Video has become the primary channel for people to access information and engage in leisure and entertainment activities. Video service platforms have emerged to meet these demands and create significant profits. These platforms aggregate diverse videos and offer subscription services, aiming to provide convenient and personalized viewing experiences. To safeguard the copyright of video content against unauthorized access, these platforms employ digital rights management (DRM) technologies. However, the use of DRM faces significant security challenges due to the presence of malware or malicious users, which can compromise the decryption program running on user devices and lead to illegal video content distribution. This paper proposes a high-performance encryption scheme for video DRM protection. We establish the security of our proposed scheme through rigorous mathematical proof and analyze its effectiveness in the context of the video DRM scenario. Furthermore, the experimental results demonstrate that our scheme outperforms other schemes. Overall, our proposed scheme offers a promising solution to the security challenges faced by DRM technologies in protecting video content. Its efficiency, security, and resilience to various attacks make it a viable option for video service platforms seeking to enhance the security of their digital content.

Reliable localization is an essential capability for marine robots navigating in GPS-denied environments. SLAM, commonly used to mitigate dead reckoning errors, still fails in feature-sparse environments or with limited-range sensors. Pose estimation can be improved by incorporating the uncertainty prediction of future poses into the planning process and choosing actions that reduce uncertainty. However, performing belief propagation is computationally costly, especially when operating in large-scale environments. This work proposes a computationally efficient planning under uncertainty framework suitable for large-scale, feature-sparse environments. Our strategy leverages SLAM graph and occupancy map data obtained from a prior exploration phase to create a virtual map, describing the uncertainty of each map cell using a multivariate Gaussian. The virtual map is then used as a cost map in the planning phase, and performing belief propagation at each step is avoided. A receding horizon planning strategy is implemented, managing a goal-reaching and uncertainty-reduction tradeoff. Simulation experiments in a realistic underwater environment validate this approach. Experimental comparisons against a full belief propagation approach and a standard shortest-distance approach are conducted.

This paper studies the problem of autonomous exploration under localization uncertainty for a mobile robot with 3D range sensing. We present a framework for self-learning a high-performance exploration policy in a single simulation environment, and transferring it to other environments, which may be physical or virtual. Recent work in transfer learning achieves encouraging performance by domain adaptation and domain randomization to expose an agent to scenarios that fill the inherent gaps in sim2sim and sim2real approaches. However, it is inefficient to train an agent in environments with randomized conditions to learn the important features of its current state. An agent can use domain knowledge provided by human experts to learn efficiently. We propose a novel approach that uses graph neural networks in conjunction with deep reinforcement learning, enabling decision-making over graphs containing relevant exploration information provided by human experts to predict a robot's optimal sensing action in belief space. The policy, which is trained only in a single simulation environment, offers a real-time, scalable, and transferable decision-making strategy, resulting in zero-shot transfer to other simulation environments and even real-world environments.

We consider an autonomous exploration problem in which a range-sensing mobile robot is tasked with accurately mapping the landmarks in an a priori unknown environment efficiently in real-time; it must choose sensing actions that both curb localization uncertainty and achieve information gain. For this problem, belief space planning methods that forward- simulate robot sensing and estimation may often fail in real-time implementation, scaling poorly with increasing size of the state, belief and action spaces. We propose a novel approach that uses graph neural networks (GNNs) in conjunction with deep reinforcement learning (DRL), enabling decision-making over graphs containing exploration information to predict a robot's optimal sensing action in belief space. The policy, which is trained in different random environments without human intervention, offers a real-time, scalable decision-making process whose high-performance exploratory sensing actions yield accurate maps and high rates of information gain.

