Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing Machine Learning for SQL Injection Detection in Web Systems

This work analyzes the machine learning techniques most used in SQL injection (SQLi) detection in order to make a comparison in terms of precision, as well as characterize the data with which the models for SQLi detection are generated. For the analysis, a systematic literature review is developed to extract the data reported from the state-of-the-art. A total of 31 primary studies are selected, of which 22 address the analysis and exploring ML techniques for SQLi detection; 20 conduct experiments to test the models in terms of performance and accuracy; and 14 explore the characteristics of the data with which ML models are prepared. In 22 of the 31 papers, 5 ML algorithms for classification problems stand out: Decision Tree, K-Nearest Neighbors, Naive Bayes, Random Forest, and Support Vector Machine. Decision Tree is the most used algorithm for detecting SQLi, appearing in 18 of 31 papers. The t-student test is applied for samples of unequal variances. The results demonstrate a marginal difference between techniques, although Random Forest is one of the techniques with the greatest consistency in accuracy.
Dynamic 3D Reconstruction using Moving Depth Camera Mounted on Robotic Arm

This work presents a robotic camera-based 3D reconstruction system and investigates the approaches for 3D reconstruction. The developed system is capable of reconstructing scenes with humans and moving objects in the scene. A moving RGB-D camera mounted on a robotic arm is utilized to capture 3D scenes dynamically. This robotic camera-based 3D reconstruction system has significant potential in human-robot collaboration in a dynamic environment. Particularly in the field of surgical robotics, the system holds great importance for advancing human-robot coexistence where timely perception of the environment is crucial even under cluttered or frequently occluded views. By accurately reconstructing real surgical scenes, it provides precise guidance to robots, enabling them to perceive, navigate, and assist doctors during operations. Ultimately, this functionality has the potential to enhance surgical safety and promote efficient collaboration between humans and robots.
Formation of Merging Stellar-mass Black Hole Binaries by Gravitational-wave Emission in Active Galactic Nucleus Disks

Many stellar-mass black holes (sBHs) are expected to orbit supermassive black holes at galactic centers. For galaxies with active galactic nuclei, it is likely that the sBHs reside in a disk. We study the formation of sBH binaries via gravitational-wave emission in such disks. We examine analytically the dynamics of two sBHs orbiting a supermassive black hole, estimate the capture cross section, and derive the eccentricity distribution of bound binaries at different frequency bands. We find that the majority of the merging sBH binaries, assembled in this manner, can be measured as highly eccentric, detectable in the LIGO-Virgo-KAGRA (LVK) band from their formation, with (1 − e) ≪ 1, through their circularization and up to their merger; the remaining binaries circularize to small eccentricities (e ≲ 0.3) before entering the LVK band. More eccentric mergers would be observed for sBHs with higher random velocities, closer to the supermassive black hole, or at lower observing frequency bands, as planned in future gravitational-wave detectors such as the Einstein Telescope and LISA.
VIRUS-NeRF -- Vision, InfraRed and UltraSonic based Neural Radiance Fields

Autonomous mobile robots are an increasingly integral part of modern factory and warehouse operations. Obstacle detection, avoidance and path planning are critical safety-relevant tasks, which are often solved using expensive LiDAR sensors and depth cameras. We propose to use cost-effective low-resolution ranging sensors, such as ultrasonic and infrared time-of-flight sensors by developing VIRUS-NeRF - Vision, InfraRed, and UltraSonic based Neural Radiance Fields. Building upon Instant Neural Graphics Primitives with a Multiresolution Hash Encoding (Instant-NGP), VIRUS-NeRF incorporates depth measurements from ultrasonic and infrared sensors and utilizes them to update the occupancy grid used for ray marching. Experimental evaluation in 2D demonstrates that VIRUS-NeRF achieves comparable mapping performance to LiDAR point clouds regarding coverage. Notably, in small environments, its accuracy aligns with that of LiDAR measurements, while in larger ones, it is bounded by the utilized ultrasonic sensors. An in-depth ablation study reveals that adding ultrasonic and infrared sensors is highly effective when dealing with sparse data and low view variation. Further, the proposed occupancy grid of VIRUS-NeRF improves the mapping capabilities and increases the training speed by 46% compared to Instant-NGP. Overall, VIRUS-NeRF presents a promising approach for cost-effective local mapping in mobile robotics, with potential applications in safety and navigation tasks. The code can be found at https://github.com/ethz-asl/virus nerf.
Towards Proactive Interactions for In-Vehicle Conversational Assistants Utilizing Large Language Models

