The increasing utilization of surveillance cameras in smart cities, coupled with the surge of online video applications, has heightened concerns regarding public security and privacy protection, which propelled automated Video Anomaly Detection (VAD) into a fundamental research task within the Artificial Intelligence (AI) community. With the advancements in deep learning and edge computing, VAD has made significant progress and advances synergized with emerging applications in smart cities and video internet, which has moved beyond the conventional research scope of algorithm engineering to deployable Networking Systems for VAD (NSVAD), a practical hotspot for intersection exploration in the AI, IoVT, and computing fields. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes, offering an exhaustive tutorial for novices in NSVAD. In addition, this article elucidates core concepts by reviewing recent advances and typical solutions and aggregating available research resources accessible at https://github.com/fdjingliu/NSVAD . Last, this article projects future development trends and discusses how the integration of AI and computing technologies can address existing research challenges and promote open opportunities, serving as an insightful guide for prospective researchers and engineers.

In device-to-device (D2D) underlaid cellular networks, several D2D pairs can share one channel to improve the system throughput. Most existing works allow D2D pairs to reuse all the channels, which may incur complicated interference management. Therefore, each D2D pair is limited to reuse at most one channel to reduce overhead. We aim to maximize the throughput of D2D pairs while suppressing the interference to cellular links. However, the optimization problem is an intractable mixed integer non-linear programming (MINLP) problem. Meanwhile, as network size increases, acquiring the global channel state information (CSI) is expensive even impossible in practice. Therefore, we propose a novel local CSI based distributed channel-power allocation scheme. The base station (BS) broadcasts a control signal to indicate its received interference from D2D pairs. Upon this signal, each D2D pair executes channel selection and power update independently and iteratively. Specifically, the channel allocation problem is formulated as a many-to-one matching game with externalities. The power control problem is modeled as a Stackelberg game. We prove the existence of two-sided swapstable matching and show that the outcomes of the Stackelberg game are locally optimal under some mild conditions. Simulation results show that our scheme is efficient with low overhead.

In object tracking, occlusions significantly undermine the performance of tracking algorithms. Unlike the existing methods that solely depend on the observed target appearance to detect occluders, we propose an algorithm that progressively analyzes the occlusion situation by exploiting the spatiotemporal context information, which is further double checked by the reference target and motion constraints. This strategy enables our proposed algorithm to make a clearer distinction between the target and occluders than existing approaches. To further improve the tracking performance, we rectify the occlusion-interfered erroneous target location by employing a variant-mask template matching operation. As a result, correct target location can always be obtained regardless of the occlusion situation. Using these techniques, the robustness of tracking under occlusions is significantly promoted. Experimental results have confirmed the effectiveness of our proposed algorithm.

In this paper, we consider a cooperative device-todevice (D2D) communication system, where the D2D transmitters (DTs) act as relays to assist the densified cellular network users (CUs) for transmission quality of service (QoS) improvement. The proposed system achieves a win-win situation, i.e. improving the spectrum efficiency of the CUs that cannot meet their rate requirement while providing spectrum access for D2D pairs. Unlike previous works, to reduce the overhead, we design a novel two-timescale resource allocation scheme, in which the pairing between CUs and D2D pairs is decided at a long timescale and transmission time for CU and D2D pair is determined at a short timescale. Specifically, to characterize the long-term payoff of each potential CU-D2D pair, we investigate the optimal cooperation policy to decide the transmission time based on the instantaneous channel state information (CSI). We prove that the optimal policy is a threshold policy which can be achieved via binary search. Since CUs and D2D pairs are self-interested, they are paired only when they agree to cooperate mutually. Therefore, to study the cooperation behaviors of CUs and D2D pairs, we formulate the pairing problem as a matching game, based on the long-term payoff achieved by the optimal cooperation policy of each possible pairing. Furthermore, unlike most previous matching models in D2D networks, we allow transfer between CUs and D2D pairs to improve the performance. To solve the pairing problem, a distributed algorithm is proposed, which converges to an -stable matching. We show that there is a tradeoff between the optimality and the computational complexity of the algorithm. We also analyze the algorithm in terms of the robustness to the unilateral deviation of D2D pairs. Finally, the simulation results verify the efficiency of the proposed matching algorithm.

The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address–transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others.

This paper proposes a new three-port LLC resonant converter which integrates input port, storage port, and load port in one converter. A pulse-frequency modulation and phaseshift combined control method are adopted for the proposed topology. It has four different operation modes with different gains and different power directions between input port, storage port, and load port, which are attractive for renewable energy with energy storage system applications. The proposed topology operates under resonant frequency, which is easy to implement with digital control, and it can achieve soft switching for almost all the switches and diodes similar to conventional LLC resonant converter. The performance of the proposed converter is validated by the experimental results from a 500-W prototype with 1.25-A maximum output current.