Machine learning techniques for cybersecurity-related software engineering tasks are becoming increasingly popular. The representation of source code is a key portion of the technique that can impact the way the model is able to learn the features of the source code. With an increasing number of these techniques being developed, it is valuable to see the current state of the field to better understand what exists and what's not there yet. This paper presents a study of these existing ML-based approaches and demonstrates what type of representations were used for different cybersecurity tasks and programming languages. Additionally, we study what types of models are used with different representations. We have found that graph-based representations are the most popular category of representation, and Tokenizers and Abstract Syntax Trees (ASTs) are the two most popular representations overall. We also found that the most popular cybersecurity task is vulnerability detection, and the language that is covered by the most techniques is C. Finally, we found that sequence-based models are the most popular category of models, and Support Vector Machines (SVMs) are the most popular model overall.

Reinforcement Learning from Human Feedback (RLHF) has proven to be a strong method to align Pretrained Large Language Models (LLMs) with human preferences. But training models with RLHF is computationally expensive, and an overall complex process. In this work, we study RLHF where the underlying models are trained using the parameter efficient method of Low-Rank Adaptation (LoRA) introduced by Hu et al. [2021]. We investigate the setup of "Parameter Efficient Reinforcement Learning" (PERL), in which we perform reward model training and reinforcement learning using LoRA. We compare PERL to conventional fine-tuning (full-tuning) across various configurations for 7 benchmarks, including 2 novel datasets, of reward modeling and reinforcement learning. We find that PERL performs on par with the conventional RLHF setting, while training faster, and with less memory. This enables the high performance of RLHF, while reducing the computational burden that limits its adoption as an alignment technique for Large Language Models. We also release 2 novel thumbs up/down preference datasets: "Taskmaster Coffee", and "Taskmaster Ticketing" to promote research around RLHF.

Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval. As the widespread adoption of LLMs continues to surge, it becomes increasingly crucial to ensure that these models yield responses that are politically impartial, with the aim of preventing information bubbles, upholding fairness in representation, and mitigating confirmation bias. In this paper, we propose a quantitative framework and pipeline designed to systematically investigate the political orientation of LLMs. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. Across topics, the results indicate that LLMs exhibit a tendency to provide responses that closely align with liberal or left-leaning perspectives rather than conservative or right-leaning ones when user queries include details pertaining to occupation, race, or political affiliation. The findings presented in this study not only reaffirm earlier observations regarding the left-leaning characteristics of LLMs but also surface particular attributes, such as occupation, that are particularly susceptible to such inclinations even when directly steered towards conservatism. As a recommendation to avoid these models providing politicised responses, users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.

Connectivity is a main driver for the ongoing megatrend of automated mobility: future Cooperative Intelligent Transport Systems (C-ITS) will connect road vehicles, traffic signals, roadside infrastructure, and even vulnerable road users, sharing data and compute for safer, more efficient, and more comfortable mobility. In terms of communication technology for realizing such vehicle-to-everything (V2X) communication, the WLAN-based peer-to-peer approach (IEEE 802.11p, ITS-G5 in Europe) competes with C-V2X based on cellular technologies (4G and beyond). Irrespective of the underlying communication standard, common message interfaces are crucial for a common understanding between vehicles, especially from different manufacturers. Targeting this issue, the European Telecommunications Standards Institute (ETSI) has been standardizing V2X message formats such as the Cooperative Awareness Message (CAM). In this work, we present V2AIX, a multi-modal real-world dataset of ETSI ITS messages gathered in public road traffic, the first of its kind. Collected in measurement drives and with stationary infrastructure, we have recorded more than 230 000 V2X messages from more than 1800 vehicles and roadside units in public road traffic. Alongside a first analysis of the dataset, we present a way of integrating ETSI ITS V2X messages into the Robot Operating System (ROS). This enables researchers to not only thoroughly analyze real-world V2X data, but to also study and implement standardized V2X messages in ROS-based automated driving applications. The full dataset is publicly available for noncommercial use at v2aix.ika.rwth-aachen.de.

