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        <title>Frontiers in Big Data | Big Data Networks section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/big-data/sections/big-data-networks</link>
        <description>RSS Feed for Big Data Networks section in the Frontiers in Big Data journal | New and Recent Articles</description>
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        <pubDate>2026-05-02T15:15:40.313+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1579332</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1579332</link>
        <title><![CDATA[Urban mobility and crime: causal inference using street closures as an instrumental variable]]></title>
        <pubdate>2025-10-31T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Karl Vachuska</author>
        <description><![CDATA[The advent of widely available cell phone mobility data in the United States has rapidly expanded the study of everyday mobility patterns in social science research. A wide range of existing literature finds ambient population (e.g., visitors) estimates of an area to be predictive of crime. Much of the past research frames neighborhood visitor flows in predictive terms without necessarily indicating or implying a causal effect. Through the use of two causal inference approaches—conventional two-way fixed effects and a novel instrumental variable approach, this brief research report explicitly formulates the causal effect of visitors in counterfactual terms. This study addresses this gap by explicitly estimating the causal effect of visitor flows on crime rates. Using high-resolution mobility and crime data from New York City for the year 2019, I estimate the additive effect of visitors on the multiple measurements of criminal activity. While two-way fixed effects models show a significant effect of visitors on a wide array of crime forms, instrumental variable estimates indicate no statistically significant causal impact, with large standard errors indicating substantial uncertainty in visitors' effect on crime rates.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1666305</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1666305</link>
        <title><![CDATA[Editorial: Interdisciplinary approaches to complex systems: highlights from FRCCS 2023/24]]></title>
        <pubdate>2025-08-12T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Roberto Interdonato</author><author>Hocine Cherifi</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1562557</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1562557</link>
        <title><![CDATA[Data visualization of complex research systems aligned with the sustainable development goals]]></title>
        <pubdate>2025-06-09T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Francisco Carlos Paletta</author><author>Audilio Gonzalez-Aguilar</author><author>Lise Verlaet</author>
        <description><![CDATA[This study presents a methodological framework for visualizing the alignment between complex research systems and the Sustainable Development Goals (SDGs), using CIRAD as a case study. By leveraging advanced data visualization and bibliometric analysis, the research maps CIRAD's publications to the SDGs and explores thematic priorities and institutional collaborations. The findings underscore CIRAD's significant contributions to climate action, food security, biodiversity conservation, and rural development. The integration of complex systems theory and network analysis enhances understanding of SDG interlinkages and provides actionable insights for strategic decision-making in research governance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1605788</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1605788</link>
        <title><![CDATA[Editorial: Applied computational social sciences]]></title>
        <pubdate>2025-05-22T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Paolo Parigi</author><author>Kinga Makovi</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1521653</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1521653</link>
        <title><![CDATA[Lightweight and hybrid transformer-based solution for quick and reliable deepfake detection]]></title>
        <pubdate>2025-04-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Geeta Rani</author><author>Atharv Kothekar</author><author>Shawn George Philip</author><author>Vijaypal Singh Dhaka</author><author>Ester Zumpano</author><author>Eugenio Vocaturo</author>
        <description><![CDATA[IntroductionRapid advancements in artificial intelligence and generative artificial intelligence have enabled the creation of fake images and videos that appear highly realistic. According to a report published in 2022, approximately 71% of people rely on fake videos and become victims of blackmail. Moreover, these fake videos and images are used to tarnish the reputation of popular public figures. This has increased the demand for deepfake detection techniques. The accuracy of the techniques proposed in the literature so far varies with changes in fake content generation techniques. Additionally, these techniques are computationally intensive. The techniques discussed in the literature are based on convolutional neural networks, Linformer models, or transformer models for deepfake detection, each with its advantages and disadvantages.MethodsIn this manuscript, a hybrid architecture combining transformer and Linformer models is proposed for deepfake detection. This architecture converts an image into patches and performs position encoding to retain spatial