Predicting the descent into extremism and terrorism

This paper proposes an approach for automatically analysing and tracking statements in material gathered online and detecting whether the authors of the statements are likely to be involved in extremism or terrorism. The proposed system comprises: online collation of statements that are then encoded in a form amenable to machine learning (ML), an ML component to classify the encoded text, a tracker, and a visualisation system for analysis of results. The detection and tracking concept has been tested using quotes made by terrorists, extremists, campaigners, and politicians, obtained from wikiquote.org. A set of features was extracted for each quote using the state-of-the-art Universal Sentence Encoder (Cer et al. 2018), which produces 512-dimensional vectors. The data were used to train and test a support vector machine (SVM) classifier using 10-fold cross-validation. The system was able to correctly detect intentions and attitudes associated with extremism 81% of the time and terrorism 97% of the time, using a dataset of 839 quotes. This accuracy was higher than that which was achieved for a simple baseline system based on n-gram text features. Tracking techniques were also used to perform a temporal analysis of the data, with each quote considered to be a noisy measurement of a person's state of mind. It was demonstrated that the tracking algorithms were able to detect both trends over time and sharp changes in attitude that could be attributed to major events.

Challenges and Opportunities for Large-Scale Exploration with Air-Ground Teams using Semantics

One common and desirable application of robots is exploring potentially hazardous and unstructured environments. Air-ground collaboration offers a synergistic approach to addressing such exploration challenges. In this paper, we demonstrate a system for large-scale exploration using a team of aerial and ground robots. Our system uses semantics as lingua franca, and relies on fully opportunistic communications. We highlight the unique challenges from this approach, explain our system architecture and showcase lessons learned during our experiments. All our code is open-source, encouraging researchers to use it and build upon.

THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large Vision-Language Models

Mitigating hallucinations in large vision-language models (LVLMs) remains an open problem. Recent benchmarks do not address hallucinations in open-ended free-form responses, which we term "Type I hallucinations". Instead, they focus on hallucinations responding to very specific question formats -- typically a multiple-choice response regarding a particular object or attribute -- which we term "Type II hallucinations". Additionally, such benchmarks often require external API calls to models which are subject to change. In practice, we observe that a reduction in Type II hallucinations does not lead to a reduction in Type I hallucinations but rather that the two forms of hallucinations are often anti-correlated. To address this, we propose THRONE, a novel object-based automatic framework for quantitatively evaluating Type I hallucinations in LVLM free-form outputs. We use public language models (LMs) to identify hallucinations in LVLM responses and compute informative metrics. By evaluating a large selection of recent LVLMs using public datasets, we show that an improvement in existing metrics do not lead to a reduction in Type I hallucinations, and that established benchmarks for measuring Type I hallucinations are incomplete. Finally, we provide a simple and effective data augmentation method to reduce Type I and Type II hallucinations as a strong baseline.

Enabling Large-scale Heterogeneous Collaboration with Opportunistic Communications

Multi-robot collaboration in large-scale environments with limited-sized teams and without external infrastructure is challenging, since the software framework required to support complex tasks must be robust to unreliable and intermittent communication links. In this work, we present MOCHA (Multi-robot Opportunistic Communication for Heterogeneous Collaboration), a framework for resilient multi-robot collaboration that enables large-scale exploration in the absence of continuous communications. MOCHA is based on a gossip communication protocol that allows robots to interact opportunistically whenever communication links are available, propagating information on a peer-to-peer basis. We demonstrate the performance of MOCHA through real-world experiments with commercial-off-the-shelf (COTS) communication hardware. We further explore the system's scalability in simulation, evaluating the performance of our approach as the number of robots increases and communication ranges vary. Finally, we demonstrate how MOCHA can be tightly integrated with the planning stack of autonomous robots. We show a communication-aware planning algorithm for a high-altitude aerial robot executing a collaborative task while maximizing the amount of information shared with ground robots. The source code for MOCHA and the high-altitude UAV planning system is available open source: http://github.com/KumarRobotics/MOCHA, http://github.com/KumarRobotics/air_router.