We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses the $f$-DRO, Wasserstein-DRO, and spectral/$L$-risk formulations used in practice. We present Drago, a stochastic primal-dual algorithm that achieves a state-of-the-art linear convergence rate on strongly convex-strongly concave DRO problems. The method combines both randomized and cyclic components with mini-batching, which effectively handles the unique asymmetric nature of the primal and dual problems in DRO. We support our theoretical results with numerical benchmarks in classification and regression.

Artists, advertisers, and photographers are routinely presented with the task of creating an image that a viewer will remember. While it may seem like image memorability is purely subjective, recent work shows that it is not an inexplicable phenomenon: variation in memorability of images is consistent across subjects, suggesting that some images are intrinsically more memorable than others, independent of a subjects' contexts and biases. In this paper, we used the publicly available memorability dataset of Isola et al. [13], and augmented the object and scene annotations with interpretable spatial, content, and aesthetic image properties. We used a feature-selection scheme with desirable explaining-away properties to determine a compact set of attributes that characterizes the memorability of any individual image. We find that images of enclosed spaces containing people with visible faces are memorable, while images of vistas and peaceful scenes are not. Contrary to popular belief, unusual or aesthetically pleasing scenes do not tend to be highly memorable. This work represents one of the first attempts at understanding intrinsic image memorability, and opens a new domain of investigation at the interface between human cognition and computer vision.

Applications such as robot control and wireless communication require planning under uncertainty. Partially observable Markov decision processes (POMDPs) plan policies for single agents under uncertainty and their decentralized versions (DEC-POMDPs) find a policy for multiple agents. The policy in infinite-horizon POMDP and DEC-POMDP problems has been represented as finite state controllers(FSCs). We introduce a novel class of periodic FSCs, composed of layers connected only to the previous and next layer. Our periodic FSC method finds a deterministic finite-horizon policy and converts it to an initial periodic infinitehorizon policy. This policy is optimized by a new infinite-horizon algorithm to yield deterministic periodic policies, and by a new expectation maximization algorithm to yield stochastic periodic policies. Our method yields better results than earlier planning methods and can compute larger solutions than with regular FSCs.

In discrete undirected graphical models, the conditional independence of node labels Y is specified by the graph structure. We study the case where there is another input random vector X (e.g.

The space travel company SpaceX has completed its most successful test yet of Starship, the world's most powerful rocket -- but as the unmanned rocket completed its flight, it was destroyed upon re-entry into Earth's atmosphere. Thursday's test flight was the third conducted with Starship rockets, ahead of planned missions with the United States space agency NASA to send astronauts to the moon. SpaceX, a company founded and owned by tech entrepreneur Elon Musk, livestreamed the latest Starship experiment, noting that the vessel flew farther and faster than it had in two previous tests. However, as the rocket returned to Earth, it lost communication with SpaceX engineers. The livestream suddenly cut off, its final image showing the rocket's heat shield flaring with friction.

This article was originally featured in Foreign Policy, the magazine of global politics and ideas. A family snap of the Princess of Wales with her three children has dominated headlines and group chats since its release on U.K. Mother's Day last weekend. Princess Catherine, whom the palace says is recovering from a January abdominal surgery, is known chiefly for never putting a foot wrong during nearly two decades of intense public scrutiny--first as the girlfriend of Prince William, then as a wife and mother to future kings, and an advocate for uncontroversial but important causes, such as early childhood development. Yet, even for a woman defined by her seeming perfection--Hilary Mantel once wrote that the former duchess appeared to have been designed by a committee and built by craftsmen--the Mother's Day photo of Catherine and her family was judged to be a little too perfect. The uncanny valley of the photo was prime territory for conspiracy theories, already circulating, that the princess is missing or perhaps even dead.