Reviewerly/exHarmony huggingface dataset:
Coordinated Motion Planning for Heterogeneous Autonomous Vehicles Based on Driving Behavior Primitives

Heterogeneous autonomous vehicle (HAV) coordinated motion planning must guide each vehicle out of the conflict zone based on the differences in vehicle platform characteristics. Decomposing complex driving tasks into primitives is an effective way to improve algorithm efficiency. Hence, the purpose of this paper is to complete the coordinated motion planning tasks through offline driving behavior primitive (DBP) library generation, online extension and selection of DBPs. The proposed algorithm applies dynamic movement primitives and singular value decomposition to learn driving behavior patterns from driving data, integrates them into a model-based optimization generation method as constraints, and builds a DBP library by fusing driving data and vehicle model. Based on the generated DBP library and primitive association probabilities learned from labeled driving segments via stochastic context-free grammar, the planning method completes the independent DBP extension of each vehicle in the conflict zone, generates an interaction DBP tree, and uses the mixed-integer linear programming algorithm to optimally select the primitives to be executed. This study demonstrates that the generated DBP library not only expands the types of primitives, but also distinguishes the characteristics of HAVs. We also present how to utilize the DBP libraries to obtain coordinated motion planning results with spatiotemporal information in the form of DBP extension and selection. The results obtained by real vehicle platforms and simulation show that the proposed method can accomplish coordinated motion planning tasks without relying on specific scene elements and highlight the unique motion characteristics of HAVs.
JHPFA-Net: Joint Head Pose and Facial Action Network for Driver Yawning Detection Across Arbitrary Poses in Videos

Yawning detection is a key means in driver fatigue detection, which suffers difficulties including head poses, facial expressions, illumination variations, occlusions, etc. Yet, most previous methods mainly focus on frontal faces, and are deficient to deal with different facial actions under arbitrary poses in the actual driving environment. In this study, we propose a novel Joint Head Pose and Facial Action Network (JHPFA-Net) for driver yawning detection across arbitrary poses in videos, with three main parts including a Geometric-based Key-frame Selection Module (GK-Module), a Face Frontalization with Warp Attention Module (FF-Module) and a dual-channel classifier for Head Pose & Facial Action Fusion Module (HF-Module). Firstly, the GK-Module is proposed to extract geometric vectors and to construct a two-stage judgment mechanism, with the purpose of dealing with frame redundancy and improving the efficiency of JHPFA-Net structure. Secondly, distinguished with existing methods, the FF-Module is proposed to synthesize photo-realistic frontal faces, which can be used for capturing the facial actions under arbitrary poses. Finally, the HF-Module is proposed to fuse head pose attributes and facial modalities together, for the purpose of achieving pose-invariant detection and improving accuracy. Extensive experiments show that the proposed JHPFA-Net achieves state-of-the-art results comparing with some representative methods on the public YawDD benchmark, and it performs well in real-time application.
SS-VPE: Semi-Supervised Variational Prototyping Encoder With Student’s-t Mixture Model

As humans can identify new targets with one or a few images, machine learning can use a few images to generalize effective representations so as to achieve few-shot learning. Previous research has established relationships between visual prototype in input space and feature representations in latent space to capture data distributions. However, such visual prototype that can compactly represent class semantics are often lacking in most datasets. This study is aimed at distinguishing samples by using a latent prototype that represents class information in latent space. However, a latent prototype is mostly sensitive to outliers; therefore, a general method using a semi-supervised variational prototyping encoder (SS-VPE) is proposed. The contributions of this study are three-fold. First, a generative unsupervised learning is employed to optimize the prototypes in latent space, such that the data distribution can be captured accurately. Second, the Student’s-t mixture model is applied to the above clustering in the latent space, resulting in high robustness against outliers. Finally, the conventional variational autoencoder (VAE) is advanced by few-shot semi-supervised learning. Intensive experiments are conducted on four popular benchmark datasets, and the experimental results show that our proposed SS-VPE outperforms the state-of-the-art methods, significantly improving both the clustering and classification ability of few-shot learning tasks.
Flock: Towards Multitasking Virtual Machines for Function-as-a-Service

