Traditional Intrusion Detection Systems (IDS) are increasingly challenged by the distributed, heterogeneous, and rapidly evolving threat landscape in Internet of Things (IoT) and Industrial IoT (IIoT) environments. Blockchain has been explored as a promising foundation for decentralized and trustworthy security mechanisms; however, the existing literature remains fragmented and lacks a clear organizing lens for comparing design choices and evaluation practices. To address this, this paper presents a problem-driven survey of blockchain-enabled IDS for IoT and IIoT. We organize prior work into four integration paradigms, Trusted Rule, ML, DL, and FL—and relate each paradigm to the recurring design tensions it primarily targets. We further distill three fundamental tensions that frequently shape system design, including distributed architectures vs. centralized security management, collaborative information sharing vs. privacy preservation, and real-time detection requirements vs. resource-constrained devices. In addition, we summarize representative frameworks by consolidating datasets, threat models, and reported performance-related metrics, and we discuss common limitations that hinder cross-paper comparability. Finally, we outline a roadmap toward more standardized benchmarking, suggesting candidate evaluation criteria and blockchain-specific KPIs to encourage more transparent and comparable reporting. Overall, this survey aims to provide a structured lens for navigating the design space of blockchain-enabled IDS and to highlight open challenges for future research.

Mobile edge computing (MEC) facilitates high reliability and low-latency applications by bringing computation and data storage closer to end-users. Intelligent computing is an important application of MEC, where computing resources are used to solve intelligent task-related problems based on task requirements. However, efficiently offloading computing and allocating resources for intelligent tasks in MEC systems is a challenging problem due to complex interactions between task requirements and MEC resources. To address this challenge, we investigate joint computing offloading and resource allocation for classification intelligence tasks (CITs) in MEC systems. Our goal is to optimize system utility by jointly considering computing accuracy and task delay to achieve maximum utility of our system. We focus on CITs and formulate an optimization problem that considers task characteristics including the accuracy requirements and the parallel computing capabilities in MEC systems. To solve the proposed problem, we decompose it into three subproblems: subcarrier allocation, computing capacity allocation and compression offloading. We use successive convex approximation and convex optimization method to derive optimized feasible solutions for the subcarrier allocation, offloading variable, computing capacity allocation, and compression ratio. Based on our solutions, we design an efficient joint computing offloading and resource allocation algorithm for CITs in MEC systems. Our simulation demonstrates that the proposed algorithm significantly improves the performance by 16.4% on average and achieves a flexible trade-off between system revenue and cost considering CITs compared with benchmarks.

Ejection fraction (EF) is a key indicator of cardiac function, crucial for diagnosing heart failure and guiding treatment. Its estimation from echocardiography is challenged by morphological changes across cardiac phases and low-quality, noisy boundaries. We propose EFusionNet, a multimodal segmentation framework that integrates echocardiographic images with structured diagnostic text to enhance segmentation and EF assessment. Clinical phrases (e.g., “irregular boundary”) are embedded via a domain-specific language model into both input fusion and UNet skip connections, enabling phase-aware feature calibration. A feature fusion enhancement module (FFEM) refines spatial localization, while a multi-objective loss enforces uncertainty learning and semantic consistency. Evaluated on CAMUS and EchoNet-Dynamic datasets, EFusionNet achieves Dice scores of 91.2%/90.1% and EFMAEof 4.8/5.0, outperforming baselines and improving reliable, interpretable EF estimation.

Large Language Models (LLMs) show strong performance in natural language processing tasks, but their application in the financial domain is limited. Current methods rely on large datasets and manual prompt engineering, resulting in high data demands, long inference times, and frequent hallucinations. To address these limitations, we propose a novel self-supervised prompt optimization framework tailored for the financial domain. Our approach involves training a critical model that evaluates and ranks generated outputs using both good and bad answers generated from various revised prompts. Experiments on a large Chinese financial corpus show that our framework significantly improves performance on tasks such as summarization and event-based question answering, as evidenced by higher scores on both automated metrics like ROUGE, BLEU, and BERTScore, and also through human evaluations. These results validate the effectiveness of our method in reducing hallucinations and improving the quality of financial text generation.

