Interaction Datasets Research Articles

HM-SYNC: A Multimodal Dataset of Human Interactions with Advanced Manufacturing Machinery

Abstract A deep understanding of manufacturing processes is essential for advancing manufacturing-oriented design and engineering complex systems. As advanced manufacturing technologies evolve, systems have grown more complex, and human interaction has become a vital component of both operation and design. This shift introduces new challenges, as human roles within these systems extend beyond traditional boundaries and are not yet fully understood in current design processes. Characterizing human interactions within manufacturing systems is therefore critical to supporting further advancements. Additionally, human behavior plays a significant role in many engineered systems beyond manufacturing, underscoring the value of developing methodologies to better analyze human behavior and interactions within complex environments. These methodologies can broadly support and enhance diverse aspects of engineering design. This study presents HM-SYNC, which is a comprehensive dataset of human interactions with advanced manufacturing machinery, specifically a wire-arc additive manufacturing machine. Depth images and 3D skeleton joints are collected over six months using privacy-preserving pose tracking with depth cameras. HM-SYNC includes thousands of interactions across various contexts, goals, and users, providing valuable insights into patterns of human-machine interaction. By capturing a diverse range of interactions in natural settings, this dataset supports advancements in human-centered manufacturing design and facilitates the development of more effective manufacturing systems. This dataset can enhance models and digital twins of manufacturing systems, help operators optimize machinery use and efficiency, and guide designers in refining machine and system design, to name just a few applications.

A global dataset of freshwater fish trophic interactions

Freshwater management and research frequently rely on trophic data to manage freshwater fishes, yet it is difficult to perform a simple search of dietary information for any one species. FishBase represents the largest effort to organize freshwater dietary data into a singular, navigable dataset. Nonetheless, FishBase excludes a large portion of the ecological literature because it was developed before the creation of most modern scientific search engines. Our project, TroPhish, builds upon FishBase by digitizing over 100 years of data from the fish predation literature. Data from 1,106 published papers, theses, dissertations, and government reports were filtered, scanned in through third-party software (Able2Extract), reorganized, and consolidated with FishBase to form a unified dataset. This dataset contains 54,750 observations of data on 4,571 unique dietary samples, representing 9% (982) of all freshwater fish species and 43% (111) of all freshwater fish families. Fish species and family representation varied by continent, ranging from 3–32% and 34–75%, respectively. Users are encouraged to submit errors or additional data through GitHub’s fork and pull model.

Digital channel-enabled distributed force decoding via small datasets for hand-centric interactions.

Tactile interfaces are essential for enhancing human-machine interactions, yet achieving large-scale, precise distributed force sensing remains challenging due to signal coupling and inefficient data processing. Inspired by the spiral structure of Aloe polyphylla and the processing principles of neuronal systems, this study presents a digital channel-enabled distributed force decoding strategy, resulting in a phygital tactile sensing system named PhyTac. This innovative system effectively prevents marker overlap and accurately identifies multipoint stimuli up to 368 regions from coupled signals. By integrating physics into model training, we reduce the dataset size to just 45 kilobytes, surpassing conventional methods that typically exceed 1 gigabyte. Results demonstrate PhyTac's impressive fidelity of 97.7% across a sensing range of 0.5 to 25 newtons, enabling diverse applications in medical evaluation, sports training, virtual reality, and robotics. This research not only enhances our understanding of hand-centric actions but also highlights the convergence of physical and digital realms, paving the way for advancements in AI-based sensor technologies.

Single Cell-Pair Proteomics for Decoding Immune-Cancer Cell Interactions.

