Interaction Data Research Articles

Biomonitoring of biocontrol across the full annual cycle in temperate climates: Post‐harvest, winter and early‐season interaction data and methodological considerations for its collection

Abstract Conservation biocontrol, the regulation of crop pests by naturally occurring biocontrol agents (e.g., predators and parasitoids), is predominantly monitored throughout periods of primary crop growth when pests exert the most observable impact on yields. Pest‐focused agricultural biomonitoring often overlooks post‐harvest, winter and even early‐season biocontrol, despite the significant predator–pest interactions during these periods that profoundly affect pest abundance and, consequently, crop yields. Rapid advances in biomonitoring, particularly in the detection of predator–pest interactions that underpin biocontrol, provide an opportunity to reconsider how and when we monitor these interactions. Advances in agricultural biomonitoring must transcend methodological innovation and encompass conceptual changes in the monitoring of agricultural systems. Here, we assess existing evidence supporting the importance of periods beyond primary crop growth for biocontrol and how predator–pest interactions are likely to evolve during these periods, subsequently influencing pest population dynamics during the primary crop growth period. We advocate for a greater concerted effort to establish continuous monitoring of biocontrol interactions, particularly beyond primary crop growth periods in temperate climates. To facilitate this, we also summarise the methodological approaches that can make it possible and explore how extending sampling across the full annual cycle might impact the practicalities and outcomes of these approaches. Year‐round monitoring of biocontrol interactions, both in crops and adjacent semi‐natural habitats, will provide a previously intractable understanding of predator–pest dynamics, offering significant potential to enhance our ability to optimise and manipulate these systems. This would manifest in reduced crop yield losses, pest infestation rates and disease transmission, with concomitant long‐term financial, environmental and land‐use benefits.

Implementasi SNA dalam Menilai Popularitas Platform Pemesanan Makanan Online Berbasis Percakapan Twitter

This research aims to understand the popularity of online food ordering platforms in Indonesia, although the data taken in Indonesian includes allied languages. This research combines the SWARA, WASPAS, and Social Network Analysis (SNA) approaches to achieve its objectives. The problem formulation proposed is how to measure and analyze the popularity of online food ordering platforms based on interaction data on Twitter social media. The SWARA method is used to determine the weight of the evaluation criteria, while WASPAS is applied to rank online food ordering platforms. Meanwhile, SNA is applied to analyze interaction patterns and user preferences for various online food ordering platforms. Twitter was chosen as the main data source because it provides direct conversation data and real depictions, allowing for in-depth and accurate analysis. Through Twitter, research can capture the dynamics of user interactions and how they communicate about various online food ordering platforms. This research aims to provide a strong foundation for decision makers in designing more effective marketing strategies and provide insight into the dynamics of competition in the online food ordering platform industry. The research results show that SNA-based analysis is able to clearly identify interaction patterns and user preferences.

Analyzing Whatsapp Chat Data for User Interaction and Sentiment: A Comprehensive Approach

Analyzing Whatsapp Chat Data for User Interaction and Sentiment: A Comprehensive Approach

Relation pruning and discriminative sampling over knowledge graph for long-tail recommendation

Long-tail recommendations have gained significant attention owing to their potential economic market. However, the scarcity of interaction data for long-tail users/items and the popularity bias present challenges in capturing high-quality embeddings for long-tail users/items. This predicament further exacerbates the long-tail recommendation problem, as current approaches tend to exhibit a bias towards making recommendations for short-head users/items, resulting in a detrimental cycle for long-tail recommendation. To this end, we propose a novel knowledge graph-based approach called LTailKG to improve long-tail recommendations. LTailKG leverages the semantic information in knowledge graph to produce high-quality embeddings and augmented samples for generating satisfactory long-tail recommendations. First, LTailKG parameterizes each node and relation as vector representations. Next, LTailKG presents a relation pruning-based graph contrastive learning operation to generate additional self-supervised signals for long-tail users/items, thereby producing high-quality embeddings for them. Furthermore, LTailKG introduces a knowledge graph-driven discriminative sampling operation to select augmented positive and negative samples from the uninteracted item set, which enables LTailKG to excel at not only identifying long-tail items that better align with the user's interests but also extracting the genuine preferences of long-tail users. Extensive experiments on real-world datasets demonstrate the superiority of LTailKG over state-of-the-art approaches in terms of long-tail recommendation.

