pwc-archive/papers-with-abstracts huggingface dataset:
Black-Box Adversarial Attacks on LLM-Based Code Completion
Authors: Slobodan Jenko, Niels Mündler, Jingxuan He, Mark Vero, Martin Vechev
Date: 2024-08-05 00:00:00
Tasks: Code Completion, Code Generation

Modern code completion engines, powered by large language models (LLMs), assist millions of developers with their strong capabilities to generate functionally correct code. Due to this popularity, it is crucial to investigate the security implications of relying on LLM-based code completion. In this work, we demonstrate that state-of-the-art black-box LLM-based code completion engines can be stealthily biased by adversaries to significantly increase their rate of insecure code generation. We present the first attack, named INSEC, that achieves this goal. INSEC works by injecting an attack string as a short comment in the completion input. The attack string is crafted through a query-based optimization procedure starting from a set of carefully designed initialization schemes. We demonstrate INSEC's broad applicability and effectiveness by evaluating it on various state-of-the-art open-source models and black-box commercial services (e.g., OpenAI API and GitHub Copilot). On a diverse set of security-critical test cases, covering 16 CWEs across 5 programming languages, INSEC increases the rate of generated insecure code by more than 50%, while maintaining the functional correctness of generated code. We consider INSEC practical -- it requires low resources and costs less than 10 US dollars to develop on commodity hardware. Moreover, we showcase the attack's real-world deployability, by developing an IDE plug-in that stealthily injects INSEC into the GitHub Copilot extension.
Stem-JEPA: A Joint-Embedding Predictive Architecture for Musical Stem Compatibility Estimation
Authors: Alain Riou, Stefan Lattner, Gaëtan Hadjeres, Michael Anslow, Geoffroy Peeters
Date: 2024-08-05 00:00:00
Tasks: Rhythm, Self-Supervised Learning

This paper explores the automated process of determining stem compatibility by identifying audio recordings of single instruments that blend well with a given musical context. To tackle this challenge, we present Stem-JEPA, a novel Joint-Embedding Predictive Architecture (JEPA) trained on a multi-track dataset using a self-supervised learning approach. Our model comprises two networks: an encoder and a predictor, which are jointly trained to predict the embeddings of compatible stems from the embeddings of a given context, typically a mix of several instruments. Training a model in this manner allows its use in estimating stem compatibility - retrieving, aligning, or generating a stem to match a given mix - or for downstream tasks such as genre or key estimation, as the training paradigm requires the model to learn information related to timbre, harmony, and rhythm. We evaluate our model's performance on a retrieval task on the MUSDB18 dataset, testing its ability to find the missing stem from a mix and through a subjective user study. We also show that the learned embeddings capture temporal alignment information and, finally, evaluate the representations learned by our model on several downstream tasks, highlighting that they effectively capture meaningful musical features.
Single-tap Latency Reduction with Single- or Double- tap Prediction
Date: 2024-08-05 00:00:00

Touch surfaces are widely utilized for smartphones, tablet PCs, and laptops (touchpad), and single and double taps are the most basic and common operations on them. The detection of single or double taps causes the single-tap latency problem, which creates a bottleneck in terms of the sensitivity of touch inputs. To reduce the single-tap latency, we propose a novel machine-learning-based tap prediction method called PredicTaps. Our method predicts whether a detected tap is a single tap or the first contact of a double tap without having to wait for the hundreds of milliseconds conventionally required. We present three evaluations and one user evaluation that demonstrate its broad applicability and usability for various tap situations on two form factors (touchpad and smartphone). The results showed PredicTaps reduces the single-tap latency from 150-500 ms to 12 ms on laptops and to 17.6 ms on smartphones without reducing usability.
The Role of Functional Muscle Networks in Improving Hand Gesture Perception for Human-Machine Interfaces
Date: 2024-08-05 00:00:00

Developing accurate hand gesture perception models is critical for various robotic applications, enabling effective communication between humans and machines and directly impacting neurorobotics and interactive robots. Recently, surface electromyography (sEMG) has been explored for its rich informational context and accessibility when combined with advanced machine learning approaches and wearable systems. The literature presents numerous approaches to boost performance while ensuring robustness for neurorobots using sEMG, often resulting in models requiring high processing power, large datasets, and less scalable solutions. This paper addresses this challenge by proposing the decoding of muscle synchronization rather than individual muscle activation. We study coherence-based functional muscle networks as the core of our perception model, proposing that functional synchronization between muscles and the graph-based network of muscle connectivity encode contextual information about intended hand gestures. This can be decoded using shallow machine learning approaches without the need for deep temporal networks. Our technique could impact myoelectric control of neurorobots by reducing computational burdens and enhancing efficiency. The approach is benchmarked on the Ninapro database, which contains 12 EMG signals from 40 subjects performing 17 hand gestures. It achieves an accuracy of 85.1%, demonstrating improved performance compared to existing methods while requiring much less computational power. The results support the hypothesis that a coherence-based functional muscle network encodes critical information related to gesture execution, significantly enhancing hand gesture perception with potential applications for neurorobotic systems and interactive machines.
Learning rheological parameters of non-Newtonian fluids from velocimetry data
Authors: Alexandros Kontogiannis, Richard Hodgkinson, Emily L. Manchester
Date: 2024-08-05 00:00:00

