During software development, developers often need appropriate but unfamiliar APIs to implement a specific functionality. Under such circumstances, developers tend to leverage search tools to seek for the relevant APIs. However, there are always semantic gaps between query words and APIs, which negatively affects the performance of these tools. In this study, we introduce Glo-APIRec, a method that combines global semantics with local context to estimate the semantic relevance between query words and APIs to recommend APIs. In this method, the Transformer model is employed to obtain global semantics, while the Word2Vec model is utilized to capture local context using a fixed-size window. First, Glo-APIRec collects millions of Java projects from GitHub to construct the corpus. Afterward, a set of tuples consisting of words and APIs is built by extracting comments and API sequences from the source code files. Finally, Transformer is employed to capture long distance semantics about API sequences and code comments. Meanwhile, Word2Vec is used to generate word vectors to capture the local context by introducing the random shuffling strategy to break the positions of words and APIs in the tuples. We evaluate the performance of Glo-APIRec with 30 sentence-level queries. Experimental results show that Glo-APIRec can achieve 0.600 in terms of SuccessRate for top-1 recommendation and 0.900 for top-10 recommendation. When recommending 10 APIs, Glo-APIRec can achieve 0.480, 0.703 and 0.717 in terms of precision, Mean Reciprocal Rank ([Formula: see text]) and Normalized Discounted Cumulative Gain ([Formula: see text]), and outperforms the state-of-the-art method by 26.2%, 31.7% and 27.9%, respectively.

This paper proposes a novel architecture of Recommendation-Enabled Edge Caching and Device-to-Device (D2D) Offloading via Incentive-driven Deep Reinforcement Learning (DRL), which can not only solve the problem of inaccurate recommendation caused by sparse rating matrix, but also encourage users to participate in D2D offloading through an effective incentive mechanism. Specifically, we define Pseudo Markov Decision Process (PMDP) for the first time, which enables the conversion of the non-sequential process (e.g. rating prediction) into a sequential one, making it suitable for DRL. Then, combining Supervised Learning (SL) and DRL, a Supervised DRL for Collaborative Filtering (CF) algorithm, named SDRLCF, is proposed to predict missing ratings. After that, from the perspective of Content Service Center (CSC), the incentive-driven recommendation-enabled edge caching and D2D offloading can be formulated as a Non-Linear Integer Programming (NLIP) problem, which belongs to NP-hard, and is difficult to obtain the optimal solution in polynomial time. To address this issue, a DRL based Edge Caching and Recommendation algorithm, named DRLECR, is proposed to minimize the cost of CSC. Finally, combining with economic theory, a Reverse Auction based Payment Determination algorithm under Vickrey-Clarke-Groves (VCG) scheme, named RAPD, is proposed, which can stimulate users to participate in edge caching and D2D offloading while guaranteeing the individual rationality and truthfulness of participants. Extensive experiment results on both realistic and synthetic datasets demonstrate that the proposed algorithms outperform other baseline methods under different scenarios.

This article proposes a novel Reverse Auction-based Computation Offloading and Resource Allocation Mechanism, named RACORAM for the mobile Cloud-Edge computing. The basic idea is that the Cloud Service Center (CSC) recruits edge server owners to replace it to accommodate offloaded computation from nearby resource-constraint Mobile Devices (MDs). In RACORAM, the reverse auction is used to stimulate edge server owners to participate in the offloading process, and the reverse auction-based computation offloading and resource allocation problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem, aiming to minimize the cost of the CSC. The original problem is decomposed into an equivalent master problem and subproblem, and low-complexity algorithms are proposed to solve the related optimization problems. Specifically, a Constrained Gradient Descent Allocation Method (CGDAM) is first proposed to determine the computation resource allocation strategy, and then a Greedy Randomized Adaptive Search Procedure based Winning Bid Scheduling Method (GWBSM) is proposed to determine the computation offloading strategy. Meanwhile, the CSC's payment determination for the winning edge server owners is also presented. Simulations are conducted to evaluate the performance of RACORAM, and the results show that RACORAM is very close to the optimal method with significantly reduced computational complexity, and greatly outperforms the other baseline methods in terms of the CSC's cost under different scenarios.

