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.

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.

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 .

Personalized patient representation learning (PPRL) is a critical element in clinical risk prediction. It aims to obtain a complete portrait of each patient based on Electronic Health Records (EHR). Although existing works have achieved remarkable progress in healthcare prediction, there are still three major issues. First, feature correlation is crucial for risk prediction, but it has not yet been fully exploited by existing works. Second, variation pattern of dynamic feature contains useful information about patient's physical status, but adaptive pattern recognition is still a challenge. Third, existing works usually adopt a two-stage embedding process to process each dimension of the EHR data. However, some useful low-level information for PPRL will be lost. To address these issues, in this paper, we propose a fine-grained PPRL architecture named FG RL- N et for clinical risk prediction based on EHR. Specifically, we propose a Medical Feature Correlation Detection Module (FCM) to effectively learn the feature correlations for each patient and a Temporal Variation Pattern Recognition Module (TVM) to effectively detect the variation patterns of each dynamic feature. Moreover, we design a Fine-Grained Representation Mechanism (FGRM) to preserve the low-level information (from both feature and visit dimensions) useful for risk prediction. In addition, in the stage of data preprocessing, We utilize generic medical classification knowledge to classify numerical dynamic data. We conduct the in-hospital mortality experiment and the decompensation experiment on a real-world dataset. The experiment results show that the FGRL-Net outperforms state-of-the-art approaches. The source code is provided in github https://github.com/JackyChio/FGRL-Net.

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.

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 this paper, we use a Spectrum Sharing/allocation algorithm to implement a Cognitive Radio network without spectral awareness. This allows radio resources to be allocated to primary users and to secondary users in such a way that primary system and secondary system are aware of each other. By doing this, sensing of spectrum holes is avoided but the primary users will be protected and will always be allocated sufficient bandwidth to meet their QoS requirements. However, secondary users also have the concept of QoS and only those that can be given sufficient resources are actually allocated any, so avoiding wasting resources on users that cannot meet their QoS requirements. The scenario adopted is a multi-cell OFDMA network and the resource allocation is formulated as a non-cooperative game: each cell is a player whose aim is to maximise the number of secondary users that can reach their bitrates subject to the premise that all primary users in that cell can achieve their bitrate. Once spectrum is allocated we implement a power-minimisation algorithm to minimise the power consumption in the network while maintaining the number of users meeting their QoS requirements. Simulation results show the benefit of the approach.

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.

In current commercial wireless networks the service providers mediate between end users and network operators. The service provider can be a good candidate to integrate the different network technologies in a flexible and expandable platform. This paper presents a framework for a service provider to perform resource management in heterogeneous wireless networks. We analyse the characteristics of the service provider, summarize the required management functions and propose an architecture that allows the service provider to support real time resource management and seamless service handover in heterogeneous wireless networks. Additionally, the resource management functions based on two service level agreements are described, and an algorithm to perform QoS degrades of live connections has been implemented. A computer simulator has been developed to demonstrate the functionality and evaluate the performance of the architecture.