With the increasing prevalence of online communication and automated services, dialogue summarization technology plays a vital role in meeting minutes, customer service, and online Q&A scenarios. However, existing methods often suffer from insufficient flexibility in topic segmentation, low efficiency in semantic information transfer, and limited role adaptability. To address these challenges, we propose DABART, a dynamic semantic optimization framework. The framework employs a dynamic semantic topic segmentation mechanism to adaptively segment topics based on the distribution characteristics of sentence embeddings within dialogues, effectively identifying key information while overcoming the limitations of fixed-parameter methods in complex dialogue scenarios. Additionally, a dynamic semantic bridging module integrates semantic and positional information, further enhancing the coherence and consistency of dialogue summarization. Experimental results demonstrate that DABART achieves superior performance on widely-used benchmarks such as SAMSum and CSDS. Notably, it surpasses state-of-the-art open-source models on the CSDS dataset in ROUGE and BERTScore metrics, while achieving more balanced and accurate role-oriented summarization. Extensive experimental analyses further validate the robustness and applicability of the DABART across diverse dialogue scenarios.

When artificial intelligence (AI) technology enters the world of acoustics, many projects that require audio processing are automated and no longer require much manual work. In particular, the use of the latest AI technologies to recognize speech and determine speakers makes it possible to generate meeting minutes entirely by machines. In the southeastern region of China (for example Hong Kong and Macao), people use Cantonese as the official language. Internal government meetings are usually conducted in Cantonese, and the executive department will request that the minutes be submitted as soon as possible after the meeting. In addition to the content of the speeches, the minutes must also include the identities of the corresponding speakers. During some periods of intensive meetings on new policy releases, our interpreters faced great pressure to script the exhausting meeting minutes. Due to the presence of local terms and personal names, even state-of-the-art large language models (LLMs) cannot fully suffice. Therefore, we propose a novel approach to solve such problems. This approach is a three-tier software architecture: the data tier (data processing), the service tier (AI models and web services), and the application tier (user interfaces). The implementation work is carried out using modern AI models (OpenAI’s Whisper and Nvidia’s TitaNet) and a dataset we created (Cantonese Policy Address, CPA). Training results (the word error rate is 33.81% and the equal error rate is 0.54) and validation results (confusion matrix up to 97%) show that our proposed approach improves automatic recognition precision, thus helping people understand the spirit of the meeting more effectively and quickly.

ABSTRACT As the volume of information on online communication platforms continues to grow, the task of dialogue summarisation becomes increasingly critical for understanding and extracting key information from diverse conversations. Traditional approaches often struggle to cope with the dynamic nature of dialogues, such as managing perspectives from multiple speakers and seamlessly transitioning between different topics. We propose a novel hierarchical topic‐driven approach to generate role‐oriented dialogue summarisation (HiSum) to address these challenges. First, we utilise VarGMM clustering technology for in‐depth topic segmentation, which enables the model to capture the key topics in a dialogue. Second, we employ a LayerAttn hierarchical attention mechanism to dynamically adjust the focus of dialogue content based on participants' importance and the topics' relevance. Experimental results on three public dialogue summarisation data sets (CSDS, MC and SAMSUM) demonstrate that our method significantly outperforms most existing strong baseline methods across various evaluation metrics and surpasses the current state‐of‐the‐art methods in certain metrics. Detailed analysis demonstrates that HiSum can perform more precise topic segmentation and effectively identify critical information. Our code is publicly available at: https://github.com/kjin0119/HiSum .

Computation offloading and resource allocation are critical in mobile edge computing (MEC) systems to handle the massive and complex requirements of applications restricted by limited resources. In a multiuser multiserver MEC network, the mobility of terminals causes computing requests to be dynamically distributed in space. At the same time, the non-negligible dependencies among tasks in some specific applications impose temporal correlation constraints on the solution as well, leading the time-adjacent tasks to experience varying resource availability and competition from parallel counterparts. To address such dynamic spatial-temporal characteristics as a challenge in the allocation of communication and computation resources, we formulate a long-term delay-energy tradeoff cost minimization problem in the view of jointly optimizing task offloading and resource allocation. We begin by designing a priority evaluation scheme to decouple task dependencies and then develop a grouped Knapsack problem for channel allocation considering the current data load and channel status. Afterward, in order to meet the rapid response needs of MEC systems, we exploit the double duel deep Q network (D3QN) to make offloading decisions and integrate channel allocation results into the reward as part of the dynamic environment feedback in D3QN, constituting the joint optimization of task offloading and channel allocation. Finally, comprehensive simulations demonstrate the performance of the proposed algorithm in the delay-energy tradeoff cost and its adaptability for various applications.

