Our findings are: the performance variance of generative DSTs is not only due to the model structure itself, but can be attributed to the distribution of cross-domain values.
This is due to an ever-growing number of cases requiring digital forensic investigation coupled with the growing volume of data to be processed per case.
Multi-year digital forensic backlogs have become commonplace in law enforcement agencies throughout the globe.
In this paper, we propose a meta-learning based semi-supervised explicit dialogue state tracker (SEDST) for neural dialogue generation, denoted as MEDST.
In this paper, a methodology for the automatic prioritisation of suspicious file artefacts (i. e., file artefacts that are pertinent to the investigation) is proposed to reduce the manual analysis effort required.
In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF.
This poses an imbalanced learning problem, since the scale of missing entries is usually much larger than that of observed entries, but they cannot be ignored due to the valuable negative signal.
To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning.
Information Retrieval Multimedia
Extensive experiments on three public real-world datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it outperforms BPR with a relative improvement of 11. 2% on average and achieves state-of-the-art performance for item recommendation.
In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering.