Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning based Bi-stage Control for Semi-enclosed Berth

In this paper, reinforcement learning is used to study the problem of semi-enclosed berth with bilateral obstacles, where the unmanned surface vessel can berthing from any universal initial position. A bi-stage path planning is developed, where Stage 1 is to arrive the boundary quickly while keeping the connection between berth center and USV mass center consistent with yaw angle. Stage 2 ensures a successful berthing with no collision. By doing so, a stable, rapid, and safe berthing can be guaranteed. Finally, simulations verify the effectiveness and robustness of our proposed method.
High-Resolution Network with Attention Module for Human Pose Estimation

Convolution neural networks (CNNs) have achieved the highest performance today not only for human posture prediction but also for other machine vision tasks (e.g., object identification, semantic segmentation, images classification). Furthermore, the Attention Module demonstrates their superiority over other conventional networks (AM). As a result, this work focuses on a useful feed-forward AM for CNNs. First, following a stage in the backbone network, feed the feature map into the attention module, which is separated into two dimensions: channel and spatial. The AM then multiplies these two feature maps and passes them on to the next level in the backbone. The network can collect more information in long-distance dependencies (channels) and geographical data, resulting in higher precision efficiency. Our experimental results would also show a difference between the employment of the attention module and current methodologies. As a result of the switch to a High-resolution network (HRNet), the predicted joint heatmap keeps accuracy while reducing the number of parameters compared to the baseline-CNN backbone. In terms of AP, the suggested design outperforms the baseline-HRNet by 2.0 points. Furthermore, the proposed network was trained using the COCO 2017 benchmarks, which are currently available as an open dataset.
Experimental Validation of Encrypted Quadratic Optimization Implemented on Raspberry Pi

Cloud-based outsourcing framework provides an effective and superior means of information processing in solving quadratic optimization problems with linear constraints involving multiple agents. However, since the data involved in solving the quadratic optimization problem might be privacy-sensitive, there are different privacy leakage problems in the data collection and calculation stages. In this study, we propose a general cryptographic framework for securely computing quadratic optimization problems with sensitive data. Simultaneously, we analyze the privacy leakage problem in the cloud-based outsourcing computing framework and the corresponding solutions are given. The correctness and efficiency of the cryptographic framework are evaluated by conducting experiments on the Raspberry Pi and laptop. The experimental results validate that the cryptographic framework not only effectively protects the privacy-sensitive information but also can complete the calculation on the device with limited computing resources.
An Effective Local Path Planning Algorithm for Intelligent Vehicle in Complex Environment

This paper proposes an intelligent vehicle local path planning algorithm for complex scenarios. Firstly, the Frenet coordinate system is used to decouple the motion of vehicle. According to the initial configuration and target configuration, the trajectory set are obtained through a polynomial method. Secondly, the feasibility of these trajectories is checked by collision, minimum distance between vehicles, curvature peak, and acceleration peak. Finally, a novel cost function based on the driving risk value and the rate of acceleration change is used to evaluate the feasible trajectories. The trajectory that minimizes the loss function is selected as the optimal solution. The results show that the proposed method can obtain a reasonable driving plan in a specific scenario.
Dissipative analysis for fractional-order complex-valued reaction–diffusion neural networks

This paper considers the dissipative analysis for a class of neural networks. The considered model consists of fractional-order factor and reaction-diffusion terms. By referring to the results proposed for real-valued neural networks, the criteria that guaranteed the dissipativity of the fractional-order complex-valued reaction-diffusion neural networks is given in terms of fractional Halanay inequality. Finally, a numerical example is provided to illustrate the feasibility of the results in this paper.
Quadratic Optimization Using Additive Homomorphic Encryption in CPS

In this paper, we propose a secure protocol to compute the quadratic optimization in a three-party outsourcing architecture in cyber-physical systems. To enable the real-world implementation, a fixed-point expression is given to represent the input of the protocol. Based on this, we define the recovery operations for decryption, addition, and multiplication. Considering the three-party-involved computation, we thoroughly analyze the privacy issues during the interaction processes. Then, the secure protocol is developed by designing encrypted addition, multiplication, and comparison based on the additive homomorphic encryption scheme. Finally, we propose a sufficient condition only related to the parameters of the fixed-point expression to guarantee that the recovered result is correct.
Total Least Squares Estimation for Motion-Induced Extrinsic Calibration in Multi-Robot Systems

