Data analysis focuses on harnessing advanced statistics, programming, and machine learning techniques to extract valuable insights from vast datasets. An increasing volume variety of research emerged, addressing datasets diverse modalities, formats, scales, resolutions across various industries. However, experienced data analysts often find themselves overwhelmed by intricate details in ad-hoc solutions or attempts the semantics grounded properly. This makes it difficult maintain scale more...

Generating collision-free formation control strategy for multiagent systems faces huge challenges in collaborative navigation tasks, especially a highly dynamic and uncertain environment. Two typical methodologies solving this problem are the conventional model-based paradigm data-driven paradigm, particularly widely used deep reinforcement learning (DRL) method. However, both paradigms encounter inherent drawbacks. In paper, we present two novel general schemes that combine these together...

Deriving a distributed, time-efficient, and connectivity-guaranteed coverage policy in multitarget environment poses huge challenges for multirobot team with limited communication. In particular, the robot needs to cover multiple targets while preserving connectivity. this article, novel deep-reinforcement-learning-based approach is proposed take both connectivity preservation into account simultaneously, which consists of four parts: hierarchical observation attention representation, an...

Reinforcement Learning (RL) has achieved remarkable success in safety-critical areas, but it can be weakened by adversarial attacks. Recent studies have introduced ``smoothed policies" to enhance its robustness. Yet, is still challenging establish a provable guarantee certify the bound of total reward. Prior methods relied primarily on computing bounds using Lipschitz continuity or calculating probability cumulative reward being above specific thresholds. However, these techniques are only...

Centralized Training with Decentralized Execution (CTDE) has emerged as a widely adopted paradigm in multi-agent reinforcement learning, emphasizing the utilization of global information for learning an enhanced joint Q-function or centralized critic. In contrast, our investigation delves into harnessing to directly enhance individual Q-functions actors. Notably, we discover that applying identical universally across all agents proves insufficient optimal performance. Consequently, advocate...

This paper presents ConvBench, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs). Unlike existing benchmarks that assess individual capabilities in single-turn dialogues, ConvBench adopts three-level multimodal capability hierarchy, mimicking human cognitive processes by stacking up perception, reasoning, and creativity. Each level focuses on distinct capability, mirroring the progression from basic perception to logical reasoning...

As Embodied AI advances, it increasingly enables robots to handle the complexity of household manipulation tasks more effectively. However, application in these settings remains limited due scarcity bimanual-mobile robot datasets. Existing datasets either focus solely on simple grasping using single-arm without mobility, or collect sensor data a narrow scope sensory inputs. result, often fail encapsulate intricate and dynamic nature real-world that are expected perform. To address...

Network security assessment is critical to the survivability and reliability of distributed systems. In this paper, we propose a novel approach that supports automatic vulnerability utilizing Bayesian attack graphs. We also integrate several major database into comprehensive build customized scanner assist graph generation. Different from existing solutions manually assign probabilities graph, design set quantitative metrics automatically analyze evaluate proposed with real-world examples....

Making efficient motion decisions for a multi-robot system is challenging problem in target encirclement with collision avoidance. Specifically, each robot local communication has to consider cooperative and avoidance simultaneously. In this paper, distributed transferable policy network framework based on deep reinforcement learning proposed solve the of The able process information uncertain number robots obstacles, which desirable property systems. particular, graph attention mechanism...

In this paper, we propose a novel decentralized method based on deep reinforcement learning using robot-level and target-level relational graphs, to solve the problem of multi-target encirclement with collision avoidance (MECA). Specifically, composed three heterogeneous graphs between each robot other robots, targets obstacles, are modeled learned through graph attention networks (GATs) for extracting different spatial representations. Moreover, target within observation robot, is built GAT...

With the advances of technologies in RFID, sensors, GPS, GPRS, IP networks and wireless networks, monitoring asset on move is becoming feasible. In this paper, we develop a trusted system for tracking based an integrated service network. Challenges requirements are discussed. An RFID sensor data integration proposed with network reliable information access delivery. We illustrate feasibility via food delivery tracking. Approach evaluation presented to help understand approachpsilas uniqueness.

