Miracle-dz/UniFAR-FTUs huggingface dataset:
L2chain: Towards High-performance, Confidential and Secure Layer-2 Blockchain Solution for Decentralized Applications

With the rapid development of blockchain, the concept of decentralized applications (DApps), built upon smart contracts, has attracted much attention in academia and industry. However, significant issues w.r. t. system throughput, transaction confidentiality, and the security guarantee of the DApp transaction execution and order correctness hinder the border adoption of blockchain DApps. To address these issues, we propose L2chain, a novel blockchain framework aiming to scale the system through a layer-2 network where DApps process transactions in the layer-2 network and only the system state digest, acting as the state integrity proof, is maintained on-chain. To achieve high performance, we introduce the split-execute-merge (SEM) transaction processing workflow with the help of the RSA accumulator, allowing DApps to lock and update a part of the state digest in parallel. We also design a witness cache mechanism for DApp executors to reduce the transaction processing latency. To fulfill confidentiality, we leverage the trusted execution environment (TEE) for DApps to execute encrypted transactions off-chain. To ensure transaction execution and order correctness, we propose a two-step execution process for DApps to prevent attacks ( i.e. , rollback attacks) from subverting the state transition. Extensive experiments have demonstrated that L2chain can achieve 1.5X to 42.2X and 7.1X to 8.9X throughput improvements in permissioned and permissionless settings respectively.
The spatial extent of the z = 2. 04 absorber in the spectrum of PKS 0458-020

Radio observations of the z = 2.29 QSO PKS 0458-020 are presented which probe the spatial extent of the gas that gives rise to damped Ly-alpha and 21 cm absorption lines at z = 2.04. Previous observations suggest an extended H I disk that would cover the large-scale structure of the background radio source, and are also consistent with smaller configurations of gas that only cover the compact core of the source. VLBI data of the line and continuum are presented which rule out small configurations such as dwarf galaxies or isolated, gravitationally bound clouds. Rather, the evidence points to an extended disklike structure with a radius of curvature larger than (8/h)kpc, where h = H(0)/100 km/s/Mpc. 21 refs.
New approach to the solution of Falkner-Skan equation

( ^tt) between the plunging and pitching motions at the elastic axis vs mass ratio are plotted for three typical Mach numbers. At MOO —0.825, the phase difference rapidly decreases to zero with increasing mass ratio, while the amplitude ratio approaches to the constant value of 1.86 when /JL is greater than 40. Since the zero phase difference between the plunging and pitching motions implies the existence of the pivotal point at xpv=a-(h/b)/a (a is the elastic axis position behind midchord in percent semichord and a =-2.0 for the present system), the flutter mode at Mx =0.825 for /x, greater than 40 is essentially a pitching oscillation with pivotal point at xpv=-3.86, which is almost identical to the first natural mode shown in Fig. 2. Therefore, when we recall the fact that the large negative peak value of the out-of-phase component in the load distribution at M^ =0.825 is due to the phase lag of the shock wave motion, it is concluded that the shock wave (especially the phase delay of the shock-wave motion) is playing the dominant role in the mechanism of the transonic dip phenomenon. It is believed that the conclusions reached in this investigation may shed some light on the mechanism of the transonic dip phenomenon of a sweptback wing flutter, although the important viscous and three-dimensional effects are still not taken into account in the present analysis.
HearFire