The efficacy of cancer immunotherapy is significantly influenced by the heterogeneity of individual tumors and immune responses. To investigate this phenomenon, a microfluidic platform is constructed for profiling immune-cancer cell interactions at the single-cell proteomics level for the first time. Based on the platform, a comprehensive workflow is proposed for achieving accurate single-cell pairing of an immune cell and a cancer cell with low cell damage and high success rate up to 95%, cell pair co-culture, and real-time microscopic monitoring of the cell-pair interactions, cell pair retrieval, mass spectrometry-based proteomic analysis of singe cell pairs, and decoupling of the proteomic information for each cell within the cell pair with the stable-isotope labeling method. With the workflow, the interactions of single natural killer (NK) cells and single K562 tumor cells are investigated based on real-time images and single cell-pair proteomics. Notably, an identification depth of over 1000 protein groups in a single cell-pair is achieved, leading to the discovery of sub-clusters of NK cells with different functions and the identification of important biomarkers for cancer treatments. This demonstrates the unique capability of the present platform in providing substantial and comprehensive datasets for profiling immune-cancer cell interactions, discovering heterogeneous immune responses, and predicting biomarkers in the study of cancer immunotherapy.

A Probabilistic View of Forbidden Links: Their Prevalence and Their Consequences for the Robustness of Plant-Hummingbird Communities.

The presence in ecological communities of unfeasible species interactions, termed forbidden links, due to physiological or morphological exploitation barriers has been long debated, but little direct evidence has been found. Forbidden links are likely to make ecological communities less robust to species extinctions, stressing the need to assess their prevalence. Here, we used a dataset of plant-hummingbird interactions, coupled with a Bayesian hierarchical model, to assess the importance of exploitation barriers in determining species interactions. We found evidence for exploitation barriers between flowers and hummingbirds across the 32 studied communities; however, the proportion of forbidden links changed drastically among communities because of changes in trait distributions. The higher the proportion of forbidden links, the more they decreased network robustness because of constraints on interaction rewiring. Our results suggest that exploitation barriers are not rare in plant-hummingbird communities and have the potential to limit the rescue of species experiencing partner extinction.

Key concepts and a world-wide look at plant recruitment networks.

Plant-plant interactions are major determinants of the dynamics of terrestrial ecosystems. There is a long tradition in the study of these interactions, their mechanisms and their consequences using experimental, observational and theoretical approaches. Empirical studies overwhelmingly focus at the level of species pairs or small sets of species. Although empirical data on these interactions at the community level are scarce, such studies have gained pace in the last decade. Studying plant-plant interactions at the community level requires knowledge of which species interact with which others, so an ecological networks approach must be incorporated into the basic toolbox of plant community ecology. The concept of recruitment networks (RNs) provides an integrative framework and new insights for many topics in the field of plant community ecology. RNs synthesise the set of canopy-recruit interactions in a local plant assemblage. Canopy-recruit interactions describe which ("canopy") species allow the recruitment of other species in their vicinity and how. Here we critically review basic concepts of ecological network theory as they apply to RNs. We use RecruitNet, a recently published worldwide data set of canopy-recruit interactions, to describe RN patterns emerging at the interaction, species, and community levels, and relate them to different abiotic gradients. Our results show that RNs can be sampled with high accuracy. The studies included in RecruitNet show a very high mean network completeness (95%), indicating that undetected canopy-recruit pairs must be few and occur very infrequently. Across 351,064 canopy-recruit pairs analysed, the effect of the interaction on recruitment was neutral in an average of 69% of the interactions per community, but the remaining interactions were positive (i.e. facilitative) five times more often than negative (i.e. competitive), and positive interactions had twice the strength of negative ones. Moreover, the frequency and strength of facilitation increases along a climatic aridity gradient worldwide, so the demography of plant communities is increasingly strongly dependent on facilitation as aridity increases. At network level, species can be ascribed to four functional types depending on their position in the network: core, satellite, strict transients and disturbance-dependent transients. This functional structure can allow a rough estimation of which species are more likely to persist. In RecruitNet communities, this functional structure most often departs from random null model expectation and could allow on average the persistence of 77% of the species in a local community. The functional structure of RNs also varies along the aridity gradient, but differently in shrubland than in forest communities. This variation suggests an increase in the probability of species persistence with aridity in forests, while such probability remains roughly constant along the gradient in shrublands. The different functional structure of RNs between forests and shrublands could contribute to explaining their co-occurrence as alternative stable states of the vegetation under the same climatic conditions. This review is not exhaustive of all the topics that can be addressed using the framework of RNs, but instead aims to present some of the interesting insights that it can bring to the field of plant community ecology.

