Expert Systems最新文献

Sarcasm detection is challenging in natural language processing (NLP) due to its implicit nature, particularly in low-resource languages. Despite limited linguistic resources, researchers have focused on detecting sarcasm on social media platforms, leading to the development of specialized algorithms and models tailored for Urdu text. Researchers have significantly improved sarcasm detection accuracy by analysing patterns and linguistic cues unique to the language, thereby advancing NLP capabilities in low-resource languages and facilitating better communication within diverse online communities. This work introduces UrduSarcasmNet, a novel architecture using cascaded group multi-head attention, which is an innovative deep-learning approach that employs cascaded group multi-head attention techniques to enhance effectiveness. By employing a series of attention heads in a cascading manner, our model captures both local and global contexts, facilitating a more comprehensive understanding of the text. Adding a group attention mechanism enables simultaneous consideration of various sub-topics within the content, thereby enriching the model's effectiveness. The proposed UrduSarcasmNet approach is validated with the Urdu-sarcastic-tweets-dataset (UST) dataset, which has been curated for this purpose. Our experimental results on the UST dataset show that the proposed UrduSarcasmNet framework outperforms the simple-attention mechanism and other state-of-the-art models. This research significantly enhances natural language processing (NLP) and provides valuable insights for improving sarcasm recognition tools in low-resource languages like Urdu.

Engagement has been defined as an attitude toward work, as a positive, satisfying, work-related state of mind characterized by high levels of vigour, dedication, and absorption. Both its definition and its assessment have been controversial; however, new methods for its assessment, including artificial intelligence (AI), have been introduced in recent years. Therefore, this research aims to determine the state of the art of AI in the study of engagement. To this end, we conducted a systematic review in accordance with PRISMA to analyse the publications to date on the use of AI for the analysis of engagement. The search, carried out in six databases, was filtered, and 15 papers were finally analysed. The results show that AI has been used mainly to assess and predict engagement levels, as well as to understand the relationships between engagement and other variables. The most commonly used AI techniques are machine learning (ML) and natural language processing (NLP), and all publications use structured and unstructured data, mainly from self-report instruments, social networks, and datasets. The accuracy of the models varies from 22% to 87%, and its main benefit has been to help both managers and HR staff understand employee engagement, although it has also contributed to research. Most of the articles have been published since 2015, and the geography has been global, with publications predominantly in India and the US. In conclusion, this study highlights the state of the art in AI for the study of engagement and concludes that the number of publications is increasing, indicating that this is possibly a new field or area of research in which important advances can be made in the study of engagement through new and novel techniques.

With the progressive augmentation of parameters in multimodal models, to optimize computational efficiency, some studies have adopted the approach of fine-tuning the unimodal pre-training model to achieve multimodal fusion tasks. However, these methods tend to rely solely on simplistic or singular fusion strategies, thereby neglecting more flexible fusion approaches. Moreover, existing methods prioritize the integration of modality features containing highly semantic information, often overlooking the influence of fusing low-level features on the outcomes. Therefore, this study introduces an innovative approach named multilevel feature fusion guided by prompts (MFF-GP), a multimodal dynamic fusion framework. It guides the dynamic neural network by prompt vectors to dynamically select the suitable fusion network for each hierarchical feature of the unimodal pre-training model. This method improves the interactions between multiple modalities and promotes a more efficient fusion of features across them. Extensive experiments on the UPMC Food 101, SNLI-VE and MM-IMDB datasets demonstrate that with only a few trainable parameters, MFF-GP achieves significant accuracy improvements compared to a newly designed PMF based on fine-tuning—specifically, an accuracy improvement of 2.15% on the UPMC Food 101 dataset and 0.82% on the SNLI-VE dataset. Further study of the results reveals that increasing the diversity of interactions between distinct modalities is critical and delivers significant performance improvements. Furthermore, for certain multimodal tasks, focusing on the low-level features is beneficial for modality integration. Our implementation is available at: https://github.com/whq2024/MFF-GP.

The current prevalence of conspiracy theories on the internet is a significant issue, tackled by many computational approaches. However, these approaches fail to recognize the relevance of distinguishing between texts which contain a conspiracy theory and texts which are simply critical and oppose mainstream narratives. Furthermore, little attention is usually paid to the role of inter-group conflict in oppositional narratives. We contribute by proposing a novel topic-agnostic annotation scheme that differentiates between conspiracies and critical texts, and that defines span-level categories of inter-group conflict. We also contribute with the multilingual XAI-DisInfodemics corpus (English and Spanish), which contains a high-quality annotation of Telegram messages related to COVID-19 (5000 messages per language). We also demonstrate the feasibility of an NLP-based automatization by performing a range of experiments that yield strong baseline solutions. Finally, we perform an analysis which demonstrates that the promotion of intergroup conflict and the presence of violence and anger are key aspects to distinguish between the two types of oppositional narratives, that is, conspiracy versus critical.

The forecasting of steel futures prices is important for the steel futures market, even for the steel industry. We propose a decomposition ensemble model that incorporates the Ensemble Empirical Mode Decomposition (EEMD), Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Back Propagation (BP) neural network to forecast steel futures prices. The forecasting procedures are as follows: (1) The price data are initially decomposed into several relatively independent Intrinsic Mode Functions (IMFs) and a residue using EEMD. (2) The IMFs are then reconstructed as components representing short-term, medium-term, and long-term frequencies via fine-to-coarse. (3) LSTM, SVR, and BP neural network are utilized to forecast the short-term, medium-term, and long-term reconstructed components, respectively. (4) The prediction results for each component are simply added to the final prediction results. The accuracy of the proposed model is compared with several benchmark models by experiments and evaluated by some prediction evaluation indexes. The experimental results show that our model outperforms other models in terms of forecast accuracy, confirming its strong predictive capabilities. This study provides some suggestions for investment and decision making by participants in the steel futures market. It may promote the smooth operation of the steel futures market and shed some light on the operation of the steel industry.

The people in the world rely on social media for gathering news, and it is mainly because of the development of technology. The approaches employed in natural language processing are still deficient in judgement factors, and these techniques frequently rely upon political or social circumstances. Numerous low-level communities in the area are curious after experiencing the negative effects caused by the spread of false information in different sectors. Low-resource languages are still distracted, because these techniques are extensively employed in the English language. This work aims to provide an analysis of regional language fake news and develop a referral system with advanced techniques to identify fake news in Hindi and Tamil. This proposed model includes (a) Regional Language Text Collection; (b) Text preprocessing; (c) Feature Extraction; (d) Weighted Stacked Feature Fusion; and (e) Fake News Detection. The text data is collected from the standard datasets. The collected text data is preprocessed and given into the feature extraction, which is done by using bidirectional encoder representations from transformers (BERT), transformer networks, and seq2seq network for extracting the three sets of language text features. These extracted feature sets are inserted into the weighted stacked feature fusion model, where the three sets of extracted features are integrated with the optimized weights that are acquired through the enhanced osprey optimization algorithm (EOOA). Finally, these resultant features are given to multi-scale atrous convolution-based one-dimensional convolutional neural network with dilated long short-term memory (MACNN-DLSTM) for detecting the fake news. Throughout the result analysis, the experimentation is conducted based on the standard Tamil and Hindi datasets. Moreover, the developed model shows 92% for Hindi datasets and 96% for Tamil datasets which shows effective performance regarding accuracy measures. The experimental analysis is carried out by comparing with the conventional algorithms and detection techniques to showcase the efficiency of the developed regional language-based fake news detection model.