Complex Query Answering (CQA) over Knowledge Graphs (KGs) is a challenging task. Given that KGs are usually incomplete, neural models are proposed to solve CQA by performing multi-hop logical reasoning. However, most of them cannot perform well on both one-hop and multi-hop queries simultaneously. Recent work proposes a logical message passing mechanism based on the pre-trained neural link predictors. While effective on both one-hop and multi-hop queries, it ignores the difference between the constant and variable nodes in a query graph. In addition, during the node embedding update stage, this mechanism cannot dynamically measure the importance of different messages, and whether it can capture the implicit logical dependencies related to a node and received messages remains unclear. In this paper, we propose Conditional Logical Message Passing Transformer (CLMPT), which considers the difference between constants and variables in the case of using pre-trained neural link predictors and performs message passing conditionally on the node type. We empirically verified that this approach can reduce computational costs without affecting performance. Furthermore, CLMPT uses the transformer to aggregate received messages and update the corresponding node embedding. Through the self-attention mechanism, CLMPT can assign adaptive weights to elements in an input set consisting of received messages and the corresponding node and explicitly model logical dependencies between various elements. Experimental results show that CLMPT is a new state-of-the-art neural CQA model. https://github.com/qianlima-lab/CLMPT.

Collision detection is critical for autonomous vehicles or robots to serve human society safely. Detecting looming objects robustly and timely plays an important role in collision avoidance systems. The locust lobula giant movement detector (LGMD1) is specifically selective to looming objects which are on a direct collision course. However, the existing LGMD1 models cannot distinguish a looming object from a near and fast translatory moving object, because the latter can evoke a large amount of excitation that can lead to false LGMD1 spikes. This article presents a new visual neural system model (LGMD1) that applies a neural competition mechanism within a framework of separated ON and OFF pathways to shut off the translating response. The competition-based approach responds vigorously to monotonous ON/OFF responses resulting from a looming object. However, it does not respond to paired ON-OFF responses that result from a translating object, thereby enhancing collision selectivity. Moreover, a complementary denoising mechanism ensures reliable collision detection. To verify the effectiveness of the model, we have conducted systematic comparative experiments on synthetic and real datasets. The results show that our method exhibits more accurate discrimination between looming and translational events-the looming motion can be correctly detected. It also demonstrates that the proposed model is more robust than comparative models.

This paper studies virtual machine (VM) scheduling in a queueing cloud computing system with stochastical arrivals of heterogeneous jobs by considering jobs’ delay requirements. The delay-optimal VM scheduling in such a cloud computing system is formulated as a multi-resource multi-class problem minimize the average job completion time, which is often NP-hard. To solve such a problem, we first propose a queueing model that buffers the same type of VM jobs in one virtual queue. The queueing model then divides the VM scheduling into two parallel low-complexity algorithms, i.e., intra-queue buffering and inter-queue scheduling. A min-min best fit (MM-BF) policy is used to schedule the jobs in different queues to minimize the remaining system resources, while a shortest-job-first (SJF) policy is used to buffer the job requests in each queue based on their job lengths in an ascending order. To avoid job starvation for the long-duration jobs in SJF-MMBF, we further propose a queue-length-based MaxWeight (QMW) policy based on Lyapunov drift to minimize the queue lengths of VM jobs, which is called SJF-QMW. Simulation results show that, SJF-MMBF and SJF-QMW achieve low delay performance in terms of average job completion time and high throughput performance in terms of job hosting ratio.

The LGMD1 neuron of locusts shows strong looming-sensitive property for both light and dark objects. Although a few LGMD1 models have been proposed, they are not reliable to inhibit the translating motion under certain conditions compare to the biological LGMD1 in the locust. To address this issue, we propose a bio-plausible model to enhance the collision-selectivity by inhibiting the translating motion. The proposed model contains three parts, the retina to lamina layer for receiving luminance change signals, the lamina to medulla layer for extracting motion cues via ON and OFF pathways separately, the medulla to lobula layer for eliminating translational excitation with neural competition. We tested the model by synthetic stimuli and real physical stimuli. The experimental results demonstrate that the proposed LGMD1 model has a strong preference for objects in direct collision course-it can detect looming objects in different conditions while completely ignoring translating objects.

