Static Information Research Articles

Designing outdoor emergency rescue stations based on the spatiotemporal behavior of outdoor adventure tourists using GPS trajectory data

While outdoor travel has become an increasingly popular phenomenon, its high accident rate remains a significant concern. It is important to reasonably design emergency rescue stations based on the behavioral characteristics of outdoor adventure tourists. However, previous studies have mainly relied on static population information, which cannot capture adventure tourists’ spatiotemporal behaviors. To overcome this disadvantage, we obtained the GPS trajectory data of adventure tourists in Shenzhen from the “2bulu” platform, covering three types of travel groups: hiking, cycling, and driving. The seasonal characteristics, intra-day activities, and trajectory distributions of outdoor adventure tourists were analyzed. On this basis, we used a genetic algorithm to determine the optimal locations of rescue stations, taking into account both socioeconomic and natural conditions. The results show that outdoor adventure tourists have seasonal preferences, with the majority traveling during the summer and being most active between 5 and 9 a.m. The travel trajectories of these tourists exhibited a linear distribution pattern with several hotspots. In addition, the optimal rescue stations are mainly distributed at the junctions of districts, which can cover most outdoor scenic spots in Shenzhen. These findings could provide decision support for ensuring the safety of outdoor adventures.

A point contextual transformer network for point cloud completion

Point cloud completion is an essential task for recovering a complete point cloud from its partial observation to support downstream applications, such as object detection and reconstruction. Existing point cloud completion networks primarily rely on large-scale datasets to learn the mapping between the partial shapes and the complete shapes. They often adopt a multi-stage strategy to progressively generate complete point clouds with finer details. However, underutilization of shape priors and complex modelling frameworks still plague these networks. To address these issues, we innovatively propose a point contextual transformer (PCoT) for point cloud completion (PCoT-Net). We design the PCoT to adaptively fuse static and dynamic point contextual information. This allows for the effective capture of fine-grained local contextual features. We then propose a one-stage network with a feature completion module to directly generate credible and detailed complete point cloud results. Furthermore, we incorporate External Attention (EA) into the feature completion module, which is lightweight and further improves the performance of learning complete features and reconstructing the complete point cloud. Extensive experiments on various datasets validate the effectiveness of our PCoT-based approach and the EA-enhanced feature completion module, which achieves superior quantitative performance in Chamfer Distance (CD) and F1-Score. In comparison to PMP-Net++ (Wen et al., 2022), our method improves the F1-Score by 0.010, 0.022, and 0.026, and reduces the CD by 0.16, 0.95, and 1.74 on the MVP, CRN, and ScanNet datasets, respectively, while providing visually superior results, capturing more fine-grained details and producing smoother reconstructed surfaces.

The Semantics of COVID-19 Web Data: Ontology Learning and Population

Background: The acquisition and exchange of meaningful, integrated, and accurate information are at the forefront of the combat against COVID-19; still, there are many countries whose health systems are disrupted. Moreover, no one is adequately equipped for COVID-19 contingencies. Many organizations have established static information systems to manage the information. This fact presents numerous issues, including delays, inconsistencies, and inaccuracies in COVID-19 information collected for pandemic control and monitoring. Objective: This paper presents a semantic representation of COVID-19 data, a domain ontology to facilitate measurement, clarification, linking, and sharing. We automatically generate a computer- intelligible knowledge base from COVID-19 case information, which contains machineunderstandable information. Furthermore, we have anticipated an ontology population algorithm from tabular data that delivers interoperable, consistent, and accurate content with COVID-19 information. Methods: We utilized the tabula package to extract the tables from PDF files and user NLP libraries to sort and rearrange tables. The proposed algorithm was then applied to all instances to automatically add to the input ontology using the Owlready Python module. Moreover, to evaluate the performance, SPARQL queries were used to retrieve answers to competency questions. Results: When there is an equivalence relationship, the suggested algorithm consistently finds the right alignments and performs at its best or very close to it in terms of precision. Moreover, a demonstration of algorithm performance and a case study on COVID-19 data to information management and visualization of the populated data are also presented. Conclusion: This paper presents an ontology learning/matching tool for ontology and populating instances automatically to ontology by emphasizing the importance of a unit's distinguishing features by unit matching.

