A great number of computer vision publications have focused on distinguishing between human action recognition and classification rather than the intensity of actions performed. Indexing the intensity which determines the performance of human actions is a challenging task due to the uncertainty and information deficiency that exists in the video inputs. To remedy this uncertainty, in this paper we coupled fuzzy logic rules with the neural-based action recognition model to rate the intensity of a human action as intense or mild. In our approach, we used a Spatio-Temporal LSTM to generate the weights of the fuzzy-logic model, and then demonstrate through experiments that indexing of the action intensity is possible. We analyzed the integrated model by applying it to videos of human actions with different action intensities, and were able to achieve an accuracy of 89.16% on our intensity indexing generated dataset. The integrated model demonstrates the ability of a neuro-fuzzy inference module to effectively estimate the intensity index of human actions.

The speed and volume at which misinformation spreads on social media have motivated efforts to automate fact-checking which begins with stance detection. For fake news stance detection, for example, many classification-based models have been proposed often with high complexity and hand-crafted features. Although these models can achieve high accuracy scores on a targeted small corpus of fake news, few are evaluated on a larger corpus of fake and conspiracy sites due to efficiency limitations and the lack of compatibility with the actual fact-checking process. In this article, we propose a practical two-stage stance detection model that is tailored to the real-life problem. Specifically, we integrate an information retrieval system with an end to end memory network model to sort articles based on their relevance to the claim and then identify the fine-grained stance of each relevant article towards its given claim. We evaluate our model on the Fake News Challenge dataset (FNC-1). The results show that the performance of our model is comparable to those of the state-of-the-art models, average weighted accuracy of 82.1, while it closely follows the real-life process of fact-checking. We also validate our model with a large dataset from a real-life fact-checking website (i.e., Snopes.com ), and the findings demonstrate the capability of the model in distinguishing false from true news headlines.

Customer-agent conversations (i.e. call transcripts) are invaluable source for companies as they convey direct information from their customers implicit and explicit behaviour. Identifying customerrelated events is an important task in customer services which is possible from the call transcripts. However, call centers produces a vast amount of transcripts which makes the manual or semi-manual processing of such raw datasets quite challenging. Furthermore, customer-agent call transcripts tend not to explicitly denote events that might be beneficial to customer services. Albeit being highly researched across multiple domains in the literature, event detection, especially implicit life event detection have not been well examined from call transcripts due to a lack of proper large-scale dataset. In this research, we propose a novel deep learning approach based on latent topic modeling and deep recurrent neural networks with memory units to automatically detect implicit events from a customer’s history of call transcripts. These implicit events are detected prior to the report date of that event thereby not containing any explicit topic/feature. We provide a case study on a real-life, large-scale data of more than 800K call transcripts from a large financial services company in the U.S. to examine the practical features and challenges of this problem. The evaluation results demonstrate the potential applicability of our implicit life event detection as it achieves a macro-recall score of 53 (macro-f1 of 47.5) on a highly imbalanced test set, negative samples are 95% of the data. Our model beats the the state-of-the-art text classification benchmarks by macro-f1 score of 5.6 and macro-recall of 8.8 on average, and performs better than the ensemble of all single-document and sequential classification benchmarks albeit being significantly less complex. The comparison results show the importance as well as our model’s capability of capturing the mutual information of a sequence of call transcripts in detecting the implicit life events.

Human faces are a core part of our identity and expression, and thus, understanding facial geometry is key to capturing this information. Automated systems that seek to make use of this information must have a way of modeling facial features in a way that makes them accessible. Hierarchical, multi-level architectures have the capability of capturing the different resolutions of representation involved. In this work, we propose using a hierarchical transformer architecture as a means of capturing a robust representation of facial geometry. We further demonstrate the versatility of our approach by using this transformer as a backbone to support three facial representation problems: face anti-spoofing, facial expression representation, and deepfake detection. The combination of effective fine-grained details alongside global attention representations makes this architecture an excellent candidate for these facial representation problems. We conduct numerous experiments first showcasing the ability of our approach to address common issues in facial modeling (pose, occlusions, and background variation) and capture facial symmetry, then demonstrating its effectiveness on three supplemental tasks.