Anomaly detection plays a critical role in ensuring safe, smooth, and efficient operation of machinery and equipment in industrial environments. With the wide deployment of multimodal sensors and the rapid development of Internet of Things (IoT), the data generated in modern industrial production has become increasingly diverse and complex. However, traditional methods for anomaly detection based on a single data source cannot fully utilize multimodal data to capture anomalies in industrial systems. To address this challenge, we propose a new model for anomaly detection in industrial environments using multimodal temporal data. This model integrates an attention-based autoencoder (AAE) and a generative adversarial network (GAN) to capture and fuse rich information from different data sources. Specifically, the AAE captures time-series dependencies and relevant features in each modality, and the GAN introduces adversarial regularization to enhance the model’s ability to reconstruct normal time-series data. We conduct extensive experiments on real industrial data containing both measurements from a distributed control system (DCS) and acoustic signals, and the results demonstrate the performance superiority of the proposed model over the state-of-the-art TimesNet for anomaly detection, with an improvement of 5.6% in F1 score.

Social status, the social influence of a user, plays an important role in many real-world applications, e.g., trust relations and information propagation in a social network. In this paper, we reveal the possibility of falsifying social status through adversarial attacks in graph neural networks (GNNs). Different from neural networks in the visual or speech domain, GNNs take the attributes of nodes and edges in a graph as features. To cater to the characteristics of GNNs, $\vphantom {_{\int }}$ we design a new paradigm of adversarial example attack, named $A^{2} S^{2}$ - GNN ( $\mathbf {GNN}$ -based $\mathbf {A}$ dversarial $\mathbf {A}$ ttacks on $\mathbf {S}$ ocial $\mathbf {S}$ tatus), aiming at manipulating the social status of a target node in social networks. The key idea is to establish relationships or break relationships between a set of compromised nodes and the target node. More specifically, we consider a signed directed graph representing complicated positive/negative asymmetric relationships between nodes. We design an efficient adversarial attack algorithm to determine the minimum set of signed links that should be created or deleted to reach the attack objective. We conduct extensive experiments on baseline datasets. Compared with the benchmark algorithms, $A^{2} S^{2}$ - GNN can effectively promote or vilify the social status of the target node up to 89.36% and 192.38%, respectively, while keeping the modification to the social network to the minimum. Furthermore, the experimental results on six status evaluating algorithms verify the transferability of our proposed attack algorithm.

The traditional 2D MUSIC algorithm fixes the azimuth or the elevation, and searches for the other without considering the directions of sources. A spectrum peak diffusion effect phenomenon is observed and may be utilized to detect the approximate directions of sources. Accordingly, a fast 2D MUSIC algorithm, which performs azimuth and elevation simultaneous searches (henceforth referred to as AESS) based on only three rounds of search is proposed. Firstly, AESS searches along a circle to detect the approximate source directions. Then, a subsequent search is launched along several straight lines based on these approximate directions. Finally, the 2D Direction of Arrival (DOA) of each source is derived by searching on several small concentric circles. Unlike the 2D MUSIC algorithm, AESS does not fix any azimuth and elevation parameters. Instead, the adjacent point of each search possesses different azimuth and elevation, i.e., azimuth and elevation are simultaneously searched to ensure that the search path is minimized, and hence the total spectral search over the angular field of view is avoided. Simulation results demonstrate the performance characters of the proposed AESS over some existing algorithms.

Performance of a collaborative task is mostly dependent on the collective effort from participants. To accomplish a participatory task effectively and efficiently, the team formation problem (TFP) outweighs all other considerations. It is even more complicated when social connections among candidates is taken into account. As we can imagine, a large number of tasks require members of the team to be socially close. On the contrary, a portion of tasks, e.g., proposal review, pay more attention to a multidimensional view, and team members should be selected from a variety of cliques. Due to the nature of tasks, it is challenging to find a subset that meets the skill requirement of the task as well as socially diversity demand of team members from a pool of candidates. In this paper, we explore the TFP in a social network. Based on different task objectives, we first formulate the TFP as TFP with strong ties (TFP-ST) and TFP with weak ties (TFP-WT), respectively. Both TFP-ST and TFP-WT are proven to be NP-hard, and we then design corresponding heuristic algorithms to solve the two problems. Through extensive simulations, we show that the solution to TFP-ST can achieve significant improvement in terms of collaboration cost, team size, as well as running time, and the solution to TFP-WT can provide better performance than existing approaches at the same time.

To enable dynamic spectrum access, service providers with spare spectrum (sellers) trade with those who are in need of additional spectrum (buyers). In a spectrum market, the transaction result is essentially a matching between sellers and buyers. Though it is tempting to optimize the matching over certain utility functions, a stable matching is more desirable, since no participants have incentives to deviate from the matching result. Existing spectrum matching algorithms only consider the maximum number of channels a buyer can purchase, but ignore minimum spectrum requirement that is essential to support proper operation of wireless communications. In this paper, we present a new framework of spectrum matching with both maximum quota and minimum requirements. Different from conventional matching problems, the spectrum market poses distinctive challenges due to spectrum reusability. To tackle this problem, we design two novel algorithms that satisfy different stability criterion: Extended Deferred Acceptance (EDA) algorithm that is fair but wasteful and the Multistage Deferred Acceptance (MDA) algorithm that is non-wasteful but weakly fair. Both algorithms converge to an interference-free matching and guarantees the minimum spectrum requirement. The simulation results show that the two proposed algorithms can raise buyer happiness and the channel utilization.

Crowdsourcing has become a popular paradigm to leverage collective intelligence of massive crowd workers to perform certain tasks in a cost-effective way. Task assignment is an essential issue in crowdsourcing platforms owing to heterogeneous tasks and works skills. In this paper, we focus on assigning workers with diversified skill levels to crowdsourcing tasks with different quality requirements and budget constraints. Task assignment is fundamentally a many-to-one matching problem, where one task is allocated to multiple users who can meet the minimum quality requirement of the task within the limited budget. While most existing works try to maximize the utility of the crowdsourcing platform, we take into account the individual preferences of crowdsourcers and workers towards each other to ensure the stability of the task assignment results. In this paper, we propose task assignment mechanisms that can guarantee stable outcomes for the many-to-one matching problem with lower and upper bounds (i.e., quality requirement and budget constraint) in regard of heterogeneous worker skill levels. Extensive simulation results show that the proposed algorithms can greatly improve the success ratio of task accomplishment and worker happiness compared with existing algorithms.