In this paper, we propose a novel approach for underwater simultaneous localization and mapping using a multibeam imaging sonar for 3D terrain mapping tasks. The high levels of noise and the absence of elevation angle information in sonar images present major challenges for data association and accurate 3D mapping. Instead of repeatedly projecting extracted features into Euclidean space, we apply optical flow within bearing-range images for tracking extracted features. To deal with degenerate cases, such as when tracking is interrupted by noise, we model the subsea terrain as a Gaussian Process random field on a Chow-Liu tree. Terrain factors are incorporated into the factor graph, aimed at smoothing the terrain elevation estimate. We demonstrate the performance of our proposed algorithm in a simulated environment, which shows that terrain factors effectively reduce estimation error. We also show ROV experiments performed in a variable-elevation tank environment, where we are able to construct a descriptive and smooth height estimate of the tank bottom.

We consider the problem of autonomous mobile robot exploration in an unknown environment taking into account the robot's mapping rate, map uncertainty, and state estimation uncertainty. This paper presents an exploration framework built upon segment-aided pose SLAM adapted for better active localization. We build on our previous work on expectation maximization (EM) exploration, which explicitly models unknown landmarks as latent variables and predicts their expected uncertainty, to resolve the lack of landmark state in denser instances of SLAM. The proposed system comprises path generation, place recognition forecasting, belief propagation and utility evaluation using a virtual map. We analyze the performance in simulated experiments, showing that our algorithm maintains higher coverage speed in exploration as well as lower mapping and localization error. The real-time applicability is demonstrated on an unmanned ground vehicle.

In this paper, we consider the problem of building descriptive three-dimensional (3-D) maps from sparse and noisy range sensor data. We expand our previously proposed method leveraging Bayesian kernel inference for prediction of occupancy in locations not directly observed by a range sensor. In this paper, we show that our kernel inference approach generalizes previous “counting sensor model” approaches from discrete occupancy grids to continuous maps. Our approach enables prediction about occupancy in regions unobserved by the range sensor based on local measurements, and smoothly transitions to a prior in regions lacking sufficient data for reliable inference. Furthermore, we demonstrate quantitatively using simulated data that the mapping performance of our method can be improved by considering rays as continuous observations, rather than sampling discrete free-space point observations along rays. Though the maps produced by our method are in principle continuous, discretizing space affords us several computational advantages, including the ability to apply recursive Bayesian updates, that allow us to perform inference very efficiently, even on large datasets. To demonstrate this advantage, we present experimental results applying this method to large-scale lidar data collected with a ground robot, showing real-time performance. Other field robotics applications, including underwater 3-D mapping with sonar, are explored qualitatively.

We study the ambiguous data association problem confronting simultaneous localization and mapping (SLAM), specifically for the autonomous exploration of environments lacking rich features. In such environments, a single false positive assignment might lead to catastrophic failure, which even robust back-ends may be unable to resolve. Inspired by multiple hypothesis tracking, we present a novel approach to effectively manage multiple hypotheses (MH) of data association inherited from traditional joint compatibility branch and bound (JCBB), which entails the generation, ordering and elimination of hypotheses. We analyze the performance of MHJCBB in two particular situations, one applying it to SLAM over a predefined trajectory and the other showing its applicability in exploring unknown environments. Statistical results demonstrate that MHJCBB's maintenance of diverse hypotheses under ambiguous conditions significantly improves map accuracy.

We consider the problem of building accurate and descriptive 3D occupancy maps of an environment from sparse and noisy range sensor data. We seek to accomplish this task by constructing a predictive model online and inferring the occupancy probability of regions we have not directly observed. We propose a novel algorithm leveraging recent advances in data structures for mapping, sparse kernels, and Bayesian nonparametric inference. The resulting inference model has several desirable properties in comparison to existing methods, including speed of computation, the ability to be recursively updated without approximation, and consistency between batch and online inference. The method also reverts to the use of a specified prior state when insufficient relevant training data exist to predict the occupancy probability of a query point, a property which is attractive for motion planning and exploration applications with mobile robots.

