--Stance Detection (SD) in social media has become a critical area of interest due to its applications in social, business, and political contexts, leading to increased research within Natural Language Processing (NLP). However, the subtlety, nuance, and complexity of texts sourced from online platforms, often containing sarcasm and figurative language, pose significant challenges for SD algorithms in accurately determining the author's stance. This paper addresses these challenges by employing sarcasm detection as an intermediate-task transfer learning approach specifically designed for SD. Additionally, it tackles the issue of insufficient annotated data for training SD models on new targets by conducting many-to-one Cross-T arget SD (CTSD). The proposed methodology involves fine-tuning BERT and RoBERT a models, followed by sequential concatenation with convolutional layers, Bidirectional Long Short T erm Memory (BiLSTM), and dense layers. Rigorous experiments are conducted on publicly available benchmark datasets to evaluate the effectiveness of our transfer-learning framework. The approach is assessed against various State-Of-The-Art (SOT A) baselines for SD, demonstrating superior performance. Notably, our model outperforms the best SOT A models in both in-domain SD and CTSD tasks, even before the incorporation of sarcasm-detection pre-training. The integration of sarcasm knowledge into the model significantly reduces misclassifications of sarcastic text elements in SD, allowing our model to accurately predict 85% of texts that were previously misclassified without sarcasm-detection pre-training on in-domain SD. This enhancement contributes to an increase in the model's average macro F1-score. The CTSD task achieves performance comparable to that of the in-domain task, despite using a zero-shot fine-tuning approach, curtailing the lack of annotated samples for training unseen targets problem. Furthermore, our experiments reveal that the success of the transfer-learning framework depends on the correlation between the lexical attributes of the intermediate task (sarcasm detection) and the target task (SD).

Stance Detection (SD) on social media has emerged as a prominent area of interest with implications for social business and political applications thereby garnering escalating research attention within NLP. The inherent subtlety and complexity of texts procured from online platforms pose challenges for SD algorithms in accurately discerning the authors stance. Mostly the inclusion of sarcastic and figurative language drastically impacts the performance of SD models. This paper addresses this by employing sarcasm detection intermediate-task transfer learning tailored for SD. The proposed methodology involves the finetuning of BERT and RoBERTa and the concatenation of convolutional BiLSTM and dense layers. Rigorous experiments are conducted on publicly available datasets to evaluate our transfer-learning framework. The performance of the approach is assessed against various State-Of-The-Art baselines for SD providing empirical evidence of its effectiveness. Notably our model outperforms the best SOTA models even prior to sarcasm-detection pretraining. The integration of sarcasm knowledge into the model proves instrumental in mitigating misclassifications of sarcastic textual elements in SD. Our model accurately predicts 85% of texts that were previously misclassified by the model without sarcasm-detection pretraining thereby amplifying the average F1-score of the model. Our experiments also revealed that the success of the transfer-learning framework is contingent upon the correlation of lexical attributes between the intermediate task and the target task. This study represents the first exploration of sarcasm detection as an intermediate transfer-learning task in the context of SD and simultaneously uses the concatenation of BERT or RoBERTa with other deep-learning techniques establishing the proposed approach as a foundational baseline for future research endeavors in this domain.

A major challenge for autonomous vehicles is handling interactive scenarios, such as highway merging, with human-driven vehicles. A better understanding of human interactive behaviour could help address this challenge. Such understanding could be obtained through modelling human behaviour. However, existing modelling approaches predominantly neglect communication between drivers and assume that some drivers in the interaction only respond to others, but do not actively influence them. Here we argue that addressing these two limitations is crucial for accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model the interaction in an integral way rather than modelling an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication and bounded rationality. We demonstrate the model in a simplified merging scenario, illustrating that it generates plausible interactive behaviour (e.g., aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model's decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles.

Recently, opinion summarization, which is the generation of a summary from multiple reviews, has been conducted in a self-supervised manner by considering a sampled review as a pseudo summary. However, non-text data such as image and metadata related to reviews have been considered less often. To use the abundant information contained in non-text data, we propose a self-supervised multimodal opinion summarization framework called MultimodalSum. Our framework obtains a representation of each modality using a separate encoder for each modality, and the text decoder generates a summary. To resolve the inherent heterogeneity of multimodal data, we propose a multimodal training pipeline. We first pretrain the text encoder--decoder based solely on text modality data. Subsequently, we pretrain the non-text modality encoders by considering the pretrained text decoder as a pivot for the homogeneous representation of multimodal data. Finally, to fuse multimodal representations, we train the entire framework in an end-to-end manner. We demonstrate the superiority of MultimodalSum by conducting experiments on Yelp and Amazon datasets.