FaaS, or function as a service, promises unprecedented cost-efficiency and elasticity thanks to its on-demand and fine-grained execution nature. However, modern FaaS platforms mainly adopt virtual machines (VMs) or containers as a computing abstraction, which incurs costs like high startup latency, large memory footprint, and high communication overhead. Multi-tasking virtual machines (MVMs), which allow co-executing multiple functions in the same managed language runtime, are appealing for FaaS due to their lightweight nature. Unfortunately, existing MVMs are not designed for FaaS. The proposed abstraction of MVMs does not provide specialized support for fine-grained, latency-sensitive functions and their chain-like execution patterns. Meanwhile, the underlying runtime still contains many global modules and lacks essential support for function-level resource accounting and isolation. To this end, this work proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flock</i> , a retrofitted MVM for FaaS execution, which provides FaaS-aware abstractions named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">funclets</i> and enhanced runtime support for isolation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flock</i> is implemented atop the HotSpot JVM of OpenJDK 8. Performance evaluation shows that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flock</i> results in up to three orders of magnitude performance improvement over state-of-the-art FaaS platforms like OpenWhisk while providing sufficient isolation support for FaaS functions.
Technology

Smart contract platforms are part of broader technological development. Such platforms, and the smart contracts that exists thereon, cannot be considered in isolation. The relevant social and technological context in which the broader technological development takes place is a relevant circumstance that helps to understand the goals and purpose of those who design such instruments. This chapter explores the distributed nature of such platforms from a purely technological perspective and does so with special attention to the public-key cryptography that forms the foundation for such platforms. The manner in which cryptography can be used to introduce transparency will be illustrated. Additionally, the ways in which trust can be created in a pseudonymous environment and the manner in which this aligns with the ideals of those who drove such developments will be outlined. Additionally, the immutable nature of the platforms will be explored. Additionally, this chapter will show how smart contracts work and explain their core-characteristics. On the basis of this exploration, it will be shown why smart contracts are, first and foremost, technical concepts rather than legal concepts. Lastly, decentralised autonomous organizations as an application of smart contracting technology will be explored. This serves as an example of the potential of the technology, and serves to illustrate the questions that might arise from this technology and the potential legal implications. This chapter aims to illustrate how the technology under investigation in this book differentiates itself through five aspects: immutability, transparency, pseudonymity, automatic execution, and automatic enforcement.
SampleHST: Efficient On-the-Fly Selection of Distributed Traces

Since only a small number of traces generated from distributed tracing helps in troubleshooting, its storage requirement can be significantly reduced by biasing the selection towards anomalous traces. To aid in this scenario, we propose SampleHST, a novel approach to sample on-the-fly from a stream of traces in an unsupervised manner. SampleHST adjusts the storage quota of normal and anomalous traces depending on the size of its budget. Initially, it utilizes a forest of Half Space Trees (HSTs) for trace scoring. This is based on the distribution of the mass scores across the trees, which characterizes the probability of observing different traces. The mass distribution from HSTs is subsequently used to cluster the traces online leveraging a variant of the mean-shift algorithm. This trace-cluster association eventually drives the sampling decision. We have compared the performance of SampleHST with a recently suggested method using data from a cloud data center and demonstrated that SampleHST improves sampling performance up to by 9.5$\times$.
EFFECTOR: DASH QoE and QoS Evaluation Framework For EnCrypTed videO tRaffic

The exponential increase in Dynamic Adaptive Streaming over HTTP (DASH) based video traffic with endto-end encryption poses many challenges for Mobile Network Operators (MNOs). To improve user-perceived video quality, MNOs must be aware of the end user’s Quality of Experience (QoE) by exploring network level Quality of Service (QoS). On the contrary, the network edge facility provides proximity for performing an intelligent operation to maintain smooth QoE from the network level QoS measurement. Therefore, in this work, we propose EFFECTOR, a framework to showcase lightweight in-band QoS features measurement technique at edge nodes from encrypted DASH video traffic. EFFECTOR uses an emulated environment with real 4G and 5G drive test traces to generate video traffic. Moreover, this work provides a massive dataset for analyzing the impact of 4G and 5G technology on video quality in the form of Interactive Jupyter Notebooks. The proposed framework is ideal for investigating QoS extracted from the network’s edge and finding its relations with QoE to ensure better video quality for end-users.
Analysis and emulation of BGP hijacking events

