Detecting anomalous behavior in dynamic networks remains a constant challenge. This problem is further exacerbated when the underlying topology of these networks is affected by individual highly-dimensional node attributes. We address this issue by tracking a network's modularity as a proxy of its community structure. We leverage Graph Neural Networks (GNNs) to estimate each snapshot's modularity. GNNs can account for both network structure and high-dimensional node attributes, providing a comprehensive approach for estimating network statistics. Our method is validated through simulations that demonstrate its ability to detect changes in highly-attributed networks by analyzing shifts in modularity. Moreover, we find our method is able to detect a real-world event within the \#Iran Twitter reply network, where each node has high-dimensional textual attributes.

Change detection and irregular object extraction in 3D point clouds is a challenging task that is of high importance not only for autonomous navigation but also for updating existing digital twin models of various industrial environments. This article proposes an innovative approach for change detection in 3D point clouds using deep learned place recognition descriptors and irregular object extraction based on voxel-to-point comparison. The proposed method first aligns the bi-temporal point clouds using a map-merging algorithm in order to establish a common coordinate frame. Then, it utilizes deep learning techniques to extract robust and discriminative features from the 3D point cloud scans, which are used to detect changes between consecutive point cloud frames and therefore find the changed areas. Finally, the altered areas are sampled and compared between the two time instances to extract any obstructions that caused the area to change. The proposed method was successfully evaluated in real-world field experiments, where it was able to detect different types of changes in 3D point clouds, such as object or muck-pile addition and displacement, showcasing the effectiveness of the approach. The results of this study demonstrate important implications for various applications, including safety and security monitoring in construction sites, mapping and exploration and suggests potential future research directions in this field.

Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detecting changes is challenging when observations are high-dimensional. In high-dimensional data, change detectors should not only be able to identify when changes happen, but also in which subspace they occur. Ideally, one should also quantify how severe they are. Our approach, ABCD, has these properties. ABCD learns an encoder-decoder model and monitors its accuracy over a window of adaptive size. ABCD derives a change score based on Bernstein's inequality to detect deviations in terms of accuracy, which indicate changes. Our experiments demonstrate that ABCD outperforms its best competitor by at least 8% and up to 23% in F1-score on average. It can also accurately estimate changes' subspace, together with a severity measure that correlates with the ground truth.

The chaos at ports continues with no end. A troubling realization is sinking into the mind: The effects of the " Great Supply Chain Distortion" are already being felt throughout the country. For example, 30% of baby formula brands may be out of stock soon. This could cause retailers to limit the number of containers they can sell and leave parents concerned that their children won't get enough food. This issue covers all industries and has an impact on automotive, healthcare IT, hospitality, manufacturing, apparel, as well as other areas.

Even small changes in your surroundings could indicate danger. Imagine a robot could detect those changes, and a warning could immediately alert you through a display in your eyeglasses. That is what U.S. Army scientists are developing with sensors, robots, real-time change detection and augmented reality wearables. Army researchers demonstrated in a real-world environment the first human-robot team in which the robot detects physical changes in 3D and shares that information with a human in real-time through augmented reality, who is then able to evaluate the information received and decide follow-on action. "This could let robots inform their Soldier teammates of changes in the environment that might be overlooked by or not perceptible to the Soldier, giving them increased situational awareness and offset from potential adversaries," said Dr. Christopher Reardon, a researcher at the U.S. Army Combat Capabilities Development Command's Army Research Laboratory.

She spoke with Ryan Herd, the founder of Caregiver Smart Solutions, about a product that uses AI and IoT to help seniors and their caregivers. The following is an edited transcript of their conversation. Karen Roby: Ryan, we talk a lot about how technology will help the aging population in a million different ways, right? And this is something that will help caregivers make sure their loved ones can stay in their home longer? Ryan Herd: Caregiver Smart Solutions will enable your loved ones to stay at home while reducing your stress as a caregiver.

She spoke with Ryan Herd, the founder of Caregiver Smart Solutions, about a product that uses AI and IoT to help seniors and their caregivers. The following is an edited transcript of their conversation. Karen Roby: Ryan, we talk a lot about how technology will help the aging population in a million different ways, right? And this is something that will help caregivers make sure their loved ones can stay in their home longer? Ryan Herd: Caregiver Smart Solutions will enable your loved ones to stay at home while reducing your stress as a caregiver.

AI is having a big impact on organizations of all sizes, across all industries. But if you don't have the proper data architecture in place to support AI and machine learning, you're likely to be disappointed in the results you're seeing. Here are seven principles to consider for an AI-ready data architecture. Artificial intelligence (AI) is all about data, all the time. Does your IT team's architecture enable computations to be performed on demand?

Real life business processes change over time, in both planned and unexpected ways. The detection of these changes is crucial for organizations to ensure that the expected and the real behavior are as similar as possible. These changes over time are called concept drift and its detection is a big challenge in process mining since the inherent complexity of the data makes difficult distinguishing between a change and an anomalous execution. In this paper, we present C2D2 (Conformance Checking-based Drift Detection), a new approach to detect sudden control-flow changes in the process models from event traces. C2D2 combines discovery techniques with conformance checking methods to perform an offline detection. Our approach has been validated with a synthetic benchmarking dataset formed by 68 logs, showing an improvement in the accuracy while maintaining a minimum delay in the drift detection.

Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary environment. However, the stationary assumption on the environment is very restrictive. In many real world problems like traffic signal control, robotic applications, one often encounters situations with non-stationary environments and in these scenarios, RL methods yield sub-optimal decisions. In this paper, we thus consider the problem of developing RL methods that obtain optimal decisions in a non-stationary environment. The goal of this problem is to maximize the long-term discounted reward achieved when the underlying model of the environment changes over time. To achieve this, we first adapt a change point algorithm to detect change in the statistics of the environment and then develop an RL algorithm that maximizes the long-run reward accrued. We illustrate that our change point method detects change in the model of the environment effectively and thus facilitates the RL algorithm in maximizing the long-run reward. We further validate the effectiveness of the proposed solution on non-stationary random Markov decision processes, a sensor energy management problem and a traffic signal control problem.