Cross-modal hashing retrieval computes similarity based on the Hamming distance among hash codes to facilitate the retrieval of multi-modal data. The primary challenge in cross-modal retrieval is how to eliminate the heterogeneous gap between different modalities. In this paper, we introduce a novel method known as Mutual Compromised Multi-Feature Fusion (MCCMR), which comprehensively combines semantic feature information and semantic structural information to address this challenge and accomplish the cross-modal retrieval task. MCCMR comprises four modules: a semantic feature guidance module, a graph attention feature fusion module, an adversarial learning feature fusion module, and a multi-task learning module. Subsequently, experiments were conducted on three cross-modal benchmark datasets to evaluate the effectiveness of our proposed method. The experimental results demonstrate that MCCMR exhibits superior performance.

Byzantine-robust federated learning aims at mitigating Byzantine failures during the federated training process, where malicious participants (known as Byzantine clients) may upload arbitrary local updates to the central server in order to degrade the performance of the global model. In recent years, several robust aggregation schemes have been proposed to defend against malicious updates from Byzantine clients and improve the robustness of federated learning. These solutions were claimed to be Byzantine-robust, under certain assumptions. Other than that, new attack strategies are emerging, striving to circumvent the defense schemes. However, there is a lack of systematical comparison and empirical study thereof. In this paper, we conduct an experimental study of Byzantine-robust aggregation schemes under different attacks using two popular algorithms in federated learning,FedSGDandFedAvg. We first survey existing Byzantine attack strategies, as well as Byzantine-robust aggregation schemes that aim to defend against Byzantine attacks. We also propose a new scheme,ClippedClustering, to enhance the robustness of a clustering-based scheme by automatically clipping the updates. Then we provide an experimental evaluation of eight aggregation schemes in the scenario of five different Byzantine attacks. Our experimental results show that these aggregation schemes sustain relatively high accuracy in some cases, but they are not effective in all cases. In particular, our proposedClippedClusteringsuccessfully defends against most attacks under independent and identically distributed (IID) local datasets. However, when the local datasets are Non-IID, the performance of all the aggregation schemes significantly decreases. With Non-IID data, some of these aggregation schemes fail even in the complete absence of Byzantine clients. Based on our experimental study, we conclude that the robustness of all the aggregation schemes is limited, highlighting the need for new defense strategies, in particular for Non-IID datasets.

Federated learning (FL) is a promising paradigm to empower on-device intelligence in Industrial Internet of Things (IIoT) due to its capability of training machine learning models across multiple IIoT devices while preserving the privacy of their local data. However, the distributed architecture of FL relies on aggregating the parameter list from the remote devices, which poses potential security risks caused by malicious devices. In this article, we propose a flexible and robust aggregation rule, called auto-weighted geometric median (AutoGM), and analyze the robustness against outliers in the inputs. To obtain the value ofAutoGM, we design an algorithm based on the alternating optimization strategy. UsingAutoGMas aggregation rule, we propose two robust FL solutionsAutoGM_FLandAutoGM_PFL.AutoGM_FLlearns a shared global model using the standard FL paradigm, andAutoGM_PFLlearns a personalized model for each device. We conduct extensive experiments on the FEMNIST and Bosch IIoT datasets. The experimental results show that our solutions are robust against both model poisoning and data poisoning attacks. In particular, our solutions sustain high performance even when 30% of the nodes perform model or 50% of the nodes perform data poisoning attacks.

Existing dialogue datasets contain lots of noise in their state annotations.Such noise can hurt model training and ultimately lead to poor generalization performance.A general framework named ASSIST has recently been proposed to train robust dialogue state tracking (DST) models.It introduces an auxiliary model to generate pseudo labels for the noisy training set.These pseudo labels are combined with vanilla labels by a common fixed weighting parameter to train the primary DST model.Notwithstanding the improvements of ASSIST on DST, tuning the weighting parameter is challenging.Moreover, a single parameter shared by all slots and all instances may be suboptimal.To overcome these limitations, we propose a meta learning-based framework MetaASSIST to adaptively learn the weighting parameter.Specifically, we propose three schemes with varying degrees of flexibility, ranging from slot-wise to both slot-wise and instance-wise, to convert the weighting parameter into learnable functions.These functions are trained in a meta-learning manner by taking the validation set as meta data.Experimental results demonstrate that all three schemes can achieve competitive performance.Most impressively, we achieve a state-of-the-art joint goal accuracy of 80.10% on MultiWOZ 2.4.

Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants without accessing their local data. Standard federated learning techniques that naively minimize an average loss function are vulnerable to data corruptions from outliers, systematic mislabeling, or even adversaries. In this article, we address this challenge by proposing Auto-weighted Robust Federated Learning ( ARFL ), a novel approach that jointly learns the global model and the weights of local updates to provide robustness against corrupted data sources. We prove a learning bound on the expected loss with respect to the predictor and the weights of clients, which guides the definition of the objective for robust federated learning. We present an objective that minimizes the weighted sum of empirical risk of clients with a regularization term, where the weights can be allocated by comparing the empirical risk of each client with the average empirical risk of the best \( p \) clients. This method can downweight the clients with significantly higher losses, thereby lowering their contributions to the global model. We show that this approach achieves robustness when the data of corrupted clients is distributed differently from the benign ones. To optimize the objective function, we propose a communication-efficient algorithm based on the blockwise minimization paradigm. We conduct extensive experiments on multiple benchmark datasets, including CIFAR-10, FEMNIST, and Shakespeare, considering different neural network models. The results show that our solution is robust against different scenarios, including label shuffling, label flipping, and noisy features, and outperforms the state-of-the-art methods in most scenarios.

The modern network consists of thousands of network devices from different suppliers that perform distinct code-pendent functions, such as routing, switching, modifying header fields, and access control across physical and virtual networks. Because of the network complexity, the network is prone to a wide range of errors, such as false-positive configuration, software errors, or unexpected interactions across protocols. These errors can lead to loops, sub-optimal routing, path leaks, black holes, and access control violations that make services unavailable, vulnerable to exploitation, or prone to attacks (e.g., DDoS attacks). To mitigate these problems, network operators deploy many different stateful network functions, like firewalls, NATs, load balancers, and intrusion-prevention boxes. They have become an important part of networks today, so it is critical to verify that these network functions are the same as expected deployments. All static network verification tools are meant to rigorously check network software or configuration for bugs before deployment. They usually use handwritten models or limited derivation models that are error-prone and ignore the fact that even the same type of network functions (from different vendors) still have different implementation details. In this paper, we propose a tool that can automatically synthesize more realistic and high-fidelity models that include stateful network functions with non-field attributes. We design an inferring algorithm, implement the transformation between data packages and symbolic packages, and obtain a finite state machine that can accurately express the actions of black-box network functions for a given configuration.

An indispensable component in task-oriented dialogue systems is the dialogue state tracker, which keeps track of users’ intentions in the course of conversation. The typical approach towards this goal is to fill in multiple pre-defined slots that are essential to complete the task. Although various dialogue state tracking methods have been proposed in recent years, most of them predict the value of each slot separately and fail to consider the correlations among slots. In this paper, we propose a slot self-attention mechanism that can learn the slot correlations automatically. Specifically, a slot-token attention is first utilized to obtain slot-specific features from the dialogue context. Then a stacked slot self-attention is applied on these features to learn the correlations among slots. We conduct comprehensive experiments on two multi-domain task-oriented dialogue datasets, including MultiWOZ 2.0 and MultiWOZ 2.1. The experimental results demonstrate that our approach achieves state-of-the-art performance on both datasets, verifying the necessity and effectiveness of taking slot correlations into consideration.

A new position regulation control algorithm is designed for the series elastic actuator (SEA) system in the presence of parameter uncertainties. To enhance the position regulation performance by using the homogeneous system theory and a non-recursive synthesis approach, the resulting dynamic stabilizing control scheme can be expressed in a simple non-nested form with explicit gain tuning mechanisms. The proposed controller guarantees the system output converge to its reference within a finite time and moreover, it could reduce the synthesis complexity. Experiments on a SEA are presented to demonstrate the effectiveness of the proposed methodology.

