Relevant Pages Research Articles

A Semantic and Optimized Focused Crawler Based on Semantic Graph and Genetic Algorithm

A focused crawler automatically retrieves, organizes, and extracts specific topic-related information from the internet for analysis and application. Currently, most focused crawlers assess the relevance of web pages to a given topic through methods such as keyword matching, semantic analysis, and link structures. However, these existing focused crawlers suffer from issues such as misleading directions and reduced accuracy due to the lack of semantic analysis of topic terms, as well as biased computation of topic relevance caused by the absence of effective weighting factors. To solve the above-mentioned problems, this study proposes a semantic and optimized focused crawler based on Semantic Graph and Genetic Algorithm. The proposed crawler eliminates ambiguous terms by constructing a semantic graph, optimizes the weighting factors of topic relevance with asymmetry by using a genetic algorithm, and combines both above two points to predict the priority of each unvisited hyperlink. The experiment results indicate that the proposed SG-GA Crawler improves the evaluation indicators compared with the other three focused crawlers, including VSM Crawler, SSRM Crawler, and SG Crawler. More specifically, the percentage improvement achieved by the proposed method exceeds 19%, 19%, and 13% in terms of three evaluation indicators, including the number of relevant web pages, acquisition rate, and average relevance, respectively. In conclusion, the proposed focused crawler can grab more quantity and higher quality topic-related web pages from the Internet.

Using artificial intelligence (AI) to assess the prevalence of false or misleading health-related claims.

Complementary healthcare in the United Kingdom is subject to voluntary, publicly funded regulation. Many such practices include pseudoscience. There are concerns that regulated practitioners make misleading health claims. This study used an artificial intelligence (AI) tool to measure the prevalence of such claims. Websites operated by practitioners of pseudoscientific complementary and alternative medicine, registered with the Complementary and Natural Healthcare Council, were downloaded and assessed by the AI, which determined whether a website was relevant to the investigation and, if so, identified health-related claims that it judged as false or misleading, supplying a rationale. Of 6096 registrants, 1326 met the selection criteria, of which 872 clinics had 725 relevant and operational websites. The AI assessed text from 11 771 web pages, identifying false or misleading claims in 704 (97%) of the websites. The AI's performance was quality-assured by four human assessors, who manually reviewed 23 relevant web pages. Humans identified on average 39.5 claims likely to be judged false or misleading by advertising regulators, the AI identified 36. Humans misidentified an average of 4.8 claims, AI misidentified two. Most practitioners of pseudoscientific therapies registered with the Complementary and Natural Healthcare Council make misleading health claims online. AI could support regulator efficiency.

РОЗРОБКА АДАПТИВНИХ АЛГОРИТМІВ РОЄВОГО ІНТЕЛЕКТУ У ПРОЕКТУВАННІ ТА УПРАВЛІННІ ІНЖЕНЕРНИМИ СИСТЕМАМИ

The article examines the design and management of engineering systems in the context of growing data volumes in local computer networks. With the development of swarm intelligence, the probability of the existence of the necessary information for managing engineering systems increases, but the possibility of finding it decreases. This creates the need to develop new methods and tools for using adaptive algorithms of swarm intelligence. The purpose of such algorithms is to ensure the ability to process information and find queries with maximum relevance, generating the largest number of search results (SERPs) containing technical information. The development of swarm intelligence algorithms is impossible without their intellectualization, which includes semantic and syntactic analysis of texts, natural language tools, and intelligent algorithms for determining the significance of information resources. In digital information retrieval systems (DIRS), a query is formed in the form of keywords or their combinations, connected by logical operations. To search for the same information, different keywords are used, the choice of which is subjective. The first stage of the algorithm consists in determining the set of meaningful correspondences and forming the initial set of requests within the engineering system. To ensure the completeness of the selection, the total relevance of the SERP in relation to the group of significance criteria is considered. There is a need to develop an efficient neural network algorithm to solve the problem of assigning one SERP to each group of query criteria. This will make it possible to distribute SERPs by criteria groups so that each page is evaluated mainly by one criteria group, and the total relevance of all groups is maximized. Thus, the article emphasizes the importance of developing new methods and tools for implementing adaptive swarm intelligence algorithms in engineering systems. Special attention is paid to the development of effective neural network algorithms that will ensure optimal distribution of the relevance of search pages, which will increase the accuracy and efficiency of information search in complex engineering systems.