We present a novel approach to perform underwater simultaneous localization and mapping (SLAM) using a small inspection-class remotely operated vehicle (ROV) equipped with a single-beam scanning sonar, amidst high levels of noise present in the sonar data, and in the absence of inertial/odometry measurements. Features are extracted from hierarchically grouped clusters of sonar returns, data association is performed via the iterative joint compatibility test, and the vehicle's trajectory and map are estimated using incremental smoothing and mapping (iSAM). The resulting point clouds derived from the ROV's sonar are used to produce Gaussian process occupancy maps, which interpolate among gaps in the acoustic range data to produce descriptive 3D maps of submerged structures. The proposed localization and mapping approach is demonstrated using data gathered in two harbor environments in close proximity to piers and seawalls.

Summary Recommender systems are playing an increasing role in improving user satisfaction as they can recommend items which might be highly interested to users. Recent advances have proven that social relations such as trust and distrust relations among users are helpful in improving recommendation accuracy. Traditional social recommendation methods directly utilize unweighted trust and distrust relations into collaborative filtering framework. These methods will lose their power when the trust or distrust relation data is sparse, which significantly hinders the improvement of rating prediction accuracy. To address this problem, we transform the unweighted trust and distrust relations into fine‐grained weighted social trust matrix which is denser and encodes the trust and distrust degree for pair of users. The weighted social trust matrix is then combined with the rating matrix in a collective matrix factorization framework to implement rating prediction task. Experimental results based on Extended Epinions dataset show that the proposed collective matrix factorization model with fine‐grained weighted social trust matrix can achieve better accuracy than conventional social recommendation algorithms such as SoRec and its extensions.

We present a novel formulation of Hilbert mapping in which we construct a global occupancy map by incrementally fusing local overlapping Hilbert maps. Rather than maintain a single supervised learning model for the entire map, a new model is trained with each of a robot's range scans, and queried at all points within the robot's perceptual field. We treat the probabilistic output of the classifier as a sensor, employing sensor fusion to merge local maps. This formulation allows Hilbert mapping to be used incrementally in real-world mapping scenarios with overlap between sensor observations. The methodology is applied to three-dimensional map-building, and evaluated using real and simulated 3D range data.

We present a novel algorithm to produce descriptive online 3D occupancy maps using Gaussian processes (GPs). GP regression and classification have met with recent success in their application to robot mapping, as GPs are capable of expressing rich correlation among map cells and sensor data. However, the cubic computational complexity has limited its application to large-scale mapping and online use. In this paper we address this issue first by proposing test-data octrees, octrees within blocks of the map that prune away nodes of the same state, condensing the number of test data used in a regression, in addition to allowing fast data retrieval. We also propose a nested Bayesian committee machine which, after new sensor data is partitioned among several GP regressions, fuses the result and updates the map with greatly reduced complexity. Finally, by adjusting the range of influence of the training data and tuning a variance threshold implemented in our method's binary classification step, we are able to control the richness of inference achieved by GPs - and its tradeoff with classification accuracy. The performance of the proposed approach is evaluated with both simulated and real data, demonstrating that the method may serve both as an improved-accuracy classifier, and as a predictive tool to support autonomous navigation.

We consider an autonomous exploration problem in which a mobile robot is guided by an information-based controller through an a priori unknown environment, choosing to collect its next measurement at the location estimated to be most informative within its current field of view. We propose a novel approach to predict mutual information (MI) using Bayesian optimization. Over several iterations, candidate sensing actions are suggested by Bayesian optimization and added to a committee that repeatedly trains a Gaussian process (GP). The GP estimates MI throughout the robot's action space, serving as the basis for an acquisition function used to select the next candidate. The best sensing action in the committee is executed by the robot. This approach is compared over several environments with two batch methods, one which chooses the most informative action from a set of pseudo-random samples whose MI is explicitly evaluated, and one that applies GP regression to this sample set. Our computational results demonstrate that the proposed method provides not only computational efficiency and rapid map entropy reduction, but also robustness in comparison with competing approaches.