Robot-to-robot extrinsic calibration (EC), which refers to determining relative pose between robots, is a fundamental requirement for many multi-robot applications. Geometric-based methods, by taking advantage of the geometric constraints arising from robot motion and inter-robot measurements, are simple and intuitive for motion-induced robot-to-robot EC. However, this approach is not available to noise-corrupted scenarios. In this paper, by considering the effect of measurement noises, we formulate the two-dimensional motion-induced EC problem as a total least squares (TLS) problem with multiple quadratic constraints. Then, a novel quadratic constrained TLS (QC-TLS) algorithm is proposed based on the Lagrange optimality condition, which is rephrased as an eigenvalue problem. Besides, an iteration scheme for the numerical computation of the QC-TLS is presented. Finally, simulation results are presented for demonstrating the effectiveness of the proposed algorithm.
Gentle Normalization and Translation in Graph Neural Network for Few-shot Learning

Few-shot learning methods based on graph neural networks (GNNs) have shown powerful capabilities. However, GNNs have a very intractable problem called oversmoothing. The oversmoothing problem refers to that as the number of GNN layers increases, the node information will converge to a similar value, which is difficult to be distinguished, thus reducing the classification performance. In this paper, a Gentle Normalization and Translation (GNT) model is proposed to solve the above problem. On the basis of the original Normalization method, a Gentle Normalization is presented to solve the oversmoothing problem and reduce the model variance by reducing the scaling range. Further, a Translation operation is developed to deal with the oversmoothing problem caused by the ReLU layer. In addition, the Initial Residual is added which can also solve the oversmoothing problem to a certain extent. The experiments on public datasets show that the classification performance has been improved considerably.
Outlier-Robust Constrained State Estimation via ℓ<sub>1</sub> and Huber Penalization

A robust approach to constrained state estimation is proposed. Classical optimal estimation methods such as Kalman filtering/smoothing have served well for the past half century. Recently, with a growing number of data sources/sensors and the demanding nature of new applications, a revisit of some of the fundamental assumptions of the aforementioned classical methods is necessary. Models that deviate from the standard Gaussian noise distribution assumption are considered in this research. This allows for the accommodation of measurement outliers, as well as impulsive disturbances on system dynamics. The particular focus in this work is on robustness to measurement outliers. The problem is framed as a convex optimization problem which enjoys global optimality guarantees. This formulation also enables the addition of convex state constraints with ease, giving more modeling flexibility. Traditional &#x2113;<inf>2</inf> penalty methods yield poor estimation performance in the face of measurement outliers. In this work, &#x2113;<inf>1</inf> and Huber penalties are introduced to handle cases that are outside of the reach of &#x2113;<inf>2</inf> methods. Simulation studies are conducted on a classic road vehicle navigation problem to show the efficacy of the proposed approaches.
Distributed cubature information filtering based on hybrid consensus strategy with event-triggered mechanism

In this paper, the cubature information filtering is investigated to deal with the distributed state estimation problem for nonlinear systems. In order to release the communication burden of sensor networks, a new event-triggered mechanism is designed based on the difference between innovations at current time instant and the latest event-triggered time instant. The hybrid consensus strategy with the new event-triggered mechanism is adopted to leverage information from neighbor nodes, which combines the advantages of the consensus on measurements method and the consensus on information method. Finally, the effectiveness of the proposed algorithm is verified by a simulation.
Patch Variational Autoencoder-based Industrial Defect Detection

Anomaly detection occupies an important position in the field of industrial automation and manufacturing intelligence. This paper aims to alleviate two of the inherent problems in variational inference-based anomaly analysis. One of the problems is the information obtained from prior latent distribution is limited, and the other one is the feature collapse phenomenon caused by the strong probability distance notion. In this paper, a patch variational autoencoder architecture with jigsaw puzzle solving is proposed, which is able to increase the expressiveness of the latent manifold. And we introduce a sliced-Wasserstein measure to solve the second problem. There is a superior performance on the MVTec AD datasets.
Developing a Realistic Simulation Environment of Off-road Terrain for UGV

Recently, the technology of Unmanned Ground Vehicle (UGV) has had rapid development. The intelligent behavior of modern UGV in off-road terrain has become a significant issue in the field of Artificial Intelligence. Experimentation on the UGV in reality not only costs too much, but is also difficult to replicate the problem of UGVs’ behavior. So, developing a realistic simulation environment of off-road terrain in software is crucially important. In this paper, a realistic simulation environment of off-road terrain is designed based on Blender and CoppeliaSim. First, the models of landform, road and plants are built and rendered in Blender. Then, they are assembled into a realistically off-road terrain for UGV in CoppeliaSim. Last, the generated simulated off-road terrain is tested using a predetermined UGV, which is running to identify the environment.
LatEst: Vertical elasticity for millisecond serverless execution