Recommender System for Group of Users using Matrix Factorization for Tourism Domain (Case Study: Bali)

Choosing a product that suits a customer's needs requires a recommendation system to provide suggestions on a collection of items of interest to the user. Recommendations can be applied in various fields such as entertainment, shopping sites, social networking, job portals, discovery of relevant web pages, and so on. There are many circumstances where recommendations are needed for a group such as in tourism and entertainment purposes. The development of a Group Recommendation System (GRS) was carried out in response to the need to provide several recommendations to a group of users. We conducted this research to build a GRS that can provide item recommendations using the Collaborative Filtering (CF) method with Matrix Factorization Technique, as well as three approaches, namely After Factorization (AF), Before Factorization (BF), and Weighted Before Factorization (WBF). Determine the best approach for the three categories of groups formed, namely small groups (three members), medium groups (five members), and large groups (ten members). The focus of this research is the tourism destination domain in Bali. In the evaluation results of the precision calculation, the medium group obtained the highest score for the AF, BF, and WBF approaches of 0.944. Meanwhile, in calculating recall, the small group achieved the highest scores for the AF, BF, and WBF approaches of 0.294, 0.259, and 0.259, respectively. From the results of this study, it appears that small groups are suitable for using the BF approach, while the AF method is more effective for large groups, and the best approach for medium groups is the WBF.

GaussDB: A Cloud-Native Multi-Primary Database with Compute-Memory-Storage Disaggregation

Cloud-native databases have been widely deployed due to high elasticity, high availability and low cost. However, most existing cloud-native databases do not support multiple writers and thus have limitations on write throughput and scalability. To alleviate this limitation, there is a need for multi-primary databases which provide high write throughput and high scalability. In this paper, we present a cloud-native multi-primary database, GaussDB, which adopts a three layer (compute-memory-storage) disaggregation framework, where the compute layer is in charge of transaction processing, the memory layer is responsible for global buffer management and global lock management, and the storage layer is used for page and log persistence. To provide multi-primary capabilities, GaussDB logically partitions the pages to different compute nodes and then assigns the ownership of each page to a compute node. For each transaction posed to a compute node, if the compute node owns all relevant pages of this query, the compute node can process the query locally; otherwise, GaussDB transfers the ownership of relevant pages to this node. To capture data affinity and reduce page transmission costs, GaussDB designs a novel page placement and query routing method. To improve recovery performance, GaussDB employs a two-tier (memory-storage) checkpoint recovery method which uses memory checkpoints combined with on-demand page recovery to significantly improve recovery performance. We have implemented and deployed GaussDB internally at Huawei and with customers, and the results show that GaussDB achieves higher throughput, lower latency, and faster recovery than state-of-the-art baselines.

A multilingual semantic search chatbot framework

Chatbots are conversational agents which interact with users and simulate a human interaction. Companies use chatbots on their customer-facing sites to enhance user experience by answering questions about their products and directing users to relevant pages on the site. Existing Chatbots which are used for this purpose give responses based on pre-defined FAQs only. In this paper, we propose a framework for a chatbot which combines two approaches - retrieval from a knowledge base consisting of question answer pairs, combined with a natural language search mechanism which can scan through the paragraphs of text information. A feedback-based knowledge base update is implemented which provides continuous improvement in user experience. The framework achieves a result of 81.73 percent answer matching on SQuAD 1.1 and 69.21 percent answer matching on SQuAD 2.0. The framework also performs well on languages such as Spanish (67.32 percent answer match), Russian (61.43 percent answer match), Arabic (51.63 percent answer match) etc. by means of zero shot learning.

Skylark Wiki: Accessing research data of the MRC National Survey of Health and Development