Stabilizing translucencies: Governing AI transparency by standardization

Standards are put forward as important means to turn the ideals of ethical and responsible artificial intelligence into practice. One principle targeted for standardization is transparency. This article attends to the tension between standardization and transparency, by combining a theoretical exploration of these concepts with an empirical analysis of standardizations of artificial intelligence transparency. Conceptually, standards are underpinned by goals of stability and solidification, while transparency is considered a flexible see-through quality. In addition, artificial intelligence-technologies are depicted as ‘black boxed’, complex and in flux. Transparency as a solution for ethical artificial intelligence has, however, been problematized. In the empirical sample of standardizations, transparency is largely presented as a static, measurable, and straightforward information transfer, or as a window to artificial intelligence use. The standards are furthermore described as pioneering and able to shape technological futures, while their similarities suggest that artificial intelligence translucencies are already stabilizing into similar arrangements. To rely heavily upon standardization to govern artificial intelligence transparency still risks allocating rule-making to non-democratic processes, and while intended to bring clarity, the standardizations could also create new distributions of uncertainty and accountability. This article stresses the complexity of governing sociotechnical artificial intelligence principles by standardization. Overall, there is a risk that the governance of artificial intelligence is let to be too shaped by technological solutionism, allowing the standardization of social values (or even human rights) to be carried out in the same manner as that of any other technical product or procedure.

Adaptable automation for a more human-centered work design? Effects on human perception and behavior

This experiment systematically examines whether, in safety-critical environments such as Air Traffic Control, the negative effects of increasing automation associated with static automation concepts can be mitigated by adaptable automation. Adaptable automation is a form of flexible automation in which the human operator (rather than the system as in adaptive automation) can decide when and to what extent to delegate tasks. A special focus is on its effects on human perception in terms of perceived autonomy and competence, satisfaction, and human role perception. We conducted two online studies using the same dual-task paradigm. Study 1 was conducted with a novice sample via Prolific (N = 93) and study 2 with an expert sample of Air Traffic Controllers (N = 126). Participants were either supported by static information automation, static decision automation, or an adaptable solution that allowed them to switch between the two automation stages. The findings of both studies are similar. Results indicated that, even when humans rarely switched, adaptable automation could increase perceived autonomy, led to high satisfaction, and had positive effects on role perceptions without impairing performance or workload. Furthermore, satisfaction was found to correlate with performance. From a human-centered perspective, flexible concepts seem to be particularly suitable when automation increasingly takes over parts of a job task not only at the stage of information analysis but also at the stage of decision-making.

Malware2ATT&CK: A sophisticated model for mapping malware to ATT&CK techniques

MITER Corporation presents the ATT&CK Matrix, which maps malware behavior to different tactics, techniques, and procedures (TTPs) providing a comprehensive view to clarify the inner mechanism of malware more accurately. However, manual mapping methods are time-consuming, while rule-based feature-based mapping methods often under- or misreport many attacks. Inspired by the successful application of image multi-label classification techniques, we propose a method called Malware2ATT&CK to automatically map malware to ATT&CK techniques. The method applies pre-trained models to extract features from the two pieces of information — the static analysis information of assembly instruction and API calls from malware. The malicious techniques are identified by a multi-label classifier based on the graph neural network and knowledge graph. In the experiments over two test sets, Malware2ATT&CK shows excellent performance achieving an average F1 score of 83.6% in the technology mapping task. Further evaluation indicates that the high accuracy of the prediction is due to our ability to accurately capture the correlation between behaviors.

