In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: The paper explores a novel methodology in source code obfuscation through the application of text-based recurrent neural network network (RNN) encoder-decoder models in ciphertext generation and key generation. Sequence-to-sequence models
 are incorporated into the model architecture to generate obfuscated code, generate the deobfuscation key, and live execution. Quantitative benchmark comparison to existing obfuscation methods indicate significant improvement in stealth and execution cost for the proposed solution, and experiments regarding the model’s properties yield positive results regarding its character variation, dissimilarity to the original codebase, and consistent length of obfuscated code.
[A]: Obfuscate code using seq2seq networks, and execute using the obfuscated code and key pair


[Q]: Vision-Language Navigation (VLN) is the task where an agent is commanded to navigate in photo-realistic unknown environments with natural language instructions. Previous research on VLN is primarily conducted on the Room-to-Room (R2R) dataset with only English instructions.   The ultimate goal of VLN, however, is to serve people speaking arbitrary languages. Towards multilingual VLN with numerous languages, we collect a cross-lingual R2R dataset, which extends the original benchmark with corresponding Chinese instructions. But it is time-consuming and expensive to collect large-scale human instructions for every existing language. Based on the newly introduced dataset, we propose a general cross-lingual VLN framework to enable instruction-following navigation for different languages. We first explore the possibility of building a cross-lingual agent when no training data of the target language is available. The cross-lingual agent is equipped with a meta-learner to aggregate cross-lingual representations and a visually grounded cross-lingual alignment module to align textual representations of different languages. Under the zero-shot learning scenario, our model shows competitive results even compared to a model trained with all target language instructions. In addition, we introduce an adversarial domain adaption loss to improve the transferring ability of our model when given a certain amount of target language data. Our methods and dataset demonstrate the potentials of building a cross-lingual agent to serve speakers with different languages.
[A]: We introduce a new task and dataset on cross-lingual vision-language navigation, and propose a general cross-lingual VLN framework for the task.


[Q]: Open information extraction (OIE) systems extract relations and their
  arguments from natural language text in an unsupervised manner. The resulting
  extractions are a valuable resource for downstream tasks such as knowledge
  base construction, open question answering, or event schema induction. In this
  paper, we release, describe, and analyze an OIE corpus called OPIEC, which was
  extracted from the text of English Wikipedia. OPIEC complements the available
  OIE resources: It is the largest OIE corpus publicly available to date (over
  340M triples) and contains valuable metadata such as provenance information,
  confidence scores, linguistic annotations, and semantic annotations including
  spatial and temporal information. We analyze the OPIEC corpus by comparing its
  content with knowledge bases such as DBpedia or YAGO, which are also based on
  Wikipedia. We found that most of the facts between entities present in OPIEC
  cannot be found in DBpedia and/or YAGO, that OIE facts 
  often differ in the level of specificity compared to knowledge base facts, and
  that OIE open relations are generally highly polysemous. We believe that the
  OPIEC corpus is a valuable resource for future research on automated knowledge
  base construction.
[A]:
An Open Information Extraction Corpus and its in-depth analysis