Objectively perceptual quality assessment of UGC videos is a challenge due to many reasons. First, the sources of UGC-videos are not available, which makes the appropriate technique is the no-reference video quality assessment (NR-VQA). Second, subjective mean option scores (MOS) of all the UGC-datasets are not uniformly distributed. Third, authentic degradations occurred in the videos are not unique, therefore, not predicable. For example, an over- or underexposure video, brightness and contrast static information is critical for evaluation. Only employing verified priori statistic knowledge or generalized learning knowledge may not cover all possible distortions. To solve these problems, we introduce a framework—Progressive Regression Network (PRNet) in this paper. For the MOS problem, a PR module is proposed, which utilizes the coarse-to-fine strategy during the training process. It can turn sparse subjective human rating scores into integers with denser samples to address the problem. For the unpredictable distortions, a wide and deep feature extraction module is developed, which employs both low-level features generated from natural scene statistics (NSS) and high-level semantic features extracted by deep neural networks, to fuse memorizing priori knowledge and generalizing learning features. Experiments demonstrate that our PRNet achieves top performance in three main datasets.

Instance segmentation exploits the potential of Internet of Things (IoT) devices to perceive environmental semantic information and achieve complex interactions with the physical world. However, IoT devices usually have limited computing and storage resources and cannot afford the intensive computational costs of instance segmentation networks. This article proposes an edge-assisted instance segmentation method for resource-limited IoT devices; it selectively offloads some computation-intensive tasks from IoT devices to edge servers to accelerate the inference processes of instance segmentation networks. To reduce the communication cost caused by computation offloading, a data compression method is proposed to adaptively adjust the downsampling interval based on an attention mechanism. Considering the susceptibility of the computation offloading scheme to network conditions, an adaptive computation offloading strategy that can jointly optimize the offloading point and the data compression ratio is proposed so that the edge-assisted instance segmentation can meet the preset latency requirements while achieving maximal accuracy under volatile network conditions. Extensive experiments are conducted to verify the feasibility and efficiency of our method. The experimental results show that our method induces less latency than existing instance segmentation methods with a slight drop in accuracy.

Traditional two-stream neural networks have shown that both appearance and motion information are important for video action recognition. However, their way of naively averaging two streams’ scores at the end of the framework neglects the underlying relationship between these two kinds of information. In this paper, we propose a two-pathway transformer network that uses memory-based attention to explore such relationship, which further improves the classification performance. Specifically, a transformer-based decoder takes one pathway’s features as the query while the other’s as the key and value. Then based on the similarity matrix estimated by the query and key, relevant information from the value can be selected to enhance the query for the final classification task. Experiments demonstrate that our proposed method outperforms existing fusion strategies at the end of the two-stream methods.

Anomalies of multitier services running in cloud platform can be caused by components of the same tenant or performance interference from other tenants. If the performance of a multitier service degrades, we need to find out the root causes precisely to recover the service as soon as possible. In this paper, we argue that cloud providers are in a better position than tenants to solve this problem, and the solution should be non-intrusive to tenants' services or applications. Based on these two considerations, we propose a solution for cloud providers to help tenants to localize root causes of any anomaly. We design a non-intrusive method to capture the dependency relationships of components, which improves the feasibility of root cause localization system. Our solution can find out root causes no matter they are in the same tenant as the anomaly or from other tenants. Our proposed two-step localization algorithm exploits measurement data of both application layer and underlay infrastructure and a random walk procedure to improve its accuracy. Our realworld experiments of a three-tier web application running in a small-scale cloud platform show a 38.9% improvement in mean average precision compared to current methods.

DDoS flooding attack is one of the top threats to the Internet. However, due to the fast development of the Internet, current detection algorithms are already inadequate to meet the growth of network traffic. In this paper, we propose a lightweight algorithm. We first observe the real Internet traffic, and find that flows of DDoS flooding attack traffic are persistent and synchronous while most flows of normal traffic are short-lived and non- synchronous. According to this difference, we propose our detection algorithm. We label the alarms firstly and then confirm the attack. Our algorithm is lightweight and sensitive to the ongoing attack. However, randomly spoofing the IP address of the attack source to different IP addresses can hide the synchronization of attack flows. Thus, we add a spoofing IP detection algorithm called hop-count filter (HCF) to our algorithm to strengthen the robustness. At last, we evaluate our detection algorithm based on the real Internet traffic from CAIDA. Results show that our detection algorithm has a high accuracy (93.3%), no false positive in attack confirmation and just 1.1% false positive rate in labeling alarms. In addition, we analyze the challenges we may face when dealing with distributed LDoS attack.