An approach for traffic pattern recognition integration of ship AIS data and port geospatial features

ABSTRACT Recognition of ship traffic patterns can provide insights into the rules of navigation, maneuvering, and collision avoidance for ships at sea. This is essential for ensuring safe navigation at sea and improving navigational efficiency. With the popularization of the Automatic Identification System (AIS), numerous studies utilized ship trajectories to identify maritime traffic patterns. However, the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior, such as ship static and maritime geospatial features, resulting in insufficient precision in ship traffic pattern recognition. This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity (STPMTS), which considers ship static feature, dynamic feature, port geospatial feature, as well as semantic relationships between these features. First, A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements. Subsequently, to quantify the degree of similarity of ship trajectories, a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features. Furthermore, trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters. The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns. Finally, the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China, ranging from September to November 2016. Compared with other methods, the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features, dynamic features, and adherence to navigation rules. This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives.

A Lightweight Context-Aware Feature Transformer Network for Human Pose Estimation

We propose a Context-aware Feature Transformer Network (CaFTNet), a novel network for human pose estimation. To address the issue of limited modeling of global dependencies in convolutional neural networks, we design the Transformerneck to strengthen the expressive power of features. Transformerneck directly substitutes 3×3 convolution in the bottleneck of HRNet with a Contextual Transformer (CoT) block while reducing the complexity of the network. Specifically, the CoT first produces keys with static contextual information through 3×3 convolution. Then, relying on query and contextualization keys, dynamic contexts are generated through two concatenated 1×1 convolutions. Static and dynamic contexts are eventually fused as an output. Additionally, for multi-scale networks, in order to further refine the features of the fusion output, we propose an Attention Feature Aggregation Module (AFAM). Technically, given an intermediate input, the AFAM successively deduces attention maps along the channel and spatial dimensions. Then, an adaptive refinement module (ARM) is exploited to activate the obtained attention maps. Finally, the input undergoes adaptive feature refinement through multiplication with the activated attention maps. Through the above procedures, our lightweight network provides powerful clues for the detection of keypoints. Experiments are performed on the COCO and MPII datasets. The model achieves a 76.2 AP on the COCO val2017 dataset. Compared to other methods with a CNN as the backbone, CaFTNet has a 72.9% reduced number of parameters. On the MPII dataset, our method uses only 60.7% of the number of parameters, acquiring similar results to other methods with a CNN as the backbone.

On the challenges of global entity-aware deep learning models for groundwater level prediction

Abstract. The application of machine learning (ML) including deep learning models in hydrogeology to model and predict groundwater level in monitoring wells has gained some traction in recent years. Currently, the dominant model class is the so-called single-well model, where one model is trained for each well separately. However, recent developments in neighbouring disciplines including hydrology (rainfall–runoff modelling) have shown that global models, being able to incorporate data of several wells, may have advantages. These models are often called “entity-aware models“, as they usually rely on static data to differentiate the entities, i.e. groundwater wells in hydrogeology or catchments in surface hydrology. We test two kinds of static information to characterize the groundwater wells in a global, entity-aware deep learning model set-up: first, environmental features that are continuously available and thus theoretically enable spatial generalization (regionalization), and second, time-series features that are derived from the past time series at the respective well. Moreover, we test random integer features as entity information for comparison. We use a published dataset of 108 groundwater wells in Germany, and evaluate the performance of the models in terms of Nash–Sutcliffe efficiency (NSE) in an in-sample and an out-of-sample setting, representing temporal and spatial generalization. Our results show that entity-aware models work well with a mean performance of NSE >0.8 in an in-sample setting, thus being comparable to, or even outperforming, single-well models. However, they do not generalize well spatially in an out-of-sample setting (mean NSE <0.7, i.e. lower than a global model without entity information). Strikingly, all model variants, regardless of the type of static features used, basically perform equally well both in- and out-of-sample. The conclusion is that the model in fact does not show entity awareness, but uses static features merely as unique identifiers, raising the research question of how to properly establish entity awareness in deep learning models. Potential future avenues lie in bigger datasets, as the relatively small number of wells in the dataset might not be enough to take full advantage of global models. Also, more research is needed to find meaningful static features for ML in hydrogeology.

