empirical results show that our proposed index and algorithms are up to 1--2 orders of magnitude faster than the baseline.

An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles.

The current technological world is growing rapidly and each aspect of life is being transformed toward automation for human comfort and reliability. With autonomous vehicle technology, the communication gap between the driver and the traditional vehicle is being reduced through multiple technologies and methods. In this regard, state-of-the-art methods have proposed several approaches for advanced driver assistance systems (ADAS) to meet the requirement of a level-5 autonomous vehicle. Consequently, this work explores the role of textual cues present in the outer environment for finding the desired locations and assisting the driver where to stop. Firstly, the driver inputs the keywords of the desired location to assist the proposed system. Secondly, the system will start sensing the textual cues present in the outer environment through natural language processing techniques. Thirdly, the system keeps matching the similar keywords input by the driver and the outer environment using similarity learning. Whenever the system finds a location having any similar keyword in the outer environment, the system informs the driver, slows down, and applies the brake to stop. The experimental results on four benchmark datasets show the efficiency and accuracy of the proposed system for finding the desired locations by sensing textual cues in autonomous vehicles.

Attention–Survival Score: A Metric to Choose Better Keywords and Improve Visibility of Information

In this paper, we propose a method to aid authors in choosing alternative keywords that help their papers gain visibility. These alternative keywords must have a certain level of popularity in the scientific community and, simultaneously, be keywords with fewer competitors. The competitors are derived from other papers containing the same keywords. Having fewer competitors would allow an author’s paper to have a higher consult frequency. In order to recommend keywords, we must first determine an attention–survival score. The attention score is obtained using the popularity of a keyword. The survival score is derived from the number of manuscripts using the same keyword. With these two scores, we created a new algorithm that finds alternative keywords with a high attention–survival score. We used ontologies to ensure that alternative keywords proposed by our method are semantically related to the original authors’ keywords that they wish to refine. The hierarchical structure in an ontology supports the relationship between the alternative and input keywords. To test the sensibility of the ontology, we used two sources: WordNet and the Computer Science Ontology (CSO). Finally, we launched a survey for the human validation of our algorithm using keywords from Web of Science papers and three ontologies: WordNet, CSO, and DBpedia. We obtained good results from all our tests.

Privacy-preserving and efficient data sharing for blockchain-based intelligent transportation systems

Recent years have witnessed the development and adoption of blockchain technology in intelligent transportation systems (ITS) because of its authenticity and traceability. However, increasing ITS devices impose grand challenges in privacy-preserving and efficient data sharing. Recent research has demonstrated that integrating searchable symmetric encryption in blockchain enables privacy-preserving data sharing among ITS devices. However, existing solutions focus only on single-keyword searches over encrypted ITS data on the blockchain and suffer from privacy and efficiency issues when extended to multi-keyword scenarios. This work proposes a bloom filter-based multi-keyword search protocol for ITS data with enhanced efficiency and privacy preservation. We design a bloom filter to select a low-frequency keyword from the multiple keywords input by the ITS data owner. The low-frequency keyword can filter out a large portion of the ITS data from the search result, thus significantly reducing the computational cost. Furthermore, each identifier-keyword pair is attached with a pseudorandom tag that enables the completion of a search operation in only one round. In this manner, privacy is preserved because there are no intermediate rounds and results. In addition to the multi-keyword search protocol, we specify the addition and deletion protocols to enable dynamic updates of data records. We conducted a comprehensive performance evaluation of the protocols. The experimental results indicate that the proposed multi-keyword search protocol saves 14.67% query time and 59.96% financial cost.

AI-Based Knowledge Extraction from the Bioprinting Literature for Identifying Technology Trends.

Bioprinting is a rapidly evolving field, as represented by the exponential growth of articles and reviews published each year on the topic. As the number of publications increases, there is a need for an automatic tool that can help researchers do more comprehensive literature analysis, standardize the nomenclature, and so accelerate the development of novel manufacturing techniques and materials for the field. In this context, we propose an automatic keyword annotation model, based on Natural Language Processing (NLP) techniques, that can be used to find insights in the bioprinting scientific literature. The approach is based on two main data sources, the abstracts and related author keywords, which are used to train a composite model based on (i) an embeddings part (using the FastText algorithm), which generates word vectors for an input keyword, and (ii) a classifier part (using the Support Vector Machine algorithm), to label the keyword based on its word vector into a manufacturing technique, employed material, or application of the bioprinted product. The composite model was trained and optimized based on a two-stage optimization procedure to yield the best classification performance. The annotated author keywords were then reprojected on the abstract collection to both generate a lexicon of the bioprinting field and extract relevant information, like technology trends and the relationship between manufacturing-material-application. The proposed approach can serve as a basis for more complex NLP-related analysis toward the automated analysis of the bioprinting literature.