PENGEMBANGAN POTENSI PARIWISATA MELALUI WISATA EDUKASI DI KAMPUNG BUDAYA SAMIN BOJONEGORO

One of the tourist attractions developed in the Samin Cultural Village which is located in Japanese Hamlet, Margomulyo Village, Margomulyo District, Bojonegoro Regency is through educational tourism on the cultivation of matoa fruit and crystal guava. The development of this educational tourism began with outreach activities to local pokdarwis to plant matoa fruit and crystal guava seeds in the camping area. The research method used by the author is qualitative research. The data collection technique used in this research is through direct observation, interviews, recording documents and journal articles. Interactive data analysis techniques. The research results show that with a variety of natural and cultural tourism potential, this village is developing educational tourism about matoa fruit and crystal guava, which are suitable for planting in the area. This development involves outreach and planting seeds, aimed at increasing tourist visits and community income.

POLITIK TUTUP JALAN

This paper describes the background of the resistance carried out by the people in three villages (Laiba, Laimpi and Wakumoro) as a form of unrest that they experienced as a result of the damage to the cross-provincial roads that had not been repaired for years by the Southeast Sulawesi provincial government. What is also interesting is that many resistance movements were pioneered by groups of mothers and resistance movements that were formed purely from the community groups themselves without depending on certain mass organizations. Using qualitative descriptive research methods. Data collection techniques through in-depth interviews and documentation studies. Interactive data analysis technique according to Milles Humerman and Saldana (2014). Community resistance against the government uses two methods of resistance, namely open reciprocity and closed resistance (James C. Scott, 2000). First, open resistance in the form of demonstrations, media hammering movements, and ina-ina resistance. Second, closed resistance is in the form of discussion of closed meetings, mass mobilization, strengthening the ideology of the movement and equalizing the vision of resistance. This research found that resistance that emerged organically then developed into a consistent resistance carried out by 3 villages (Laimpi, Laiba and Wakumoro Villages), so as to achieve the desired goals. Namely, the budgeted repair of the road.

Higher-order correlations reveal complex memory in temporal hypergraphs

Many real-world complex systems are characterized by interactions in groups that change in time. Current temporal network approaches, however, are unable to describe group dynamics, as they are based on pairwise interactions only. Here, we use time-varying hypergraphs to describe such systems, and we introduce a framework based on higher-order correlations to characterize their temporal organization. The analysis of human interaction data reveals the existence of coherent and interdependent mesoscopic structures, thus capturing aggregation, fragmentation and nucleation processes in social systems. We introduce a model of temporal hypergraphs with non-Markovian group interactions, which reveals complex memory as a fundamental mechanism underlying the emerging pattern in the data.

Rational gaze: Presenting the open-source HYPEtools R package for analysis, visualization, and interpretation of hydrological models and datasets

The open-source HYPEtools R package aims to address current challenges in hydrological modeling and data analysis by providing a suite of tools for data management, visualization, interpretation, and exploration. Originally developed as a supporting toolbox for the HYPE hydrological model, HYPEtools has since evolved into a standalone package with features ranging from diverse aggregation and evaluation routines to interactive apps and mapping. Specifically, functions are included for data manipulation and conversion, data summarization, plotting and mapping, and interactive data exploration. Two case studies are provided to highlight how HYPEtools can simplify hydrological workflows and analyses both independently of and within HYPE modeling contexts. First, HYPEtools is used for data exploration and mapping of water transfers in South Africa. Then, a case study for the Baltic Sea region of a pan-European HYPE model setup illustrates how HYPEtools can assist model calibration and validation.