In the multiple unmanned aerial vehicle (UAV) mobile edge computing (MEC) systems, the cooperative computation among multiple UAVs can improve the overall computation service capability. Multi-UAV MEC systems can meet the quality of service requirements for computation intensive applications of ground terminals (GTs) in complex field environments, emergency disaster relief and other special scenarios. In this paper, a multi-UAV cooperative computation framework is proposed while taking the GT movement and random arrival of computation tasks into consideration. A long-term optimization problem is formulated for the joint optimization of UAV trajectory and resource allocation, subject to minimizing the total GT computation task completion time and the total system energy consumption. To solve this problem, a joint multiple time-scale optimization algorithm is proposed. In particular, the optimization problem is decomposed into a long time-scale multi-UAV trajectory planning subproblem and a short time-scale resource allocation subproblem. The proximal policy optimization algorithm is invoked to solve the long time-scale subproblem. The greedy algorithm and the successive convex approximation (SCA) method are employed to solve the short time-scale subproblem. Finally, a joint multiple time-scale optimization algorithm with a two-layer loop structure is proposed. Simulation results show that: 1) the proposed multi-UAV cooperative computation MEC system outperforms the conventional MEC system without collaboration among UAVs; and 2) the proposed algorithm can quickly adapt to different degrees of environmental dynamics and outperforms the benchmark algorithm for different network sizes, task requirements, and available resources.

Unmanned aerial vehicles (UAVs) are deployed in emergency disaster-relief operations to provide communication services as substitutes for damaged ground base stations (BSs), as well as to offload computational tasks for applications such as target recognition. In view of the limited computing power of a single UAV, we focus on the edge computing offloading problem with multiple-UAV cooperation. As a single UAV is not enough to offload massive delay-sensitive computing tasks in the emergency communication scenarios, we have built up a multi-UAV cooperation computing architecture. By exploring the multiple-UAV cooperation computing offloading capacity, we formulated an optimization problem of minimizing the total time slot size. Since the proposed problem is relevant to mixed integer nonlinear programming, it can be decomposed into two sub-problems: computing task scheduling and UAV trajectory. To handle the formulated problems, we developed a joint optimization algorithms by invoking the penalty method and successive convex approximation (SCA) method. The simulation results show that, compared with the benchmark algorithms, the proposed algorithm can significantly reduce the computation task delay and improve the execution efficiency of the UAVs.

This paper investigates the intelligent computing task-oriented computing offloading and semantic compression in mobile edge computing (MEC) systems. With the popularity of intelligent applications in various industries, terminals increasingly need to offload intelligent computing tasks with complex demands to MEC servers for computing, which is a great challenge for bandwidth and computing capacity allocation in MEC systems. Considering the accuracy requirement of intelligent computing tasks, we formulate an optimization problem of computing offloading and semantic compression. We jointly optimize the system utility which are represented as computing accuracy and task delay respectively to acquire the optimized system utility. To solve the proposed optimization problem, we decompose it into computing capacity allocation subproblem and compression offloading subproblem and obtain solutions through convex optimization and successive convex approximation. After that, the offloading decisions, computing capacity and compressed ratio are obtained in closed forms. We design the computing offloading and semantic compression algorithm for intelligent computing tasks in MEC systems then. Simulation results represent that our algorithm converges quickly and acquires better performance and resource utilization efficiency through the trend with total number of users and computing capacity compared with benchmarks.

Current state-of-the-art single shot object detection pipelines, composed by an object detector such as Yolo, generate multiple detections for each object, requiring a post-processing Non-Maxima Suppression (NMS) algorithm to remove redundant detections. However, this pipeline struggles to achieve high accuracy, particularly in object counting applications, due to a trade-off between precision and recall rates. A higher NMS threshold results in fewer detections suppressed and, consequently, in a higher recall rate, as well as lower precision and accuracy. In this paper, we have explored a new pedestrian detection pipeline which is more flexible, able to adapt to different scenarios and with improved precision and accuracy. A higher NMS threshold is used to retain all true detections and achieve a high recall rate for different scenarios, and a Pedestrian Similarity Extraction (PSE) algorithm is used to remove redundant detentions, consequently improving counting accuracy. The PSE algorithm significantly reduces the detection accuracy volatility and its dependency on NMS thresholds, improving the mean detection accuracy for different input datasets.

