Improving context faithfulness in large language models is essential for developing trustworthy retrieval augmented generation systems and mitigating hallucinations, especially in long-form question answering (LFQA) tasks or scenarios involving knowledge conflicts. Existing methods either intervene LLMs only at inference without addressing their inherent limitations or overlook the potential for self-improvement. In this paper, we introduce GenDiE (Generate, Discriminate, Evolve), a novel self-evolving framework that enhances context faithfulness through fine-grained sentence-level optimization. GenDiE combines both generative and discriminative training, equipping LLMs with self-generation and self-scoring capabilities to facilitate iterative self-evolution. This supports both data construction for model alignment and score-guided search during inference. Furthermore, by treating each sentence in a response as an independent optimization unit, GenDiE effectively addresses the limitations of previous approaches that optimize at the holistic answer level, which may miss unfaithful details. Experiments on ASQA (in-domain LFQA) and ConFiQA (out-of-domain counterfactual QA) datasets demonstrate that GenDiE surpasses various baselines in both faithfulness and correctness, and exhibits robust performance for domain adaptation.

Thanks to the rapid advancement of computer hardware, deep learning has made significant progress in the application of unstructured data, such as images (Cao & Chen, 2025) and text (Li et al., 2024). Specifically, the success of representation learning (Wang & Lian, 2025; Zhang et al., 2025) has gradually replaced the earlier approaches of transforming unstructured data into structured formats. The key to the success of representation learning lies in leveraging a large number of parameters for backpropagation, enabling the model to adapt to data with non-normal distributions. Although models based on backpropagation neural networks (Yang et al., 2019; Banerjee et al., 2023) have achieved significant technical advancements, their application in many sensitive domains, such as medicine (Zhang et al., 2025) and industrial inspection (Rathee et al., 2021), still faces considerable challenges due to the difficulty in understanding the basis of their decision-making. Explainable Artificial Intelligence (XAI) aims to reveal the inner mechanisms of neural network decisions, thereby making these models more reliable for applications in sensitive domains. In recent years, several studies (Li et al., 2025; Jing et al., 2025; Liu et al., 2024; Guan et al., 2024) have focused on injecting explainability into deep learning models and using various visualization techniques to explain the decisions of these "black box" models. While these models have achieved a certain level of interpretability, two pressing issues remain (Huang & Marques, 2023; Huang & Marques, 2024): first, whether the correlations between different attributes are correctly evaluated, and second, whether the model's decision-making pathway truly aligns with human reasoning, even when the model's understanding appears consistent with user expectations.

Recently, research based on pre-trained models has demonstrated outstanding performance in violence surveillance tasks. However, most of them were black-box systems which faced challenges regarding explainability during training and inference processes. An important question is how to incorporate explicit knowledge into these implicit models, thereby designing expertdriven and interpretable violence surveillance systems. This paper proposes a new paradigm for weakly supervised violence monitoring (WSVM) called Rule base Violence Monitoring (RuleVM). The proposed RuleVM uses a dual-branch structure with different designs for images and text. One of the branches is called the implicit branch, which uses only visual features for coarse-grained binary classification. In this branch, image feature extraction is divided into two channels: one responsible for extracting scene frames and the other focusing on extracting actions. The other branch is called the explicit branch, which utilizes language-image alignment to perform fine-grained classification. For the language channel design in the explicit branch, the proposed RuleVM uses the state-of-the-art YOLOWorld model to detect objects in video frames, and association rules are identified through data mining methods as descriptions of the video. Leveraging the dual-branch architecture, RuleVM achieves interpretable coarse-grained and fine-grained violence surveillance. Extensive experiments were conducted on two commonly used benchmarks, and the results show that RuleVM achieved the best performance in both coarse-grained and finegrained monitoring, significantly outperforming existing state-ofthe-art methods. Moreover, interpretability experiments uncovered some interesting rules, such as the observation that as the number of people increases, the risk level of violent behavior also rises.

Reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems. However, most RL algorithms lack an explicit method that would allow learning from contextual information. Humans use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. On the other hand, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This paper proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state using contextual key frames (CKFs), which can then be used to extract a function that represents the affordances of the state; in addition, two loss functions are introduced with respect to the affordances of the state. The novelty of the IECR framework lies in its capacity to extract contextual information from the environment and learn from the CKFs' representation. We validate the framework by developing four new algorithms that learn using context: Iota deep Q-network (IDQN), Iota double deep Q-network (IDDQN), Iota dueling deep Q-network (IDuDQN), and Iota dueling double deep Q-network (IDDDQN). Furthermore, we evaluate the framework and the new algorithms in five discrete environments. We show that all the algorithms, which use contextual information, converge in around 40,000 training steps of the neural networks, significantly outperforming their state-of-the-art equivalents.