Indoor conflagration causes a large number of casualties and property losses worldwide every year. Yet existing indoor fire detection systems either suffer from short sensing range (e.g., ≤ 0.5m using a thermometer), susceptible to interferences (e.g., smoke detector) or high computational and deployment overhead (e.g., cameras, Wi-Fi). This paper proposes HearFire, a cost-effective, easy-to-use and timely room-scale fire detection system via acoustic sensing. HearFire consists of a collocated commodity speaker and microphone pair, which remotely senses fire by emitting inaudible sound waves. Unlike existing works that use signal reflection effect to fulfill acoustic sensing tasks, HearFire leverages sound absorption and sound speed variations to sense the fire due to unique physical properties of flame. Through a deep analysis of sound transmission, HearFire effectively achieves room-scale sensing by correlating the relationship between the transmission signal length and sensing distance. The transmission frame is carefully selected to expand sensing range and balance a series of practical factors that impact the system's performance. We further design a simple yet effective approach to remove the environmental interference caused by signal reflection by conducting a deep investigation into channel differences between sound reflection and sound absorption. Specifically, sound reflection results in a much more stable pattern in terms of signal energy than sound absorption, which can be exploited to differentiate the channel measurements caused by fire from other interferences. Extensive experiments demonstrate that HireFire enables a maximum 7m sensing range and achieves timely fire detection in indoor environments with up to 99.2% accuracy under different experiment configurations.
WISE: Predicting the Performance of Sparse Matrix Vector Multiplication with Machine Learning

Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse kernel. Numerous methods have been developed to accelerate SpMV. However, no single method consistently gives the highest performance across a wide range of matrices. For this reason, a performance prediction model is needed to predict the best SpMV method for a given sparse matrix. Unfortunately, predicting SpMV's performance is challenging due to the diversity of factors that impact it. In this work, we develop a machine learning framework called WISE that accurately predicts the magnitude of the speedups of different SpMV methods over a baseline method for a given sparse matrix. WISE relies on a novel feature set that summarizes a matrix's size, skew, and locality traits. WISE can then select the best SpMV method for each specific matrix. With a set of nearly 1,500 matrices, we show that using WISE delivers an average speedup of 2.4× over using Intel's MKL in a 24-core server.
The non-rhombohedral low-temperature structure of PMN–10% PT

The phase diagram of the Pb(Mg1/3Nb2/3)O3 and PbTiO3 solid solution (PMN–xPT) depicts a boundary between cubic and rhombohedral phases for x ⩽ 0.32. X-ray powder measurements reported by Dkhil et al show a rhombohedrally split (222) Bragg peak for PMN–10% PT at 80 K that is consistent with this phase boundary. Remarkably, our neutron data taken on a single crystal of the same compound with comparable q-resolution reveal a single resolution-limited (111) peak down to 50 K, and thus no rhombohedral distortion. Given the marked difference in penetration depths between x-rays and neutrons in these lead-oxide relaxor materials, our results suggest that the bulk structure, which is nearly cubic, differs from that of the outer layer in PMN–10% PT, a situation that has also recently been observed in PZN by Xu et al.
Autonomous Drone Surveillance in a Known Environment Using Reinforcement Learning

We utilize deep reinforcement learning to develop both single-agent and multi-agent methods that can accomplish autonomous drone surveillance tasks in a known indoor environment in this research. We combine the benefits of both visual and obstacle information to boost efficacy while ensuring low time consumption. And we devise a separate reinforcement learning training and test technique that both enhance training efficiency and ensure task completion. This method also creates a new field for sim-to-real transfer. Our experimental results show that the trained agents can detect all targets at a relatively fast speed while maintaining a high level of security, and the patrol completion rate is more than 98% in both single-agent and multi-agent tasks.
Multivariate Weibull mixtures with proportional hazard restrictions for dwell‐time‐based session clustering with incomplete data

Summary. Emanating from classical Weibull mixture models we propose a framework for clustering survival data with various more parsimonious models by imposing restrictions on the distributional parameters. We show that these restrictions on the Weibull mixtures correspond to different proportional hazard restrictions across mixture components and Web page areas. A parametric cluster approach based on the EM algorithm is carried out on a multivariate data set. Our model set‐up encompasses incomplete‐data structures as well as censoring observations. We apply the methodology on retail data stemming from a global e‐commerce company. Sessions are clustered with respect to the dwell times that a user spends on certain page areas. The cluster solution that is found allows for a detailed examination of the navigation behaviour in terms of the hazard and survivor functions within each component.
Toward Performance Efficient UAV Task Scheduling in Cloud Native Edge