Joint green dynamic order batching and picker routing problem using PSO with global worst experience

With the increasing importance of various countries’ initiatives on reducing carbon emissions (e.g., carbon border adjustment mechanism, CBAM), intelligent order picking systems have assisted electronic retailers in realizing a green supply chain. Previous studies on picker routing operations focused on adopting offline static customer order information to carry out operational decisions. However, in practice, the customer order information is updated dynamically; and high-efficiency warehouse layouts have been increasingly concerned. Therefore, this study creates a mixed-integer programming model for minimizing both the total carbon footprint and the total penalty cost for delayed orders in the joint order batching and picker routing problem with dynamic arriving orders and shipping time constraints in a high-efficiency fishbone warehouse layout. To solve this complex problem, a particle swarm optimization algorithm that integrates the swarm’s previous global and local worst experiences and a migration mechanism is further proposed to increase solution quality and computing efficiency. In addition, a three-dimensional space for the warehouse floorplan and shipping time constraints is designed to divide orders into batches to address the constraints. Theoretical analysis of this algorithm is conducted, and experimental analysis shows that this algorithm finds superior solutions than current practical strategies, to offer a valuable reference for carbon emission reduction in greening warehousing.

Unveiling community structures in static networks through graph variational Bayes with evolution information

With the rapid development of graph variational Bayes theory, some representative community detection methods have been proposed. Although these methods are well designed, they are limited by the inherent constraints of static networks. Therefore, how to re-examine the information in static networks from a new perspective becomes a challenge. To this end, in this paper, we propose EG-VGAE and its variant EGC-VGAE, going beyond the constraints of static networks from a new perspective. Specifically, we first demonstrate that the evolution relations on static networks can be simulated reasonably. And then, our EG-VGAE method combines evolution information with static network information to realize a fine-grained propagation of local low-frequency signals to global low-frequency signals, thereby improving the accuracy of community assignment for each node. Building on this progress, our method EGC-VGAE imposes the smoothness constraint on adjacent slices, significantly enhancing the sensitivity of the method to evolution information and mitigating the impact of network noise. The comprehensive experimental results on real static networks well validate that our methods outperform state-of-the-art methods in most cases. The code is available at https://github.com/GDM-SCNU/EG-VGAE.

Novel Human Activity Recognition and Recommendation Models for Maintaining Good Health of Mobile Users

With the continuous improvement of the living standard, people have changed their concept from disease treatment to health management. However, most of the current health management software makes recommendations based on users’ static information, with low updating frequency. The effect of targeted suggestions becomes weak with time, and it is hard for the recommendation effect to be satisfactory. Based on the use of smartphones for recognizing human activities in real-time, firstly, a novel 'CNN+GRU' model is proposed in this paper, utilizing both convolutional neural networks (CNNs) and gated recurrent units (GRUs). 'CNN+GRU' can improve the recognition speed and extract the features in sensor data more accurately by achieving in the conducted experiments an average accuracy of 91.27%, thus outperforming other models compared. Secondly, another model, named SimilRec, is proposed for physical activity recommendation to users based on their health profile, the similarities between their current physical activity sequence, and the historical physical activity sequence of other (similar) users.

Feedforward Photoadaptive Organic Neuromorphic Transistor with Mixed‐Weight Plasticity for Augmenting Perception

AbstractOrganic photoelectric neuromorphic devices that mimic the brain are widely explored for advanced perceptual computing. However, current individual neuromorphic synaptic devices mainly focus on utilizing linear models to process optoelectronic signals, which means that there is a lack of effective response to nonlinear structural information from the real world, severely limiting the computational efficiency and adaptability of networks to static and dynamic information. Here, a feedforward photoadaptive organic neuromorphic transistor with mixed‐weight plasticity is reported. By introducing the potential of the space charge to couple gate potential, photoexcitation, and photoinhibition occur successively in the channel under the interference of constant light intensity, which enables the device to transform from a linear model to a nonlinear model. As a result, the device exhibits a dynamic range of over 100 dB, exceeding the currently reported similar neuromorphic synaptic devices. Further, the device achieves adaptive tone mapping within 5 s for static information and achieves over 90% robustness recognition accuracy for dynamic information. Therefore, this work provides a new strategy for developing advanced neuromorphic devices and has great potential in the fields of intelligent driving and brain‐like computing.

Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables

Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static positional information and dynamic variables. Additionally, most existing deep learning models have not integrated the global system characteristics of the Earth’s features and teleconnection during the learning phase. Here, we propose a static location-aware ConvLSTM (SLA-ConvLSTM) model that is aware of static positional elements and interconnected with global information and teleconnection. The model we propose can discern the influence of dynamic variables across various geographical locations on predictive outcomes. Compared with other deep learning models, our SLA-ConvLSTM model has achieved commendable performance. The outcomes indicate that the collaborative interplay of spatiotemporal features and the extraction of static positional information present a promising technique for wildfire prediction. Moreover, the incorporation of climate indices and global feature variables enhances the predictive capability of the model in wildfire prediction.

Application research of deep learning-based BIM Technology in Intelligent Construction

Abstract With the increasing importance of energy-efficient renovations in architecture, the pressing issue of how to enhance the effectiveness of such renovations through advanced technology has come to the forefront. This article affirms the significance of building information modeling (BIM) technology in energy consumption renovations. It introduces a BIM-based building energy monitoring system, which not only provides comprehensive and effective static information for energy consumption analysis but also offers real-time visualization of energy consumption status for daily management. By collecting all necessary data for energy management and storing it in the cloud, this system greatly facilitates data analysis. Moreover, the article proposes a short-term electricity load forecasting method for community buildings based on empirical mode decomposition (EMD)-gated recurrent unit (GRU). The EMD-GRU method proposed in this article exhibits superior accuracy compared to other methods, with a mean absolute percentage error value of 14.43%. By considering factors such as historical load, weather, and date, this method accurately predicts short-term electricity loads for communities. This research provides a robust framework for improving energy management in buildings through advanced technologies, ultimately leading to more effective energy-saving renovations and optimizations.

Investigating Driver Preferences for Traffic Information Using Digital Signage and Road Surface Holograms

This study investigates road users' information preferences to enhance traffic information provision strategies. Traditional road signs primarily convey static information like speed limits and restricted areas. However, smart street poles now offer the capability to collect real-time road conditions and deliver dynamic traffic updates promptly. Leveraging these smart poles allows for more effective and tailored information delivery strategies. The research examines three types of media for information dissemination: digital signage, road surface hologram, and a combination of both. Additionally, it surveys road users' preferences for information delivery methods, including text, graphics, or a mixture, based on the type of information. Conjoint analysis of 220 responses reveals essential attributes influencing users when they receive information. The findings indicate that a majority of road users prefer a blend of digital signage and road surface holograms, with a notable preference among younger individuals for digital signage. For road surface holograms, the favored approach involves a mix of text and graphics. This study lays the foundation for user-centric information strategies, offering insights into tailoring approaches based on the type of information and user preferences.

Recognizing facial expressions of emotion amid noise: A dynamic advantage.

Humans communicate internal states through complex facial movements shaped by biological and evolutionary constraints. Although real-life social interactions are flooded with dynamic signals, current knowledge on facial expression recognition mainly arises from studies using static face images. This experimental bias might stem from previous studies consistently reporting that young adults minimally benefit from the richer dynamic over static information, whereas children, the elderly, and clinical populations very strongly do (Richoz, Jack, Garrod, Schyns, & Caldara, 2015, Richoz, Jack, Garrod, Schyns, & Caldara, 2018b). These observations point to a near-optimal facial expression decoding system in young adults, almost insensitive to the advantage of dynamic over static cues. Surprisingly, no study has yet tested the idea that such evidence might be rooted in a ceiling effect. To this aim, we asked 70 healthy young adults to perform static and dynamic facial expression recognition of the six basic expressions while parametrically and randomly varying the low-level normalized phase and contrast signal (0%-100%) of the faces. As predicted, when 100% face signals were presented, static and dynamic expressions were recognized with equal efficiency with the exception of those with the most informative dynamics (i.e., happiness and surprise). However, when less signal was available, dynamic expressions were all better recognized than their static counterpart (peaking at ∼20%). Our data show that facial movements increase our ability to efficiently identify emotional states of others under the suboptimal visual conditions that can occur in everyday life. Dynamic signals are more effective and sensitive than static ones for decoding all facial expressions of emotion for all human observers.