Underwater Acoustic Sensor Networks (UW-ASNs) are predominantly used for underwater environments and find applications in many areas. However, a lack of security considerations, the unstable and challenging nature of the underwater environment, and the resource-constrained nature of the sensor nodes used for UW-ASNs (which makes them incapable of adopting security primitives) make the UW-ASN prone to vulnerabilities. This paper proposes an Adaptive decentralised Intrusion Detection and Prevention System called AIDPS for UW-ASNs. The proposed AIDPS can improve the security of the UW-ASNs so that they can efficiently detect underwater-related attacks (e.g., blackhole, grayhole and flooding attacks). To determine the most effective configuration of the proposed construction, we conduct a number of experiments using several state-of-the-art machine learning algorithms (e.g., Adaptive Random Forest (ARF), light gradient-boosting machine, and K-nearest neighbours) and concept drift detection algorithms (e.g., ADWIN, kdqTree, and Page-Hinkley). Our experimental results show that incremental ARF using ADWIN provides optimal performance when implemented with One-class support vector machine (SVM) anomaly-based detectors. Furthermore, our extensive evaluation results also show that the proposed scheme outperforms state-of-the-art bench-marking methods while providing a wider range of desirable features such as scalability and complexity.

Steganography and steganalysis are two interrelated aspects of the field of information security. Steganography seeks to conceal communications, whereas steganalysis is aimed to either find them or even, if possible, recover the data they contain. Steganography and steganalysis have attracted a great deal of interest, particularly from law enforcement. Steganography is often used by cybercriminals and even terrorists to avoid being captured while in possession of incriminating evidence, even encrypted, since cryptography is prohibited or restricted in many countries. Therefore, knowledge of cutting-edge techniques to uncover concealed information is crucial in exposing illegal acts. Over the last few years, a number of strong and reliable steganography and steganalysis techniques have been introduced in the literature. This review paper provides a comprehensive overview of deep learning-based steganalysis techniques used to detect hidden information within digital media. The paper covers all types of cover in steganalysis, including image, audio, and video, and discusses the most commonly used deep learning techniques. In addition, the paper explores the use of more advanced deep learning techniques, such as deep transfer learning (DTL) and deep reinforcement learning (DRL), to enhance the performance of steganalysis systems. The paper provides a systematic review of recent research in the field, including data sets and evaluation metrics used in recent studies. It also presents a detailed analysis of DTL-based steganalysis approaches and their performance on different data sets. The review concludes with a discussion on the current state of deep learning-based steganalysis, challenges, and future research directions.

Garbage disposal is a challenging problem throughout the developed world. In Cyprus, as elsewhere, illegal ``fly-tipping" is a significant issue, especially in rural areas where few legal garbage disposal options exist. However, there is a lack of studies that attempt to measure the scale of this problem, and few resources available to address it. A method of automating the process of identifying garbage dumps would help counter this and provide information to the relevant authorities. The aim of this study was to investigate the degree to which artificial intelligence techniques, together with satellite imagery, can be used to identify illegal garbage dumps in the rural areas of Cyprus. This involved collecting a novel dataset of images that could be categorised as either containing, or not containing, garbage. The collection of such datasets in sufficient raw quantities is time consuming and costly. Therefore a relatively modest baseline set of images was collected, then data augmentation techniques used to increase the size of this dataset to a point where useful machine learning could occur. From this set of images an artificial neural network was trained to recognise the presence or absence of garbage in new images. A type of neural network especially suited to this task known as ``convolutional neural networks" was used. The efficacy of the resulting model was evaluated using an independently collected dataset of test images. The result was a deep learning model that could correctly identify images containing garbage in approximately 90\% of cases. It is envisaged that this model could form the basis of a future system that could systematically analyse the entire landscape of Cyprus to build a comprehensive ``garbage" map of the island.

Air quality plays a crucial role in human health and the well-being of the environment. Unfortunately, air pollution has been on the rise due to various sources such as vehicle emissions, industrial activities, energy production, and natural disasters like wildfires. Understanding and assessing the quality of the air we breathe is of utmost importance. Air Quality Monitoring (AQM) systems, integrated with sensors and advanced technologies, are utilized to measure particulate matter and air pollutants like ozone, nitrogen oxides, and sulfur dioxide. The data collected by these systems helps formulate policies, monitor pollution reduction efforts, and empower the public to make informed decisions regarding their health and well-being. Currently, AQM stations are primarily used for calculating the Air Quality Index (AQI) and monitoring pollution. However, the infrastructure requirements, operational complexities, and ongoing expenses associated with these stations limit the expansion of AQM networks and the availability of air pollution data. To overcome these limitations, it is imperative to develop low-cost, efficient, and real-time data-sensing devices.

New Delhi, Oct 27: Indian researchers have developed a new technique to assess fracture recovery. This technique, based on an artificial intelligence (AI) simulation model, could be helpful in predicting the improvement in fractures of the thigh bone after surgery. In addition, this technique can also help the surgeon choose the right implant or procedure before the surgery required for fracture healing. The researchers say this technique may be used to assess the healing outcomes of various fracture fixation strategies. This allows the optimal approach to be selected based on the patient's specific anatomical build and fracture type.

Artificial Intelligence is machine intelligence or ability to think and process information like natural human intelligence in order to create expert systems with human intelligence (reasoning, learning, and problem solving) with help from science and technology disciplines such as Mathematics, Engineering, Biology, Computer Science, Linguistics and Psychology. The term intelligence, literally, means the ability to acquire and apply knowledge and skills. The term Artificial Intelligence ( Artificial Intelligence) is pretty self-explanatory. It is the ability to acquire and apply knowledge and skills artificially. In 1956, a group of researchers from different disciplines of technology gathered for the summit called Dartmouth Summer Research Project.