Analysis and evaluation of Worldwide Carbon emission topics in economic journals: a review on articles published during 2016-2021

Carbon dioxide is the most prevalent gas, prompting researchers to investigate carbon reduction and mitigation measures. Carbon emission reporting is just one aspect of this domain’s research, which also includes identifying and implementing carbon mitigation and reduction measures. However, there isn’t a thorough study accessible to track global research on carbon emissions. As a result, this study examined the global literature on carbon emissions using a scientometric analytic method. From 2016 to 2021, 167 bibliographic records were taken from the Scopus collection database and evaluated utilizing author, journal, country, and keyword analysis approaches. China, Turkey, the United States, and the United Kingdom made the most substantial contributions to the domain. The findings revealed that over the study period, the publication of papers relating to carbon emissions increased, and China produced a greater number of publications and authors (45.12 percent of total). Furthermore, this page examines the most commonly used input keywords. The major findings of this study will be useful for politicians, researchers, and institutions in determining future research directions and identifying whom they might consult to assist in formulating carbon emission control policies and future carbon reduction objectives, according to the authors.

Contribution Title Analysis and evolution of mortality and morbity: a review of articles published during 2016–2021

Morbidity and mortality are two frequent epidemiological monitoring measures. These parameters indicate how a health issue develops and how severe it becomes. They're important for learning about illness risk factors and comparing health events and populations. From 2016 to 2021, 241 bibliographic records were extracted from the Scopus database and evaluated through author, journal, country, and keyword analyses. The United States, United Kingdom, Australia, France, and Canada made the most substantial contributions to the domain. Moreover, the findings revealed that during the study period, the publication of papers relating to mortality and morbity increased, and the United States produced the largest proportion of publications and authors (22 percent of total). The researchers are interested in strongly highly loaded citation. This work also examines the most used input keywords. Accordingly, the major findings of this study will be useful for politicians, researchers, and institutions to determine future research directions and identify potential consultants to assist formulating their mortality and morbity control policies and future mortality reduction objectives

Adaptive Emulation Framework for Multi-Architecture IoT Firmware Testing

Internet of things (IoT) devices are being increasingly used in numerous areas. However, the low priority on security and various IoT types have made these devices vulnerable to attacks. To prevent this, recent studies have analyzed firmware in an emulation environment that does not require actual devices and is efficient for repeated experiments. However, these studies focused only on major firmware architectures and rarely considered exotic firmware. In addition, because of the diversity of firmware, the emulation success rate is not high in terms of large-scale analyses. In this study, we propose the adaptive emulation framework for multi-architecture (AEMA). In the field of automated emulation frameworks for IoT firmware testing, AEMA considers the following issues: (1) limited compatibility for exotic firmware architectures, (2) emulation instability when configuring an automated environment, and (3) shallow testing range resulting from structured inputs. To tackle these problems, AEMA can emulate not only major firmware architectures but also exotic firmware architectures not previously considered, such as Xtensa, ColdFire, and reduced instruction set computer (RISC) version five, by implementing a minority emulator. Moreover, we applied the emulation arbitration technique and input keyword extraction technique for emulation stability and efficient test case generation. We compared AEMA with other existing frameworks in terms of emulation success rates and fuzz testing. As a result, AEMA succeeded in emulating 864 out of 1,083 overall experimental firmware and detected vulnerabilities at least twice as fast as the experimental group. Furthermore, AEMA found a 0-day vulnerability in real-world IoT devices within 24 h.

Searching for Researchers: an Ontology-based NoSQL Database System Approach and Practical Implementation

This work presents the design and implementation of two web-based search systems, Busc@NIMA and Quem@PUC. Both systems allow the identification of research and development projects, besides existing competencies in laboratories and departments involving professors and researchers at PUC-Rio University. Our applications are based on a list of search-related terms that are matched to the dataset composed of PUC-Rio’s Lattes CVs offered courses, information from administrative systems, and specific keywords that are input by the professors/researchers themselves. To integrate all the needed data, we consider multiple database and search technologies, such as XML, RDF, TripleStores, and Relational Databases. Search results include professor’s name, academic papers, teaching activities, contact links, keywords, and laboratories of those involved with the subject represented by the set of keywords input. We describe the main features that show how our systems work.

Design and Communication of City Brand Image Based on Big Data and Personalized Recommendation System

In order to define the positioning of the urban brand image, design the urban brand image, integrate and optimize the communication channels, improve the public participation awareness, and enhance the core competitiveness of the city. In this paper, a personalized recommendation search engine based on big data identifies keywords input by urban users. And give more accurate results based on some relevant information that can be extracted. This paper analyzes how to make better use of big data for tourism destination brand image management, and the existing shortcomings, and puts forward relevant suggestions. The industries related to cultural creative design and tourism elements constitute an intertwined cultural and tourism industry chain, and data technology plays an important role in the cultural and tourism industry chain. Through the development of tourism, tourists will produce comprehensive and diversified consumption in the city. Based on the analysis of big data, it can provide strong decision support for the government and industry managers, and realize the image design and communication of the urban brand identification system. Through the big data platform, establish the brand management strategy, improve the communication content of the city’s brand image, and timely feedback the opinions and suggestions of tourists on the tourism destination, so as to adjust the communication strategy of the tourism image according to the feedback information of tourists. The results show that the big data personalized recommendation system can achieve ideal results in urban brand value and urban tourism related factors.