The Role of Data Science in Enhancing Web Security

With the rise of digital transformation, web security has become a critical concern for organizations, governments, and individuals. This study explores the role of data science in enhancing web security by leveraging machine learning algorithms and advanced analytics to predict and identify potential attacks in real-time. The main objective is to demonstrate how data-driven techniques, including predictive analytics, anomaly detection, and behavioral analysis, can be integrated into existing security frameworks to reduce vulnerabilities and strengthen defenses against cyber threats. The research gap addressed by this study lies in the insufficient application of comprehensive, data-driven methodologies for threat detection and classification in web security. The problem gap is the absence of integrated frameworks that combine feature engineering, classification models, and anomaly detection for both known and unknown threats. This study bridges these gaps by employing a structured dataset of web interactions to model, detect, and predict security threats using advanced data science techniques. Using a dataset of simulated web traffic and previous attack records, this research applies data preprocessing, feature engineering, and machine learning classification models, such as decision trees and random forests, to predict threat levels and identify anomalies. Results show that machine learning models can effectively classify threat levels, with a threat classification accuracy of 80 percent. This study contributes to the field by demonstrating how data science can improve web security practices, offering a proactive approach to detecting and mitigating cyber-attacks.

Coordinate-Corrected and Graph-Convolution-Based Hand Pose Estimation Method.

To address the problem of low accuracy in joint point estimation in hand pose estimation methods due to the self-similarity of fingers and easy self-obscuration of hand joints, a hand pose estimation method based on coordinate correction and graph convolution is proposed. First, the standard coordinate encoding is improved by generating an unbiased heat map, and the distribution-aware method is used for decoding coordinates to reduce the error in decoding the coordinate encoding of joints. Then, the complex dependency relationship between the joints and the relationship between pixels and joints of the hand are modeled by using graph convolution, and the feature information of the hand joints is enhanced by determining the relationship between the hand joints. Finally, the skeletal constraint loss function is used to impose constraints on the joints, and a natural and undistorted hand skeleton structure is generated. Training tests are conducted on the public gesture interaction dataset STB, and the experimental results show that the method in this paper can reduce errors in hand joint point detection and improve the estimation accuracy.

Prediction of antibody-antigen interaction based on backbone aware with invariant point attention.

Antibodies play a crucial role in disease treatment, leveraging their ability to selectively interact with the specific antigen. However, screening antibody gene sequences for target antigens via biological experiments is extremely time-consuming and labor-intensive. Several computational methods have been developed to predict antibody-antigen interaction while suffering from the lack of characterizing the underlying structure of the antibody. Beneficial from the recent breakthroughs in deep learning for antibody structure prediction, we propose a novel neural network architecture to predict antibody-antigen interaction. We first introduce AbAgIPA: an antibody structure prediction network to obtain the antibody backbone structure, where the structural features of antibodies and antigens are encoded into representation vectors according to the amino acid physicochemical features and Invariant Point Attention (IPA) computation methods. Finally, the antibody-antigen interaction is predicted by global max pooling, feature concatenation, and a fully connected layer. We evaluated our method on antigen diversity and antigen-specific antibody-antigen interaction datasets. Additionally, our model exhibits a commendable level of interpretability, essential for understanding underlying interaction mechanisms. Quantitative experimental results demonstrate that the new neural network architecture significantly outperforms the best sequence-based methods as well as the methods based on residue contact maps and graph convolution networks (GCNs). The source code is freely available on GitHub at https://github.com/gmthu66/AbAgIPA .

"I'm in the Bluesky Tonight": Insights from a year worth of social data.

Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. We present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social to address this pressing issue. The dataset contains the complete post history of over 4M users (81% of all registered accounts), totalling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions. Since Bluesky allows users to create and like feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped "like" interactions. This dataset allows novel analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection and performing content virality and diffusion analysis.

The JIBO Kids Corpus: A speech dataset of child-robot interactions in a classroom environment.

This paper describes an original dataset of children's speech, collected through the use of JIBO, a social robot. The dataset encompasses recordings from 110 children, aged 4-7 years old, who participated in a letter and digit identification task and extended oral discourse tasks requiring explanation skills, totaling 21 h of session data. Spanning a 2-year collection period, this dataset contains a longitudinal component with a subset of participants returning for repeat recordings. The dataset, with session recordings and transcriptions, is publicly available, providing researchers with a valuable resource to advance investigations into child language development.