Visual simultaneous localization and mapping (vSLAM) algorithm based on improved Vision Transformer semantic segmentation in dynamic scenes

Abstract. Identifying dynamic objects in dynamic scenes remains a challenge for traditional simultaneous localization and mapping (SLAM) algorithms. Additionally, these algorithms are not able to adequately inpaint the culling regions that result from excluding dynamic objects. In light of these challenges, this study proposes a novel visual SLAM (vSLAM) algorithm based on improved Vision Transformer semantic segmentation in dynamic scenes (VTD-SLAM), which leverages an improved Vision Transformer semantic segmentation technique to address these limitations. Specifically, VTD-SLAM utilizes a residual dual-pyramid backbone network to extract dynamic object region features and a multiclass feature transformer segmentation module to enhance the pixel weight of potential dynamic objects and to improve global semantic information for precise identification of potential dynamic objects. The method of multi-view geometry is applied to judge and remove the dynamic objects. Meanwhile, according to static information in the adjacent frames, the optimal nearest-neighbor pixel-matching method is applied to restore the static background, where the feature points are extracted for pose estimation. With validation in the public dataset TUM (The Entrepreneurial University Dataset) and real scenarios, the experimental results show that the root-mean-square error of the algorithm is reduced by 17.1 % compared with dynamic SLAM (DynaSLAM), which shows better map composition capability.

Quantifying and Learning Static Vs. Dynamic Information in Deep Spatiotemporal Networks.

There is limited understanding of the information captured by deep spatiotemporal models in their intermediate representations. For example, while evidence suggests that action recognition algorithms are heavily influenced by visual appearance in single frames, no quantitative methodology exists for evaluating such static bias in the latent representation compared to bias toward dynamics. We tackle this challenge by proposing an approach for quantifying the static and dynamic biases of any spatiotemporal model, and apply our approach to three tasks, action recognition, automatic video object segmentation (AVOS) and video instance segmentation (VIS). Our key findings are: (i) Most examined models are biased toward static information. (ii) Some datasets that are assumed to be biased toward dynamics are actually biased toward static information. (iii) Individual channels in an architecture can be biased toward static, dynamic or jointly encode a combination static and dynamic information. (iv) Most models converge to their culminating biases in the first half of training. We then explore how these biases affect performance on dynamically biased datasets. For action recognition, we propose StaticDropout, a semantically guided dropout that debiases a model from static information toward dynamics. For AVOS, we design a better combination of fusion and cross connection layers compared with previous architectures.

Stimulus-responsive polymer materials toward multi-mode and multi-level information anti-counterfeiting: recent advances and future challenges.

Information storage and security is one of the perennial hot issues in society, while the further advancements of related chemical anti-counterfeiting systems remain a formidable challenge. As emerging anti-counterfeiting materials, stimulus-responsive polymers (SRPs) have attracted extensive attention due to their unique stimulus-responsiveness and charming discoloration performance. At the same time, single-channel decryption technology with low-security levels has been unable to effectively prevent information from being stolen or mimicked. As a result, it would be of great significance to develop SRPs with multi-mode and multi-level anti-counterfeiting characteristics. This study summarizes the latest achievements in advance anti-counterfeiting strategies based on SRPs, including multi-mode anti-counterfeiting (static information) and multi-level anti-counterfeiting (dynamic information). In addition, the promising applications of such materials in anti-counterfeiting labels, identification platforms, intelligent displays, and others are briefly reviewed. Finally, the challenges and opportunities in this emerging field are discussed. This review serves as a useful resource for manipulating SRP-based anti-counterfeiting materials and creating cutting-edge information security and encryption systems.