Passive acoustic monitoring is a sustainable method of monitoring wildlife and environments that leads to the generation of large datasets and, currently, a processing backlog. Academic research into automating this process is focused on the application of resource intensive convolutional neural networks which require large pre-labelled datasets for training and lack flexibility in application. We present a viable alternative relevant in both wild and captive settings; a transparent, lightweight and fast-to-train associative memory AI model with Hopfield neural network (HNN) architecture. Adapted from a model developed to detect bat echolocation calls, this model monitors captive endangered black-and-white ruffed lemur (Varecia variegata) vocalisations. Lemur social calls of interest when monitoring welfare are stored in the HNN in order to detect other call instances across the larger acoustic dataset. We make significant model improvements by storing an additional signal caused by movement and achieve an overall accuracy of 0.94. The model can perform 340 classifications per second, processing over 5.5 hours of audio data per minute, on a standard laptop running other applications. It has broad applicability and trains in milliseconds. Our lightweight solution reduces data-to-insight turnaround times and can accelerate decision making in both captive and wild settings.

Even if you have never had your smartphone stolen, you probably know someone who has. In London, 80,000 phones were stolen last year alone. And as victims of phone theft know, while the loss of a pricey gadget can sting, the dreary administrative slog in replacing a device that runs your entire life can, in some ways, be worse. So why can't we stop phone thieves – and is there a better way to protect your personal data? The answer is partly down to the numerous ways that criminals profit from stolen phones, but it is also about technology firms prioritising usability over security and international governments failing to arrive at a global solution.

The rapid spread of digital technologies has produced data-driven feedback loops, wearable devices, social media networks, and mobile applications that shape user behavior, motivation, and mental well-being. While these systems encourage self-improvement and the development of healthier habits through real-time feedback, they also create psychological risks such as technostress, addiction, and loss of autonomy. The present study also aims to investigate the positive and negative psychological consequences of feedback mechanisms on users' behaviour and well-being. Employing a descriptive survey method, the study collected data from 200 purposely selected users to assess changes in behaviour, motivation, and mental well-being related to health, social, and lifestyle applications. Results indicate that while feedback mechanisms facilitate goal attainment and social interconnection through streaks and badges, among other components, they also enhance anxiety, mental weariness, and loss of productivity due to actions that are considered feedback-seeking. Furthermore, test subjects reported that their actions are unconsciously shaped by app feedback, often at the expense of personal autonomy, while real-time feedback minimally influences professional or social interactions. The study shows that data-driven feedback loops deliver not only motivational benefits but also psychological challenges. To mitigate these risks, users should establish boundaries regarding their use of technology to prevent burnout and addiction, while developers need to refine feedback mechanisms to reduce cognitive load and foster more inclusive participation. Future research should focus on designing feedback mechanisms that promote well-being without compromising individual freedom or increasing social comparison.

Academics and departments are sometimes judged by how their research has benefitted society. For example, the UK Research Excellence Framework (REF) assesses Impact Case Studies (ICS), which are five-page evidence-based claims of societal impacts. This study investigates whether ChatGPT can evaluate societal impact claims and therefore potentially support expert human assessors. For this, various parts of 6,220 public ICS from REF2021 were fed to ChatGPT 4o-mini along with the REF2021 evaluation guidelines, comparing the results with published departmental average ICS scores. The results suggest that the optimal strategy for high correlations with expert scores is to input the title and summary of an ICS but not the remaining text, and to modify the original REF guidelines to encourage a stricter evaluation. The scores generated by this approach correlated positively with departmental average scores in all 34 Units of Assessment (UoAs), with values between 0.18 (Economics and Econometrics) and 0.56 (Psychology, Psychiatry and Neuroscience). At the departmental level, the corresponding correlations were higher, reaching 0.71 for Sport and Exercise Sciences, Leisure and Tourism. Thus, ChatGPT-based ICS evaluations are simple and viable to support or cross-check expert judgments, although their value varies substantially between fields.

Entity Linking (EL) is a fundamental task for Information Extraction and Knowledge Graphs. The general form of EL (i.e., end-to-end EL) aims to first find mentions in the given input document and then link the mentions to corresponding entities in a specific knowledge base. Recently, the paradigm of retriever-reader promotes the progress of end-to-end EL, benefiting from the advantages of dense entity retrieval and machine reading comprehension. However, the existing study only trains the retriever and the reader separately in a pipeline manner, which ignores the benefit that the interaction between the retriever and the reader can bring to the task. To advance the retriever-reader paradigm to perform more perfectly on end-to-end EL, we propose BEER$^2$, a Bidirectional End-to-End training framework for Retriever and Reader. Through our designed bidirectional end-to-end training, BEER$^2$ guides the retriever and the reader to learn from each other, make progress together, and ultimately improve EL performance. Extensive experiments on benchmarks of multiple domains demonstrate the effectiveness of our proposed BEER$^2$.

