Hasil untuk "Information resources (General)"

Chun Hei Michael Shiu, Hei Victor Cheng, Lele Wang

The graph alignment problem aims to identify the vertex correspondence between two correlated graphs. Most existing studies focus on the scenario in which the two graphs share the same vertex set. However, in many real-world applications, such as computer vision, social network analysis, and bioinformatics, the task often involves locating a small graph pattern within a larger graph. Existing graph alignment algorithms and analysis cannot directly address these scenarios because they are not designed to identify the specific subset of vertices where the small graph pattern resides within the larger graph. Motivated by this limitation, we introduce the subgraph alignment problem, which seeks to recover both the vertex set and/or the vertex correspondence of a small graph pattern embedded in a larger graph. In the special case where the small graph pattern is an induced subgraph of the larger graph and both the vertex set and correspondence are to be recovered, the problem reduces to the subgraph isomorphism problem, which is NP-complete in the worst case. In this paper, we formally formulate the subgraph alignment problem by proposing the Erdos-Renyi subgraph pair model together with some appropriate recovery criterion. We then establish almost-tight information-theoretic results for the subgraph alignment problem and present some novel approaches for the analysis.

Touseef Hasan, Laila Cure, Souvika Sarkar

Public service information systems are often fragmented, inconsistently formatted, and outdated. These characteristics create low-resource retrieval environments that hinder timely access to critical services. We investigate retrieval challenges in such settings through the domain of food pantry access, a socially urgent problem given persistent food insecurity. We develop an AI-powered conversational retrieval system that scrapes and indexes publicly available pantry data and employs a Retrieval-Augmented Generation (RAG) pipeline to support natural language queries via a web interface. We conduct a pilot evaluation study using community-sourced queries to examine system behavior in realistic scenarios. Our analysis reveals key limitations in retrieval robustness, handling underspecified queries, and grounding over inconsistent knowledge bases. This ongoing work exposes fundamental IR challenges in low-resource environments and motivates future research on robust conversational retrieval to improve access to critical public resources.

Clare Daniel, Enilda Romero-Hall, Jacquelyne Thoni Howard et al.

This paper explores our experiences as scholars in higher education who collaborate as part of an informal collective supporting the Feminist Pedagogy for Teaching Online digital guide[1]. We, the authors, have diverse professional and educational backgrounds; our areas of research interest also vary significantly. However, we have a passion for humanizing online learning experiences and practically applying feminist pedagogical tenets to these interactions. The purpose of this paper is to explore, through a process of self-reflection, our experiences as scholars in higher education as part of an informal collective supporting the Feminist Pedagogy for Teaching Online digital guide. To share our experiences as editors of this digital guide, we included our individual stories using a collaborative autoethnography approach. In our stories, we specifically discussed our interest in joining this collective of feminist scholars, the evolving nature of our efforts in support of the digital guide, the success experienced, the challenges that we encountered, and the internal and external support we received throughout this journey. Ideally, through this critical reflection, we can aid other collectives who already engage, or are considering engaging, in similar scholarly communication endeavors. [1] This digital scholarship project works towards communicating liberatory pedagogical principles for educators in digital modalities.

Marie-E. Zubler, (née Godefroid), Julian Koch, Ralf Plattfaut

Non-governmental organizations (NGOs) typically have restrained information and communication technology (ICT) budgets and resources. At the same time, they face high pressure to reduce administrative costs. A possible solution to the resulting conundrum could be user-driven technology. This term describes a selection of technologies, including intelligent process automation, low-code platforms, and business intelligence tools that push innovation and user-centricity by letting operational employees directly deploy comparably cheap solutions without the need for central ICT support. Practitioner literature indicates, however, that user-driven technologies are lagging in the social sector despite evidence from some individual success stories published by researchers. Thus, a systematic assessment of user-driven technologies within NGOs and of potential challenges in their introduction is necessary. To close this research gap, we employ the method of computationally intensive theory construction, combining data mining with qualitative interviews. Results indicate that user-driven technologies are indeed lagging and that forming a problem-mindset and creating adequate governance structures are the main challenges to their introduction within NGOs.

Lingyi Chen, Shitong Wu, Sicheng Xu et al.

The information bottleneck (IB) method is a technique designed to extract meaningful information related to one random variable from another random variable, and has found extensive applications in machine learning problems. In this paper, neural network based estimation of the IB problem solution is studied, through the lens of a novel formulation of the IB problem. Via exploiting the inherent structure of the IB functional and leveraging the mapping approach, the proposed formulation of the IB problem involves only a single variable to be optimized, and subsequently is readily amenable to data-driven estimators based on neural networks. A theoretical analysis is conducted to guarantee that the neural estimator asymptotically solves the IB problem, and the numerical experiments on both synthetic and MNIST datasets demonstrate the effectiveness of the neural estimator.

