Learning Observation-Based Certifiable Safe Policy for Decentralized Multi-Robot Navigation

Safety is of great importance in multi-robot navigation problems. In this paper, we propose a control barrier function (CBF) based optimizer that ensures robot safety with both high probability and flexibility, using only sensor measurement. The optimizer takes action commands from the policy network as initial values and then provides refinement to drive the potentially dangerous ones back into safe regions. With the help of a deep transition model that predicts the evolution of surrounding dynamics and the consequences of different actions, the CBF module can guide the optimization in a reasonable time horizon. We also present a novel joint training framework that improves the cooperation between the Reinforcement Learning (RL) based policy and the CBF-based optimizer both in training and inference procedures by utilizing reward feedback from the CBF module. We observe that the policy using our method can achieve a higher success rate while maintaining the safety of multiple robots in significantly fewer episodes compared with other methods. Experiments are conducted in multiple scenarios both in simulation and the real world, the results demonstrate the effectiveness of our method in maintaining the safety of multi-robot navigation. Code is available at url{https://github.com/YuxiangCui/MARL-OCBF

EvilModel 2.0: Hiding Malware Inside of Neural Network Models

While artificial intelligence (AI) is widely applied in various areas, it is also being used maliciously. It is necessary to study and predict AI-powered attacks to prevent them in advance. Turning neural network models into stegomalware is a malicious use of AI, which utilizes the features of neural network models to hide malware while maintaining the performance of the models. However, the existing methods have a low malware embedding rate and a high impact on the model performance, making it not practical. Therefore, by analyzing the composition of the neural network models, this paper proposes new methods to embed malware in models with high capacity and no service quality degradation. We used 19 malware samples and 10 mainstream models to build 550 malware-embedded models and analyzed the models performance on ImageNet dataset. A new evaluation method that combines the embedding rate, the model performance impact and the embedding effort is proposed to evaluate the existing methods. This paper also designs a trigger and proposes an application scenario in attack tasks combining EvilModel with WannaCry. This paper further studies the relationship between neural network models embedding capacity and the model structure, layer and size. With the widespread application of artificial intelligence, utilizing neural networks for attacks is becoming a forwarding trend. We hope this work can provide a reference scenario for the defense of neural network-assisted attacks.

Socially-Aware Multi-Agent Following with 2D Laser Scans via Deep Reinforcement Learning and Potential Field

Target following in dynamic pedestrian environments is an important task for mobile robots. However, it is challenging to keep tracking the target while avoiding collisions in crowded environments, especially with only one robot. In this paper, we propose a multi-agent method for an arbitrary number of robots to follow the target in a socially-aware manner using only 2D laser scans. The multi-agent following problem is tackled by utilizing the complementary strengths of both reinforcement learning and potential field, in which the reinforcement learning part handles local interactions while navigating to the goals assigned by the potential field. Specifically, with the help of laser scans in obstacle map representation, the learning-based policy can help the robots avoid collisions with both static obstacles and dynamic obstacles like pedestrians in advance, namely socially aware. While the formation control and goal assignment for each robot is obtained from a target-centered potential field constructed using aggregated state information from all the following robots. Experiments are conducted in multiple settings, including random obstacle distributions and different numbers of robots. Results show that our method works successfully in unseen dynamic environments. The robots can follow the target in a socially compliant manner with only 2D laser scans.

Multilingual Compositional Wikidata Questions

Semantic parsing allows humans to leverage vast knowledge resources through natural interaction. However, parsers are mostly designed for and evaluated on English resources, such as CFQ (Keysers et al., 2020), the current standard benchmark based on English data generated from grammar rules and oriented towards Freebase, an outdated knowledge base. We propose a method for creating a multilingual, parallel dataset of question-query pairs, grounded in Wikidata, and introduce such a dataset called Compositional Wikidata Questions (CWQ). We utilize this data to train and evaluate semantic parsers for Hebrew, Kannada, Chinese and English, to better understand the current strengths and weaknesses of multilingual semantic parsing. Experiments on zero-shot cross-lingual transfer demonstrate that models fail to generate valid queries even with pretrained multilingual encoders. Our methodology, dataset and results will facilitate future research on semantic parsing in more realistic and diverse settings than has been possible with existing resources.

Meaning Representation of Numeric Fused-Heads in UCCA

We exhibit that the implicit UCCA parser does not address numeric fused-heads (NFHs) consistently, which could result either from inconsistent annotation, insufficient training data or a modelling limitation. and show which factors are involved. We consider this phenomenon important, as it is pervasive in text and critical for correct inference. Careful design and fine-grained annotation of NFHs in meaning representation frameworks would benefit downstream tasks such as machine translation, natural language inference and question answering, particularly when they require numeric reasoning, as recovering and categorizing them. We are investigating the treatment of this phenomenon by other meaning representations, such as AMR. We encourage researchers in meaning representations, and computational linguistics in general, to address this phenomenon in future research.