relationships between patches. Its encoder captures the contextual information from the input patches, and Gaussian Error Linear Unit resolves the vanishing gradient problem.ResultsThe Linformer component reduces the size of the attention matrix. Thus, it reduces the execution time to half without compromising accuracy. Moreover, it utilizes the unique features of transformer and Linformer models to enhance the robustness and generalization of deepfake detection techniques. The low computational requirement and high accuracy of 98.9% increase the real-time applicability of the model, preventing blackmail and other losses to the public.DiscussionThe proposed hybrid model utilizes the strength of the transformer model in capturing complex patterns in data. It uses the self-attention potential of the Linformer model and reduces the computation time without compromising the accuracy. Moreover, the models were implemented on patch sizes of 6 and 11. It is evident from the obtained results that increasing the patch size improves the performance of the model. This allows the model to capture fine-grained features and learn more effectively from the same set of videos. The larger patch size also enables the model to better preserve spatial details, which contributes to improved feature extraction.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1506443</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1506443</link>
        <title><![CDATA[Toward a physics-guided machine learning approach for predicting chaotic systems dynamics]]></title>
        <pubdate>2025-01-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Liu Feng</author><author>Yang Liu</author><author>Benyun Shi</author><author>Jiming Liu</author>
        <description><![CDATA[Predicting the dynamics of chaotic systems is crucial across various practical domains, including the control of infectious diseases and responses to extreme weather events. Such predictions provide quantitative insights into the future behaviors of these complex systems, thereby guiding the decision-making and planning within the respective fields. Recently, data-driven approaches, renowned for their capacity to learn from empirical data, have been widely used to predict chaotic system dynamics. However, these methods rely solely on historical observations while ignoring the underlying mechanisms that govern the systems' behaviors. Consequently, they may perform well in short-term predictions by effectively fitting the data, but their ability to make accurate long-term predictions is limited. A critical challenge in modeling chaotic systems lies in their sensitivity to initial conditions; even a slight variation can lead to significant divergence in actual and predicted trajectories over a finite number of time steps. In this paper, we propose a novel Physics-Guided Learning (PGL) method, aiming at extending the scope of accurate forecasting as much as possible. The proposed method aims to synergize observational data with the governing physical laws of chaotic systems to predict the systems' future dynamics. Specifically, our method consists of three key elements: a data-driven component (DDC) that captures dynamic patterns and mapping functions from historical data; a physics-guided component (PGC) that leverages the governing principles of the system to inform and constrain the learning process; and a nonlinear learning component (NLC) that effectively synthesizes the outputs of both the data-driven and physics-guided components. Empirical validation on six dynamical systems, each exhibiting unique chaotic behaviors, demonstrates that PGL achieves lower prediction errors than existing benchmark predictive models. The results highlight the efficacy of our design of data-physics integration in improving the precision of chaotic system dynamics forecasts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1448481</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1448481</link>
        <title><![CDATA[Cybermycelium: a reference architecture for domain-driven distributed big data systems]]></title>
        <pubdate>2024-11-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Pouya Ataei</author>
        <description><![CDATA[IntroductionThe ubiquity of digital devices, the infrastructure of today, and the ever-increasing proliferation of digital products have dawned a new era, the era of big data (BD). This era began when the volume, variety, and velocity of data overwhelmed traditional systems that used to analyze and store that data. This precipitated a new class of software systems, namely, BD systems. Whereas BD systems provide a competitive advantage to businesses, many have failed to harness the power of them. It has been estimated that only 20% of companies have successfully implemented a BD project.MethodsThis study aims to facilitate BD system development by introducing Cybermycelium, a domain-driven decentralized BD reference architecture (RA). The artifact was developed following the guidelines of empirically grounded RAs and evaluated through implementation in a real-world scenario using the Architecture Tradeoff Analysis Method (ATAM).ResultsThe evaluation revealed that Cybermycelium successfully addressed key architectural qualities: performance (achieving <1,000 ms response times), availability (through event brokers and circuit breaking), and modifiability (enabling rapid service deployment and configuration). The prototype demonstrated effective handling of data processing, scalability challenges, and domain-specific requirements in a large-scale international company setting.DiscussionThe results highlight important architectural trade-offs between event backbone implementation and service mesh design. While the domain-driven distributed approach improved scalability and maintainability compared to traditional monolithic architectures, it requires significant technical expertise for implementation. This contribution advances the field by providing a validated reference architecture that addresses the challenges of adopting BD in modern enterprises.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1422546</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1422546</link>
