Anomalous Situations Research Articles

“Don't Forget to Put the Milk Back!” Dataset for Enabling Embodied Agents to Detect Anomalous Situations

“Don't Forget to Put the Milk Back!” Dataset for Enabling Embodied Agents to Detect Anomalous Situations

Three-dimensional forward modeling and quantitative assessment of electrode offset effects in ERT

Resistivity data has important applications in geophysical exploration, but the impact of electrode offsets on resistivity response characteristics remains unclear. This study aims to explore the influence of horizontal electrode offset angles and vertical offsets caused by topographical variations on the forward modeling of resistivity data. By analyzing experimental models with different measurement arrays, the paper revealed their influence laws on the buried depth of the target body and resistivity resolution. Utilizing tools like ZondRes3D, we conducted 3D resistivity forward modeling and analyzed the results in detail. It is found that horizontal electrode offsets lead to pseudo-anomalies in the apparent resistivity response, which is related to the offset angles and the number of electrodes. Under different conditions, the horizontal electrode offsets exhibit a “gradient variation” pattern. In addition, topographical variations can also cause distortions and offsets in the apparent resistivity curves and the locations of the anomaly response. Specifically, the measuring lines near the edge of the target bodies are more susceptible to these effects. Based on the comprehensive experimental results, we have drawn several conclusions regarding the impact of electrode offsets and topographical variations, including the effects of offset angles on the pseudo-anomalies, the anomalous response laws under different topographic conditions, as well as anomalous situations under specific angles. These findings provide crucial insights for interpreting resistivity data in geophysical exploration and addressing practical engineering problems, and offer guidance for optimizing measuring line layouts and post-processing terrain correction algorithms.

Adaptive Internal Model Backstepping Control for a Class of Second-Order Electromagnetic Micromirror with Output Performance Constraints and Anomaly Control.

This paper investigates the asymptotic tracking problem for a class of second-order electromagnetic micromirror model with output performance constraints and anomaly control, which is subject to model parameter uncertainties and external disturbances. Specifically, this paper formulates the trajectory tracking control problem of an electromagnetic micromirror as a closed-loop control trajectory tracking problem based on the general solution framework of output regulation. Moreover, the extended internal model is introduced to reformulate the closed-loop control problem into a state stabilization problem of the augmented system. Based on the augmented system, an internal model backstepping controller is proposed by integrating the barrier Lyapunov Functions (BLF) and the Nussbaum gain function with the backstepping structure.This controller not only satisfies the output performance constraints of the micromirror, but also maintains the control performance in anomalous control situations. The final performance simulation demonstrates the efficacy of the proposed controller.

MarMot: Metamorphic Runtime Monitoring of Autonomous Driving Systems

Autonomous Driving Systems (ADSs) are complex Cyber-Physical Systems (CPSs) that must ensure safety even in uncertain conditions. Modern ADSs often employ Deep Neural Networks (DNNs), which may not produce correct results in every possible driving scenario. Thus, an approach to estimate the confidence of an ADS at runtime is necessary to prevent potentially dangerous situations. In this paper we propose MarMot, an online monitoring approach for ADSs based on Metamorphic Relations (MRs), which are properties of a system that hold among multiple inputs and the corresponding outputs. Using domain-specific MRs, MarMot estimates the uncertainty of the ADS at runtime, allowing the identification of anomalous situations that are likely to cause a faulty behavior of the ADS, such as driving off the road. We perform an empirical assessment of MarMot with five different MRs, using two different subject ADSs, including a small-scale physical ADS and a simulated ADS. Our evaluation encompasses the identification of both external anomalies, e.g., fog, as well as internal anomalies, e.g., faulty DNNs due to mislabeled training data. Our results show that MarMot can identify up to 65% of the external anomalies and 100% of the internal anomalies in the physical ADS, and up to 54% of the external anomalies and 88% of the internal anomalies in the simulated ADS. With these results, MarMot outperforms or is comparable to other state-of-the-art approaches, including SelfOracle, Ensemble, and MC Dropout-based ADS monitors.