Unmanned Aerial Vehicle (UAV) has been widely applied in many domains. But the computation and energy resource limitation severely hinders its development and application. Mobile Edge Computing (MEC) emerges as a promising platform to process the tasks offloaded from the UAVs to effectively improve the Quality-of-Service (QoS). To this vision, it is first required that the edge servers must be deployed with the needed service to handle the offloaded task. Fortunately, by exploring cloud native computing technology, it is possible to deploy container-based microservice to MEC in a prompt way. In this case, it raises the task scheduling problem on whether to deploy a new service or to utilize an existing service to balance the overhead between data transmission and the microservice deployment (i.e., container image pulling) for overall task completion time minimization. In this paper, the problem is first formulated in Integer Linear Programming (ILP) form, and proved to be NP-hard. We further propose an incentive-based request scheduling algorithm. Experiments based on track-driven simulations show that the total completion time for all tasks is reduced by 21.37% compared to the state-of-the-art solution.
Collision-Backpropagation based Obstacle Avoidance Method for a Legged Robot Expressed as a Simplified Dynamics Model

Simplified dynamics models have been widely adopted to reduce the computational complexity of motion planning for legged robots. However, not much research has been conducted on the collision avoidance for a simplified dynamics model. To contribute to this problem, we present the collision-backpropagation based obstacle avoidance method (CBOA), in which we employ the gradient flow of the collision cost to optimize the trajectory, thus avoiding collisions with obstacles. Our experiment shows that the CBOA reduces the collision rate of planned trajectories by up to 15.89 times compared to a previous implicit collision avoidance method.
Hypergraphs: Concepts, Applications and Analysis

Complex networks are a mainstream tool for understanding and modeling complex systems. Hypergraphs have been extensively studied in many fields due to its strong ability to represent higher-order group relationships among objects. In this paper, we give a comprehensive overview of hypergraphs. We first introduce the background of hypergraph and some basic terminologies. Then, we review hypergraph generation methods and representation methods combined with some downstream tasks, such as vertex classification, hyperedge prediction. Finally, we look into topological properties of some typical hypergraphs, including vertex degree distribution, hyperedge degree distribution, connectivity, etc. The paper concludes with a discussion of application and promising future directions of hypergraphs.
Federated Edge Learning via Reconfigurable Intelligent Surface with One-Bit Quantization

In this paper, the problem of model aggregation for the federated edge learning (FEEL) over a realistic wireless network is investigated, where multiple distributed edge devices collaboratively train a global learning model using local data. In the considered model, the one-bit gradient quantization with majority vote is adopted at edge devices, which sends the local gradient sign to the edge server instead of transmitting high-dimensional stochastic gradients directly. After aggregating the quantified signs, the edge server sends back only the majority decision to significantly minimize the transmission overhead since all communications are compressed to one bit. Nevertheless, it turns out that the quality of training will be inevitably deteriorated by the undesirable propagation error introduced by wireless channels. To address this issue, we propose in this work a reconfigurable intelligent surface (RIS) assisted one-bit communication scheme under orthogonal frequency division multiplexing (OFDM) system to reduce the signal distortion during iterative model exchange of FEEL. Specifically, a learning convergence analysis with respect to wireless communication error is first established. After that, we further formulate a unified communication-learning design problem to jointly optimize the power allocation vector and the RIS configuration matrix. Numerical results demonstrate that the proposed design achieves substantial performance improvement compared with the state-of-the-art solutions.
Performance Analysis of Visual-Inertial Navigation System with Feature Track Parameters