Edge caching has been a research hotspot of the Mobile Edge Computing (MEC) in recent years, which is an effective way to ease the burden of traffic in cellular networks. It places contents to the edge of the networks and assists contents transmission via Device-to-Device (D2D) links. Traditional caching strategies strictly depend on the personal preferences of users, and they are scarcely possible to reduce the transmission cost while ensuring the high effectiveness. Fortunately, recent studies have found that the combination of caching and recommendation can effectively improve the efficiency of edge caching and reduce the transmission cost. In this article, we jointly consider the content caching and recommendation through Opportunistic Mobile Networks (OMNs) to reduce the cost of Content Service Center (CSC). In order to obtain the optimal caching and recommendation solutions with sparse rating matrix, we propose a Joint Content Caching and Recommender System (JCCRS). In JCCRS, a Broad Incremental Learning based Collaborative Filtering algorithm, named BILCF, is first proposed to predict the missing ratings. Afterwards, we quantify the relationship between each pair of Mobile Users (MUs) according to their mobility, similarity and preference. The content caching and recommendation problem is then modeled as a Non-Linear Integer Programming (NLIP) problem and we prove that it belongs to NP-hard. To solve this problem, a Deep Deterministic Policy Gradient (DDPG) based Content Caching and Recommendation method, named DCRM, is further proposed to obtain the approximate optimal solutions. Extensive experiments on both a realistic dataset and a synthetic dataset validated by the realistic data show that the proposed algorithms outperform other baseline methods under different scenarios.

Vehicular ad hoc network (VANET) has become an accessible technology for improving road safety and driving experience, the problems of heterogeneity and lack of resources it faces have also attracted widespread attention. With the development of software-defined networking (SDN) and multiaccess edge computing (MEC), a variety of resource allocation strategies in MEC-enabled software-defined networking-based VANET (SDVN) have been proposed to solve these problems. However, we note that few of these work involves the situation where SDVN is under Distributed Denial of Service (DDoS) attacks. Actually, Internet of Things (IoT) devices are extremely easy to be compromised by malicious users, and compromised IoT devices may be used to launch edge DDoS attacks against the MEC servers in MEC-enabled SDVN at any time. In this article, we propose a graph neural network (GNN)-based collaborative deep reinforcement learning (GCDRL) model to generate the resource provisioning and mitigating strategy. The model evaluates the trust value of the vehicles, formulates mitigation of edge DDoS attacks and resource provisioning strategies to ensure that the MEC servers can work normally under edge DDoS attacks. In addition, GNN is adopted in the DRL model to extract the structure feature of the graph composed of MEC servers, and help transfer computing tasks between MEC servers to alleviate the problem of resources imbalance between them. Experimental results show that the method of estimating the vehicular trust value is effective, and our method can make the average throughput of edge nodes more stable and lower down the average delay and the average energy consumption under the edge DDoS attack. Also, a real-world case study is conducted to verify our conclusion.

Weakly Supervised Semantic Segmentation (WSSS) with image-level annotation uses class activation maps from the classifier as pseudo-labels for semantic segmentation. However, such activation maps usually highlight the local discriminative regions rather than the whole object, which deviates from the requirement of semantic segmentation. To explore more comprehensive class-specific activation maps, we propose an Embedded Discriminative Attention Mechanism (EDAM) by integrating the activation map generation into the classification network directly for WSSS. Specifically, a Discriminative Activation (DA) layer is designed to explicitly produce a series of normalized class-specific masks, which are then used to generate class-specific pixel-level pseudo-labels demanded in segmentation. For learning the pseudo-labels, the masks are multiplied with the feature maps after the backbone to generate the discriminative activation maps, each of which encodes the specific information of the corresponding category in the input images. Given such class-specific activation maps, a Collaborative Multi-Attention (CMA) module is proposed to extract the collaborative information of each given category from images in a batch. In inference, we directly use the activation masks from the DA layer as pseudo-labels for segmentation. Based on the generated pseudo-labels, we achieve the mIoU of 70.60% on PASCAL VOC 2012 segmentation testset, which is the new state-of-the-art, to our best knowledge. Code and pre-trained models are available online soon.

In this paper, we propose a computation offloading method based on the game theory, which is suitable for cloud-edge computing networks. We consider that the Cloud Server (CS) can offload the computation tasks to wireless Access Points (APs) associated with Edge Servers (ESs) to accelerate processing. ESs can gain benefits through computation offloading, while the CS can reduce its cost and computing pressure. We model the interaction between the CS and ESs as a Stackelberg game, and use the backward induction method to analyze the proposed game. We prove that the game can achieve a unique Nash equilibrium. Then, we propose a Gradient-based Iterative Search Algorithm (GISA) to maximize the utility of the CS and ESs. Finally, numerical simulation results show that our proposed method greatly outperforms other benchmark schemes under different scenarios, and can encourage ESs to trade their computation resources with the CS effectively.