Background & IntroductionThe Medical Research Council National Survey of Health and Development (NSHD) is the oldest and longest-running of the British birth cohort studies. In 2018 we launched the Skylark Wiki, a central metadata repository and the first port of call for any data sharing queries. In 2023 we utilised the HDRUK funding initiative to work with our advisory group and research community to improve the transparency of our data access process and make the content more accessible. MethodsWe conducted a focus group session with the NSHD Participant Advisory Panel to understand their views on the data sharing process and whether the information we provided them, sufficiently explained how their data are shared. We also sent out a questionnaire to all Skylark Users to identify areas of improvement. Lastly, we undertook an audit of the website’s content and used Google Analytics to see how Users navigated across the site. Combining all this information we identified areas requiring improvements, from the point of view of our stakeholders, and comparing these to the requirements indicated in the HDRUK Transparency Standards we mapped out updates to the Skylark Wiki. The work was carried out by an external agency (Aubergine262 Ltd). ResultsTo increase transparency and searchability, we improved the flow diagram of the data sharing process to include links to the relevant web pages. We also created guidance to explain why the information in the data request application form is being asked. As feedback from Skylark Users suggested difficulties in finding the required information, the navigation has been updated. We have also changed the layout of the front page to include navigation boxes that takes users straight to the content. The NSHD Advisory Panel reinforced the notion of trust that study participants place in the study and its approach to data sharing. They confirmed the information provided on data sharing was adequate and appreciated that this information was placed on the main study website (https://nshd.mrc.ac.uk/) for their benefit. Initial feedback from Users indicates that it is easier to find information on the website. We will keep monitoring access to the website, the number of queries, and data access requests to evaluate long-term benefits. We have changed the sitemap and added in large link boxes, to make it easier to find the different sections. We hope this will improve the searchability of the site so users can find information they required. We hope this will therefore lead to fewer emails and correspondence between the data sharing team and users. We have also updated the flow diagram of the data sharing process to include links to the web pages which are related to that section. We hope this will help the users find the information they require. ConclusionsThe new Skylark data-sharing website supports easier navigation providing a better User experience. In return, this should lead to fewer queries from Users. The website will be easier to maintain having changed the content management system from a Wiki to WordPress. WordPress requires less technical knowledge, allowing more staff to contribute to regular maintenance. References Skylark data-sharing website: https://skylark.ucl.ac.uk/NSHD/ Aubergine: https://www.aubergine262.com/

Efficient intelligent crawler for hamming distance based on prioritization of web documents

Search engines play a crucial role in today's Internet landscape, especially with the exponential increase in data storage. Ranking models are used in search engines to locate relevant pages and rank them in decreasing order of relevance. They are an integral component of a search engine. The offline gathering of the document is crucial for providing the user with more accurate and pertinent findings. With the web’s ongoing expansions, the number of documents that need to be crawled has grown enormously. It is crucial to wisely prioritize the documents that need to be crawled in each iteration for any academic or mid-level organization because the resources for continuous crawling are fixed. The advantages of prioritization are implemented by algorithms designed to operate with the existing crawling pipeline. To avoid becoming the bottleneck in pipeline, these algorithms must be fast and efficient. A highly efficient and intelligent web crawler has been developed, which employs the hamming distance method for prioritizing the pages to be downloaded in each iteration. This cutting-edge search engine is specifically designed to make the crawling process more streamlined and effective. When compared with other existing methods, the implemented hamming distance method achieves a high value of 99.8% accuracy.

Web Page Ranking Based on Text Content and Link Information Using Data Mining Techniques

Thanks to the rapid expansion of the Internet, anyone can now access a vast array of information online. However, as the volume of web content continues to grow exponentially, search engines face challenges in delivering relevant results. Early search engines primarily relied on the words or phrases found within web pages to index and rank them. While this approach had its merits, it often resulted in irrelevant or inaccurate results. To address this issue, more advanced search engines began incorporating the hyperlink structures of web pages to help determine their relevance. While this method improved retrieval accuracy to some extent, it still had limitations, as it did not consider the actual content of web pages. The objective of the work is to enhance Web Information Retrieval methods by leveraging three key components: text content analysis, link analysis, and log file analysis. By integrating insights from these multiple data sources, the goal is to achieve a more accurate and effective ranking of relevant web pages in the retrieved document set, ultimately enhancing the user experience and delivering more precise search results the proposed system was tested with both multi-word and single-word queries, and the results were evaluated using metrics such as relative recall, precision, and F-measure. When compared to Google’s PageRank algorithm, the proposed system demonstrated superior performance, achieving an 81% mean average precision, 56% average relative recall, and a 66% F-measure.