In this paper we evaluate the game-theory approach we have used for radio resource block allocation in sliced multi-cell networks with distributed control against a centralized control strategy. This is done in the context of an approach that allocates resources according to the QoS requirements of users. The objective is to see whether the game-theory approach is worth using throughout a network with mixed decentralized and centralized control and the conclusion is that the performance of the game-theory approach is equally suitable for both types.

In this paper, we demonstrate how isolation can be achieved in dynamic network slicing using an appropriate Connection Admission Control (CAC) mechanism. The scenario adopted is a multi-cell OFDMA network and the resource allocation is performed using a non-cooperative game. Simulation results show that such an approach leads to implicit isolation (a term we define in the paper) being achieved, at the same time reaping the capacity benefits of dynamic slicing.

In today's wireless communications, the need to consider the Quality of Service (QoS) requirements of individual users is gaining widespread acceptance. Previous work showed that an OFDMA spectrum sharing network solution based on semi-smart antennas gave a performance improvement of around 10% in terms of the number of qualified users. Optimization of the semi-smart antenna radiation patterns using Genetic Algorithm produced a dynamic cell boundary and flexible cell coverage. However in a realistic network, it is not sensible to expect cell boundaries to change rapidly as this would lead to increased handovers and signalling. In this paper the optimization approach is tested with users moving as movement would lead to the greatest likelihood of patterns changing and hence increased handovers to research when to change antenna pattern with user movement. This paper proposes a periodic optimization scheme that is found to lead to a significant improvement in the ratio of qualified users.

Location based services are the hottest applications on mobile devices nowadays and the growth is continuing. Indoor wireless positioning is the key technology to enable location based services to work well indoors, where GPS normally could not work. Bluetooth has been widely used in mobile devices like phone, PAD etc. therefore Bluetooth based indoor positioning has great market potential. Radio Signal Strength (RSS) is a key parameter for wireless positioning. New Bluetooth standard (since version 2.1) enables RSS to be discovered without time consuming pre-connection. In this research, general wireless positioning technologies are firstly analysed. Then RSS based Bluetooth positioning using the new feature is studied. The mathematical model is established to analyse the relation between RSS and the distance between two Bluetooth devices. Three distance-based algorithms are used for Bluetooth positioning: Least Square Estimation, Three-border and Centroid Method. Comparison results are analysed and the ways to improve the positioning accuracy are discussed.

This paper proposes a learning approach to solve adaptive Connection Admission Control (CAC) schemes in future wireless networks. Real time connections (that require lower delay bounds than non-real-time) are subdivided into hard realtime (requiring constant bandwidth capacity) or adaptive (that have flexible bandwidth requirements). The CAC for such a mix of traffic types is a complex constraint reinforcement learning problem with noisy fitness. Noise deteriorates the final location and quality of the optimum, and brings a lot of fitness fluctuation in the boundary of feasible and infeasible region. This paper proposes a novel approach that learns adaptive CAC policies through NEAT combined with Superiority of Feasible Points. The objective is to maximize the network revenue and also maintain predefined several QoS constraints.

Orthogonal frequency division multiplexing (OFDMA) offers great spectrum efficiency and flexible frequency allocation to users without intra-cell interferences in LTE system. However, the cell edge users will experience high interferences from neighbouring cells. Many frequency reuse schemes have been proposed for improving the Signal to Interference and Noise Ratio (SINR) performance for cell edge users, most of them dividing available frequencies into groups for cell centre and cell edge users. In this research, we combine the traditional frequency scheme with novel semi-smart antennas and learning algorithm - Genetic Algorithm (GA). The semi-smart antennas can produce flexible coverage patterns for base stations (BSs) and the learning algorithm can coordinate the coverage patterns between BSs to minimise interferences for mobile units (MUs). A system level simulation contains 25 BSs and 1000 MUs has been developed. Simulation results show that the proposed scheme improves the total traffic load and reduces antenna propagation power for all cells compares to system with fixed coverage patterns.

IEEE 802.11 based WLANs have been widely deployed. Currently research is concentrating on how to find the best location for these access points plus tuning their antennas in order to seamlessly cover large areas. With more and more WLAN users, the air interface acts as bottleneck even in high-speed WLAN systems such as IEEE 802.11a. To improve the overall performance of IEEE 802.11 WLAN systems under congestion conditions, an agent-based radio resource management system is proposed that can dynamically change an AP's radio coverage pattern in cooperation with surrounding APs. A low-cost four sector semismart antenna array is used for access points. In this paper we present results from a simulation showing system performance. The use of intelligent agents and negotiations for indoor radio resource management is presented and discussed.