In this paper, we study the estimation performance of a visual-inertial navigation system (VINS) with two visual feature parameters: the number of visual feature tracks and vertical resolution of an image. While VINS have been intensively researched in the recent two decades, how quantitatively these parameters affect the estimation performance is still unclear. We try to answer this question by designing numerical simulation that is based on the real-world sensor setup. Specifically, we design two simulation experiments with decreasing number of visual features to simulate a visually challenging environment and decreasing vertical resolution of an image to mimic a restricted field of view of a ground vehicle with a frontal view. Our simulation results reveal that the 90% of visual feature loss induces 4.37 and 2.03 error gain for the position and attitude, respectively. Furthermore, the study shows that the vertical resolution has significant negative effect on the vertical position estimates.
Dynamic Output-Feedback Controller Design for Interval Positive Linear Systems

This paper addresses the dynamic output-feedback control design for positive linear systems with interval uncertainties. Both continuous- and discrete-time cases are investigated. For this purpose, sufficient linear matrix inequality (LMI) conditions are developed to assure the existence of full order dynamic output-feedback controllers. As main property, in order to simplify the treatment, both Lyapunov matrix and control realization are described in an affine manner at the LMI conditions. An optimization algorithm with initial feasible conditions is given to design the controller. The results are illustrated by means of numerical examples and comparisons with previous approaches from the literature.
Safe Integration of Autonomous Machines in Underground Mining Environments

Autonomous and Semi-Autonomous Machines (ASAMs) provide several benefits and have already emerged in mining environments. However, for cost-efficiency reasons and for ASAMs to reach their full potential, they should be capable of operating seamlessly with manually operated machines. Establishing the requirements for sufficient safety for such integration is a non-trivial task. This paper proposes a methodology for safely integrating ASAMs in underground mining environments. First, we describe the purpose of the integration and define the constituent components. Second, we identify the conditions that ASAMs will likely encounter using ODD-UM, an operational design domain specification for underground mining. Third, we derive high-level requirements for individual components based on ODD-UM attributes. Such requirements are allocated into the constituent components and considered as assumptions for the safety analysis. Fourth, we perform STPA (System-theoretic Process Analysis) to analyze safety-related control requirements for the integrated system. Our methodology could help the system integrator to systematically identify integration requirements to be enforced in constituent components and safety control systems. Index Terms—Underground Autonomous Mining, Integration Requirements, ODD-UM, STPA, Safety-guided Design.
Learning Optimal Team-Decisions

In this paper, we linear quadratic team decision problems, where a team of agents minimizes a convex quadratic cost function over T time steps subject to possibly distinct linear measurements of the state of nature. We assume that the state of nature is a Gaussian random variable and that the agents do not know the cost function nor the linear functions mapping the state of nature to their measurements. We present a gradient-descent based algorithm with an expected regret of O(log(T)) for full information gradient feedback and $\mathcal{O}\left( {\sqrt {\left( T \right)} } \right)$ for bandit feedback. In the case of bandit feedback, the expected regret has an additional multiplicative term O(d) where d reflects the number of learned parameters.
Constructing a DRL Decision Making Scheme for Multi-Path Routing in All-IP Access Network

Despite the massive increase in network capacities and devices capabilities, intra-domain TE problems have remained a challenge due to high traffic demand in dynamic network settings. In this paper, we fundamentally adopt and construct the use of Deep Reinforcement Learning (DRL) algorithm to improve end-to-end delay and maximum link utilisation (MLU) in a wireless All-IP access network. In an experimental pseudo setting, we propose a routing method DRL-MPR (Multi-Plane Routing) within a Dueling Deep Q-Network (DDQN) framework as a controller in which by simultaneously allocating multiple robust IP sessions, each session will take the near optimal path/plane. We first revisit Multi-Plane Routing Protocol (MPR) to configure a set of routing paths/Routing Planes (RP) from a physical topology as pre-condition ahead of the traffic injection. We compare our decision making scheme results with some traditional routing methodologies such as OSPF and standard Multi-Plane Routing (MPR). Our simulation results show that DRL obtains lower maximum link utilisation (MLU) and end-to-end delay. For example, in the 19 nodes and 41 links topology, a small traffic load results in 60% maximum link utilisation for DRL-MPR, whereas those of OSPF and MPR are 100% and 94% respectively. In this simulation environment, the traffic demand in the IP access network is gradually increasing to the point of saturated network link utilisation.
Enhanced Recursive Feature Elimination for IoT Intrusion Detection Systems