        <title><![CDATA[An enhanced whale optimization algorithm for task scheduling in edge computing environments]]></title>
        <pubdate>2024-10-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Li Han</author><author>Shuaijie Zhu</author><author>Haoyang Zhao</author><author>Yanqiang He</author>
        <description><![CDATA[The widespread use of mobile devices and compute-intensive applications has increased the connection of smart devices to networks, generating significant data. Real-time execution faces challenges due to limited resources and demanding applications in edge computing environments. To address these challenges, an enhanced whale optimization algorithm (EWOA) was proposed for task scheduling. A multi-objective model based on CPU, memory, time, and resource utilization was developed. The model was transformed into a whale optimization problem, incorporating chaotic mapping to initialize populations and prevent premature convergence. A nonlinear convergence factor was introduced to balance local and global search. The algorithm's performance was evaluated in an experimental edge computing environment and compared with ODTS, WOA, HWACO, and CATSA algorithms. Experimental results demonstrated that EWOA reduced costs by 29.22%, decreased completion time by 17.04%, and improved node resource utilization by 9.5%. While EWOA offers significant advantages, limitations include the lack of consideration for potential network delays and user mobility. Future research will focus on fault-tolerant scheduling techniques to address dynamic user needs and improve service robustness and quality.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1420344</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1420344</link>
        <title><![CDATA[Equitable differential privacy]]></title>
        <pubdate>2024-08-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Vasundhara Kaul</author><author>Tamalika Mukherjee</author>
        <description><![CDATA[Differential privacy (DP) has been in the public spotlight since the announcement of its use in the 2020 U.S. Census. While DP algorithms have substantially improved the confidentiality protections provided to Census respondents, concerns have been raised about the accuracy of the DP-protected Census data. The extent to which the use of DP distorts the ability to draw inferences that drive policy about small-populations, especially marginalized communities, has been of particular concern to researchers and policy makers. After all, inaccurate information about marginalized populations can often engender policies that exacerbate rather than ameliorate social inequities. Consequently, computer science experts have focused on developing mechanisms that help achieve equitable privacy, i.e., mechanisms that mitigate the data distortions introduced by privacy protections to ensure equitable outcomes and benefits for all groups, particularly marginalized groups. Our paper extends the conversation on equitable privacy by highlighting the importance of inclusive communication in ensuring equitable outcomes for all social groups through all the stages of deploying a differentially private system. We conceptualize Equitable DP as the design, communication, and implementation of DP algorithms that ensure equitable outcomes. Thus, in addition to adopting computer scientists' recommendations of incorporating equity parameters within DP algorithms, we suggest that it is critical for an organization to also facilitate inclusive communication throughout the design, development, and implementation stages of a DP algorithm to ensure it has an equitable impact on social groups and does not hinder the redressal of social inequities. To demonstrate the importance of communication for Equitable DP, we undertake a case study of the process through which DP was adopted as the newest disclosure avoidance system for the 2020 U.S. Census. Drawing on the Inclusive Science Communication (ISC) framework, we examine the extent to which the Census Bureau's communication strategies encouraged engagement across the diverse groups of users that employ the decennial Census data for research and policy making. Our analysis provides lessons that can be used by other government organizations interested in incorporating the Equitable DP approach in their data collection practices.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1287442</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1287442</link>
        <title><![CDATA[Data science's cultural construction: qualitative ideas for quantitative work]]></title>
        <pubdate>2024-08-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Philipp Brandt</author>