Design-by-Analogy: Effects of Exploration-Based Approach on Analogical Retrievals and Design Outcomes

Abstract This paper presents VISION (Visual Interaction tool for Seeking Inspiration based on Nonnegative Matrix Factorization), a computational design-by-analogy (DbA) tool that enables designers to visually explore a space of analogical inspiration for creative idea generation. While many currently available DbA tools use a query-based approach for retrieving analogies (i.e., input keywords or functions to return a set of relevant results), VISION allows designers to retrieve a collection of design analogies that are related to topics of interest and explore a space of potential inspiration, the way one would gather books of particular topics from multiple shelves at the library to find potential resources. Two cognitive engineering design studies were conducted to evaluate the efficacy of VISION during the conceptual design process. In the first study, conducted in a controlled-lab setting, VISION was evaluated based on its effect on the quantity, quality, novelty, and direct physical similarity ratings of design outcomes. In the second study, conducted in a graduate engineering design class, VISION was evaluated based on designers’ abilities to retrieve analogies from different domains and analogies that are different from already existing design solutions. Studies show that VISION could provide an alternative to the query-based search that many DbA computational support systems use and open up new opportunities for designers to benefit from computationally supported analogies.

A Recommender System Method for Children’s Education Using Mobile Technology

In the last decade, we have seen attention and scientific research in using technology, especially the mobile technology, in education. The critical effects of technology in child education are increasing motivation, the self-confidence of children and their group cooperation. The purpose of this research is to provide a child educational recommender system using mobile technology, which includes decision making and real-time monitoring of the educational status of each child, personalization of educational materials based on the educational status of each child, recommendation of educational materials based on the extent of their dissemination in the children’s educational network, etc. In this research, the type of research based on the objective is applied research. The statistical population includes children between the ages of 8 and 10 years. The child’s educational recommender system includes input, processing and output. The mobile technology was used to track each child’s educational activity and educational status and turn them into input keywords of the recommender system. In the processing section, various matching functions are provided to match each child’s detected educational status with the educational documents. Finally, in the output section, educational documents adapted to each child’s needs are e-mailed to the mobile phone of the child’s parents. Because children cannot recognize and express their educational needs, in the proposed recommender system, all phases, from the recognition of children’s educational activity and diagnosis of the educational status of each child to the presentation of educational documents related to the diagnosed educational status, have been done automatically. The educational recommender system is simulated using NetLogo and R. Also, the implementation of the educational recommender system has been done on 36 children. According to the results, the educational recommender system was able to increase children’s learning more than twice.

Efficacy of probiotics in the treatment of acute diarrhea in children: a systematic review and meta-analysis of clinical trials.

BackgroundIf acute diarrhea in children is not treated promptly and effectively, it can lead to severe dehydration and serious sequelae. Due to the imbalance of intestinal bacteria in children with acute diarrhea, the supplementation with probiotics is important, which can improve the intestinal microenvironment, promote immunity, and enhance resistance. This meta-analysis provides further evidence and discussion of the therapeutic effect of probiotics on acute diarrhea in children.MethodsMEDLINE, EMBASE, PubMed, and the Cochrane Library databases were searched by rapid matching. The input keywords were as follows: (probiotics/synbiotics) and (child/children) and (acute diarrhea/acute gastroenteritis). Articles reporting on randomized controlled trials (RCTs) of probiotics in treating acute diarrhea in children were retrieved. The studies were published from 2010 to 2020. After screening and quality evaluation, Stata 16.0 software was used for the analysis.ResultsTwelve articles with 744 patients were included in the study, and the overall quality of the articles was excellent. Meta-analysis showed that the duration of diarrhea in the probiotics group was shorter than that in the control group [standard mean difference (SMD) =−0.74, 95% CI: −1.11 to −0.37, Z=−3.935, P=0.000], the 2-day treatment efficacy for diarrhea in the probiotics group was greater than that in the control group [odds ratio (OR) =2.12, 95% CI: 1.47–3.05, Z=3.998, P=0.000], and the length of hospital stay in the probiotics group was shorter than that of the control group (SMD =−0.60, 95% CI: −0.74 to −0.47, Z=−8.781, P=0.000). In the subgroup analysis, combined probiotics shortened the duration of diarrhea compared with single probiotic use, and Lactobacillus reuteri and Saccharomyces boulardii had a better therapeutic effect than Lactobacillus rhamnosus or Lactobacillus acidophilus.DiscussionIn the treatment of acute diarrhea in children, the addition of probiotics can shorten the duration of diarrhea, increase treatment efficacy after 2 days of treatment, and shorten the length of hospital stay. However, because of possible publication bias in the current study, further high-quality RCT studies in clinical settings are needed to verify the current results and continue the exploration of this topic.