Comparative analysis of single-board computers for the development of a microarchitectural computing system for fire detection

Objectives. The purpose of the work is to select the basic computing microplatform of the onboard microarchitectural computing complex for the detection of anomalous situations in the territory of the Republic of Belarus from space on the basis of artificial intelligence methods.Methods. The method of comparative analysis is used to select a computing platform. A series of performance tests and comparative analysis (benchmarking) are performed on the selected equipment. The methods of comparative and benchmarking analysis are performed in accordance with the terms of reference to the current project.Results. A comparative analysis and performance testing of Raspberry Pi 4 Model B and Cool Pi 4 Model B single-board computers, as well as AI-accelerator Google Coral USB Accelerator with Google Edge TPU have been performed. The comparative analysis showed that Raspberry Pi 4 Model B and Cool Pi 4 Model B fully meet the terms of reference to the current project. At the same time Cool Pi 4 Model B handles neural network calculations well, but four times slower than similar calculations on Google Coral USB Accelerator. Neural network computations on the Raspberry Pi 4 Model B are 22 times slower than similar computations on the Google Coral USB Accelerator. Cool Pi 4 Model B outperforms Raspberry Pi 4 Model B by the factor of two to three for data copying and compression and almost six times faster for neural network computations.Conclusion. Despite the fact that Raspberry Pi 4 Model B meets the terms of reference to the project as a computational basis, when developing an on-board microarchitectural computing system for detecting anomalous situations, it is worth using more powerful alternatives with built-in AI-accelerators (e.g., Radxa Rock 5 Model A) or with an additional external AI-accelerator (e.g., a combination of Cool Pi 4 Model B and Google Coral USB Accelerator). Using a Raspberry Pi 4 Model B with an additional AI-accelerator is also acceptable and will speed up computations by several dozen times. AI-accelerators provide the fastest neural network computations, but there are features related to the novelty of the technology that will be explored in further development.

Migration and Culture Shock: Neo-Cultural Praxis and Mathe-Logic Reformations in Selected Texts

The study examines ‘Migration and Culture Shock: Neo-Cultural Praxis and Mathe-logic Reformations in Babel of Voices, Tinuke’s Last Dance. It also reviews various feelings and excruciating experiences of the people. This provides a discursive analysis of society and constant need to x-ray those aspects of cultural trends that are problematic. The research is library oriented. Psychological and historical approaches are used in the study as theoretical framework to achieve greater result concerning issues raised in this study and writer’s lenses through the society. In a nutshell, the findings of this research highlights those social problems to draw attention of the audience on various aspects of acculturated behaviours vis-à-vis education that may have touched the playwright to initiate discourse in the guise of critiquing these anomalous situations for highlighted reforms. Babel of Voices situates Charles Okwelume as the likely person who will advance or take Ola Rotimi’s banner in creativity. It also attempts to look at the society to inform audience on the relevance of exposing these abnormal practices and challenges that are inimical to culture and progressive society. These necessitate trends, prospects and challenges of Nigerian culture in this twenty-first century.

Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments1

Detecting anomalies in patterns of sensor data is important in many practical applications, including domestic activity monitoring for Active Assisted Living (AAL). How to represent and analyse these patterns, however, remains a challenging task, especially when data is relatively scarce and an explicit model is required to be fine-tuned for specific scenarios. This paper, therefore, presents a new approach for temporal modelling of long-term human activities with smart-home sensors, which is used to detect anomalous situations in a robot-assisted environment. The model is based on wavelet transforms and used to forecast smart sensor data, providing a temporal prior to detect unexpected events in human environments. To this end, a new extension of Hybrid Markov Logic Networks has been developed that merges different anomaly indicators, including activities detected by binary sensors, expert logic rules, and wavelet-based temporal models. The latter in particular allows the inference system to discover deviations from long-term activity patterns, which cannot be detected by simpler frequency-based models. Two new publicly available datasets were collected using several smart-sensors to evaluate the approach in office and domestic scenarios. The experimental results demonstrate the effectiveness of the proposed solutions and their successful deployment in complex human environments, showing their potential for future smart-home and robot integrated services.