Haoran Ma, Yanwen Li, Xiaoying Zhang et al.

Dwarf apple is one of the most important forms of garden economy, which has become a new engine for rural revitalization. The effective detection of coloring and ripening apples in complex environments is important for the sustainable development of smart agricultural operations. Addressing the issues of low detection efficiency in the greenhouse and the challenges associated with deploying complex target detection algorithms on low-cost equipment, we propose an enhanced lightweight model rooted in YOLOv7. Firstly, we enhance the model training performance by incorporating the Squeeze-and-Excite attention mechanism, which can enhance feature extraction capability. Then, an SCYLLA-IoU (SIoU) loss function is introduced to improve the ability of extracting occluded objects in complex environments. Finally, the model was simplified by introducing depthwise separable convolution and adding a ghost module after up-sampling layers. The improved YOLOv7 model has the highest AP value, which is 10.00%, 5.61%, and 6.00% higher compared to YOLOv5, YOLOv7, and YOLOX, respectively. The improved YOLOv7 model has an MAP value of 95.65%, which provides higher apple detection accuracy compared to other detection models and is suitable for potted dwarf anvil apple identification and detection.

Molline Natanah C. Gusha, Christopher D. McQuaid

ABSTRACT The environment shapes the spatial distribution of species, but species also comprise suites of traits which may indicate their adaptability to a specific environment. This forms the basis of trait biogeography studies. We thus examined how a species distribution is not only influenced by its environment and traits, but by interactions among its traits. Trait information was collected for 150 intertidal macroinvertebrates along a 3000 km environmental and biogeographic gradient on the South African coast. This information was analysed, as functional entities (FEs) were species performing similar functions that have the same trait values and were further condensed into two trait domains (Reproduction and Lifestyle). We then defined Life History Strategies (LHS) as specific combinations of Lifestyle and Reproduction FEs. Seven combinations of Lifestyle and Reproduction formed LHS that dominated total biomass. Some of these LHS were ubiquitous, while others showed geographic patterns across our west‐east environmental gradient. For Lifestyle, filter‐feeders exhibited high abundances on the East (subtropical, oligotrophic) and West (cool‐temperate, eutrophic) extremes of the biogeographic gradient, but differed between the two in size at reproductive maturity and larval development type. This similarity in functionality of feeding mechanism and mobility with different reproductive strategies suggests a trait trade–off (investment in one trait reduces resources for others) between the Reproduction and Lifestyle domains. Within the Reproduction domain, gonochoristic, annual planktotrophic reproduction was common across bioregions, reflecting spin‐offs (investment in one trait facilitates another trait) among these traits. Gonochoristic investment in less frequent episodic reproduction is another trade‐off, with investment in large size and delayed maturation being a trade–off for many reproductive cycles. Overall, although our data supports the habitat templet model (i.e., the importance of environmental drivers), it further indicates that species distribution patterns observed along the South African coast reflect strong trait interactions and biomass patterns related to their LHS.

Jingping Zhou, Xiaohe Gu, Huili Gong et al.

China has abundant straw resources, but challenges in utilization persist. Utilization rates need improvement, and environmental pollution from straw burning remains a significant issue. Accurate and intelligent remote sensing classification of straw types is crucial for enhancing straw utilization and preventing straw burning. This paper proposed a new approach for the intelligent classification of maize straw types, using the DenseNet201 deep transfer learning algorithm based on RGB images captured by Unmanned Aerial Vehicle (UAV). The sample labels dataset was established for maize straw types, utilizing DenseNet201 deep transfer learning algorithm to pre-train the sample set. This pre-training facilitated model transfer and parameter initialization. Subsequently, the second round of deep transfer learning was performed to construct the final maize straw type remote sensing classification models using DenseNet201 deep transfer learning algorithm. This model and results were subsequently compared with maize straw type classification by the ResNet50 and GoogLeNet deep transfer learning algorithms, as well as maize straw type classification using DenseNet201, ResNet50, and GoogLeNet deep learning algorithms. The results showed that the accuracy of the pre-trained maize straw type deep transfer learning remote sensing classification model surpassed that of the untrained maize straw type deep learning remote sensing classification model, resulting in an enhancement of accuracy by 8.59%, 7.38%, and 1.28%, respectively. The DenseNet201 deep transfer learning model for maize straw types exhibited the highest accuracy with the overall accuracy of 95.57%, and the kappa coefficient of 0.9410. Hence, the DenseNet201 deep transfer learning classification of maize straw types enabled the attainment of intelligent remote sensing recognition of maize straw types. The classification methodology, model, and results presented in this paper can serve as valuable technical references, offering essential information support for agricultural and environmental protection departments actively involved in the comprehensive utilization of straw resources and atmospheric environmental protection efforts.