Offloading cellular traffic via Device-to-Device communication (or D2D offloading) has been proved to be an effective way to ease the traffic burden of cellular networks. However, mobile nodes may not be willing to take part in D2D offloading without proper financial incentives since the data offloading process will incur a lot of resource consumption. Therefore, it is imminent to exploit effective incentive mechanisms to motivate nodes to participate in D2D offloading. Furthermore, the design of the content caching strategy is also crucial to the performance of D2D offloading. In this paper, considering these issues, a novel Incentive-driven and Deep Q Network (DQN) based Method, named IDQNM is proposed, in which the reverse auction is employed as the incentive mechanism. Then, the incentive-driven D2D offloading and content caching process is modeled as Integer Non-Linear Programming (INLP), aiming to maximize the saving cost of the Content Service Provider (CSP). To solve the optimization problem, the content caching method based on a Deep Reinforcement Learning (DRL) algorithm, named DQN is proposed to get the approximate optimal solution, and a standard Vickrey-Clarke-Groves (VCG)-based payment rule is proposed to compensate for mobile nodes' cost. Extensive real trace-driven simulation results demonstrate that the proposed IDQNM greatly outperforms other baseline methods in terms of the CSP's saving cost and the offloading rate in different scenarios.

Offloading cellular traffic through Opportunistic Mobile Networks (OMNs) is an effective way to relieve the burden of cellular networks. Providing data offloading services requires a lot of resources, and nodes in OMNs are selfish and rational, they are not willing to provide data offloading services for others without any compensation. Therefore, it is urgent to design an incentive mechanism to stimulate mobile nodes to participate in data offloading process. In this paper, we propose a Reverse Auction-based Incentive Mechanism to stimulate mobile nodes in OMNs to provide data offloading services, and take the cache management into consideration. We model the incentive-driven data offloading process as a non-linear integer programming problem, then a Greedy Helper Selection Method (GHSM) and a Caching Replacement Scheme (CRS) are proposed to solve the problem. In addition, we also propose an innovative payment rule based on the Vickrey-Clarke-groves (VCG) model to ensure the individual rationality and authenticity of the proposed algorithm. Trace-driven simulation results show that the proposed algorithm can reduce the cost of Content Service Provider (CSP) significantly in different scenarios.

Actions always refer to complex vision variations in a long-range redundant video sequence. Instead of focusing on limited range sequence, i.e. convolution on adjacent frames, in this paper, we proposed an action recognition approach with bootstrapping based long-range temporal context attention. Specifically, due to vision variations of the local region across frames, we target at capturing temporal context by proposing the Temporal Pixels based Parallel-head Attention (TPPA) block. In TPPA, we apply the self-attention mechanism between local regions at the same position across temporal frames to capture the interaction impacts. Meanwhile, to deal with video redundancy and capture long-range context, the TPPA is extended to the Random Frames based Bootstrapping Attention (RFBA) framework. While the bootstrapping sampling frames have the same distribution of the whole video sequence, the RFBA not only captures longer temporal context with only a few sampling frames but also has comprehensive representation through multiple sampling. Furthermore, we also try to apply this temporal context attention to image-based action recognition, by transforming the image into "pseudo video" with the spatial shift. Finally, we conduct extensive experiments and empirical evaluations on two most popular datasets:UCF101 for videos andStanford40 for images. In particular, our approach achieves top-1 accuracy of $91.7%$ in UCF101 and mAP of $90.9%$ in Stanford40.

As the energy consumption of multi-core systems becomes increasingly prominent, it's a challenge to design an energy-efficient real-time scheduling algorithm in multi-core systems for reducing the system energy consumption while guaranteeing the feasibility of real-time tasks. In this paper, we focus on multi-core processors, with the global Dynamic Voltage Frequency Scaling (DVFS) and Dynamic Power Management (DPM) technologies. In this setting, we propose an energy-efficient real-time scheduling algorithm, the Time Local remaining execution plane based Dynamic Voltage Frequency Scaling (TL-DVFS). TL-DVFS utilizes the concept of Time Local remaining execution (TL) plane to dynamically scale the voltage and frequency of a processor at the initial time of each TL plane as well as at the release time of a sporadic task in each TL plane. Consequently, TL-DVFS can obtain a reasonable tradeoff between the real-time constraint and the energy-saving while realizing the optimal feasibility of sporadic tasks. Mathematical analysis and extensive simulations demonstrate that TL-DVFS always saves more energy than existing algorithms, especially in the case of high workloads, and guarantees the optimal feasibility of sporadic tasks at the same time.