Theories used to develop or evaluate social prescribing in studies: a scoping review

ObjectiveThis scoping review aims to provide an overview of how theories were used in the development or evaluation of social prescribing (SP) intervention studies.BackgroundSP describes a patient pathway where general practitioners (GPs) connect patients with community activities through referrals to link workers. This review seeks to understand the explanations provided for the outcomes and implementation process of SP.Inclusion criteriaStudies using a defined theory to develop or evaluate a specific SP intervention in primary care and the community sector.MethodsThis scoping review was conducted in accordance with JBI methodology. The following databases were searched on 8th of July 2022: PubMed, ASSIA, Cochrane, Cinahl, PsycINFO, Social Care Online, Sociological s, Scopus, and Web of Science. The search only considered English language texts. Additional literature was identified by searching relevant web pages and by contacting experts. The selection of sources and the data extraction was done by two reviewers independently.ResultsThe search resulted in 4240 reports, of which 18 were included in the scoping review. Of these, 16 were conducted in the UK, one in Canada and one in Australia. The majority of reports employed a qualitative approach (11/18). Three were study protocols. 11 distinct theories were applied to explain outcomes (4 theories), differences in outcomes (3 theories), and the implementation of the intervention (4 theories). In terms of practical application, the identified theories were predominantly used to explain and understand qualitative findings. Only one theory was used to define variables for hypothesis testing. All theories were used for the evaluation and none for the development of SP.ConclusionThe theories influenced which outcomes the evaluation assessed, which causal pathway was expected to generate these outcomes, and which methodological approaches were used. All three groups of theories that were identified focus on relevant aspects of SP: fostering positive patient/community outcomes, addressing inequalities by considering the context of someone’s individual circumstances, and successfully implementing SP by collaboratively working across professions and institutional boundaries. Additional insight is required regarding the optimal use of theories in practical applications.

Investigating pedagogical agents' scaffolding of self‐regulated learning in relation to learners' subgoals

AbstractIntelligent tutoring systems (ITSs) incorporate pedagogical agents (PAs) to scaffold learners' self‐regulated learning (SRL) via prompts and feedback to promote learners' monitoring and regulation of their cognitive, affective, metacognitive and motivational processes to achieve their (sub)goals. This study examines PAs' effectiveness in scaffolding and teaching SRL during learning with MetaTutor, an ITS on the human circulatory system. Undergraduates (N = 118) were randomly assigned to a condition: Control Condition (i.e. learners could only self‐initiate SRL strategies) and Prompt and Feedback Condition (i.e. PAs prompted learners to engage in SRL). Learners' log‐file data captured when strategies were used, the initiator of the strategy (i.e. learner and PA), and the relevance of instructional content pages in relation to learner subgoals. While results showed that PAs were effective scaffolders of SRL in which they prompted learners to engage in SRL strategies more when content was relevant towards their subgoals and as time on page and task increased, there were mixed findings about the effectiveness of PAs as teachers of SRL. Findings show how production rules guiding PA prompts can improve their scaffolding and teaching of SRL across the learning task – through contextualizing SRL strategies to the instructional content and in relation to the relevance of the content to learners' subgoals.Practitioner notesWhat is already known about this topic Most learners struggle to efficiently and effectively use self‐regulated learning (SRL) strategies to attain goals and subgoals. There is a need for SRL to be scaffolded for learners to manage multiple goals and subgoals while learning about complex STEM topics. Intelligent tutoring systems (ITSs) typically incorporate pedagogical agents (PAs) to prompt learners to engage in SRL strategy and provide feedback. There are mixed findings on the effectiveness of PAs in scaffolding learners' SRL. What this paper adds We consider PAs not only scaffolders but also teachers of SRL. Results showed that while PAs encouraged the use of SRL strategies when the content was relevant to subgoals, they did not discourage the use of SRL strategies when the content was not relevant. Results for this study were mixed in their support of PAs as teachers of SRL. Learners increasingly depended on PAs to prompt SRL strategies as time on task progressed. Implications for practice and/or policy PAs are effective scaffolders of SRL with more research needed to understand their role as teachers of SRL. PA scaffolding is more essential as time on task progresses. When deploying specific cognitive and metacognitive SRL strategies, the relevance of the content to learners' subgoals should be taken into account.