Carolina Fernandes, Denise Campbell-Scherer, Aisha Lofters et al.

Abstract Background Clinical practice guidelines (CPGs) synthesize high-quality information to support evidence-based clinical practice. In primary care, numerous CPGs must be integrated to address the needs of patients with multiple risks and conditions. The BETTER program aims to improve prevention and screening for cancer and chronic disease in primary care by synthesizing CPGs into integrated, actionable recommendations. We describe the process used to harmonize high-quality cancer and chronic disease prevention and screening (CCDPS) CPGs to update the BETTER program. Methods A review of CPG databases, repositories, and grey literature was conducted to identify international and Canadian (national and provincial) CPGs for CCDPS in adults 40–69 years of age across 19 topic areas: cancers, cardiovascular disease, chronic obstructive pulmonary disease, diabetes, hepatitis C, obesity, osteoporosis, depression, and associated risk factors (i.e., diet, physical activity, alcohol, cannabis, drug, tobacco, and vaping/e-cigarette use). CPGs published in English between 2016 and 2021, applicable to adults, and containing CCDPS recommendations were included. Guideline quality was assessed using the Appraisal of Guidelines for Research and Evaluation (AGREE) II tool and a three-step process involving patients, health policy, content experts, primary care providers, and researchers was used to identify and synthesize recommendations. Results We identified 51 international and Canadian CPGs and 22 guidelines developed by provincial organizations that provided relevant CCDPS recommendations. Clinical recommendations were extracted and reviewed for inclusion using the following criteria: 1) pertinence to primary prevention and screening, 2) relevance to adults ages 40–69, and 3) applicability to diverse primary care settings. Recommendations were synthesized and integrated into the BETTER toolkit alongside resources to support shared decision-making and care paths for the BETTER program. Conclusions Comprehensive care requires the ability to address a person’s overall health. An approach to identify high-quality clinical guidance to comprehensively address CCDPS is described. The process used to synthesize and harmonize implementable clinical recommendations may be useful to others wanting to integrate evidence across broad content areas to provide comprehensive care. The BETTER toolkit provides resources that clearly and succinctly present a breadth of clinical evidence that providers can use to assist with implementing CCDPS guidance in primary care.

Yusef Hassan-Montero, Victor Herrero-Solana

Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to refinding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud's tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.

Nadiia Davydenko, Yuliya Lutsyk, Alina Buriak et al.

The article is devoted to the modern development of high technologies and computer technology greatly enhanced the development of automated banking systems of banking sector organizations and allowed the synthesis of information and communication technologies for their formation.The main purpose of the article is to select the main indicators for assessing the level of financial security of the banking system of the state and identify promising areas of its development using forecasting models. In the process of research such analytical functions have been used: polynomial, exponential, power and logarithmic. The authors believe that the information and analytical provision of the financial security of the bank is an information provision that combines, on the one hand, information work, that is, ways, means and methods of collecting the necessary information, and on the other - analytical work, which includes forms and methods of information analysis and processing, which ensures an objective assessment of the situation and the adoption of a balanced management decision. As a result, forecast models were built for each of the indicators and also, it has been found that most indicators of the banking system of Ukraine in 2021-2023 will remain at “unsatisfactory” and “critical” levels. In conclusions it was proposed to introduce measures that would be aimed at improving the reliability and stability of the banking system of Ukraine.

Chen Chuan, Liu Linlin, Zhang Xinli

China’s construction industry is the pillar industry of the national economy, but the market competition faced by construction enterprises is becoming increasingly fierce. The general contractor must carry out close business cooperation with other subcontractors and integrate the valuable resources of all parties to cope with the competition jointly. Based on this, this paper constructs the evolutionary game model between the general contractor and the subcontractor. The equilibrium point is calculated by copying the dynamic equation, and the influence of parameters on the evolution probability of both sides is explored. Finally, combined with the numerical simulation, some suggestions on the matching of construction subcontractor suppliers are put forward: improve the cognitive level of the supply chain and reduce the contract cost of the enterprise; adopt an efficient and transparent supervision mechanism to reduce the risk of cooperation; improve the information transmission network of the supply chain and reduce the cost of failure; clear project claims and reward and punishment measures to enhance the enthusiasm of enterprises.