We solve a Bayesian inverse Navier-Stokes (N-S) problem that assimilates velocimetry data in order to jointly reconstruct the flow field and learn the unknown N-S parameters. By incorporating a Carreau shear-thinning viscosity model into the N-S problem, we devise an algorithm that learns the most likely Carreau parameters of a shear-thinning fluid, and estimates their uncertainties, from velocimetry data alone. We then conduct a flow-MRI experiment to obtain velocimetry data of an axisymmetric laminar jet through an idealised medical device (FDA nozzle) for a blood analogue fluid. We show that the algorithm can successfully reconstruct the flow field by learning the most likely Carreau parameters, and that the learned parameters are in very good agreement with rheometry measurements. The algorithm accepts any algebraic effective viscosity model, as long as the model is differentiable, and it can be extended to more complicated non-Newtonian fluids (e.g. Oldroyd-B fluid) if a viscoelastic model is incorporated into the N-S problem.
Command-line Obfuscation Detection using Small Language Models
Authors: Vojtech Outrata, Michael Adam Polak, Martin Kopp
Date: 2024-08-05 00:00:00
Tasks: Language Modeling, Language Modelling, Obfuscation Detection

To avoid detection, adversaries often use command-line obfuscation. There are numerous techniques of the command-line obfuscation, all designed to alter the command-line syntax without affecting its original functionality. This variability forces most security solutions to create an exhaustive enumeration of signatures for even a single pattern. In contrast to using signatures, we have implemented a scalable NLP-based detection method that leverages a custom-trained, small transformer language model that can be applied to any source of execution logs. The evaluation on top of real-world telemetry demonstrates that our approach yields high-precision detections even on high-volume telemetry from a diverse set of environments spanning from universities and businesses to healthcare or finance. The practical value is demonstrated in a case study of real-world samples detected by our model. We show the model's superiority to signatures on established malware known to employ obfuscation and showcase previously unseen obfuscated samples detected by our model.
Detection of Compromised Functions in a Serverless Cloud Environment
Authors: Danielle Lavi, Oleg Brodt, Dudu Mimran, Yuval Elovici, Asaf Shabtai
Date: 2024-08-05 00:00:00

Serverless computing is an emerging cloud paradigm with serverless functions at its core. While serverless environments enable software developers to focus on developing applications without the need to actively manage the underlying runtime infrastructure, they open the door to a wide variety of security threats that can be challenging to mitigate with existing methods. Existing security solutions do not apply to all serverless architectures, since they require significant modifications to the serverless infrastructure or rely on third-party services for the collection of more detailed data. In this paper, we present an extendable serverless security threat detection model that leverages cloud providers' native monitoring tools to detect anomalous behavior in serverless applications. Our model aims to detect compromised serverless functions by identifying post-exploitation abnormal behavior related to different types of attacks on serverless functions, and therefore, it is a last line of defense. Our approach is not tied to any specific serverless application, is agnostic to the type of threats, and is adaptable through model adjustments. To evaluate our model's performance, we developed a serverless cybersecurity testbed in an AWS cloud environment, which includes two different serverless applications and simulates a variety of attack scenarios that cover the main security threats faced by serverless functions. Our evaluation demonstrates our model's ability to detect all implemented attacks while maintaining a negligible false alarm rate.
Abstraction in Neural Networks
Authors: Nancy Lynch
Date: 2024-08-04 00:00:00