Prompt2Rec: Prompt based user and item re-characterizing method for recommendation

Collaborative Filtering (CF), which utilizes user-item interaction data is widely adopted in Recommendation System; however, CF encounters challenges such as the cold-start problem and data sparsity. To address this issue, research incorporating Natural Language Processing (NLP) has made progress in leveraging review texts that contain rich information about user preferences and item attributes. Nevertheless, the conventional approach of integrating the entire review text and using it as an input, which has been widely used in previous research, can be vulnerable to noise (i.e., data with little relevance to user preferences or item attributes). In this study, we propose a novel user and item re-characterizing method called Prompt2Rec, which introduces the Prompt-based learning paradigm of NLP. It generates key factors that newly defined essential user and item characteristics from review texts and uses them as new information to train the recommendation model. We validate our proposed method through experiments on five benchmark datasets. The results show that Prompt2Rec leads to improved performance compared to existing methods that rely on review texts. Furthermore, we explore the potential to provide explanations for recommendations by visualizing the model's attention weights on the key factors.

Graph Neural Networks for Recommendation Leveraging Multimodal Information

Recommender systems act as filtering algorithms to provide users with items that might meet their interests according to the expressed preferences and items' characteristics. As of today, the collaborative filtering paradigm, along with deep learning techniques to learn high-quality users' and items' representations, constitute the de facto standard for personalized recommendation, showing remarkable recommendation accuracy performance. Nevertheless, recommendation remains a highly-challenging task. Among the most debated open issues in the community, this thesis considers two algorithmic and conceptual ones, namely: (i) the inexplicable nature of users' preferences, especially when they come in the form of implicit feedback; (ii) the effective exploitation of the collaborative information in the designing and training of recommendation models. In domains such as fashion, food, and media content recommendation, the shallow item's profile can be enhanced through the multimodal characteristics describing items [Malitesta et al., 2023]. Driven by these assumptions, in the first part of this thesis, we apply multimodal deep learning strategies for multimedia recommendation; the scope is to study and design recommendation algorithms based upon the principles of multimodality to possibly match each item's characteristic to the implicit preference expressed by the user [Deldjoo et al., 2022], thus addressing the (i) issue. Recent collaborative filtering approaches profile users and items through embedding vectors in the latent space. However, such models disregard structural properties naturally encoded into the user-item interaction data. Indeed, recommendation datasets are easily describable under the topology of a bipartite and undirected graph, with users and items being the graph nodes connected at multiple distance hops. In this respect, the application of graph neural networks , recent deep learning techniques specifically tailored to learn from non-euclidean data, can provide a refined representation of users and items to mine near- and long-distance relationships in the user-item graphs [Anelli et al., 2023b]. Indeed, this is one possible solution to exploit the collaborative information, which is effectively propagated within the user-item graph, addressing the (ii) issue. Conclusively, this thesis aims to match the two families of recommendation strategies by leveraging graph neural networks and multimodal information data [Anelli et al., 2022]. In doing so, other numerous micro-aspects within the two macro-areas (introduced above) are examined. Indeed, the thesis is a systematic compendium of careful analyses regarding, among others, reproducibility, novel evaluation dimensions [Anelli et al., 2023a], and tasks/scenarios complementary to recommendation. Awarded by : Politecnico di Bari, Bari, Italy on 30 January 2024. Supervised by : Tommaso Di Noia. Available at : https://hdl.handle.net/11589/264941.