Job scheduling is a necessary prerequisite for performance optimization and resource management in the cloud computing system. Focusing on accurate scaled cloud computing environment and efficient job scheduling under Virtual Machine (VM) resource and Server Level Agreement (SLA) constraints, we introduce the architecture of cloud computing platform and optimization job scheduling scheme in this study. The system model is comprised of clearly defined separate constituent parts, including portal, job scheduler, and resources pool. By analyzing the execution process of user jobs, we designed a novel job scheduling scheme based on reinforcement learning to minimize the makespan and Average Waiting Time (AWT) under the VM resource and deadline constraints, and employ parallel multi-age parallel technologies to balance the exploration and exploitation in learning process and accelerate the convergence of Q-learning algorithm. Both simulation and real cloud platform experiment results demonstrate the efficiency of the proposed job scheduling scheme.

Person re-identification is an important task in forensics applications. Most existing person re-identification methods focus on matching persons captured by different true-color cameras. In practice, the captured pedestrian videos may be grayscale in some cases due to camera malfunction or special treatment for gray mode. In these cases, the person re-identification between true-color and grayscale pedestrian videos, which we call color to gray video person re-identification (CGVPR), will be needed. Since the color information that is very important to represent a pedestrian is usually intensity information and monochrome in grayscale videos, the CGVPR problem is very challenging. To relieve the difficulties in CGVPR, we propose an asymmetric within-video projection based Semicoupled Dictionary Pair Learning (SDPL) approach. SDPL simultaneously learns two within-video projection matrices, a pair of true-color and grayscale dictionaries, as well as a semi-coupled mapping matrix. The learned within-video projection matrices can make each video (true-color or grayscale) more compact. The learned dictionary pair and the mapping matrix can work together to bridge the gap between features of true-color and grayscale videos. To date there exists no true-color and grayscale pedestrian video dataset, so we contribute a new one, called true-color and grayscale video person re-identification dataset (CGVID). Our dataset is collected under a real-world scenario and consists of over 50K frames. Extensive evaluations demonstrate that the collected CGVID dataset is very challenging and can be used for further research on person re-identification. The experimental results show that our approach outperforms the compared methods on the CGVPR task.

The present study constructs a multi-agent organizational framework with virtual machine clusters as the unit of granularity based on the results of that previous study. In addition, the present study also investigates the requirements for an orderly and efficient workflow of this multi-agent organizational framework and constructs an abstract system structure based on collaborative agents (including vms-agents and apps-agents) and a collaborative mechanism. Finally, the present study reports a comparative experiment conducted on the CloudSim platform. The experimental results show that the proposed multi-agent organization method with virtual machine clusters as the unit of granularity improves the efficiency of resource utilization and accounts for the interests of both cloud users and providers while ensuring the maintenance of the application service level agreement. Compared with the mechanism of the multi-agent organizational method proposed in the present study, existing studies have generally considered the configuration of a given party only locally and in isolation, severing the intrinsic connection between the resources of virtual machines and the application systems/components that are operating on those virtual machines. As a result, the interests of the cloud users and providers cannot be simultaneously ensured.

Cloud computing is the next generation of computation and is regarded as the fifth utility service. A core issue of cloud computing is the deployment of virtual machine (VM). A new method had been constructed for VM cluster based on graph theory in this study. The authors first described VM cluster by energy minimisation. Then, they changed deployment of VM cluster into maximum flow minimum cut problem, added sink and source point, and found activity node. Finally, activity tail‐nodes were connected and cut formed for VM cluster. Experimental results show that the segmentation method can effectively realise the VM deployment clusters, can reduce the overall bandwidth requirements after deployment.

Job scheduling is a necessary prerequisite for performance optimization and resource management in the cloud computing system. Focusing on accurate scaled cloud computing environment and efficient job scheduling under Virtual Machine (VM) resource and Server Level Agreement (SLA) constraints, we introduce the architecture of cloud computing platform and optimization job scheduling scheme in this study. The system model is comprised of clearly defined separate constituent parts, including portal, job scheduler, and resources pool. By analyzing the execution process of user jobs, we designed a novel job scheduling scheme based on reinforcement learning to minimize the makespan and Average Waiting Time (AWT) under the VM resource and deadline constraints, and employ parallel multi-age parallel technologies to balance the exploration and exploitation in learning process and accelerate the convergence of Q-learning algorithm. Both statistical and numerical analysis results demonstrate the efficiency of the proposed job scheduling scheme.

Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Recently reinforcement learning has been given abroad attention, but when it is applied to solve problems with large-scale discrete or contiguous state space environments, the results are likely to be unsatisfactory and even fail to find optimal policies. In order to solve this problem, we establish a new generative model about the value function and use Gaussian Process Regression to approximate the state-action pairs which were never or seldom visited. We testify to the performance of the proposed algorithm by an access-control queuing job in a cloud computing environment. The computational results demonstrate the scheme can balance the exploration and exploitation in the learning process and accelerate the convergence to a certain extent.