We show how brain networks, modeled as Spiking Neural Networks, can be viewed at different levels of abstraction. Lower levels include complications such as failures of neurons and edges. Higher levels are more abstract, making simplifying assumptions to avoid these complications. We show precise relationships between executions of networks at different levels, which enables us to understand the behavior of lower-level networks in terms of the behavior of higher-level networks. We express our results using two abstract networks, A1 and A2, one to express firing guarantees and the other to express non-firing guarantees, and one detailed network D. The abstract networks contain reliable neurons and edges, whereas the detailed network has neurons and edges that may fail, subject to some constraints. Here we consider just initial stopping failures. To define these networks, we begin with abstract network A1 and modify it systematically to obtain the other two networks. To obtain A2, we simply lower the firing thresholds of the neurons. To obtain D, we introduce failures of neurons and edges, and incorporate redundancy in the neurons and edges in order to compensate for the failures. We also define corresponding inputs for the networks, and corresponding executions of the networks. We prove two main theorems, one relating corresponding executions of A1 and D and the other relating corresponding executions of A2 and D. Together, these give both firing and non-firing guarantees for the detailed network D. We also give a third theorem, relating the effects of D on an external reliable actuator neuron to the effects of the abstract networks on the same actuator neuron.
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization
Authors: Andoni I. Garmendia, Quentin Cappart, Josu Ceberio, Alexander Mendiburu
Date: 2024-08-05 00:00:00
Tasks: Combinatorial Optimization

Neural Combinatorial Optimization (NCO) is an emerging domain where deep learning techniques are employed to address combinatorial optimization problems as a standalone solver. Despite their potential, existing NCO methods often suffer from inefficient search space exploration, frequently leading to local optima entrapment or redundant exploration of previously visited states. This paper introduces a versatile framework, referred to as Memory-Augmented Reinforcement for Combinatorial Optimization (MARCO), that can be used to enhance both constructive and improvement methods in NCO through an innovative memory module. MARCO stores data collected throughout the optimization trajectory and retrieves contextually relevant information at each state. This way, the search is guided by two competing criteria: making the best decision in terms of the quality of the solution and avoiding revisiting already explored solutions. This approach promotes a more efficient use of the available optimization budget. Moreover, thanks to the parallel nature of NCO models, several search threads can run simultaneously, all sharing the same memory module, enabling an efficient collaborative exploration. Empirical evaluations, carried out on the maximum cut, maximum independent set and travelling salesman problems, reveal that the memory module effectively increases the exploration, enabling the model to discover diverse, higher-quality solutions. MARCO achieves good performance in a low computational cost, establishing a promising new direction in the field of NCO.
A Landscape-Aware Differential Evolution for Multimodal Optimization Problems
Authors: Guo-Yun Lin, Zong-Gan Chen, Chuanbin Liu, Yuncheng Jiang, Sam Kwong, Jun Zhang, Zhi-Hui Zhan
Date: 2024-08-05 00:00:00

How to simultaneously locate multiple global peaks and achieve certain accuracy on the found peaks are two key challenges in solving multimodal optimization problems (MMOPs). In this paper, a landscape-aware differential evolution (LADE) algorithm is proposed for MMOPs, which utilizes landscape knowledge to maintain sufficient diversity and provide efficient search guidance. In detail, the landscape knowledge is efficiently utilized in the following three aspects. First, a landscape-aware peak exploration helps each individual evolve adaptively to locate a peak and simulates the regions of the found peaks according to search history to avoid an individual locating a found peak. Second, a landscape-aware peak distinction distinguishes whether an individual locates a new global peak, a new local peak, or a found peak. Accuracy refinement can thus only be conducted on the global peaks to enhance the search efficiency. Third, a landscape-aware reinitialization specifies the initial position of an individual adaptively according to the distribution of the found peaks, which helps explore more peaks. The experiments are conducted on 20 widely-used benchmark MMOPs. Experimental results show that LADE obtains generally better or competitive performance compared with seven well-performed algorithms proposed recently and four winner algorithms in the IEEE CEC competitions for multimodal optimization.
An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms
Authors: Leonardo Lucio Custode, Fabio Caraffini, Anil Yaman, Giovanni Iacca
Date: 2024-08-05 00:00:00
Tasks: Evolutionary Algorithms, Hyperparameter Optimization

Hyperparameter optimization is a crucial problem in Evolutionary Computation. In fact, the values of the hyperparameters directly impact the trajectory taken by the optimization process, and their choice requires extensive reasoning by human operators. Although a variety of self-adaptive Evolutionary Algorithms have been proposed in the literature, no definitive solution has been found. In this work, we perform a preliminary investigation to automate the reasoning process that leads to the choice of hyperparameter values. We employ two open-source Large Language Models (LLMs), namely Llama2-70b and Mixtral, to analyze the optimization logs online and provide novel real-time hyperparameter recommendations. We study our approach in the context of step-size adaptation for (1+1)-ES. The results suggest that LLMs can be an effective method for optimizing hyperparameters in Evolution Strategies, encouraging further research in this direction.
GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS
Authors: Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse, Brennan Shacklett, Eugene Vinitsky
Date: 2024-08-02 00:00:00
Tasks: GPU, Navigate