In this paper, a Critical Sensor Determination and Substitution (CSDS) strategy is proposed to address the important problem of network partitions in mobile sensor networks (MSN) due to the failure of particular sensors. CSDS utilizes a graph-theoretic method to locally identify critical sensors with 2-hop neighboring information. Then, an efficient backup sensor selection algorithm is cooperated to monitor the critical sensors and, if necessary, substitute it in order to eliminate the partitions in MSN. The main contribution of our proposed work is that CSDS only requires the relocation of only one sensor in each partition elimination process, so that the impacts on the primary missions of the MSN is minimized. Experimental simulations are conducted to evaluate the correctness and effectiveness of CSDS.

Robotic sensor network (RSN) contains mobile sensors and robots provides feasible solution for many multiagent applications. One of the most critical issues in RSN and its application is how to effectively assign tasks. This paper presents a novel threshold-based task allocation strategy to answer the question particularly for RSN. Firstly, we model the task allocation in RSN to distinguish the discovering and allocating processes. Secondly, a fully distributed simple Threshold-based Unoccupied Neighbor Algorithm, namely TUNA, is developed to allocate tasks with only partial view of the network topology. A connectivity controller is finally developed and integrated into the strategy to guarantee the global connectivity of entire RSN, which is critical in most RSN applications. Experimental simulations are also provided to evaluate the performance of the proposed strategy.

Restoring connectivity subjected to the failure of mobile node(s) is an important research challenge in mobile ad hoc networks. To achieve this objective, a Connectivity Restoration Strategy (CRS) is developed, aiming at restoring the connectivity of mobile network whenever it is partitioned from multiple simultaneous node failures. The proposed strategy is fully distributed and requires only local information of k-hop neighbors. Furthermore, with respect to the fact that connectivity may not be restorable without accessing network-wide information of neighbor status, an extensive investigation of connectivity restorability is conducted through computer simulations.

Topology control serves as an important technique in dynamic networks for its capabilities of reducing energy consumption and radio interference. Additionally, the sparse network structure generated by topology control algorithms is beneficial for enhancing network coverage. In this paper, a local two-tier topology control method is developed to address the issues of topology control and coverage enhancement of dynamic networks. By combing a topology control algorithm with mobility control technique, the proposed method can disperse a team of mobile agents to reduce radio interference and to enhance area coverage. In the meantime, connectivity maintenance of the underlying network and collision avoidance between mobile agents are also handled effectively.

In mobile multi-agent networks, connectivity is a crucial factor for network-wide collaborations. However, such connectivity can be easily sabotaged by a single or multiple simultaneous agent failure(s). To handle this problem, in this paper, a hybrid connectivity restoration framework, namely HERO, is developed. By integrating a distributed connectivity restoration algorithm with a potential function based motion controller, HERO can restore the connectivity of the disconnected networks subjected to a single or multiple simultaneous agent failure(s). In the mean time, inter-agent collisions and further partitions of the underlying networks can also be effectively avoided.

This paper addresses the problem of coverage enhancement for a team of mobile agents with consideration of global connectivity. A hybrid coverage enhancement system is developed, consisting of two distinctive layers. In network control layer, a link removal algorithm is designed to generate a sparse network structure while preserving the global connectivity. A potential function based motion controller is synthesized in motion control layer to disperse a team of mobile agents while avoiding any inter-agent collisions. The proposed hybrid coverage enhancement system can reconfigure an initially compact multi-agent system to achieve a certain level of coverage with guaranteed connectivity and collision avoidance. Moreover, the proposed method can be used in both two dimensional area coverage enhancement and three dimensional space coverage enhancement.

This paper presents a connectivity improvement strategy for sparse free-flow Vehicular Ad hoc Networks(VANETs) subject to channel randomness. We first model the connectivity of VANETs in the form of mean broadcast percolation distance based on the equivalent M/G/∞ queue theory. Thereafter, to address the problem of poor connectivity in sparse VANETs, a distributed connectivity improvement strategy based on the deployment of Road Side Units (RSUs) is developed, aiming at improving the connectivity of VANETs to a desired level while minimizing the energy consumption and signal conflict. Simulation studies have been conducted to verify the analytical model and evaluate the efficiency of the proposed strategy, and the results have shown that the connectivity in sparse VANETs has been improved in an efficient and economic way.

Grid services scheduling is a challenging problem under open grid service architecture (OGSA). Grid services running on clusters must have mechanism and policies for establishing and supporting QoS. A graph theory formal description is introduced into the service grid model in this paper. The necessary and sufficient condition of complete matching of user job and service resources has been given and proved. Optimal solution to matchmaking of grid jobs and grid services is developed based on the running time weight matrix, and the arithmetic has been verified by simulation analysis which proved to be more efficient than the alike arithmetic. The arithmetic has been implemented and running well

The dynamical Grid environment requires flexible and adaptive workflow system. This paper integrates Agents and Web Services into Grid Service Workflow System based on Colored Petri Nets. The combination of Agents and Web Services enhances the adaptability and dynamics of the framework. How Web Service can be located dynamically and the services provided by it can be invoked are discussed. Using an example of ordering railway ticket, we show how our framework can achieve adaptability from process, resource and task perspectives using both Agents and Web Services.