Yutong Tian, Yan Zhang, Qingyun Cheng et al.

Background: Integrated care is an important initiative to respond positively to the ageing of society and information and communication technology(ICT) plays an important role in facilitating the integration of functional and normative health and social care. The scoping review aims to synthesize evidence on the experience and practice of ICT-based implementation of integrated care for older adults. Methods: This study followed the research framework developed by Arksey and O’malley for the scoping review and systematically searched for relevant studies published between 1 January 2000 and 30 March 2022 from nine electronic databases, three specialist journals, three key institutional websites, 11 integrated care project websites, google scholar and references of the studies to be included. Two reviewers independently screened and extracted data and used thematic analysis to sort out and summarize the core elements, hindrances and facilitators of ICT-based integrated care. Results: A total of 77 studies were included in this study, including 36 ICT-based practice models of integrated care with seven core elements of implementation including single entry point, comprehensive geriatric assessment, personalized care planning, multidisciplinary case conferences, coordinated care, case management and patient empowerment, which generally had a positive effect on improving quality of life, caregiver burden and primary care resource utilization for older adults, but effectiveness evaluations remained Heterogeneity exists. The barriers and facilitators to ICT-based implementation of integrated care were grouped into four themes: demand-side factors, provider factors, technology factors and system factors. Conclusion: The implementation of ICT-based integrated care for the elderly is expected to improve the health status of both the supply and demand of services, but there is still a need to strengthen the supply of human resources, team training and collaboration, ICT systems and financial support in order to promote the wider use of ICT in integrated care.

Songhua Li, Lingjie Duan

In location-based social networks (LBSNs), users sense urban point-of-interest (PoI) information in the vicinity and share such information with friends in online social networks. Given users' limited social connections and severe lags in disseminating fresh PoI to all, major LBSNs aim to enhance users' social PoI sharing by selecting $k$ out of $m$ users as hotspots and broadcasting their fresh PoI information to the entire user community. This motivates us to study a new combinatorial optimization problem that involves the interplay between an urban sensing network and an online social network. We prove that this problem is NP-hard and also renders existing approximation solutions not viable. Through analyzing the interplay effects between the two networks, we successfully transform the involved PoI-sharing process across two networks to matrix computations for deriving a closed-form objective to hold desirable properties (e.g., submodularity and monotonicity). This finding enables us to develop a polynomial-time algorithm that guarantees a ($1-\frac{m-2}{m}(\frac{k-1}{k})^k$) approximation of the optimum. Furthermore, we allow each selected user to move around and sense more PoI information to share and propose an augmentation-adaptive algorithm with decent performance guarantees. Finally, our theoretical results are corroborated by our simulation findings using both synthetic and real-world datasets.

Layla Anbarsetani, Rashid Zoalfaghari Zafarani, BiBi Sadat Miresmaeili et al.

The present study was conducted with the aim of developing and validating a quality management model in order to implement the educational goals of the document of fundamental transformation of education and upbringing with a combined approach. In terms of applied purpose, the present research method was a combination of consecutive exploratory type and data collection method in the qualitative part, field using interviews and in the quantitative field using a questionnaire. The statistical population in the qualitative section includes professors of educational management familiar with the document of education and training as well as familiar with quality management, who were purposefully selected as a standard for qualitative interviews on the subject of research (19 interviews with 19 people and up to The theoretical saturation limit continued) and in the second (quantitative) part, after collecting information from qualitative research, a questionnaire was constructed. Therefore, in general, it was found that the categories of strategic quality management model in order to implement the educational goals of the document of fundamental transformation of education and training, in order of importance, including infrastructure, evaluation, finance, human resources, participatory education, communication, human, organizational structure, program is planning and training. The directors of education in the country are recommended to use the results of the present study to achieve the educational goals of the document of fundamental change in education.

Maryam Talei, Farhad Handjani, Behrooz Astaneh et al.