Revealing Spatial–Temporal Patterns of Sea Surface Temperature in the South China Sea Based on Spatial–Temporal Co-Clustering

To discover the spatial–temporal patterns of sea surface temperature (SST) in the South China Sea (SCS), this paper proposes a spatial–temporal co-clustering algorithm optimized by information divergence. This method allows for the clustering of SST data simultaneously across temporal and spatial dimensions and is adaptable to large volumes of data and anomalous data situations. First, the SST data are initially clustered using the co-clustering algorithm. Second, we use information divergence as the loss function to refine the clustering results iteratively. During the iterative optimization of spatial clustering results, we treat the temporal dimension as a constraint; similarly, during the iterative optimization of temporal clustering, we treat the spatial dimension as a constraint. This is to ensure better robustness of the algorithm. Finally, this paper conducts experiments in the SCS to verify our algorithm. According to the analysis of the experimental results, we have drawn the following conclusions. First, the use of the spatial–temporal co-clustering algorithm reveals that the SST in the SCS exhibits strong seasonal patterns in the temporal clustering results. The spatial distribution of SST varies significantly in different seasons. There is a slight difference in SST between the northern and southern regions of the SCS in winter, but the largest difference is in summer. Second, during ocean anomalies, our proposed algorithm can identify the corresponding abnormal patterns. When ENSO occurs, the seasonal distribution pattern of SST in the SCS is destroyed and replaced by an abnormal temporal pattern. The results indicate that during ENSO events, the SST in specific months in the SCS exhibits a correlation with the SST observed 4–5 months afterward.

Estimation of Intercepted Solar Radiation and Stem Water Potential in a Table Grape Vineyard Covered by Plastic Film Using Sentinel-2 Data: A Comparison of OLS-, MLR-, and ML-Based Methods.

In the framework of precision viticulture, satellite data have been demonstrated to significantly support many tasks. Specifically, they enable the rapid, large-scale estimation of some viticultural parameters like vine stem water potential (Ψstem) and intercepted solar radiation (ISR) that traditionally require time-consuming ground surveys. The practice of covering table grape vineyards with plastic films introduces an additional challenge for estimation, potentially affecting vine spectral responses and, consequently, the accuracy of estimations from satellites. This study aimed to address these challenges with a special focus on the exploitation of Sentinel-2 Level 2A and meteorological data to monitor a plastic-covered vineyard in Southern Italy. Estimates of Ψstem and ISR were obtained using different algorithms, namely, Ordinary Least Square (OLS), Multivariate Linear Regression (MLR), and machine learning (ML) techniques, which rely on Random Forest Regression, Support Vector Regression, and Partial Least Squares. The results proved that, despite the potential spectral interference from the plastic coverings, ISR and Ψstem can be locally estimated with a satisfying accuracy. In particular, (i) the OLS regression-based approach showed a good performance in providing accurate ISR estimates using the near-infrared spectral bands (RMSE < 8%), and (ii) the MLR and ML algorithms could estimate both the ISR and vine water status with a higher accuracy (RMSE < 7 for ISR and RMSE < 0.14 MPa for Ψstem). These results encourage the adoption of medium-high resolution multispectral satellite imagery for deriving satisfying estimates of key crop parameters even in anomalous situations like the ones where plastic films cover the monitored vineyard, thus marking a significant advancement in precision viticulture.

YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories

Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications including transportation modeling, disaster management, and urban planning. The recent availability of large-scale human movement data collected from mobile devices has enabled the development of complex human mobility prediction models. However, human mobility prediction methods are often trained and tested on different datasets, due to the lack of open-source large-scale human mobility datasets amid privacy concerns, posing a challenge towards conducting transparent performance comparisons between methods. To this end, we created an open-source, anonymized, metropolitan scale, and longitudinal (75 days) dataset of 100,000 individuals’ human mobility trajectories, using mobile phone location data provided by Yahoo Japan Corporation (currently renamed to LY Corporation), named YJMob100K. The location pings are spatially and temporally discretized, and the metropolitan area is undisclosed to protect users’ privacy. The 90-day period is composed of 75 days of business-as-usual and 15 days during an emergency, to test human mobility predictability during both normal and anomalous situations.

An unsupervised machine learning approach for real-time damage detection in bridges

The bridge network is progressively aging, with an alarming proportion of bridges over 100 years. This situation engenders substantial risks to the overall reliability of transportation networks, requiring innovative methods for efficient management. Monitoring can provide a direct source of information about structural behavior generating alerts when changes occur. Real-time alerts enable effective infrastructure management and decision-making during damage or anomalous situations. However, monitoring can result in a large amount of data that is often difficult to convert into valuable information in real time. This paper presents an approach for real-time detection of abrupt damage occurrence in bridges using unsupervised anomaly detection algorithms and strain/acceleration measurements. The approach incorporates the separation of measurements into events having the same loading nature and the construction of three feature matrices based on statistical features, time-frequency features, and wavelet spectrum features. It includes the evaluation of five anomaly detection algorithms including Isolation Forest, One-Class Support Vector Machine, Robust Random Cut Forest, Local Outlier Factor, and Mahalanobis Distance. The approach is illustrated with a case study of a steel-bascule-railway bridge, that has experienced a brittle cracking event during monitoring. Results highlight the robustness of One-Class Support Vector Machine, Isolation Forest, and Local Outlier Factor algorithms in promptly detecting abrupt changes across different features. The separation of strain and acceleration data into loading-based events, coupled with the comparison of previous and new event features, provides robust feature matrices for effective damage detection. Enhanced detection and higher scores are particularly attributed to time-frequency domain features during damage occurrence. The presented approach can be used as a base on how to perform real-time anomaly detection within the context of bridge monitoring.

Real-Time Intelligent Anomaly Detection and Prevention System

Real-time anomaly detection in network traffic is a method that detects unexpected and anomalous behaviour by identifying normal behaviour and statistical patterns in network traffic data. This method is used to detect potential attacks or other anomalous conditions in network traffic. Real-time anomaly detection uses different algorithms to detect abnormal activities in network traffic. These include statistical methods, machine learning and deep learning techniques. By learning the normal behaviour of network traffic, these methods can detect unexpected and anomalous situations. Attackers use various techniques to mimic normal patterns in network traffic, making it difficult to detect. Real-time anomaly detection allows network administrators to detect attacks faster and respond more effectively. Real-time anomaly detection can improve network performance by detecting abnormal conditions in network traffic. Abnormal traffic can overuse the network's resources and cause the network to slow down. Real-time anomaly detection detects abnormal traffic conditions, allowing network resources to be used more effectively. In this study, blockchain technology and machine learning algorithms are combined to propose a real-time prevention model that can detect anomalies in network traffic.

PEAR: Positional-encoded Asynchronous Autoregression for satellite anomaly detection

This paper proposes a Positional-Encoded Asynchronous AutoRegression (PEAR) method for satellite anomaly detection. We empirically observe that a single classification model can hardly detect unknown anomalous situations and neglect the Markov nature of temporal satellite data. To address this, we adopt an autoregressive model to deal with the prediction of unknown anomaly for satellite data. We further propose a non-uniform temporal encoding method for asynchronous data and a median filtering method for more accurate detection. To reduce the effect of outliers, we employ an adaptive threshold selection method to achieve a more robust classification boundary. We test the proposed method on the time series prediction models including LSTM and transformers and we further employ the positional encoding strategy to improve the modeling capabilities of the Transformer model on high-frequency information. Experiments on real satellite data demonstrate that the proposed PEAR method outperforms the baseline method by 55.79%.