Communication-efficient federated learning via personalized filter pruning

With the popularity of mobile devices and the continuous growth of interactive data, FL (Federated Learning) has gradually become an effective mean to address the problems of privacy leakage and data silos. However, due to the heterogeneity and imbalance of participants, FL faces many challenges, including model accuracy, security, heterogeneous devices and data, privacy preservation, as well as communication overhead and efficiency. To address challenges such as high communication overhead and low model accuracy in FL, we innovatively introduce model pruning into the FL framework and propose a personalized filter pruning-based FL method named PF2 Learning (Personalized Filter Pruning Federal Learning). This method achieves reasonable filter pruning for all local models by performing personalized pruning on each local model. Specifically, on the device side, we use a pruning strategy based on the norm geometric median, which also considers the order dependence between adjacent layers and evaluates the contribution of filters involved in pruning to optimize the pruning for local models at a unified pruning rate. On the server side, we calculate the unified pruning ratio of the model based on the contribution of participants' model filters and training errors to maintain the consistency of the participants' model structures. To validate the effectiveness of our proposed method, we conducted federated image classification tasks on ResNet-18, VGG-11, DenseNet-121, and InceptionNet-V1 models using CIFAR-10, FEMNIST, and ImageNet datasets. The experimental results show that PF2 Learning outperforms most FL pruning methods and exhibits better model performance and accuracy.

A novel method for predicting ecological interactions with an unsupervised machine learning algorithm

Abstract This gap in knowledge regarding ecological interactions has prompted the development of various predictive approaches. Traditionally, ecological interactions have been inferred using traits. However, the lack of trait information for numerous organisms necessitates using phylogenetic data and statistical insights from interaction matrices for prediction. Previous studies have overlooked the validation of model‐predicted interactions. This study used a novel method in predicting ecological interactions using a self‐organizing map (SOM), an unsupervised machine learning algorithm. The SOM learns from the input interaction matrix by grouping the nodes into output layers based on their interactions. Subsequently, the trained model predicts the interactions as scores. To distinguish between interactions and non‐interactions, we employed F1 score maximization, setting scores above a specific threshold as interactions and the remainder as non‐interactions. We applied this method to three unipartite metawebs and one bipartite metaweb and subsequently validated the predicted interactions using two innovative approaches: taxonomic and interaction recovery validation. Our method exhibited outstanding predictive performance, particularly for large networks. Various binary classification performance indicators, including F1 score (0.84–0.97) and accuracy (0.97–0.99), indicated high performance. Moreover, the method generated minimal predicted interactions, signifying low noise in the predictions, particularly for large networks. Taxonomic validation excels in metawebs with a connectance >0.1 but performs poorly in metawebs with very low connectance. In contrast, interaction recovery was most effective in larger metawebs. Our proposed method excels at making highly accurate predictions of ecological interactions with minimal noise, solely utilizing input interaction data without relying on traits or phylogenetic information regarding interacting nodes. These predictions are particularly precise for large networks, underscoring their potential to address knowledge gaps in emerging extensive metawebs. Notably, taxonomic validation and interaction recovery methods are sensitive to connectance and network size, respectively, suggesting prospects for developing robust interaction validation methods.

Peta Digital Kependudukan Berbasis QGIS: Inovasi Teknologi untuk Visualisasi Data kependudukan dan Populasi di Dusun Mrisi, Kabupaten Bantul, Daerah Istimewa Yogyakarta

Digital mapping has become a valuable tool for managing and visualizing geographical information. In this context, this community service aims to create a digital map using WebGIS in Mrisi Hamlet. Mrisi requires accurate and efficient mapping of population data to optimize data collection methods. This is to make decisions related to regional development, and map resources such as UMKM. This activity method involves collecting spatial and attribute data through field surveys, remote sensing, and relevant secondary data sources. The data obtained is then processed and compiled into a web-based geographical information system. In developing digital maps, WebGIS was chosen because it provides easy accessibility, interactive data visualization, and the ability to share geographical information with other users. This activity results in a digital map that includes various relevant information about Mrisi Hamlet. The map includes area boundaries, infrastructure, land use, public facilities location, and other related information. These digital maps are internet-based access and use decision-making by local governments, communities, and other related parties.