        <description><![CDATA[Introduction“Data scientists” quickly became ubiquitous, often infamously so, but they have struggled with the ambiguity of their novel role. This article studies data science's collective definition on Twitter.MethodsThe analysis responds to the challenges of studying an emergent case with unclear boundaries and substance through a cultural perspective and complementary datasets ranging from 1,025 to 752,815 tweets. It brings together relations between accounts that tweeted about data science, the hashtags they used, indicating purposes, and the topics they discussed.ResultsThe first results reproduce familiar commercial and technical motives. Additional results reveal concerns with new practical and ethical standards as a distinctive motive for constructing data science.DiscussionThe article provides a sensibility for local meaning in usually abstract datasets and a heuristic for navigating increasingly abundant datasets toward surprising insights. For data scientists, it offers a guide for positioning themselves vis-à-vis others to navigate their professional future.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1379921</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1379921</link>
        <title><![CDATA[From theory to practice: insights and hurdles in collecting social media data for social science research]]></title>
        <pubdate>2024-05-30T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Yan Chen</author><author>Kate Sherren</author><author>Kyung Young Lee</author><author>Lori McCay-Peet</author><author>Shan Xue</author><author>Michael Smit</author>
        <description><![CDATA[Social media has profoundly changed our modes of self-expression, communication, and participation in public discourse, generating volumes of conversations and content that cover every aspect of our social lives. Social media platforms have thus become increasingly important as data sources to identify social trends and phenomena. In recent years, academics have steadily lost ground on access to social media data as technology companies have set more restrictions on Application Programming Interfaces (APIs) or entirely closed public APIs. This circumstance halts the work of many social scientists who have used such data to study issues of public good. We considered the viability of eight approaches for image-based social media data collection: data philanthropy organizations, data repositories, data donation, third-party data companies, homegrown tools, and various web scraping tools and scripts. This paper discusses the advantages and challenges of these approaches from literature and from the authors' experience. We conclude the paper by discussing mechanisms for improving social media data collection that will enable this future frontier of social science research.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1330392</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1330392</link>
        <title><![CDATA[Analyzing political party positions through multi-language twitter text embeddings]]></title>
        <pubdate>2024-05-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jinghui Chen</author><author>Takayuki Mizuno</author><author>Shohei Doi</author>
        <description><![CDATA[Traditional monolingual word embedding models transform words into high-dimensional vectors which represent semantics relations between words as relationships between vectors in the high-dimensional space. They serve as productive tools to interpret multifarious aspects of the social world in social science research. Building on the previous research which interprets multifaceted meanings of words by projecting them onto word-level dimensions defined by differences between antonyms, we extend the architecture of establishing word-level cultural dimensions to the sentence level and adopt a Language-agnostic BERT model (LaBSE) to detect position similarities in a multi-language environment. We assess the efficacy of our sentence-level methodology using Twitter data from US politicians, comparing it to the traditional word-level embedding model. We also adopt Latent Dirichlet Allocation (LDA) to investigate detailed topics in these tweets and interpret politicians' positions from different angles. In addition, we adopt Twitter data from Spanish politicians and visualize their positions in a multi-language space to analyze position similarities across countries. The results show that our sentence-level methodology outperform traditional word-level model. We also demonstrate that our methodology is effective dealing with fine-sorted themes from the result that political positions towards different topics vary even within the same politicians. Through verification using American and Spanish political datasets, we find that the positioning of American and Spanish politicians on our defined liberal-conservative axis aligns with social common sense, political news, and previous research. Our architecture improves the standard word-level methodology and can be considered as a useful architecture for sentence-level applications in the future.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1304806</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1304806</link>
        <title><![CDATA[How different are offline and online diplomacy? A comparative analysis of public statements and SNS posts by delegates to the United Nations]]></title>
        <pubdate>2024-04-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Takuto Sakamoto</author><author>Momoko Araki</author><author>Hiroto Ito</author><author>Tomoyuki Matsuoka</author>