Research demonstrates that the proactivity of in-vehicle conversational assistants (IVCAs) can help to reduce distractions and enhance driving safety, better meeting users' cognitive needs. However, existing IVCAs struggle with user intent recognition and context awareness, which leads to suboptimal proactive interactions. Large language models (LLMs) have shown potential for generalizing to various tasks with prompts, but their application in IVCAs and exploration of proactive interaction remain under-explored. These raise questions about how LLMs improve proactive interactions for IVCAs and influence user perception. To investigate these questions systematically, we establish a framework with five proactivity levels across two dimensions-assumption and autonomy-for IVCAs. According to the framework, we propose a"Rewrite + ReAct + Reflect"strategy, aiming to empower LLMs to fulfill the specific demands of each proactivity level when interacting with users. Both feasibility and subjective experiments are conducted. The LLM outperforms the state-of-the-art model in success rate and achieves satisfactory results for each proactivity level. Subjective experiments with 40 participants validate the effectiveness of our framework and show the proactive level with strong assumptions and user confirmation is most appropriate.
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tokamak fusion reactor or minimizing the drag force exerted on an object in a fluid flow. However, these algorithms require an abundance of training examples and may become prohibitively expensive for many applications. In addition, the reliance on deep neural networks often results in an uninterpretable, black-box policy that may be too computationally expensive to use with certain embedded systems. Recent advances in sparse dictionary learning, such as the sparse identification of nonlinear dynamics (SINDy), have shown promise for creating efficient and interpretable data-driven models in the low-data regime. In this work we introduce SINDy-RL, a unifying framework for combining SINDy and DRL to create efficient, interpretable, and trustworthy representations of the dynamics model, reward function, and control policy. We demonstrate the effectiveness of our approaches on benchmark control environments and challenging fluids problems. SINDy-RL achieves comparable performance to state-of-the-art DRL algorithms using significantly fewer interactions in the environment and results in an interpretable control policy orders of magnitude smaller than a deep neural network policy.
Reflexivity and Hochschild Cohomology

Reflexive DG-categories were defined by Kuznetsov and Shinder as generalisations of smooth and proper DG-categories. Over a perfect field, they include all projective schemes and finite dimensional algebras. Smooth and proper DG-categories are the dualizable objects in the symmetric monoidal category of DG-categories localised at Morita equivalences. We show the reflexive DG-categories are the reflexive objects in this monoidal category. Using this perspective we prove that the Hochschild cohomology of a reflexive DG-category is isomorphic to that of its derived category of cohomologically finite modules.
Ventilation and Temperature Control for Energy-efficient and Healthy Buildings: A Differentiable PDE Approach

In this paper, we introduce a novel framework for building learning and control, focusing on ventilation and thermal management to enhance energy efficiency. We validate the performance of the proposed framework in system model learning via two case studies: a synthetic study focusing on the joint learning of temperature and CO2 fields, and an application to a real-world dataset for CO2 field learning. For building control, we demonstrate that the proposed framework can optimize the control actions and significantly reduce the energy cost while maintaining a comfort and healthy indoor environment. When compared to existing traditional methods, an optimization-based method with ODE models and reinforcement learning, our approach can significantly reduce the energy consumption while guarantees all the safety-critical air quality and control constraints. Promising future research directions involve validating and improving the proposed PDE models through accurate estimation of airflow fields within indoor environments. Additionally, incorporating uncertainty modeling into the PDE framework for HVAC control presents an opportunity to enhance the efficiency and reliability of building HVAC system management.
Algorithmic syntactic causal identification

Causal identification in causal Bayes nets (CBNs) is an important tool in causal inference allowing the derivation of interventional distributions from observational distributions where this is possible in principle. However, most existing formulations of causal identification using techniques such as d-separation and do-calculus are expressed within the mathematical language of classical probability theory on CBNs. However, there are many causal settings where probability theory and hence current causal identification techniques are inapplicable such as relational databases, dataflow programs such as hardware description languages, distributed systems and most modern machine learning algorithms. We show that this restriction can be lifted by replacing the use of classical probability theory with the alternative axiomatic foundation of symmetric monoidal categories. In this alternative axiomatization, we show how an unambiguous and clean distinction can be drawn between the general syntax of causal models and any specific semantic implementation of that causal model. This allows a purely syntactic algorithmic description of general causal identification by a translation of recent formulations of the general ID algorithm through fixing. Our description is given entirely in terms of the non-parametric ADMG structure specifying a causal model and the algebraic signature of the corresponding monoidal category, to which a sequence of manipulations is then applied so as to arrive at a modified monoidal category in which the desired, purely syntactic interventional causal model, is obtained. We use this idea to derive purely syntactic analogues of classical back-door and front-door causal adjustment, and illustrate an application to a more complex causal model.
Exploring Prompt Engineering Practices in the Enterprise