A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding Events

Accurate prediction of individual mobility is crucial for developing intelligent transportation systems. However, while previous models usually focused on predicting individual mobility under ordinary conditions, the models that are applicable to large crowding events are still lacking. Here, we employ the smart card data of 6.5 million subway passengers of the Shenzhen Metro to develop a Markov chain-based individual mobility prediction model (i.e., SCMM) applicable to both ordinary and anomalous passenger flow situations. The proposed SCMM model improves the Markov chain model by incorporating the station-level anomalous passenger flow index and the collective mobility patterns of similar passengers. Compared with the benchmark models, the SCMM model achieves the highest prediction accuracy in both ordinary conditions and large crowding events. Our results highlight the importance of combining an individual’s own historical mobility data with collective mobility data and suggest the appropriate weights of individual and collective information considered in individual mobility modeling.

Spatio-Temporal Meta-Graph Learning for Traffic Forecasting

Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (i.e., METR-LA and PEMS-BAY) and a new large-scale traffic speed dataset called EXPY-TKY that covers 1843 expressway road links in Tokyo. Our model outperformed the state-of-the-arts on all three datasets. Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle the road links and time slots with different patterns and be robustly adaptive to any anomalous traffic situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.

Implementing and evaluating a GDPR-compliant open-source SIEM solution

Security Information and Event Management (SIEM) solutions collect events from the IT infrastructure and concentrate information from the various components in a single place, allowing the detection of anomalous situations and attacks, and helping to protect confidential data. But real-world network environments may be complex and heterogeneous (e.g., in terms of devices, applications, and operating systems), and the attack surface can be vast, which makes increases the amount that a SIEM solution must collect and analyze. The General Data Protection Regulation (GDPR) has increased the level of complexity in such context, as organizations must ensure the monitoring of access to personal data and various levels of security in their infrastructure.In this work, we deal with the implementation of an open-source SIEM solution that incorporates technical measures for the protection and control of personal data, ensuring compliance with the GDPR. We identify the main functionalities and describe a solution based on the Elastic Stack and additional open-source external tools.To validate our proposals, we implemented a prototype of our solution in a real-world environment. We simulated internal and external attacks that show the solution capacity to deal in real-time with the detection of threats and incidents. We also evaluated the performance and resource consumption of personal data pseudonymization processes. Obtained results show our solution presents good performance and scalability.

Dynamic dielectric function and phonon self-energy from electrons strongly correlated with acoustic phonons in 2D Dirac crystals

The unique structure of two-dimensional (2D) Dirac crystals, with electronic bands linear in the proximity of the Brillouin-zone boundary and the Fermi energy, creates anomalous situations where small Fermi-energy perturbations critically affect the electron-related lattice properties of the system. The Fermi-surface nesting (FSN) conditions determining such effects via electron–phonon interaction require accurate estimates of the crystal’s response function as a function of the phonon wavevector q for any values of temperature, as well as realistic hypotheses on the nature of the phonons involved. Numerous analytical estimates of for 2D Dirac crystals beyond the Thomas–Fermi approximation have been so far carried out only in terms of dielectric response function , for photon and optical-phonon perturbations, due to relative ease of incorporating a q-independent oscillation frequency in calculation. Models accounting for Dirac-electron interaction with acoustic phonons, for which ω is linear to q and is therefore dispersive, are essential to understand many critical crystal properties, including electrical and thermal transport. The lack of such models has often led to the assumption that the dielectric response function in these systems can be understood from free-electron behavior. Here, we show that, different from free-electron systems, calculated for acoustic phonons in 2D Dirac crystals using the Lindhard model, exhibits a cuspidal point at the FSN condition. Strong variability of persists also at finite temperatures, while tend to infinity in the dynamic case where the speed of sound is small, albeit non negligible, over the Dirac-electron Fermi velocity. The implications of our findings for electron-acoustic phonon interaction and transport properties such as the phonon line width derived from the phonon self-energy will also be discussed.