        <description><![CDATA[IntroductionThis article investigates the evolving landscape of diplomacy in the digital age, focusing on diplomats at the United Nations (UN) Headquarters in New York. The central inquiry revolves around how diplomatic actors use digital tools to complement or augment traditional face-to-face diplomacy.MethodsWe systematically compare a substantial corpus of X posts (tweets) from UN diplomats with their public statements at the United Nations Security Council (UNSC), employing advanced computational social science techniques. This study applies a range of large-scale text analysis methods, including word embedding, topic modeling, and sentiment analysis, to investigate systematic differences between offline and online communication.ResultsOur analysis reveals that, while the essence of diplomacy remains consistent across both domains, there is strategic selectivity in the use of online platforms by diplomats. Online communication emphasizes non-security topics, ceremonial matters, and prominent policy stances, in contrast to the operational issues common in UNSC deliberations. Additionally, online discourse adopts a less confrontational, more public diplomacy-oriented tone, with variations among countries.DiscussionThis study offers one of the first systematic comparisons between offline and online diplomatic messages. It illuminates how diplomats navigate the digital realm to complement traditional roles. The findings indicate that some elements of public diplomacy and nation branding, directed toward a wider audience far beyond the council chamber, have become an integral part of multilateral diplomacy unfolding at the UNSC.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1356116</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1356116</link>
        <title><![CDATA[Urban delineation through a prism of intraday commute patterns]]></title>
        <pubdate>2024-03-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yuri Bogomolov</author><author>Alexander Belyi</author><author>Stanislav Sobolevsky</author>
        <description><![CDATA[IntroductionUrban mobility patterns are crucial for effective urban and transportation planning. This study investigates the dynamics of urban mobility in Brno, Czech Republic, utilizing the rich dataset provided by passive mobile phone data. Understanding these patterns is essential for optimizing infrastructure and planning strategies.MethodsWe developed a methodological framework that incorporates bidirectional commute flows and integrates both urban and suburban commute networks. This comprehensive approach allows for a detailed representation of Brno's mobility landscape. By employing clustering techniques, we aimed to identify distinct mobility patterns within the city.ResultsOur analysis revealed consistent structural features within Brno's mobility patterns. We identified three distinct clusters: a central business district, residential communities, and an intermediate hybrid cluster. These clusters highlight the diversity of mobility demands across different parts of the city.DiscussionThe study demonstrates the significant potential of passive mobile phone data in enhancing our understanding of urban mobility patterns. The insights gained from intraday mobility data are invaluable for transportation planning decisions, allowing for the optimization of infrastructure utilization. The identification of distinct mobility patterns underscores the practical utility of our methodological advancements in informing more effective and efficient transportation planning strategies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1354007</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1354007</link>
        <title><![CDATA[City composition and accessibility statistics in and around Paris]]></title>
        <pubdate>2024-03-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Marie-Olive Thaury</author><author>Simon Genet</author><author>Léopold Maurice</author><author>Paola Tubaro</author><author>Sarah J. Berkemer</author>
        <description><![CDATA[IntroductionIs Paris a 15-min city, where inhabitants can access essential amenities such as schools and shops with a 15-min walk or bike ride? The concept of a 15-min (more generally, X-minute) city was launched in the French capital and was part of the current mayor's plan in her latest re-election campaign. Yet, its fit with the existing urban structure had not been previously assessed.MethodsThis article combines open map data from a large participatory project and geo-localized socio-economic data from official statistics to fill this gap.ResultsWe show that, while the city of Paris is rather homogeneous, it is nonetheless characterized by remarkable inequalities between a highly accessible city center (though with some internal differences in terms of types of amenities) and a less well-equipped periphery, where lower-income neighborhoods are more often found. The heterogeneity increases if we consider Paris together with its immediate surroundings, the "Petite Couronne," where large numbers of daily commuters and other users of city facilities live.DiscussionWe thus conclude that successful implementation of the X-minute-city concept requires addressing existing socio-economic inequalities, and that especially in big cities, it should be extended beyond the narrow boundaries of the municipality itself to encompass the larger area around it.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1358486</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1358486</link>
        <title><![CDATA[Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application]]></title>