The perceptions of task cohesion in collaborative learning teams

Team cohesion is critical in driving successful outcomes for teams in collaborative learning settings. It shapes team behaviour, fostering shared perceptions, group synchrony and a common goal-oriented approach. This affinity becomes evident in dynamic interactions, offering insights into team behaviour through interaction data analysis. Interpreting interaction data proves complex, hampering our understanding and insights into shared team perceptions and task cohesion development. This paper used temporal motif analysis to examine the changes in team members’ cohesive perceptions and behaviours, including task cohesion, performance outcomes, engagement and group synchrony. Trace data from an online work-integrated learning environment captured learning behaviours, while responses to a questionnaire at different stages of a study program captured task cohesion and cohesive perceptions. The findings reveal teams with strong task cohesion and high performance tend to share similar cohesive perceptions driven by interdependent interactions. Conversely, teams with different cohesion perceptions have lower interaction interdependence and poorer performance. Through analysing team interaction data, this study uncovered key insights to promote positive adjustments aligning team perceptions, enhancing collaborative learning and offering support for improved performance, engagement and synchrony among teams, ultimately benefiting learning outcomes and the cultivation of skills and competencies.

Effective Leadership Achieves Organizational Goals in Indonesia

This study examines the crucial role of leadership in achieving organizational goals at the Food and Agriculture Office of Sidoarjo Regency. Using a qualitative descriptive method, data were collected through observation, interviews, and documentation, and analyzed using the Interactive Data Model by Miles, Huberman, and Saldana. Findings show that effective leadership—particularly directive, participative, and supportive styles—significantly contributes to achieving organizational goals by providing direction, guidance, and support, involving subordinates in decision-making, and recognizing their performance. This research highlights the importance of appropriate leadership practices in enhancing organizational performance. Highlight: Leadership styles boost organizational performance: directive, participative, supportive. Data collected through observation, interviews, documentation; analyzed interactively. Leaders guide, support, involve in decisions, recognize performance. Keywoard: Leadership, Organizational Goals, Qualitative Research, Sidoarjo Regency, Food and Agriculture Office

EFEKTIVITAS PENERAPAN SISTEM INFORMASI PEMERINTAHAN DAERAH (SIPD) DALAM PERENCANAAN PEMBANGUNAN DI KOTA TOMOHON

This research aims to analyze and determine the effectiveness of implementing the Regional Government Information System (SIPD) in development planning in Tomohon City. This research adopted a qualitative descriptive research method and was conducted at the Bappelitbangda office in Tomohon City, North Sulawesi. The data used in this research comes from primary and secondary data. The data analysis technique used interactive data analysis. The research results show the effectiveness of the Regional Government Information System (SIPD) application in development planning in Tomohon City, this application is considered innovative and able to increase the efficiency of planning preparation because it is electronic-based and integrated with the centre and regions. However, there are several obstacles that need to be taken into account, such as effectiveness indicators not yet being met due to frequently changing legal bases, lack of regular outreach and training for human resources, and limited infrastructure. Although this application is very helpful compared to manual methods, it still requires better adjustments and support in order to achieve optimal effectiveness according to Duncan's theory in Steers (2012).

CEGAH MALADMINISTRASI: Fungsi Pengawasan Ombudsman Sulawesi Tenggara di Pelabuhan Feri Kendari-Langara

The aim of this research is to determine the supervisory function of the Ombudsman of the Republic of Indonesia Representative of Southeast Sulawesi in preventing maladministration and improving services at the Kendari-Langara Ferry Port. Using qualitative descriptive research methods. The informants in this research were 2 Heads of Representatives and Prevention Assistants for the Republic of Indonesia Ombudsman for Southeast Sulawesi Province as well as the Head of the Operations Section and UPTD Levy Collection Officer Pel. There were 2 people on the Kendari-Langara Ferry and 2 people from the public/passengers who were determined using a purposive sampling technique. Data collection techniques through in-depth interviews and documentation studies. Interactive data analysis technique according to Milles Humerman and Saldana (2014). The results of the research show that the implementation of supervision by the Indonesian Ombudsman Representative for Southeast Sulawesi on services at the Kendari-Langara Ferry Port has gone well, however, the effectiveness of supervision of services carried out by the Kendari-Langara Ferry Port UPTD, Southeast Sulawesi Province is not good enough as seen from one of the Tangibles indicators. (touchable) has not been met, while the 4 indicators of Reliability, Responsiveness, Assurance and Empathy have been running well in providing services at the Kendari-Langara Ferry Port.