Prediction of strain level phage-host interactions across the Escherichia genus using only genomic information.

Predicting bacteriophage infection of specific bacterial strains promises advancements in phage therapy and microbial ecology. Whether the dynamics of well-established phage-host model systems generalize to the wide diversity of microbes is currently unknown. Here we show that we could accurately predict the outcomes of phage-bacteria interactions at the strain level in natural isolates from the genus Escherichia using only genomic data (area under the receiver operating characteristic curve (AUROC) of 86%). We experimentally established a dataset of interactions between 403 diverse Escherichia strains and 96 phages. Most interactions are explained by adsorption factors as opposed to antiphage systems which play a marginal role. We trained predictive algorithms and pinpoint poorly predicted interactions to direct future research efforts. Finally, we established a pipeline to recommend tailored phage cocktails, demonstrating efficiency on 100 pathogenic E. coli isolates. This work provides quantitative insights into phage-host specificity and supports the use of predictive algorithms in phage therapy.

“Saying ‘thank you’ or something more than lip service”: a variational analysis of the influence of gender and social status on responses to congratulations on Chinese WeChat

Abstract Existing studies on responses to congratulations to date have displayed a general culturally-bound finding that Chinese speakers prefer to take a detour approach. However, the effect of social constraints on response strategies have not received the attention it deserves. Drawing on a variational pragmatic approach, this study explores the constraints of gender and social status on the pragmatic variation of Chinese WeChat users’ responses to congratulations. Building upon an integrative data set of congratulatory interactions in actual online messages, the findings suggest that gender and social status overlap in complex ways, but with a clear tendency: both men and women significantly tend towards an acceptance strategy when responding to congratulations on WeChat, while social status influences the sub-strategies used in accepting congratulations across both male and female groups. The findings challenge previous stereotypes about Chinese language variation in responses to congratulations. The current results suggest that Chinese speakers have already made a transition from following the Modesty Maxim to using the Agreement Maxim, and have also incorporated the Obligation Maxim. Chinese speakers also consider rapport and reciprocity when responding to kind congratulatory remarks.

Co-Occurrence Patterns Do Not Predict Mutualistic Interactions Between Plant and Butterfly Species.

Biotic interactions are crucial for determining the structure and dynamics of communities; however, direct measurement of these interactions can be challenging in terms of time and resources, especially when numerous species are involved. Inferring species interactions from species co-occurrence patterns is increasingly being used; however, recent studies have highlighted some limitations. To our knowledge, no attempt has been made to test the accuracy of the existing methods for detecting mutualistic interactions in terrestrial ecosystems. In this study, we compiled two literature-based, long-term datasets of interactions between butterflies and herbaceous plant species in two regions of Germany and compared them with observational abundance and presence/absence data collected within a year in the same regions. We tested how well the species associations generated by three different co-occurrence analysis methods matched those of empirically measured mutualistic associations using sensitivity and specificity analyses and compared the strength of associations. We also checked whether flower abundance data (instead of plant abundance data) increased the accuracy of the co-occurrence models and validated our results using empirical flower visitation data. The results revealed that, although all methods exhibited low sensitivity, our implementation of the Relative Interaction Intensity index with pairwise null models performed the best, followed by the probabilistic method and Spearman's rank correlation method. However, empirical data showed a significant number of interactions that were not detected using co-occurrence methods. Incorporating flower abundance data did not improve sensitivity but enhanced specificity in one region. Further analysis demonstrated incongruence between the predicted co-occurrence associations and actual interaction strengths, with many pairs exhibiting high interaction strength but low co-occurrence or vice versa. These findings underscore the complexity of ecological dynamics and highlight the limitations of current co-occurrence methods for accurately capturing species interactions.