        <pubdate>2024-02-21T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Muhammad Saad</author><author>Rabia Noor Enam</author><author>Rehan Qureshi</author>
        <description><![CDATA[As the volume and velocity of Big Data continue to grow, traditional cloud computing approaches struggle to meet the demands of real-time processing and low latency. Fog computing, with its distributed network of edge devices, emerges as a compelling solution. However, efficient task scheduling in fog computing remains a challenge due to its inherently multi-objective nature, balancing factors like execution time, response time, and resource utilization. This paper proposes a hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) algorithm to optimize multi-objective task scheduling in fog computing environments. The hybrid approach combines the strengths of GA and PSO, achieving effective exploration and exploitation of the search space, leading to improved performance compared to traditional single-algorithm approaches. The proposed hybrid algorithm results improved the execution time by 85.68% when compared with GA algorithm, by 84% when compared with Hybrid PWOA and by 51.03% when compared with PSO algorithm as well as it improved the response time by 67.28% when compared with GA algorithm, by 54.24% when compared with Hybrid PWOA and by 75.40% when compared with PSO algorithm as well as it improved the completion time by 68.69% when compared with GA algorithm, by 98.91% when compared with Hybrid PWOA and by 75.90% when compared with PSO algorithm when various tasks inputs are given. The proposed hybrid algorithm results also improved the execution time by 84.87% when compared with GA algorithm, by 88.64% when compared with Hybrid PWOA and by 85.07% when compared with PSO algorithm it improved the response time by 65.92% when compared with GA algorithm, by 80.51% when compared with Hybrid PWOA and by 85.26% when compared with PSO algorithm as well as it improved the completion time by 67.60% when compared with GA algorithm, by 81.34% when compared with Hybrid PWOA and by 85.23% when compared with PSO algorithm when various fog nodes are given.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1214029</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1214029</link>
        <title><![CDATA[Climbing crags recommender system in Arco, Italy: a comparative study]]></title>
        <pubdate>2023-10-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Iustina Ivanova</author><author>Mike Wald</author>
        <description><![CDATA[Outdoor sport climbing is popular in Northern Italy due to its vast amount of rock climbing places (such as crags). New climbing crags appear yearly, creating an information overload problem for tourists who plan their sport climbing vacation. Recommender systems partly addressed this issue by suggesting climbing crags according to the most visited places or the number of suitable climbing routes. Unfortunately, these methods do not consider contextual information. However, in sport climbing, as in other outdoor activities, the possibility of visiting certain places depends on several contextual factors, for instance, a suitable season (winter/summer), parking space availability if traveling with a car, or the possibility of climbing with children if traveling with children. To address this limitation, we collected and analyzed the crag visits in Arco (Italy) from an online guidebook. We found that climbing contextual information, similar to users' content preferences, can be modeled by a correlation between recorded visits and crags features. Based on that, we developed and evaluated a novel context-aware climbing crags recommender system Visit & Climb, which consists of three stages as follows: (1) contextual information and content tastes are learned automatically from the users' logs by computing correlation between users' visits and crags' features; (2) those learned tastes are further made adjustable in a preference elicitation web interface; (3) the user receives recommendations on the map according to the number of visits made by a climber with similar learned tastes. To measure the quality of this system, we performed an offline evaluation (where we calculated Mean Average Precision, Recall, and Normalized Discounted Cumulative Gain for top-N), a formative study, and an online evaluation (in a within-subject design with experienced outdoor climbers N = 40, who tried three similar systems including Visit & Climb). Offline tests showed that the proposed system suggests crags to climbers accurately as the other classical models for top-N recommendations. Meanwhile, online tests indicated that the system provides a significantly higher level of information sufficiency than other systems in this domain. The overall results demonstrated that the developed system provides recommendations according to the users' requirements, and incorporating contextual information and crag characteristics into the climbing recommender system leads to increased information sufficiency caused by transparency, which improves satisfaction and use intention.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1144793</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1144793</link>
        <title><![CDATA[Efficient community detection in multilayer networks using boolean compositions]]></title>
        <pubdate>2023-08-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abhishek Santra</author><author>Fariba Afrin Irany</author><author>Kamesh Madduri</author><author>Sharma Chakravarthy</author><author>Sanjukta Bhowmick</author>