Great Service! Fine-grained Parsing of Implicit Arguments

Broad-coverage meaning representations in NLP mostly focus on explicitly expressed content. More importantly, the scarcity of datasets annotating diverse implicit roles limits empirical studies into their linguistic nuances. For example, in the web review Great service!, the provider and consumer are implicit arguments of different types. We examine an annotated corpus of fine-grained implicit arguments (Cui and Hershcovich, 2020) by carefully re-annotating it, resolving several inconsistencies. Subsequently, we present the first transition-based neural parser that can handle implicit arguments dynamically, and experiment with two different transition systems on the improved dataset. We find that certain types of implicit arguments are more difficult to parse than others and that the simpler system is more accurate in recovering implicit arguments, despite having a lower overall parsing score, attesting current reasoning limitations of NLP models. This work will facilitate a better understanding of implicit and underspecified language, by incorporating it holistically into meaning representations.

Learning World Transition Model for Socially Aware Robot Navigation

Moving in dynamic pedestrian environments is one of the important requirements for autonomous mobile robots. We present a model-based reinforcement learning approach for robots to navigate through crowded environments. The navigation policy is trained with both real interaction data from multi-agent simulation and virtual data from a deep transition model that predicts the evolution of surrounding dynamics of mobile robots. The model takes laser scan sequence and robots own state as input and outputs steering control. The laser sequence is further transformed into stacked local obstacle maps disentangled from robots ego motion to separate the static and dynamic obstacles, simplifying the model training. We observe that our method can be trained with significantly less real interaction data in simulator but achieve similar level of success rate in social navigation task compared with other methods. Experiments were conducted in multiple social scenarios both in simulation and on real robots, the learned policy can guide the robots to the final targets successfully while avoiding pedestrians in a socially compliant manner. Code is available at https://github.com/YuxiangCui/model-based-social-navigation

HUJI-KU at MRP~2020: Two Transition-based Neural Parsers

This paper describes the HUJI-KU system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task. Both are transition-based parsers using BERT contextualized embeddings. We generalized TUPA to support the newly-added MRP frameworks and languages, and experimented with multitask learning with the HIT-SCIR parser. We reached 4th place in both the cross-framework and cross-lingual tracks.

DeepC2: AI-powered Covert Botnet Command and Control on OSNs

Command and control (C&C) is the essential component of a botnet. In previous C&C using online social networks (OSNs), the botmasters identifiers are reversible. After a bot is analyzed, the botmasters accounts can be predicted in advance. Additionally, abnormal content from explicit commands may expose botmasters and raise anomalies on OSNs. To overcome these deficiencies, we proposed DeepC2, an AI-powered covert C&C method on OSNs. By leveraging neural networks, bots can find botmasters by avatars, which are converted into feature vectors and built into bots. Defenders cannot predict the botmasters accounts from the vectors in advance. Commands are embedded into normal contents (e.g., tweets and comments) using easy data augmentation and hash collision. Experiments on Twitter show that command-embedded contents can be generated efficiently, and bots can find botmasters and obtain commands accurately. Security analysis on different scenarios show that it is hard to predict the botmasters avatars. By demonstrating how AI may help promote covert communication on OSNs, this work provides a new perspective on botnet detection and confrontation.

The First Step Towards Modeling Unbreakable Malware

Constructing stealthy malware has gained increasing popularity among cyber attackers to conceal their malicious intent. Nevertheless, the constructed stealthy malware still fails to survive the reverse engineering by security experts. Therefore, this paper modeled a type of malware with an unbreakable security attribute-unbreakable malware (UBM), and made a systematical probe into this new type of threat through modeling, method analysis, experiments, evaluation and anti-defense capacity tests. Specifically, we first formalized the definition of UBM and analyzed its security attributes, put forward two core features that are essential for realizing the unbreakable security attribute, and their relevant tetrad for evaluation. Then, we worked out and implemented four algorithms for constructing UBM, and verified the unbreakable security attribute based on our evaluation of the abovementioned two core features. After that, the four verified algorithms were employed to construct UBM instances, and by analyzing their volume increment and anti-defense capacity, we confirmed real-world applicability of UBM. Finally, to address the new threats incurred by UBM to the cyberspace, this paper explored some possible defense measures, with a view to establishing defense systems against UBM attacks.