A Vision Transformer Approach for Traffic Congestion Prediction in Urban Areas

Traffic problems continue to deteriorate because of increasing population in urban areas that rely on many modes of transportation, the transportation infrastructure has achieved considerable strides in the last several decades. This has led to an increase in congestion control difficulties, which directly affect citizens through air pollution, fuel consumption, traffic law breaches, noise pollution, accidents, and loss of time. Traffic prediction is an essential aspect of an intelligent transportation system in smart cities because it helps reduce overall traffic congestion. This article aims to design and enforce a traffic prediction scheme that is efficient and accurate in forecasting traffic flow. Available traffic flow prediction methods are still unsuitable for real-world applications. This fact motivated us to work on a traffic flow forecasting issue using Vision Transformers (VTs). In this work, VTs were used in conjunction with Convolutional neural networks (CNN) to predict traffic congestion in urban spaces on a city-wide scale. In our proposed architecture, a traffic image is fed to a CNN, which generates feature maps. These feature maps are then fed to the VT, which employs the dual techniques of tokenization and projection. Tokenization is used to convert features into tokens containing Vision information, which are then sent to projection, where they are transformed into feature maps and ultimately delivered to LSTM. The experimental results demonstrate that the vision transformer prediction method based on Spatio-temporal characteristics is an excellent way of predicting traffic flow, particularly during anomalous traffic situations. The proposed technology surpasses traditional methods in terms of precision, accuracy and recall and aids in energy conservation. Through rerouting, the proposed work will benefit travellers and reduce fuel use.

Evaluation of the Impact of the Sustainable Development Goals on an Activity Recognition Platform for Healthcare Systems

The Sustainable Development Goals (SDGs), also known as the Global Goals, were adopted by the United Nations in 2015 as a universal call to end poverty, protect the planet and ensure peace and prosperity for all by 2030. The 17 SDGs have been designed to end poverty, hunger, AIDS and discrimination against women and girls. Despite the clear SDG framework, there is a significant gap in the literature to establish the alignment of systems, projects or tools with the SDGs. In this research work, we assess the SDG alignment of an activity recognition platform for healthcare systems, called ACTIVA. This new platform, designed to be deployed in environments inhabited by vulnerable people, is based on sensors and artificial intelligence, and includes a mobile application to report anomalous situations and ensure a rapid response from healthcare personnel. In this work, the ACTIVA platform and its compliance with each of the SDGs is assessed, providing a detailed evaluation of SDG 7-ensuring access to affordable, reliable, sustainable and modern energy for all. In addition, a website is presented where the ACTIVA platform's compliance with the 17 SDGs has been evaluated in detail. The comprehensive assessment of this novel platform's compliance with the SDGs provides a roadmap for the evaluation of future and past systems in relation to sustainability.

Practicing conjunctural methodologies: Engaging Chinese capitalism

Conjunctural analysis is a rather enigmatic practice, observed mostly after the fact and in feats of exemplary execution, apparently somewhat resistant to codification, and maybe too modish for methodological rules. Predicated on the analysis of politically salient ‘situations’, conjunctural approaches combine reflexive theorizing with socially engaged inquiry and context-rich, historicized modes of analysis. Exploring the potential of conjunctural analysis in economic geography, the article moves first to tease out the methodological implications of this rather elusive approach, including: attention to complex states of causal codetermination, ‘in articulation’; an orientation to relational and ‘unbounded’ modes of inquiry; an emphasis on the stress-testing of received theory claims and conceptual categories, often in anomalous (as opposed to ‘typical’) situations; and a commitment to reflexivity, context-engaged analysis, and ‘thick’ theorization. Second, the article interrogates these methodological dispositions and propositions by placing them in dialogue, indicatively, with the case of Chinese capitalism. This is a case that routinely frustrates and confounds extant theoretical frameworks and conceptual categories, sometimes prompting theoretical defeatism. Although conjunctural analysis is not methodologically prescriptive, it implies distinctive criteria for problem formulation and research design; for ‘casing’, case selection, and specification; and for the exposition and (re)construction of contextualized theory claims.