From petals to healing: consolidated network pharmacology and molecular docking investigations of the mechanisms underpinning Rhododendron arboreum flower's anti-NAFLD effects.

Rhododendron arboreum: Sm., also known as Burans is traditionally used as an anti-inflammatory, anti-diabetic, hepatoprotective, adaptogenic, and anti-oxidative agent. It has been used since ancient times in Indian traditional medicine for various liver disorders. However, the exact mechanism behind its activity against NAFLD is not known. The aim of the present study is to investigate the molecular mechanism of Rhododendron arboreum flower (RAF) in the treatment of NAFLD using network pharmacology and molecular docking methods. Bioactives were also predicted for their drug-likeness score, probable side effects and ADMET profile. Protein-protein interaction (PPI) data was obtained using the STRING platform. For the visualisation of GO analysis, a bioinformatics server was employed. Through molecular docking, the binding affinity between potential targets and active compounds were assessed. A total of five active compounds of RAF and 30 target proteins were selected. The targets with higher degrees were identified through the PPI network. GO analysis indicated that the NAFLD treatment with RAF primarily entails a response to the fatty acid biosynthetic process, lipid metabolic process, regulation of cell death, regulation of stress response, and cellular response to a chemical stimulus. Molecular docking and molecular dynamic simulation exhibited that rutin has best binding affinity among active compounds and selected targets as indicated by the binding energy, RMSD, and RMSF data. The findings comprehensively elucidated toxicity data, potential targets of bioactives and molecular mechanisms of RAF against NAFLD, providing a promising novel strategy for future research on NAFLD treatment.

Subjective Norms of Health Students Related to Parent's Social Control on Intention to Stop Smoking

Smoking among students is a significant global issue. Health students, expected to be knowledgeable about smoking dangers and serve as role models, are found to engage in smoking behavior, which is concerning. This study aimed to analyze health students' perceptions regarding parental social control and their intention to quit smoking using the Theory of Planned Behaviour, specifically focusing on the role of subjective norms. The study used a descriptive qualitative approach with a phenomenological methodology involving in-depth interviews and observations. The research was conducted in Jember Regency with five primary informants, health students at the University of Jember, and key informants who were close friends and parents of primary informants. Data processing used the interactive data analysis model proposed by Miles and Huberman, involving three steps: data reduction, data presentation, and conclusion drawing. The study found that health students who smoke positively perceived their parents' opinions and social support regarding smoking cessation behavior. They viewed their parents' efforts to prevent them from smoking as beneficial and effective in influencing their behavior. health students who smoke acknowledged that the social control exerted by their parents was crucial in helping them overcome their smoking habits. Most health students who smoke expressed a strong intention to quit smoking, indicating a willingness to change their behavior in response to the support they received from their parents. The study suggests that parents should set a good example and communicate effectively to influence adolescent smoking behavior. Health students and health offices can also support this effort through smoking cessation activities and education.

Unearthing Heritage: Empowering High School History Teachers to Illuminate Local Narratives

Purpose of the study: This research aims to discuss the role of history teachers in local history learning at State 3 Palu high school, the development of local history learning at State 3 Palu high school, and the role of history teachers in local history learning. Methodology:This research is descriptive qualitative research. The sample in this research was high school history teachers with a sampling technique using purposive sampling. Data collection techniques use interviews, observation and documentation. The analysis was carried out using interactive model data analysis. Main Findings: The research results show that in the history learning process, history teachers have carried out their role well. History teachers try to build students' historical awareness by teaching local history of Central Sulawesi and visiting historical sites in the environment around students. Local history is integrated into history subjects so that in implementing learning, history teachers adjust learning plans to the basic competencies specified in curriculum. Adding historical material with local history, especially by utilizing historical sources that are close to the students' environment. History teachers are one of the important elements who must play an active role in developing local history learning. Novelty/Originality of this study: This research provides valuable insight into the role of history teachers in the context of local history learning, with a focus on integration, local resources, and the development of students' historical awareness.