Optical Wireless Communication (OWC) propagation channel characterization plays a key role on the design and performance analysis of Vehicular Visible Light Communication (VVLC) systems. Current OWC channel models based on deterministic and stochastic methods, fail to address mobility induced ambient light, optical turbulence and road reflection effects on channel characterization. Therefore, alternative machine learning (ML) based schemes, considering ambient light, optical turbulence, road reflection effects in addition to intervehicular distance and geometry, are proposed to obtain accurate VVLC channel loss and channel frequency response (CFR). This work demonstrates synthesis of ML based VVLC channel model frameworks through multi layer perceptron feed-forward neural network (MLP), radial basis function neural network (RBF-NN) and Random Forest ensemble learning algorithms. Predictor and response variables, collected through practical road measurements, are employed to train and validate proposed models for various conditions. Additionally, the importance of different predictor variables on channel loss and CFR is assessed, normalized importance of features for measured VVLC channel is introduced. We show that RBF-NN, Random Forest and MLP based models yield more accurate channel loss estimations with 3.53 dB, 3.81 dB, 3.95 dB root mean square error (RMSE), respectively, when compared to fitting curve based VVLC channel model with 7 dB RMSE. Moreover, RBF-NN and MLP models are demonstrated to predict VVLC CFR with respect to distance, ambient light and receiver inclination angle predictor variables with 3.78 dB and 3.60 dB RMSE respectively.

Fifteen global companies have taken up Singapore's AI Model Governance Framework; Practical examples for organisations to follow suit. Singapore sees Artificial Intelligence ("AI") as an important and fundamental technology for the Digital Economy, with AI-powered products offering a level of personalised service at scale that was previously unimaginable. In the global discourse on AI ethics and governance issue, Singapore believes that its balanced approach can facilitate innovation, safeguard consumer interests, and serve as a common global reference point. These initiatives follow Singapore's launch of the Model AI Governance Framework in Davos in 2019, as well as the announcement of Singapore's National AI Strategy in November 2019, and demonstrate the progress made in supporting organisations in deploying responsible AI. The new initiatives were announced by Mr S Iswaran, Singapore's Minister for Communications and Information, and Ms Kay Firth-Butterfield, AI Portfolio Lead at the World Economic Forum, at a joint press conference with the WEF's Centre for the Fourth Industrial Revolution ("WEF C4IR") at WEF's Annual Meeting in Davos.

The PDPC presents the first edition of a Model AI Governance Framework (Model Framework) - an accountability-based framework to help chart the language and frame the discussions around harnessing AI in a responsible way. The Model Framework translates ethical principles into practical measures that can be implemented by organisations deploying AI solutions at scale. Through the Model Framework, we aim to promote AI adoption while building consumer confidence and trust in providing their personal data for AI. We encourage organisations to use this Model Framework for internal discussion and implementation. Trade associations and chambers professional bodies and interest groups are welcome to use this document for their discussions, and adapt it for their own use.

Self-driving vehicles, dating apps which give out relationship advice, humanoid robots that crack jokes and get upset... With a global market that is expected to reach US$35,870 million by 2025 from its direct revenue sources, artificial intelligence (AI) is no longer just the subject of science fiction books. According to a study carried out by IDC, in the ASEAN region, AI adoption rates are currently on the rise and growth has almost doubled in comparison to last year. When it comes to adopting this emerging technology, Indonesia is leading the way, with 24.6% of companies already embracing AI in some capacity. Thailand comes in second and the bronze medal goes to Singapore. This is somewhat surprising, considering the city state is normally something of a trailblazer in the region when it comes to embracing new technologies.

The Singapore government has released an artificial intelligence (AI) governance framework to help businesses tackle the ethical and governance challenges arising from the growing use of AI across industries. Want to know what will dominate the world of IT in 2019? You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

Classification is the problem of categorising data in one of two or more possible classes. In the classical supervised learning framework, examples of each class have already been obtained and the task of the decision maker is to accurately categorise new observations, whose class is unknown. The accuracy is either measured in terms of the rate of misclassification, or in terms of the average cost, for problems where different types of errors carry different costs. In that setting, the problem has three phases: (a) the collection of training data, (b) the estimation of a decision rule based on the training data and (c) the application 1 of the decision rule to new data. Typically, the decision rule remains fixed after the second step.