Background: Peer review is a necessary but costly and time-consuming process to identify good-quality and methodologically sound articles and improve them before publication. Finding good peer reviewers is often difficult.Objective: To identify the incentives that make Iranian biomedical researchers accept invitations to be a peer reviewer and factors that affect these incentives.Methods: Twelve reviewers selected at random from the reviewers pool of each of 26 biomedical journals published from Fars province, Iran, were surveyed using a questionnaire that we had developed and tested in a pilot study of 30 reviewers (Cronbach’s alpha of 0.779). The data included the reviewers’ demographics, history of their reviews, and choice of 11 reasons each for accepting or declining the invitation to review.Results: A total of 233 reviewers completed the questionnaire. The most important reasons for accepting the invitation to review were the journal’s practice to publish the names of the reviewers alongside the article they had reviewed, acknowledgement by the journals by publishing the names of reviewers once a year, free access to journals’ content, and lower publication charges as authors. The most common reasons to decline the invitation were lack of time, busy schedules, and lack of sufficient incentive to review.Conclusion: Acknowledgement by the journal, offering to publish the names of reviewers alongside the articles they had reviewed, and monetary rewards will be effective incentives for biomedical researchers in Iran to serve as peer reviewers.

Svitlana Vakulenko, Evangelos Kanoulas, Maarten de Rijke

Conversational search is a relatively young area of research that aims at automating an information-seeking dialogue. In this paper we help to position it with respect to other research areas within conversational Artificial Intelligence (AI) by analysing the structural properties of an information-seeking dialogue. To this end, we perform a large-scale dialogue analysis of more than 150K transcripts from 16 publicly available dialogue datasets. These datasets were collected to inform different dialogue-based tasks including conversational search. We extract different patterns of mixed initiative from these dialogue transcripts and use them to compare dialogues of different types. Moreover, we contrast the patterns found in information-seeking dialogues that are being used for research purposes with the patterns found in virtual reference interviews that were conducted by professional librarians. The insights we provide (1) establish close relations between conversational search and other conversational AI tasks; and (2) uncover limitations of existing conversational datasets to inform future data collection tasks.

Aljaž Zrnec, Marko Poženel, Dejan Lavbič

Digital information exchange enables quick creation and sharing of information and thus changes existing habits. Social media is becoming the main source of news for end-users replacing traditional media. This also enables the proliferation of fake news, which misinforms readers and is used to serve the interests of the creators. As a result, automated fake news detection systems are attracting attention. However, automatic fake news detection presents a major challenge; content evaluation is increasingly becoming the responsibility of the end-user. Thus, in the present study we used information quality (IQ) as an instrument to investigate how users can detect fake news. Specifically, we examined how users perceive fake news in the form of shorter paragraphs on individual IQ dimensions. We also investigated which user characteristics might affect fake news detection. We performed an empirical study with 1123 users, who evaluated randomly generated stories with statements of various level of correctness by individual IQ dimensions. The results reveal that IQ can be used as a tool for fake news detection. Our findings show that (1) domain knowledge has a positive impact on fake news detection; (2) education in combination with domain knowledge improves fake news detection; and (3) personality trait conscientiousness contributes significantly to fake news detection in all dimensions.

Islam Akef Ebeid, Elizabeth Pierce

Here we study the semantic search and retrieval problem in biomedical digital libraries. First, we introduce MedGraph, a knowledge graph embedding-based method that provides semantic relevance retrieval and ranking for the biomedical literature indexed in PubMed. Second, we evaluate our method using PubMed's Best Match algorithm. Moreover, we compare our method MedGraph to a traditional TFIDF based algorithm. We use a dataset extracted from PubMed, including 30 million articles' metadata such as abstracts, author information, citation information, and extracted biological entity mentions. We do that by pulling a subset of the dataset to evaluate MedGraph using predefined queries with ground truth ranked results. To our knowledge, this technique has not been explored before in biomedical information retrieval. In addition, our results provide evidence that semantic approaches to search and relevance in biomedical digital libraries that rely on knowledge graph modeling offer better search relevance results when compared with traditional approaches in terms of objective metrics.

Rafael Bellan Rodrigues de Souza

RESUMO O artigo propõe um diagnóstico do território digital como via para analisar as possibilidades de uma ação comunicativa contra-hegemônica em seu interior. A investigação segue as trilhas do materialismo histórico e percorre a indissociabilidade entre as dimensões econômico-materiais, os elementos culturais e as práticas comunicacionais centralizadas pela internet. Assim, o texto põe em relevo as amarrações estruturais existentes entre o capitalismo e a comunicação no contexto da internet. Os resultados apontam para um reconhecimento dos limites da atuação no ambiente digital, bem como a atualidade de elaboração de um novo projeto societário. Palavras-chave: Internet; Contra-hegemonia; Materialismo Histórico; Capitalismo Digital.