ОСОБЛИВОСТІ ТЕХНОЛОГІЇ ПОШУКОВОЇ ОПТИМІЗАЦІЇ ЯК ОДНІЄЇ З ОСНОВНИХ СКЛАДОВИХ ІНСТРУМЕНТУ ЦИФРОВОГО МАРКЕТИНГУ

The rapid development of information technology, based on the principles of technological convergence, has led to the fact that sales through websites and online business have recently become particularly popular. You can attract the target audience to your website by using search engine optimization. Search engine optimization is the comprehensive development and promotion of a company's website to ensure that it reaches the top positions as a result of search engine results for selected queries, with the aim of increasing traffic and further generating income. The paper examines the technology of search engine optimization as one of the elements of digital marketing aimed at increasing the volume of online sales and improving the customer experience in using a company's website. It is substantiated that the higher the position of the site, the more users go to it. It is found that the following tasks are important for the results of website promotion: increasing the relevance of pages to search queries (relevance); optimization of structure and content; improvement of commercial factors; increasing external factors of resource significance. The paper notes that search engine optimization technology has great prospects for further use in business, as well as high efficiency in promoting goods or services and increasing sales through websites. The paper also considers a component that is part of the digital marketing system – search engine optimization SEO. SEO is the comprehensive strategy for developing and promoting a company's website to secure top positions in search engine results pages (SERPs) for relevant user queries. This strategic approach aims to dramatically increase website traffic, ultimately leading to enhanced sales and revenue generation. The process of creating a website optimization algorithm, which includes the necessary steps for internal and external optimization, is formulated, and a comparison of some Internet marketing tools is made. The paper also considers some theoretical issues of changing marketing technologies for interaction with customers in the digital environment.

AUDIENCE-GENERATED FEEDBACK ON CONSPIRATORIAL CONTENT ON FACEBOOK AND REDDIT IN SERBIA

Conspiracy theories are a ubiquitous phenomenon in the socio-political discussion. By trusting these theories, society justifies possible events like the COVID-19 pandemic. Through mixed methods research, the article seeks to provide the amount of page-generated and user-generated conspiratorial content that links chemtrails with the COVID-19 pandemic, along with audience-generated feedback on social media content. Facebook and Reddit have been chosen for this research because of their inherently contrasting approaches to content regulation. Facebook’s content moderation tools lacked the impact to remove misinformation, as only 8% of content was flagged as false information. Additionally, even if researched subreddits had two times the amount of relevant content in comparison to relevant Facebook pages, most of the content had 0 upvotes, meaning that the content was either negatively received or lacked adequate support. Serbian-speaking Facebook users expressed support for conspiratorial content on this platform, while Serbian redditors used conspiratorial narratives to ridicule conspiracy theorists. Likes were the most utilized type of feedback on content of relevant Facebook pages, while comments were the most utilized type of feedback on content of relevant subreddits. The importance of this research lies in understanding what conspiratorial narratives try to imply through social media and how the audience interprets and communicates with this content. Keywords: conspiracy theories, social media, chemtrails, pandemic, COVID-19

Otto Jespersen (1860-1943) romanista: La rilevanza della sua analisi dei Passive tenses in italiano ed altre lingue

Otto Jespersen (1860-1943) was a Danish linguist, best known for his studies of English, of phonetics and ofgeneral linguistics. In my paper, I introduce a lesser-known side of Jespersen, i.e. as an Italianist and aRomanist. A special focus is on how he deals with the passive tenses in Romance languages, in particular Italianand French. Drawing on the theory developed by the German Romanist Friedrich Diez (1794-1876), whonotably differentiated between perfective and imperfective verbs, and subsequently connecting thisdifferentiation to the general discussion of aspect and Aktionsart around 1900, Jespersen in a few pageselaborates a rather advanced analysis of the passive tenses in the Romance (and some other) languages. Isuggest that Jespersen's statement forestalls an important part of the description of the passive tenses in Italianelaborated by the Danish Italianist (and Romanist) Jørgen Schmitt Jensen (1931-2004) and published in acomprehensive Italian grammar (1990). I further propose that even though Jespersen’s text probably is not adirect source for Schmitt Jensen, it helps understanding the general frame of the latter's presentation of thesubject. Finally, I claim that the relevant pages in Jespersen (1924) are interesting concerning the historiographyof the concept of Aktionsart.