-RAMP3 promotes hepatocellular carcinoma tumor cell-mediated CCL2 degradation by supporting membrane distribution of ACKR2

This study aimed to explore the potential bind of Receptor Activity-Modifying Protein 3 (RAMP3) with atypical chemokine receptor 2 (ACKR2), and their cooperative regulation on the degradation of the immunosuppressive chemokine CCL2 in the tumor microenvironment of HCC. Bioinformatic analysis was conducted using available bulk-tissue RNA-seq, single-cell RNA-seq, and protein–protein interaction datasets. Human HCC cell line Huh7 and HepG2 and mouse HCC cell line Hepa1-6 were utilized for experiments. Results showed that RAMP3 binds with ACKR2 in HCC tumor cells and promotes the membrane distribution of ACKR2 through RAB4-positive vesicles. RAMP3 promotes CCL2 scavenging through ACKR2 in HCC cells. Mouse RAMP3 inhibited the proliferation of mouse liver cancer cell line (Hepa1-6)-derived syngeneic tumors through ACKR2, reduced the intratumoral concentration of CCL2 in the tumor, and inhibited the phosphorylation of Signal Transducer and Activator of Transcription 3 (STAT3) and protein kinase B (AKT). In addition, mouse RAMP3 inhibited CD11b+/Gr-1 + myeloid cell infiltration and neovascularization in the tumors through ACKR2. In TCGA-LIHC, RAMP3low/ACKR2low group had the worst progression-free interval (PFI), while the RAMP3high/ACKR2high group had the best overall survival (OS). In summary, restoring RAMP3 expression in HCC cells may generate synergistic support for the anticancer effect of ACKR2.

Impact of genotype × environment interaction and selection history on genomic prediction in maize (Zea mays L.)

AbstractBreeders made remarkable progress in improving productivity and stability of cultivars. Breeding progress relies on selecting favorable alleles for performance and stability to produce productive varieties across diverse environments. In this study, we analyzed the Genomes to Fields Initiative 2018–2019 genotype by environment interaction (G × E) dataset, focusing on three populations of double haploid (DH) lines derived from crossing inbrexpired Plant Variety Protection (ex‐PVP) inbred line PHW65 with inbred lines PHN11, Mo44, and MoG. PHW65 is an Iodent/Lancaster‐type inbred; PHN11 is an Iodent type ex‐PVP line; Mo44 is a tropical‐derived inbred; and MoG is an agronomically poor line derived from the variety Mastadon. Hybrids were produced by crossing the resulting DHs with Stiff Stalk testers PHT69 and LH195. The study's objective was to determine the donor inbreds' relative value and understand the impact of selection history on genomic prediction. We conducted a two‐stage analysis to compare hybrid performance and G × E variance of the populations. G × E variance for yield was significantly lower in the PHW65 × PHN11 population relative to the PHW65 × MoG population. The reduced G × E variance of the PHN11 population led to increased indirect prediction accuracy (when training and testing data are drawn from the same population but different environments). In cross‐validation, the PHN11 population had the greatest indirect prediction accuracy 45% of the time, followed by the Mo44 population (30%) and the MoG population (25%). Results demonstrate that prediction accuracy was greater in the population with the longest history of selection for favorable alleles (PHN11), contributing to greater yield stability.

Predicting protein complexes in protein interaction networks using Mapper and graph convolution networks

Protein complexes are groups of interacting proteins that are central to multiple biological processes. Studying protein complexes can enhance our understanding of cellular functions and malfunctions and thus support the development of effective disease treatments. High-throughput experimental techniques allow the generation of large-scale protein-protein interaction datasets. Accordingly, various computational approaches to predict protein complexes from protein-protein interactions were presented in the literature. They are typically based on networks in which nodes and edges represent proteins and their interactions, respectively. State-of-the-art approaches mainly rely on clustering static networks to identify complexes. However, since protein interactions are highly dynamic in nature, recent approaches seek to model such dynamics by typically integrating gene expression data and identifying protein complexes accordingly. We propose MComplex, a method that utilizes time-series gene expression with interaction data to generate a temporal network which is passed to a generative adversarial network whose generator is a graph convolutional network. This creates embeddings which are then analyzed using a modified graph-based version of the Mapper algorithm to predict corresponding protein complexes. We test our approach on multiple benchmark datasets and compare identified complexes against gold-standard protein complex datasets. Our results show that MComplex outperforms existing methods in several evaluation aspects, namely recall and maximum matching ratio as well as a composite score covering aggregated evaluation measures. The code and data are available for free download from https://github.com/LeonardoDaou/MComplex.