Entity Set Expansion (ESE) is a critical task aiming at expanding entities of the target semantic class described by seed entities. Most existing ESE methods are retrieval-based frameworks that need to extract contextual features of entities and calculate the similarity between seed entities and candidate entities. To achieve the two purposes, they iteratively traverse the corpus and the entity vocabulary, resulting in poor efficiency and scalability. Experimental results indicate that the time consumed by the retrieval-based ESE methods increases linearly with entity vocabulary and corpus size. In this paper, we firstly propose Generative Entity Set Expansion (GenExpan) framework, which utilizes a generative pre-trained auto-regressive language model to accomplish ESE task. Specifically, a prefix tree is employed to guarantee the validity of entity generation, and automatically generated class names are adopted to guide the model to generate target entities. Moreover, we propose Knowledge Calibration and Generative Ranking to further bridge the gap between generic knowledge of the language model and the goal of ESE task. For efficiency, expansion time consumed by GenExpan is independent of entity vocabulary and corpus size, and GenExpan achieves an average 600% speedup compared to strong baselines. For expansion effectiveness, our framework outperforms previous state-of-the-art ESE methods.

Text classification is an area of research which has been studied over the years in Natural Language Processing (NLP). Adapting NLP to multiple domains has introduced many new challenges for text classification and one of them is long document classification. While state-of-the-art transformer models provide excellent results in text classification, most of them have limitations in the maximum sequence length of the input sequence. The majority of the transformer models are limited to 512 tokens, and therefore, they struggle with long document classification problems. In this research, we explore on employing Model Fusing for long document classification while comparing the results with well-known BERT and Longformer architectures.

Chinese Text Error Correction (CTEC) aims to detect and correct errors in the input text, which benefits human's daily life and various downstream tasks. Recent approaches mainly employ Pre-trained Language Models (PLMs) to resolve CTEC task and achieve tremendous success. However, previous approaches suffer from issues of over-correction and under-correction, and the former is especially conspicuous in the precision-critical CTEC task. To mitigate the issue of overcorrection, we propose a novel model-agnostic progressive multitask learning framework for CTEC, named ProTEC, which guides a CTEC model to learn the task from easy to difficult. We divide CTEC task into three sub-tasks from easy to difficult: Error Detection, Error Type Identification, and Correction Result Generation. During the training process, ProTEC guides the model to learn text error correction progressively by incorporating these sub-tasks into a multi-task training objective. During the inference process, the model completes these sub-tasks in turn to generate the correction results. Extensive experiments and detailed analyses fully demonstrate the effectiveness and efficiency of our proposed framework.

In the online world, Machine Translation (MT) systems are extensively used to translate User-Generated Text (UGT) such as reviews, tweets, and social media posts, where the main message is often the author's positive or negative attitude towards the topic of the text. However, MT systems still lack accuracy in some low-resource languages and sometimes make critical translation errors that completely flip the sentiment polarity of the target word or phrase and hence delivers a wrong affect message. This is particularly noticeable in texts that do not follow common lexico-grammatical standards such as the dialectical Arabic (DA) used on online platforms. In this research, we aim to improve the translation of sentiment in UGT written in the dialectical versions of the Arabic language to English. Given the scarcity of gold-standard parallel data for DA-EN in the UGT domain, we introduce a semi-supervised approach that exploits both monolingual and parallel data for training an NMT system initialised by a cross-lingual language model trained with supervised and unsupervised modeling objectives. We assess the accuracy of sentiment translation by our proposed system through a numerical 'sentiment-closeness' measure as well as human evaluation. We will show that our semi-supervised MT system can significantly help with correcting sentiment errors detected in the online translation of dialectical Arabic UGT.

The domain of Botany is rich with metaphorical terms. Those terms play an important role in the description and identification of flowers and plants. However, the identification of such terms in discourse is an arduous task. This leads in some cases to committing errors during translation processes and lexicographic tasks. The process is even more challenging when it comes to machine translation, both in the cases of single-word terms and multi-word terms. One of the recent concerns of Natural Language Processing (NLP) applications and Machine Translation (MT) technologies is the automatic identification of metaphor-based words in discourse through Deep Learning (DL). In this study, we seek to fill this gap through the use of thirteen popular transformer based models, as well as ChatGPT, and we show that discriminative models perform better than GPT-3.5 model with our best performer reporting 92.2349% F1 score in metaphoric flower and plant names identification task.