Border Gateway Protocol (BGP) is the standard protocol used for inter-domain routing in the Internet. Since it was designed without built-in security mechanisms, nowadays it results in being vulnerable to various security issues. Although countermeasures exist to secure BGP sessions, they are not widely used due to lack of knowledge and complexity of the setup. The aim of this paper is to raise awareness about routing security in BGP, to provide a methodology to deepen the analysis of BGP incidents and a tool to reproduce them in a sandbox environment, to better understand how these issues arise and why it is crucial to have security countermeasures in place. The paper examines a recent BGP incident in March 2022, where a Russian ISP hijacked an IP prefix belonging to Twitter. A comprehensive analysis of the incident is performed, including how it spread throughout the Internet and presenting the powerful toolkit used for the analysis. In the last section, the paper explains the usage and the potentiality of the tool KathBGPBuilder, which can recreate a real BGP deployment with minimal manual configuration using open data collected from RIPEstat. This tool can be utilized to experiment and recreate real BGP incidents, or to test security mechanisms.
A Comprehensive Framework for Intent-Based Networking, Standards-Based and Open-Source

This paper presents a comprehensive framework for Intent-Based Networking (IBN). The framework is an open-source project, and its implementation is standards-based. Relevant IBN concepts from the standards organizations, the framework’s architecture, and its implementation on key IBN aspects and features including Intent life-cycle, Intent translation, Intent orchestration, and Intent assurance using closed-loops are discussed. The paper also demonstrates a real intent-based use case realized by the framework in order to show and validate the proof-of-concept. The Future work of this project is also discussed.
Low Latency Federated Learning over Wireless Edge Networks via Efficient Bandwidth Allocation

Federated learning has emerged as a solution to preserve data privacy while processing a large amount of data in parallel at the network edge, thus ensuring low latency and high data security. The involvement of a massive amount of edge devices in federated learning has raised various challenges due to device heterogeneity in computation and communication and limited resources. To reduce the latency of a resource-constrained network, optimal bandwidth sharing among edge devices participating in federated learning is necessary. This paper proposes an efficient bandwidth allocation method to reduce overall latency in federated learning rounds over resource-constrained edge networks. First, the optimization problem is formulated as the minimization of total latency in one round of federated learning. Then a fast converging iterative bandwidth allocation (FCI-BA) method is proposed to allocate the available bandwidth among participating edge devices efficiently. Based on experimental results, the proposed framework achieves optimal solutions regardless of the initial guess. Further experiments demonstrate the effectiveness of the approach for a large number of devices, and it is observed that the performance of the FCI-BA method is independent of the increase in the number of devices in the network. Therefore, the proposed method can be effectively applied to resource-constrained massive networks.
MPC-as-a-Service: A Customizable Management Protocol for Running Multi-party Computation on IoT Devices

Techniques to perform computations without disclosing the input values have notably improved in the last decade. One such technology, called Secure Multiparty Computation (MPC), where two or more computation nodes hold secret pieces of private data and jointly execute a protocol to obtain a function output, has proven effective for preserving privacy in many applications (e.g., distributed signing, financial scores, machine learning, and more). Nonetheless, in many cases, the data source and computation nodes are often assumed to be the same, with the existence of a manually preconfigured network before they start the computation. This challenge is typical of many IoT applications where the IoT devices need to collaborate using MPC but do not have the resources, and thus outsource the tasks to powerful MPC nodes. Nonetheless, in such a scenario, the IoT devices do not know the MPC nodes, and vice-versa to manage the overall process. To fill this gap, we propose an MPC management protocol that automates the registration and authentication of a group of clients (i.e., sources and consumers of data) and MPC servers (the private computation providers), the requesting of MPC jobs, and receiving the results thereafter. Our experiments over a cloud environment demonstrate the first protocol that efficiently and securely automates the management of MPC systems on many use cases, which would otherwise take considerable time and effort.
Gaussian dynamics equation in normal product form

In this paper, we discuss the normal product form of the density operator of multimode Gaussian states and obtain the correlation equation between the kernel matrix $$\textbf{R}$$ of the Gaussian density operator in the norm al product form and its kernel matrix $$\textbf{G}$$ in the standard quadratic form. Further, we explore the time evolution mechanism of $$\textbf{R}$$ and obtain the Gaussian dynamical equation under the normal product $$\overset{ \cdot }{\textbf{R}}=i(\mathbf {RJH-HJR})$$ . Our work is devoted to searching for another mechanism for Gaussian dynamics. By exploring the description of the normal ordered density matrix under the coherent state representation, we find that our mechanism is feasible and easy to operate.
Data Fusion and Pattern Classification in Dynamical Systems Via Symbolic Time Series Analysis