Lo sguardo di Thérèse Rivière al Musée de l’Homme di Parigi. Le “tecniche del corpo” per il capovolgimento del paradigma coloniale (1928-1946)

The article describes the work of ethnographer Thérèse Rivière (1901-1970) at the Musée d’Ethnographie du Trocadéro, later Musée de l’Homme, in Paris. Analysing the scholar’s contribution, in particular the method she adopted during her fieldwork in Algeria among the Amazigh populations and its subsequent impact on the museum, the essay brings to light not only a half-forgotten profile, but also a relevant page in the history of the Musée de l’Homme. The analysis develops around the themes of gender, distance/proximity to the object of study, and adherence or non-adherence to a normativity imposed from above in the strongly hierarchical structure of the museum. What emerges is the figure of an ethnographer who entrusts relationships and imaginative power with the ability to understand and represent a culture. The collection of photographs, the letters, and the album of the ‘dessins indigènes’ show how her practice was not at odds with an unobjectionable scientific rigour. The long years in a psychiatric hospital, however, also suggest a reflection on the boundaries between truth and fiction, the relationship between sight and mental health, the impact of war and much more to be explored.

Wild and backyard food use during COVID-19 in upstate New York, United States.

COVID-19 acutely shocked both socio-economic and food systems in 2020. We investigated the impact of COVID-19 on production and consumption of gardened produce, backyard poultry, wild game and fish, and foraged mushrooms, berries, and other plants in New York State, aiming to understand crisis influenced food choice and motivations, including food security. We conducted an online, cross-sectional survey in October-December 2020 with a convenience sample of participants (n = 505) with an interest in gardening, poultry rearing, foraging, hunting, and/or fishing from six counties in upstate New York. We recruited through the New York Department of Environmental Conservation, Cornell Cooperative Extension, and other relevant email and social media pages. Across the wild and backyard food production strategies, 4.0-14.3% of respondents reported engaging for the first time and 39.6-45.7% reported increased production (a little or a lot more), and 31.6-42.7% of respondents' production was the same as the previous year. Consumption of foods produced was widespread, including fruit and vegetables (97.6% of producers also consumed), backyard eggs (92.7%), and foraged foods (93.8%). For meats, a majority consumed backyard poultry meat (51.2%), wild-caught fish (69.7%), and wild game they hunted (80.1%). The frequency of consumption of fruit and vegetables (average of 13.5 times/month) and eggs (16.4 times/month) was very high, while average consumption of poultry meat, foraged foods, fish, and wild game ranged from 3.1 to 5.8 times/month. The number of respondents who reported "have more control over food availability" as motivation to produce all wild and backyard foods increased from 2019 to 2020 (p < 0.05 - p < 0.001). There was also a significant relationship between experiences of COVID-19 related hardship (i.e., food insecurity, income loss) with gardening and poultry-rearing (p ≤ 0.05), but not with other production methods or with consumption of wild and backyard foods. Our findings help to locate wild and backyard foods within COVID-19 impacted food environments, and describe food security as a particularly relevant motivation, among others, reported by respondents in 2020. Given this, New York State service providers can use these findings to tailor current future support for households exerting control over their own food environments with wild and backyard foods, allowing the state to be better prepared for future crises.

Analysis of the key influencing factors of China's cross-border e-commerce ecosystem based on the DEMATEL-ISM method.

Systematically analysing the relative importance and hierarchical relationships among the influencing factors of the cross-border e-commerce ecosystem holds rich theoretical value and practical significance for the development of this ecosystem. A total of 19 influencing factors covering four aspects affecting the cross-border e-commerce ecosystem are identified by means of the relevant literature, web pages, research, and discussions with relevant experts and scholars, and the decision-making trial and evaluation laboratory (DEMATEL) and interpretative structural modeling (ISM) method is used to analyse the cause-effect correlation of each factor and to obtain a cause-effect diagram and a multi-level recursive structure model. The results show that three factors, i.e., the e-commerce platform development level, cross-border e-commerce competitiveness, and the cross-border e-commerce transaction scale, have a greater degree of influence on the other influencing factors. Additionally, three factors, i.e., the information development level, GDP, and cross-border online shopping demand, are vulnerable to the influence of the other factors. The level of cross-border e-commerce platform development, cross-border e-commerce competitiveness, and inter-firm competition are the root factors and occupy an important position in the cross-border e-commerce ecosystem as influencing factors and influence the stability of the cross-border e-commerce ecosystem by affecting the other factors.