Edge computing is a promising paradigm to expand the capability of Internet of Things (IoT) devices by computation offloading. To establish a distributed ledger to provide a secure and trusted environment for the resource allocation between edge servers and IoT devices, the emerging blockchain technology has attracted a lot of attention recently. However, in practice, edge resource allocation in IoT devices often involves multi-layer structures, which poses a challenge due to information incompleteness among different layers. Moreover, how to design a suitable and efficient blockchain framework for hierarchical resource allocation markets is a critical issue. In this paper, we apply blockchain to propose a secure and efficient hierarchical resource allocation framework for edge computing. First, we study the edge computing resource allocation problem in the hierarchical market of IoT devices, in which the IoT devices beyond the coverage of Access Points can participate in the resource allocation through middlemen. To solve the problem, a smart contract-based hierarchical auction mechanism is developed. The edge computing resources allocated in the top market can be continually reallocated to the sub-markets based on the mechanism, which then leads an efficient solution that maximizes the social welfare of the whole participants. Moreover, the mechanism is implemented as a smart contract in the blockchain, which enforces the rule of the hierarchical auction in a non-deniable and automated manner. Finally, the extensive simulations demonstrate the correctness and performance of the proposed mechanism.

Service recommendation is widely used to locate developers' desired services. Previous methods mainly focus on employing collaborative filtering (CF) techniques to recommend services to developers. However, these methods have some problems, such as being sensitive to sparse data and having limited predictive ability to new developers. Generative adversarial network (GAN) can solve the above mentioned problems, since it can learn the data distribution from a limited amount of data and generate a new developer's preference score for a service, even if he/she has not invoked the service. In this paper, we propose a novel GAN based service recommendation method. It first constructs a heterogeneous information network (HIN) by utilizing mashup information, service information and their respective attribute information. Then, it samples meta-paths of different semantic relationships and constructs similarity matrices between mashups and services through meta-paths based similarity measurement. Finally, by leveraging the adversarial training between the discriminator and the generator, the discriminator can effectively guide the generator to generate a preference vector for the developer, thus recommending a list of services for him/her according to his/her given mashup attribute information. Comprehensive experimental results on a real-world dataset demonstrate the superiority of the proposed method.

Traditional robotic systems are often incapable of handling complex tasks due to hardware constraints, such as computing ability, storage space, and battery capacity. Cloud robotic systems, characterized by allowing multi-robot systems to access the powerful cloud infrastructures, is a promising solution to fulfill complex tasks, such as disaster management, real-time object recognition, 3D Simultaneous Localization And Mapping (SLAM). However, the destabilizing factors of network could lead to high latency of data transmission in cloud robotic systems, which have made great challenges to the fields that have high real-time requirements. What's more, the existence of heterogeneity of robots further complicates cloud robotics cooperation. In order to minimize the average response time in latency-aware scenarios, we jointly investigate task assignment and scheduling in Collaborative Cloud Robotic Systems (CCRS). We first formulate the problem into a Mixed-Integer Non-Linear Programming (MINLP) and then linearize it into an Integer Linear Programming (ILP) using discrete time structure. To meet the extensibility requirement, we further propose a partitioning-based algorithm to deal with large-scale task graphs. The results show that our two approaches outperform the existing genetic algorithm and greedy algorithm.

The task of community detection has become a fundamental research problem in complex network analysis. Intuitively, similar nodes are more likely to be contained in the same community. However, most existing community detection methods cannot extract the intrinsic similarity between nodes. Thus, they may fail to identify the real community structures. In this paper, we propose to learn an affinity matrix adaptively, which can capture the intrinsic similarity between nodes accurately, and therefore benefit the community detection results. Specifically, the proposed model first embeds each node into a low-dimensional space through a transformation matrix with the community structures being preserved. Then, our model learns the affinity matrix in this low-dimensional space. The affinity matrix is further utilized to guide the learning of the community membership matrix via manifold regularization. The above three matrices are learned simultaneously and updated iteratively under the framework of Alternating Direction Method of Multipliers (ADMM). Extensive experiments show that our model can outperform the state-of-the-art approaches.