Current state-of-the-art serverless frameworks can-not execute functions within a few milliseconds for bursty work-loads. The reason for that is that typically they rely on horizontal elasticity to cope with the varying demand for resources, which induces high overhead in the event of a cold -start. Recent literature has focused on minimizing the overhead of horizontal elasticity using mechanisms such as snapshots. However, the spawning of new function instances still requires several tens of milliseconds. This paper proposes vertical elasticity to scale resources of serverless functions to cope with bursting workloads. We design LatEst, a controller for serverless frameworks that adapts the allocated resources of active function instances. U sing vertical scaling, LatEst can adjust to bursts of function invocations within a few milliseconds. LatEst implements a feed-back control loop to: (1) predict the required resources during workload changes and (2) react rapidly and accurately to such changes. Moreover, LatEst spawns new instances for functions when the resources of the underlying server are reaching their limit. We evaluate LatEst as an extension of vHive [1] and find that LatEst can improve tail latency of serverless functions up to 25x compared to vHive.
Multimorbidity profiles and stochastic block modeling improve ICU patient clustering

Identifying groups of patients with similar morbid-ity profiles can help us understand the relationships between their pre-existing conditions and the risks of adverse events in the ICU. To find such groups, common approaches apply clustering algorithms such as k-means and latent class analysis. However, these techniques present drawbacks such as the lack of principled methods for choosing the number of clusters, the need for assumptions about the relationships between variables, and outputs which are hard to explain. To overcome these limitations, we map the problem of patient clustering to that of community detection in complex networks. We construct a bipartite network in which nodes represent patients and their features, including morbidities and demographics. Then, we find homogeneous groups of patients using stochastic block modeling (SBM), an unsupervised probabilistic approach to find structure in networks. We show that this approach has several advantages over traditional clustering methods, and enables us to retrieve more fine-grained clusters that are commonly missed by existing approaches. We also show that these clusters have a stronger relationship with mortality and sepsis rates of patients in the ICU.
Neural Network Model Obfuscation through Adversarial Training

With the increased commercialization of deep learning (DL) models, there is also a growing need to protect them from illicit usage. For cost- and ease of deployment reasons it is becoming increasingly common to run DL models on the hardware of third parties. Although there are some hardware mechanisms, such as Trusted Execution Environments (TEE), to protect sensitive data, their availability is still limited and not well suited to resource demanding tasks, like DL models, that benefit from hardware accelerators. In this work, we make model stealing more difficult, presenting a novel way to divide up a DL model, with the main part on normal infrastructure and a small part in a remote TEE, and train it using adversarial techniques. In initial experiments on image classification models for the Fashion MNIST and CIFAR 10 datasets, we observed that this obfuscation protection makes it significantly more difficult for an adversary to leverage the exposed model components.
Deverlay: Container Snapshots For Virtual Machines

The Cloud Native paradigm has quickly emerged as a new trend in Web Services architectures. Applications are now developed as a network of microservices and functions that can be quickly re-deployed anywhere, decoupled from their state. In this scenario, workloads are usually packaged as container images that can be quickly provisioned anywhere in a provider web service. To enforce security, traditional Docker container runtime mechanisms are now being enhanced by stronger isolation techniques such as lightweight hardware level virtualization. Such sandboxing inserts a strong boundary - the guest space - and therefore security containers do not share filesystem semantics with the host Operating System. However, the existing container storage drivers are designed and optimized to run directly on the host. In this paper we bridge the gap between traditional containers and virtualized containers. We present Deverlay, a container storage driver that prepares a block-based container root filesystem view, targeting lightweight Virtual Machines and keeping host native execution compatibility. We show that, in contrast to other block-based drivers, Deverlay can boot 80 micro VM containers in less than 4s by efficiently sharing host cache buffers among containers and reducing I/O disk access by 97.51 %.
Improving Server Re-Consolidation for Datacenters via Resource Exchange and Load Adjustment

Server re-consolidation refers to online migrating applications among servers for minimizing the number of servers used. To avoid interruptions to online services, resources (i.e., transient resources) are occupied at both original and destination servers during the migrations. Transient resources reduce the remaining capacity of servers, making server re-consolidation challenging. To tackle this problem, we leverage the previously proposed concept of resource exchange, which refers to a set of resource configurations such that an application running under any one of them has similar performance, thus these configurations are exchangeable and such a set is called exchangeable configuration set (ECS). Moreover, we adjust the load pressure of an application to change its ECS when there is no suitable configuration, and such a strategy is called load adjustment. In this paper, we propose a new server re-consolidation problem that maintains applications' QoS target and availability at the same time. We leverage a machine-learning algorithm to estimate the performance of applications, based on which the ECSs are constructed efficiently. We design a simulated annealing-based (SA) algorithm to optimally solve the scheduling plan. We evaluate the proposed approach using data from real-world latency-critical applications. The results show that the proposed approach outperforms two state-of-the-art baselines in terms of reducing the number of used servers by 10% on average and up to 29%. To our knowledge, this is the first time that resource exchange and load adjustment strategies have been applied to the server re-consolidation problem.
On Task Assignment and Scheduling for Distributed Job Execution