Indian Railways: enhancing northeast connectivity with Half-Y connection

Research methodology The case has been developed as an illustrative case study using primary data. The data and images used for developing the case have been collected from the Alipurduar Division of Indian Railways with suitable permissions. The specific newspaper studies used in developing the case content have been referenced as footnotes on the relevant pages of the case study document. Case overview/synopsis Train operations in Alipurduar Division of Northeast Frontier Railway zone of Indian Railways are difficult. The division provides the vital rail link between the northeast states and the rest of India. Railway lines in the Alipurduar Division pass through several forests with a significant elephant population. As an outcome of train–elephant conflicts, train operations on one of the railway lines operate under severe speed restrictions. Moreover, the region is known for heavy rainfall and thunderstorms for almost six months every year. For the Divisional Railway Manager (DRM), the protagonist of the case, this implied repeated congestion, traffic disruptions, inadvertent delays and lack of time for critical maintenance of the tracks. A solution was planned several years ago for creating an alternative path to de-congest the traffic; however, it ran into a roadblock due to land acquisition issues.With all these limitations, could a solution be found and implemented? How could it be executed?The case illustrates how a simple yet innovative solution was proposed by the DRM in 2015 and implemented in 2016.With this case, students will be able to understand the innovation process that is embedded within long-term infrastructure projects. The case will help students understand how innovation can take place even in the later stages of project implementation, and how simple and creative solutions can have a long-term impact. Complexity academic level The case can be used in graduate and executive education courses in General Management and in Public Policy Management. It can also be used in Doctoral-Level Programmes such as those taught to scholars pursuing Fellow Programme in Management. Since the case brings out elements of problem framing and critical thinking, the case can be used for courses in strategic management. Many professionals, particularly those working in large organizations dealing with large infrastructure projects, will identify with the DRM and the challenges faced by him.

Mining relevant solutions for programming tasks from search engine results

AbstractOfficial documentation of software development technologies, for example, APIs, may not be sufficient for all developer needs, so searching on the Internet is a usual practice. Nonetheless, finding useful information may be challenging because the best solutions are not always among the first ranked pages. Developers need to read and discard irrelevant pages, that is, those without code examples or those that have content with little focus on the desired solution. This work aims at proposing an approach to mine relevant solutions for programming tasks from search engine results by removing irrelevant pages. The authors evaluated the top‐20 pages returned by the Google search engine, for 10 different queries, and observed that only 31% of the evaluated pages are relevant to developers. Then, the authors proposed and evaluated three different approaches to mine the relevant pages returned by the search engine. Google's search engine has been used as a baseline, and authors’ results have shown that it returns a reasonable number of irrelevant pages for developers, and the authors could establish an effective approach to remove irrelevant pages, suggesting that developers could benefit from a customised web search filter for development content.

CovidCoughNet: A new method based on convolutional neural networks and deep feature extraction using pitch-shifting data augmentation for covid-19 detection from cough, breath, and voice signals

This study proposes a new deep learning-based method that demonstrates high performance in detecting Covid-19 disease from cough, breath, and voice signals. This impressive method, named CovidCoughNet, consists of a deep feature extraction network (InceptionFireNet) and a prediction network (DeepConvNet). The InceptionFireNet architecture, based on Inception and Fire modules, was designed to extract important feature maps. The DeepConvNet architecture, which is made up of convolutional neural network blocks, was developed to predict the feature vectors obtained from the InceptionFireNet architecture. The COUGHVID dataset containing cough data and the Coswara dataset containing cough, breath, and voice signals were used as the data sets. The pitch-shifting technique was used to data augmentation the signal data, which significantly contributed to improving performance. Additionally, Chroma features (CF), Root mean square energy (RMSE), Spectral centroid (SC), Spectral bandwidth (SB), Spectral rolloff (SR), Zero crossing rate (ZCR), and Mel frequency cepstral coefficients (MFCC) feature extraction techniques were used to extract important features from voice signals. Experimental studies have shown that using the pitch-shifting technique improved performance by around 3% compared to raw signals. When the proposed model was used with the COUGHVID dataset (Healthy, Covid-19, and Symptomatic), a high performance of 99.19% accuracy, 0.99 precision, 0.98 recall, 0.98 F1-Score, 97.77% specificity, and 98.44% AUC was achieved. Similarly, when the voice data in the Coswara dataset was used, higher performance was achieved compared to the cough and breath studies, with 99.63% accuracy, 100% precision, 0.99 recall, 0.99 F1-Score, 99.24% specificity, and 99.24% AUC. Moreover, when compared with current studies in the literature, the proposed model was observed to exhibit highly successful performance. The codes and details of the experimental studies can be accessed from the relevant Github page: (https://github.com/GaffariCelik/CovidCoughNet).