        <description><![CDATA[Networks (or graphs) are used to model the dyadic relations between entities in complex systems. Analyzing the properties of the networks reveal important characteristics of the underlying system. However, in many disciplines, including social sciences, bioinformatics, and technological systems, multiple relations exist between entities. In such cases, a simple graph is not sufficient to model these multiple relations, and a multilayer network is a more appropriate model. In this paper, we explore community detection in multilayer networks. Specifically, we propose a novel network decoupling strategy for efficiently combining the communities in the different layers using the Boolean primitives AND, OR, and NOT. Our proposed method, network decoupling, is based on analyzing the communities in each network layer individually and then aggregating the analysis results. We (i) describe our network decoupling algorithms for finding communities, (ii) present how network decoupling can be used to express different types of communities in multilayer networks, and (iii) demonstrate the effectiveness of using network decoupling for detecting communities in real-world and synthetic data sets. Compared to other algorithms for detecting communities in multilayer networks, our proposed network decoupling method requires significantly lower computation time while producing results of high accuracy. Based on these results, we anticipate that our proposed network decoupling technique will enable a more detailed analysis of multilayer networks in an efficient manner.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1085571</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1085571</link>
        <title><![CDATA[Synthetic biomedical data generation in support of In Silico Clinical Trials]]></title>
        <pubdate>2023-08-15T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Alena Simalatsar</author>
        <description><![CDATA[Living in the era of Big Data, one may advocate that the additional synthetic generation of data is redundant. However, to be able to truly say whether it is valid or not, one needs to focus more on the meaning and quality of data than on the quantity. In some domains, such as biomedical and translational sciences, data privacy still holds a higher importance than data sharing. This by default limits access to valuable research data. Intensive discussion, agreements, and conventions among different medical research players, as well as effective techniques and regulations for data anonymization, already made a big step toward simplification of data sharing. However, the situation with the availability of data about rare diseases or outcomes of novel treatments still requires costly and risky clinical trials and, thus, would greatly benefit from smart data generation. Clinical trials and tests on animals initiate a cyclic procedure that may involve multiple redesigns and retesting, which typically takes two or three years for medical devices and up to eight years for novel medicines, and costs between 10 and 20 million euros. The US Food and Drug Administration (FDA) acknowledges that for many novel devices, practical limitations require alternative approaches, such as computer modeling and engineering tests, to conduct large, randomized studies. In this article, we give an overview of global initiatives advocating for computer simulations in support of the 3R principles (Replacement, Reduction, and Refinement) in humane experimentation. We also present several research works that have developed methodologies of smart and comprehensive generation of synthetic biomedical data, such as virtual cohorts of patients, in support of In Silico Clinical Trials (ISCT) and discuss their common ground.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1135191</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1135191</link>
        <title><![CDATA[Modeling information diffusion in social media: data-driven observations]]></title>
        <pubdate>2023-05-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Adriana Iamnitchi</author><author>Lawrence O. Hall</author><author>Sameera Horawalavithana</author><author>Frederick Mubang</author><author>Kin Wai Ng</author><author>John Skvoretz</author>
        <description><![CDATA[Accurately modeling information diffusion within and across social media platforms has many practical applications, such as estimating the size of the audience exposed to a particular narrative or testing intervention techniques for addressing misinformation. However, it turns out that real data reveal phenomena that pose significant challenges to modeling: events in the physical world affect in varying ways conversations on different social media platforms; coordinated influence campaigns may swing discussions in unexpected directions; a platform's algorithms direct who sees which message, which affects in opaque ways how information spreads. This article describes our research efforts in the SocialSim program of the Defense Advanced Research Projects Agency. As formulated by DARPA, the intent of the SocialSim research program was “to develop innovative technologies for high-fidelity computational simulation of online social behavior ... [focused] specifically on information spread and evolution.” In this article we document lessons we learned over the 4+ years of the recently concluded project. Our hope is that an accounting of our experience may prove useful to other researchers should they attempt a related project.]]></description>
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