PB-Trajectron: Physics bounded neural network for generalized trajectory prediction

Vehicle trajectory prediction is a critical technology in autonomous driving systems, as the quality of prediction directly affects downstream path planning and vehicle control. Although recent studies have shown that models combining kinematic rules with data-driven methods exhibit better interpretability in trajectory prediction, these models often adopt fixed constraint strengths, limiting their generalization ability in diverse traffic scenarios. This fixed constraint strength design restricts the model’s adaptability to different traffic environments.To address this issue, we propose PB-Trajectron, a dynamic integration mechanism that enhances the model’s prediction performance and generalization capability. PB-Trajectron is a dynamic integration mechanism that combines vehicle kinematic rules with deep learning prediction models for generalized vehicle trajectory prediction. The model integrates physics-based motion models and Deep Neural Networks (DNNs) to provide reasonable physical explanations for predicting vehicle trajectories at different speeds. First, we establish two vehicle kinematic constraints applicable to different prediction scenarios, enabling the agent to switch between these constraints based on the causal relationships between vehicle motion states to avoid generating non-physical trajectory results. Second, we propose a kinematic constraint decision framework based on velocity thresholds, which demonstrates adaptive adjustment, allowing the agent to switch tasks according to real-time state conditions and adaptively adjust the contribution of different physical constraints based on actual vehicle speeds. Finally, we investigate the advantages of PB-Trajectron in Out-of-Domain(OOD) generalization.Cross-scenario experiments on the nuScenes dataset show that, compared to previous methods, PB-Trajectron reduces the final displacement error by 7.69% when the prediction step length is 4. Furthermore, out-of-domain generalization tests on the INTERACTION dataset demonstrate that PB-Trajectron achieves a 12.23% reduction in average prediction error on datasets from different countries and scenarios compared to previous methods. The proposed mechanism can better adapt to complex and diverse traffic scenarios, laying the foundation for explainable and robust autonomous driving systems.

Click to Correction: Interactive Bidirectional Dynamic Propagation Video Object Segmentation Network.

High-quality video object segmentation is a challenging visual computing task. Interactive segmentation can improve segmentation results. This paper proposes a multi-round interactive dynamic propagation instance-level video object segmentation network based on click interaction. The network consists of two parts: a user interaction segmentation module and a bidirectional dynamic propagation module. A prior segmentation network was designed in the user interaction segmentation module to better segment objects of different scales that users click on. The dynamic propagation network achieves high-precision video object segmentation through the bidirectional propagation and fusion of segmentation masks obtained from multiple rounds of interaction. Experiments on interactive segmentation datasets and video object segmentation datasets show that our method achieves state-of-the-art segmentation results with fewer click interactions.

Research on Digital Media Algorithm Recommendation based on Support Vector Machine

Within digital media, the effectiveness of content material advice systems is pivotal for boosting user engagement and satisfaction. This study’s article delves into the development and implementation of a singular set of rules recommendation gadgets based totally on the principles of support Vector device (SVM), a distinguished machine learning approach. The objective is to address the demanding situations faced by using traditional recommendation structures, such as media content problems, by leveraging the type and regression talents of SVM. The methodology encompasses the usage of a large dataset of person interactions and alternatives extracted from diverse virtual media systems. This statistic is then processed via an SVM version and relationships among user behaviors and content material characteristics. The particular issue of this technique lies in its adaptability and precision in dealing with excessive-dimensional facts, which is ordinary in digital media environments. The SVM model is high-quality-tuned to optimize content recommendation via not most effective matching consumer choices but additionally introducing a degree of content material variety to combat echo chambers. This research evaluates the performance of the SVMbased recommendation system towards traditional algorithms via a sequence of metrics inclusive of accuracy, range, and consumer engagement charges. This assessment gives insights into the efficacy of SVM in delivering extra applicable and various content to users, thereby enhancing their digital media experience.