Abstract Symbolic time series analysis (STSA) plays an important role in the investigation of continuously evolving dynamical systems, where the capability to interpret the joint effects of multiple sensor signals is essential for adequate representation of the embedded knowledge. This technical brief develops and validates, by simulation, an STSA-based algorithm to make timely decisions on dynamical systems for information fusion and pattern classification from ensembles of multisensor time series data. In this context, one of the most commonly used methods has been neural networks (NN) in their various configurations; however, these NN-based methods may require large-volume data and prolonged computational time for training. An alternative feasible method is the STSA-based probabilistic finite state automata (PFSA), which has been shown in recent literature to require significantly less training data and to be much faster than NN for training and, to some extent, for testing. This technical brief reports a modification of the current PFSA methods to accommodate (possibly heterogeneous and not necessarily tightly synchronized) multisensor data fusion and (supervised learning-based) pattern classification in real-time. Efficacy of the proposed method is demonstrated by fusion of time series of position and velocity sensor data, generated from a simulation model of the forced Duffing equation.
Privacy-Preserved Video Monitoring Method with 3D Human Pose Estimation

With the fast growth of aging population and the spread of various chronic diseases such as heart disease and arthritis among older adults, elderly care has become an urgent topic facing today’s society. Consequently, technologies mediated remote care has become a widely-used method, with the significant promise of reducing cost and improving the efficiency and quality of healthcare. Yet, most remote-caring technologies, especially surveillance video based remote care, face the challenge of privacy issues. For addressing this issue, this paper proposes a privacy- preserved remote care method. Specially, we use ROMP to extract the 3D human model of the elderly in the surveillance video, and use KNN pose estimation algorithm to detect the potential abnormal behaviors. Compared to existing methods, which mainly replace the privacy information with totally different contents, our method not only protects the personal privacy information of the elderly, but also provides clear and identifiable posture information which could better support remote care.
A Robust and Accurate Encrypted Video Traffic Identification Method via Graph Neural Network

The explosive growth of video traffic has brought major challenges for network providers to improve user experience. On account of traffic encryption, network providers need to identify encrypted video traffic first before adopting optimization approaches to them. Traditional encrypted video traffic identification methods try to reveal the pattern of video traffic by using statistical features, which are not robust enough in different network environments. Some sophisticated graph-based methods recently have shown their advantages for encrypted traffic identification. However, these works lack optimization when it comes to the video streaming scenario. Inspired by these works, we propose GraphV, a GNN-based approach for identifying encrypted video traffic. Specifically, we construct an information-rich graph structure enhanced by unique features of video transmission. Then the embedding representation of each graph can be obtained through a Bi-LSTM layer added to all the sequential nodes embedding on this graph. The experiments on a well-known dataset and two open-world datasets from different network environments we collected show that GraphV outperforms the existing methods, especially on the generalization ability of the model.
Face Image Synthesis From Speech Using Conditional Generative A Adversarial Network

A Human Brain may translate a person's voice to its corresponding face image even if never seen before. Training a deep learning network to do the same can be used in detecting human faces based on their voice, which may be used in finding a criminal that we only have a voice recording for. The goal in this paper is to build a Conditional Generative Adversarial Network that produces face images from human speeches which can then be recognized by a face recognition model to identify the owner of the speech. The model was trained and the face recognition model gave an accuracy of 80.08% in training and 56.2% in testing. Compared to the basic GAN model, this model has improved the results by about 30%.
Path Planning based on Reinforcement Learning with Improved APF model for Synergistic Multi-UAVs

As the emerging technology of Unmanned Aerial Vehicles (UAVs) becomes mature, UAVs are widely used in environmental monitoring, communication and other fields. In view of this, this paper analyzed the task of synergistic multi-UAVs exploration of unknown environments, and proposed a path planning method for them based on reinforcement learning. Firstly, the path planning task of the UAVs was divided into two parts: the path travel strategy module and the information exploration strategy module. Models of the two modules were based on the Deep Deterministic Policy Gradient algorithm (DDPG), and an improved Artificial Potential Field (APF) force traction mechanism was introduced in the path travel strategy module. Its aim was to assist in guiding the generation of UAV flight path trajectories. Also it could enhance the learning capability of the model. The path travel strategy module would generate the complete flight path of the whole cluster in a distributed manner. A series of temporary target points provided by the information exploration strategy module helped. In maps with 21.5%, 25.3% and 29.6% of obstacles, multi-UAVs could achieve 84.2%, 76.7% and 69.9% of environmental exploration by the designed method. Compared with the APF method, the A star method and the Breath First Search (BFS) method, the proposed method is not only able to plan feasible paths in a more complex map model, but also the curvature of the planned paths is smoother, thus achieving the goal of reducing the energy cost of UAVs.