You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: We propose a deep cascade learning framework to address the challenge of joint optimization of effectiveness

Keyphrases: multi-document machine reading comprehension text paragraph-level

Dataset description:
TriviaQA: TriviaQA is a realistic text-based question answering dataset which includes 950K question-answer pairs from 662K documents collected from Wikipedia and the web. This dataset is more challenging than standard QA benchmark datasets such as Stanford Question Answering Dataset (SQuAD), as the answers for a question may not be directly obtained by span prediction and the context is very long.
REDS: The realistic and dynamic scenes (REDS) dataset was proposed in the NTIRE19 Challenge. The dataset is composed of 300 video sequences with resolution of 720×1,280, and each video has 100 frames, where the training set, the validation set and the testing set have 240, 30 and 30 videos, respectively
DuReader: DuReader is a large-scale open-domain Chinese machine reading comprehension dataset. The dataset consists of 200K questions, 420K answers and 1M documents.
JHMDB: JHMDB is an action recognition dataset that consists of 960 video sequences belonging to 21 actions. It is a subset of the larger HMDB51 dataset collected from digitized movies and YouTube videos.
COG: A configurable visual question and answer dataset (COG) to parallel experiments in humans and animals. COG is much simpler than the general problem of video analysis, yet it addresses many of the problems relating to visual and logical reasoning and memory -- problems that remain challenging for modern deep learning architectures.
WFLW: The Wider Facial Landmarks in the Wild or WFLW database contains 10000 faces (7500 for training and 2500 for testing) with 98 annotated landmarks. This database also features rich attribute annotations in terms of occlusion, head pose, make-up, illumination, blur and expressions.

Options:- A: TriviaQA  B: REDS  C: DuReader  D: JHMDB  E: COG  F: WFLW
["TriviaQA", "DuReader"]