Finding collision-free and efficient paths in an uncertain dynamic environment is a challenge for robot navigation tasks, especially when there are external autonomous agents that also have decision-making abilities the same environment. This paper develops novel method based on DRL with graph attention network (GAT) to solve problem of among (other agents). Specifically, GAT adopted describe other as specific graph, extract spatial structural influence features from graph. Multi-head...

This paper considers a multi-target coverage problem where robot team aims to efficiently cover multi-targets while maintaining connectivity in distributed manner. A novel knowledge-incorporated policy framework is proposed derive distributed, efficient, and guaranteed policy. In particular, knowledge-guided network (KGPnet) designed, which consists of observation attention representation, interaction learning. Giving credit the KGPnet, can be applied different number targets. Moreover,...

Distant e-learning emerges as one of promising means for people to learn online. Although there is a substantial increase in computer and network performance recent years, mainly result faster hardware more sophisticated software, are still problems the fields integrating various resources towards enabling distant e-learning. Further, with advances technologies RFID, sensors, GPS, GPRS, IP networks, wireless mobile learning becoming viable teaching learning. In this book chapter, we develop...

Applying deep reinforcement learning to football games has recently received extensive attention. However, this remains challenging due the excessively high complexity of environment, such as high-dynamical game states, sparse rewards, and multiple roles with different capabilities. Existing works aim address these problems without considering abundant domain knowledge football. In article, a knowledge-embedded framework is proposed. Specifically, pitch control concept innovatively...

Recently, a lot of works have been devoted to researching how agents can learn efficient cooperation in multiagent systems. However, it still remains challenging large-scale systems (MASs) due the complex dynamics between and environment dimension explosion state-action space. In this paper, we propose novel MultiAgent Automatic Curriculum Learning method (MA-ACL) solve learning problems cooperative MASs by beginning from on scenario with few automatically progressively increasing number...

In this paper, we propose a novel distributed method based on attention-based deep reinforcement learning using individual reward shaping, for multiple unmanned aerial vehicles (UAVs) cooperative short-range combat mission. Specifically, two-level attention policy, composed of observation-level and communication-level networks, is designed to enable each UAV selectively focus important environmental features messages, enhancing the effectiveness policy. Moreover, due high complexity...

In this paper, we present mobile intelligence (MI) to develop delay tolerant RFID network for enabling logistics and supply chain applications. It provides intelligent ubiquitous information access, relay, search delivery over relay networks (MRN) - a critical infrastructure with convergence of RFID, wireless sensor networks, IP networks. leverages nodes "bridge the gap" towards pervasive management.

In Wireless Sensor Network (WSN), one of the primary issues is energy conservation for extending network lifetime. Communication protocols WSN help reduce consumption via adopting sleep scheduling in network. Random a desirable mechanism its simplicity and steady duty cycle. However, low cycle sleeping nodes may destroy connectivity network, which assumption taken by most traditional routing protocols. If certain delay acceptable, partially connected can achieve successful packet forwarding...

Social learning, especially social incentives, is extremely important for humans to achieve a high level of coordination. Inspired by this, we introduce this concept into cooperative multiagent reinforcement learning (MARL), implicitly address the credit assignment problem and promote interagent direct interactions cooperations among agents in games. In article, propose novel intrinsic reward method with peer incentives (IRPI) based on actor–critic policy gradient. This can enable...

In Wireless Sensor Network (WSN), because battery and energy supply are constraints, sleep scheduling is always needed to save while maintaining connectivity for packet delivery. Traditional schemes have ensure high duty cycling enough percentage of active nodes then derogate the efficiency. This paper proposes an RFID based non-preemptive random scheme with stable low cycle. It employs delay tolerant network routing protocol tackle frequent disconnections. A low-power wakeup signal used...