Due to the rapidly growing reputation and utilization of IoT devices, The threat to our privacy and security is increasing exponentially, since it is a convenient platform for cyber-attacks. in 2017 the number of attacks utilizing/against IoT devices increased by 600%. As there are many areas of security that utilize machine learning (ML) technology. Based on previous research, using ML to detect intrusions appears to be realistic and workable. While several studies have been conducted using ML for intrusion detection, they are either not highly accurate, only compatible with specific types of attacks or devices, or require a long duration to classify the attack. In this paper, we present an Intrusion Detection system that utilizes a Recursive Feature Elimination wrapper with the Decision Tree classifier to remove the redundant features. Experimental results on the Bot-IoT and NSL-KDD data-set minimized the number of features to only 16 and 15 respectively while maintaining high accuracy, precision, recall, and f1-score in comparison to previous works.
Evaluation of a novel DSO-based Indoor Ceiling-Vision Odometry System

Indoor localization for mobile industrial robots is a crucial step toward an autonomous system. A mobile robot needs a reliable and robust localization system to achieve its task autonomously. A reasonable estimate of the robot's state can be achieved through Visual Odometry (VO); however, with dynamic objects in the scene, classical VO approaches need to detect and filter these moving objects. Alternatively, we can use an up-facing camera to track the movement with respect to the ceiling, which represents a static and invariant space. This paper presents Ceiling-DSO: an indoor ceiling-vision (CV) system based on Direct Sparse Odometry (DSO). We take advantage of the generic formulation of DSO to avoid making assumptions about the observable shapes or landmarks on the ceiling, making the method generic and applicable to multiple ceiling types. We built a ceiling-vision dataset in a real-world scenario; we then used it to test our approach with different DSO parameters to identify the best fit for robot pose estimation. This paper provides a qualitative and quantitative analysis of the obtained results that showed an acceptable error rate compared to the ground truth.
How to easily make a policy network of reinforcement learning robust without physical modeling

In this paper, we propose ensemble inverse model network based disturbance observer (EIMN-DOB) to improve the robustness of the policy network (PN) which is a training result of policy based reinforcement learning (RL), without physical modeling. EIMN-DOB uses the ensemble model of the inverse model network (IMN), which acts as a nominal inverse model, and can estimate and cancel model uncertainty and disturbance like a typical disturbance observer (DOB) without a physical modeling. Because EIMN is trained from the data used in training RL, the additional training data for expressing the inverse model are not required. The experiments in this paper appeared that the PN of soft actor critic(SAC) combined with EIMN-DOB maintains control performance even in the presence of disturbance in continuous control benchmark tasks based on Mujoco physics engine. When the trained PN is used with EIMN-DOB in the real environment, the control performance in simulator can be preserved in the real environment, and it is expected to be utilized to minimize the sim-to-real gap of RL.
A Comprehensive Literature Review for Optimal Planning of Distributed Energy Resources in Distribution Grids

With the increasing integration of distributed energy resources (DERs) such as distributed generators (DGs) and energy storage systems (ESSs) in the distribution grid, their optimal planning in terms of the type, size, and site is attracting significant research attention from both the industry and academia. This paper conducts a comprehensive literature review on the existing methods for DG and ESS planning and state-of-the-art optimization approaches. At first, the optimal DER planning framework and mathematical models are introduced. Subsequently, uncertainty modeling methods and optimization techniques are classified into different categories, reviewed, and compared in detail. Finally, conclusions and future works are discussed. This review can provide valuable insights into current research achievements and future research directions of the DER planning for worldwide researchers and engineers.