Multi-agent learning algorithms have been successful at generating superhuman planning in various games but have had limited impact on the design of deployed multi-agent planners. A key bottleneck in applying these techniques to multi-agent planning is that they require billions of steps of experience. To enable the study of multi-agent planning at scale, we present GPUDrive. GPUDrive is a GPU-accelerated, multi-agent simulator built on top of the Madrona Game Engine capable of generating over a million simulation steps per second. Observation, reward, and dynamics functions are written directly in C++, allowing users to define complex, heterogeneous agent behaviors that are lowered to high-performance CUDA. Despite these low-level optimizations, GPUDrive is fully accessible through Python, offering a seamless and efficient workflow for multi-agent, closed-loop simulation. Using GPUDrive, we train reinforcement learning agents on the Waymo Open Motion Dataset, achieving efficient goal-reaching in minutes and scaling to thousands of scenarios in hours. We open-source the code and pre-trained agents at https://github.com/Emerge-Lab/gpudrive.
Review of Cloud Service Composition for Intelligent Manufacturing
Authors: Cuixia Li, Liqiang Liu, Li Shi
Date: 2024-08-03 00:00:00
Tasks: Service Composition

Intelligent manufacturing is a new model that uses advanced technologies such as the Internet of Things, big data, and artificial intelligence to improve the efficiency and quality of manufacturing production. As an important support to promote the transformation and upgrading of the manufacturing industry, cloud service optimization has received the attention of researchers. In recent years, remarkable research results have been achieved in this field. For the sustainability of intelligent manufacturing platforms, in this paper we summarize the process of cloud service optimization for intelligent manufacturing. Further, to address the problems of dispersed optimization indicators and nonuniform/unstandardized definitions in the existing research, 11 optimization indicators that take into account three-party participant subjects are defined from the urgent requirements of the sustainable development of intelligent manufacturing platforms. Next, service optimization algorithms are classified into two categories, heuristic and reinforcement learning. After comparing the two categories, the current key techniques of service optimization are targeted. Finally, research hotspots and future research trends of service optimization are summarized.
MAO: A Framework for Process Model Generation with Multi-Agent Orchestration
Authors: Leilei Lin, Yumeng Jin, Yingming Zhou, Wenlong Chen, Chen Qian
Date: 2024-08-04 00:00:00
Tasks: Hallucination, software testing

Process models are frequently used in software engineering to describe business requirements, guide software testing and control system improvement. However, traditional process modeling methods often require the participation of numerous experts, which is expensive and time-consuming. Therefore, the exploration of a more efficient and cost-effective automated modeling method has emerged as a focal point in current research. This article explores a framework for automatically generating process models with multi-agent orchestration (MAO), aiming to enhance the efficiency of process modeling and offer valuable insights for domain experts. Our framework MAO leverages large language models as the cornerstone for multi-agent, employing an innovative prompt strategy to ensure efficient collaboration among multi-agent. Specifically, 1) generation. The first phase of MAO is to generate a slightly rough process model from the text description; 2) refinement. The agents would continuously refine the initial process model through multiple rounds of dialogue; 3) reviewing. Large language models are prone to hallucination phenomena among multi-turn dialogues, so the agents need to review and repair semantic hallucinations in process models; 4) testing. The representation of process models is diverse. Consequently, the agents utilize external tools to test whether the generated process model contains format errors, namely format hallucinations, and then adjust the process model to conform to the output paradigm. The experiments demonstrate that the process models generated by our framework outperform existing methods and surpass manual modeling by 89%, 61%, 52%, and 75% on four different datasets, respectively.
Visual Grounding for Object-Level Generalization in Reinforcement Learning
Authors: Haobin Jiang, Zongqing Lu
Date: 2024-08-04 00:00:00
Tasks: Language Modelling, Object, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Visual Grounding, Zero-shot Generalization

Generalization is a pivotal challenge for agents following natural language instructions. To approach this goal, we leverage a vision-language model (VLM) for visual grounding and transfer its vision-language knowledge into reinforcement learning (RL) for object-centric tasks, which makes the agent capable of zero-shot generalization to unseen objects and instructions. By visual grounding, we obtain an object-grounded confidence map for the target object indicated in the instruction. Based on this map, we introduce two routes to transfer VLM knowledge into RL. Firstly, we propose an object-grounded intrinsic reward function derived from the confidence map to more effectively guide the agent towards the target object. Secondly, the confidence map offers a more unified, accessible task representation for the agent's policy, compared to language embeddings. This enables the agent to process unseen objects and instructions through comprehensible visual confidence maps, facilitating zero-shot object-level generalization. Single-task experiments prove that our intrinsic reward significantly improves performance on challenging skill learning. In multi-task experiments, through testing on tasks beyond the training set, we show that the agent, when provided with the confidence map as the task representation, possesses better generalization capabilities than language-based conditioning. The code is available at https://github.com/PKU-RL/COPL.