It is common for big data applications to run across multiple datacenters or machine clusters because the data inputs are distributed over different locations. This paper studies a job scheduling problem for distributed job execution in which the data inputs to jobs may be replicated across multiple locations so that each task of a job can be executed at any one of these locations. To schedule the jobs, we need to determine the processing locations for the tasks of each job and the execution order of the tasks at each location. We focus on the objective of minimizing the average job response time. We first design a task assignment algorithm to balance the task allocation among various locations. We then further develop integrated solutions that conduct task assignment and scheduling together. We experimentally evaluate our algorithms using real job traces. The results show that our algorithms can significantly reduce the job response times compared to a baseline that allocates each task to a fixed location for processing.
VeloxDFS: Streaming Access to Distributed Datasets to Reduce Disk Seeks

In this work, we design and implement VeloxDFS, a distributed file system for ETL MapReduce frameworks, which has compatible APIs with HDFS. VeloxDFS is a decentralized, consistent, hash-based file system that dynamically adjusts the size of partitioned blocks. Rather than the conventional static and coarse-grained partitioning scheme, VeloxDFS employs a fine- grained logical partitioning scheme and provides an abstraction of various sized blocks based on the I/O consumption rate. VeloxDFS avoids I/O contention and straggler problems by employing a block stream manager that coordinates the I/O requests of multiple tasks during runtime. By reducing the I/O contention of concurrent tasks, VeloxDFS enables overcommit scheduling that schedules a larger number of tasks than the available physical CPU cores, leveraging the OS scheduler to improve the computing resource utilization of a cluster. Our extensive performance study shows that VeloxDFS with the over- commit scheduling policy shows up to 1.7x higher job processing throughput than HDFS for multiple concurrent workloads.
Using Multi-Resolution Data to Accelerate Neural Network Training in Scientific Applications

Neural networks are powerful solutions to many scientific applications; however, they usually require long model training time due to large training data sets or large model size. Research has been focused on developing numerical optimization algorithms and parallel processing to reduce the training time. In this work, we propose a multi-resolution strategy that can reduce the training time by training the model with the reduced-resolution data samples at the beginning and later switching to the original resolution data samples. This strategy is motivated by the observation that coarser versions of many applications can be solved faster than their denser counterparts, and the solution to a coarser problem could be used to initialize the solution to the denser problem. When applying the idea to neural network training, coarse data can have a similar effect on the learning curves at the early stage as the dense data but requires less time. Once the curves no longer improve significantly, our strategy switches to using the data in original resolution. The key in this process is the ability to generate multiple resolutions of a problem automatically, which could usually be done with scientific applications with spatial and temporal continuity. We use two real-world scientific applications, CosmoFlow and DeepCAM, to evaluate the proposed mixed-resolution training strategy. Our experiment results demonstrate that the proposed training strategy effectively reduces the end-to-end training time while achieving a comparable accuracy to that of the training only with the original data. While maintaining the same model accuracy, our multi-resolution training strategy reduces the end-to-end training time up to 30% and 23% for CosmoFlow and DeepCAM, respectively.
A Continuous Risk Assessment Methodology for Cloud Infrastructures

Cloud systems are dynamic environments which make it difficult to keep track of security risks that resources are exposed to. Traditionally, risk assessment is conducted for individual assets to evaluate existing threats-their results, however, are quickly outdated in such a dynamic environment. In this paper, we propose an adaptation of the traditional risk assessment methodology for cloud infrastructures which loosely couples manual, in-depth analyses with continuous, automatic application of their results. These two parts are linked by a novel threat profile definition that allows to reusably describe configuration weaknesses based on properties that are common across assets and cloud providers. This way, threats can be identified automatically for all resources that exhibit the same properties, including new and modified ones. We also present a prototype implementation which automatically evaluates an infrastructure as code template of a cloud system against a set of threat profiles, and we evaluate its performance. Our methodology not only enables organizations to reuse their threat analysis results, but also to collaborate on their development, e.g. with the public community. To that end, we propose an initial open-source repository of threat profiles.