Pedestrian trajectory prediction plays a crucial role in understanding human behavior and intentions. Due to the inherent randomness in human movement, current research constructs trajectories in stochastic space and uses diffusion models to reverse the denoising process. The commonly used denoising model, Transformer, is affected by uneven noise sampling, leading to confusion between temporal consistency and randomness across multiple time points. To address this issue, we propose a novel framework named Stochastic-Aware Mamba Diffusion (SAMD), which combines Stochastic-Aware Mamba with the Diffusion model to predict motion noise and motion states in the stochastic space. It utilizes a temporal aggregator to extract temporal consistency features. We construct a motion-selective state space model, which includes the adaptive transition between consistency motion states and stochastic states for balancing stability and diversity. The motion gating unit activates pertinent information within motion states to extract motion noise for trajectory prediction. Our approach achieves state-of-the-art results on the ETH-UCY and SDD datasets while significantly reducing the computational cost.

For intelligent driving systems, predicting the lane change intentions of surrounding vehicles in advance is essential to improve safety and efficiency in dynamic traffic conditions. In this paper, a lane-changing intention identification method with driving style recognition is proposed to identify lane-changing intentions for intelligent vehicles, incorporating driving style recognition to enhance prediction accuracy. Firstly, a dynamic clustering framework integrating the Gaussian Mixture Model is introduced to identify the driving style of vehicles under different traffic conditions. Subsequently, a lane-changing intention recognition model based on bidirectional long short-term memory networks is proposed. By leveraging driving style enhancements, the model is refined to better simulate and comprehend driving behaviors on the road. Finally, the NGSIM dataset is used to train and evaluate the proposed prediction identification method. The results show that the accuracy is improved by 2.1% compared to state-of-the-art methods.

With the coming of the Internet of Things (IoT) era, malware attacks targeting IoT networks have posed serious threats to users. Recently, the emerging of edge computing have paved the way for new data processing paradigms in IoT networks, but it is still a challenge for deploying malware detection systems on the IoT devices. This paper develops an IoT malware detection system based on trust hybrid user-edge evaluation, namely MDHE. This system decomposes a large and complex deep learning model into two parts, which are deployed on edge servers and end devices, respectively. Specifically, a trust evaluation mechanism is used to select the trusted devices to participate the model training. Moreover, we develop a private feature generation that leverages a graph mining technology to extract the subgraph features, which then are perturbed by leveraging the differential privacy technology to prevent user privacy from leaking. Finally, we reconstruct the perturbed features on edge server, and propose a Capsule Network (CapsNet) to identify malware. Experimental results show that MDHE can effectively detect malware. Specifically, it can reduce sensitive inference while maintaining the utility of data.

Pedestrian trajectory prediction is a challenging and important task in many applications, which aims to predict future pedestrians' trajectory coordinates from the input historical data. The existing methods usually use ready-made trajectory coordinates as inputs, which is, however, unavailable in video-based scenarios. In this paper, we propose a relation reasoning hypergraph (RRH) model to directly predict multiple pedestrian trajectories from raw videos. It is a challenging issue for the input and output are in different modalities and a video may contain multiple pedestrians. Our model integrates historical trajectory tracking, pedestrian relation reasoning, and future trajectory prediction into one framework. For capturing the subtle social relationships among pedestrians, we design a relation reasoning hypergraph network. We tested the proposed method on two public pedestrians datasets and the performance demonstrates the power of the model.

Recognizing time-varying object states in complex tasks is an important and challenging issue. In this paper, we propose a novel model to jointly infer object fluents and complex tasks in videos. A task is a complex human activity with specific goals and a fluent is defined as a time-varying object state. A hierarchical graph represents a task as a human action stream and multiple concurrent object fluents which vary as the human performs the actions. In this process, the human actions serve as the causes of object state changes which conversely reflect the effects of human actions. For a given input video, a causal sampling search algorithm is proposed to jointly infer the task category and the states of objects in each video frame. For model learning, a structural SVM framework is adopted to jointly train the task, fluent, cause, and effect parameters. We test the proposed method on a task and fluent dataset. Experimental results demonstrate the effectiveness of the proposed method.

In recent years, Dropbox, Google, and Microsoft have been competing in the market of consumer cloud storage (CCS) services. While once the key comparative metric, storage capacity per user has outgrown the needs of most users. Today, third-party applications based on CCS's RESTful Web APIs are becoming a primary way for users to utilize their expanded storage resources. Unfortunately, there is very little visibility into the performance of these Web APIs, even though they are primary determinants of the end user experience on these storage applications. In this paper, we report results from a comprehensive measurement study of the Web APIs of five popular CCS providers. Our results reveal significant differences and limitations in API performance, which result in performance bottlenecks visible to the user through the storage application. We analyze the underlying system designs of the five providers' Web APIs, and present the performance implications of their different design choices. Our research provides practical guidance for service providers to optimize their API performance, for developers to improve the experience of third-party applications, and for users to pick appropriate services that best match their requirements.

Consumer cloud storage (CCS) services have become popular among users for storing and synchronizing files via apps installed on their devices. A single CCS, however, has intrinsic limitations on networking performance, service reliability, and data security. To overcome these limitations, we present UniDrive, a CCS app that synergizes multiple CCSs (multi-cloud) by using only few simple public RESTful Web APIs. UniDrive follows a server-less, client-centric design, in which synchronization logic is purely implemented at client devices and all communication is conveyed through file upload and download operations. Strong consistency of the metadata is guaranteed via a quorum-based distributed mutual-exclusive lock mechanism. UniDrive improves reliability and security by judiciously distributing erasure coded files across multiple CCSs. To boost networking performance, UniDrive leverages all available clouds to maximize parallel transfer opportunities, but the key insight behind is the concept of data block over-provisioning and dynamic scheduling. This suite of techniques masks the diversified and varying network conditions of the underlying clouds, and exploits more the faster clouds via a simple yet effective in-channel probing scheme. Extensive experimental results on the global Amazon EC2 platform and a real-world trial by 272 users confirmed significantly superior and consistent sync performance of UniDrive over any single CCS.

With wide application of virtualization technology, tenants are able to access isolated cloud services by renting the shared resources in datacenters. Unlike resources such as CPU and memory, datacenter network, which relies on traditional transport-layer protocols, suffers unfairness due to a lack of VM-level network isolation. In this paper, we propose Falloc, a new bandwidth allocation protocol, towards VM-based fairness across the datacenter with two main objectives: (i) guarantee bandwidth for VMs based on their base bandwidth requirements, and (ii) share residual bandwidth in proportion to weights of VMs. To design Falloc, we model the datacenter bandwidth allocation as a bargaining game and propose a distributed algorithm to achieve the asymmetric Nash bargaining solution (NBS). We apply the theory to practice by implementing Falloc with OpenFlow in experiments under diversed scenarios, which shows that Falloc can achieve fairness by adapting to different network requirements of VMs, and balance the tradeoff between bandwidth guarantee and proportional bandwidth share. By carrying out large scale trace-driven simulations using real-world Mapreduce workload, we show that Falloc achieves high utilization and maintains fairness among VMs in datacenters.