Interaction with Large Language Models (LLMs) is primarily carried out via prompting. A prompt is a natural language instruction designed to elicit certain behaviour or output from a model. In theory, natural language prompts enable non-experts to interact with and leverage LLMs. However, for complex tasks and tasks with specific requirements, prompt design is not trivial. Creating effective prompts requires skill and knowledge, as well as significant iteration in order to determine model behavior, and guide the model to accomplish a particular goal. We hypothesize that the way in which users iterate on their prompts can provide insight into how they think prompting and models work, as well as the kinds of support needed for more efficient prompt engineering. To better understand prompt engineering practices, we analyzed sessions of prompt editing behavior, categorizing the parts of prompts users iterated on and the types of changes they made. We discuss design implications and future directions based on these prompt engineering practices.
Quantum Fourier Transform using Dynamic Circuits

In dynamic quantum circuits, classical information from mid-circuit measurements is fed forward during circuit execution. This emerging capability of quantum computers confers numerous advantages that can enable more efficient and powerful protocols by drastically reducing the resource requirements for certain core algorithmic primitives. In particular, in the case of the $n$-qubit quantum Fourier transform followed immediately by measurement, the scaling of resource requirements is reduced from $O(n^2)$ two-qubit gates in an all-to-all connectivity in the standard unitary formulation to $O(n)$ mid-circuit measurements in its dynamic counterpart without any connectivity constraints. Here, we demonstrate the advantage of dynamic quantum circuits for the quantum Fourier transform on IBM's superconducting quantum hardware with certified process fidelities of $>50\%$ on up to $16$ qubits and $>1\%$ on up to $37$ qubits, exceeding previous reports across all quantum computing platforms. These results are enabled by our contribution of an efficient method for certifying the process fidelity, as well as of a dynamical decoupling protocol for error suppression during mid-circuit measurements and feed-forward within a dynamic quantum circuit that we call ``feed-forward-compensated dynamical decoupling"(FC-DD). Our results demonstrate the advantages of leveraging dynamic circuits in optimizing the compilation of quantum algorithms.
Urban mapping in Dar es Salaam using AJIVE

Mapping deprivation in urban areas is important, for example for identifying areas of greatest need and planning interventions. Traditional ways of obtaining deprivation estimates are based on either census or household survey data, which in many areas is unavailable or difficult to collect. However, there has been a huge rise in the amount of new, non-traditional forms of data, such as satellite imagery and cell-phone call-record data, which may contain information useful for identifying deprivation. We use Angle-Based Joint and Individual Variation Explained (AJIVE) to jointly model satellite imagery data, cell-phone data, and survey data for the city of Dar es Salaam, Tanzania. We first identify interpretable low-dimensional structure from the imagery and cell-phone data, and find that we can use these to identify deprivation. We then consider what is gained from further incorporating the more traditional and costly survey data. We also introduce a scalar measure of deprivation as a response variable to be predicted, and consider various approaches to multiview regression, including using AJIVE scores as predictors.
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning

The Intelligent Transportation System (ITS) environment is known to be dynamic and distributed, where participants (vehicle users, operators, etc.) have multiple, changing and possibly conflicting objectives. Although Reinforcement Learning (RL) algorithms are commonly applied to optimize ITS applications such as resource management and offloading, most RL algorithms focus on single objectives. In many situations, converting a multi-objective problem into a single-objective one is impossible, intractable or insufficient, making such RL algorithms inapplicable. We propose a multi-objective, multi-agent reinforcement learning (MARL) algorithm with high learning efficiency and low computational requirements, which automatically triggers adaptive few-shot learning in a dynamic, distributed and noisy environment with sparse and delayed reward. We test our algorithm in an ITS environment with edge cloud computing. Empirical results show that the algorithm is quick to adapt to new environments and performs better in all individual and system metrics compared to the state-of-the-art benchmark. Our algorithm also addresses various practical concerns with its modularized and asynchronous online training method. In addition to the cloud simulation, we test our algorithm on a single-board computer and show that it can make inference in 6 milliseconds.
Tracking of charged particles with nanosecond lifetimes at LHCb

A method is presented to reconstruct charged particles with lifetimes between 10 ps and 10 ns, which considers a combination of their decay products and the partial tracks created by the initial charged particle. Using the $\Xi^-$ baryon as a benchmark, the method is demonstrated with simulated events and proton-proton collision data at $\sqrt{s}=13$ TeV, corresponding to an integrated luminosity of 2.0 fb${}^{-1}$ collected with the LHCb detector in 2018. Significant improvements in the angular resolution and the signal purity are obtained. The method is implemented as part of the LHCb Run 3 event trigger in a set of requirements to select detached hyperons. This is the first demonstration of the applicability of this approach at the LHC, and the first to show its scaling with instantaneous luminosity.
EfficientMFD: Towards More Efficient Multimodal Synchronous Fusion Detection

Multimodal image fusion and object detection play a vital role in autonomous driving. Current joint learning methods have made significant progress in the multimodal fusion detection task combining the texture detail and objective semantic information. However, the tedious training steps have limited its applications to wider real-world industrial deployment. To address this limitation, we propose a novel end-to-end multimodal fusion detection algorithm, named EfficientMFD, to simplify models that exhibit decent performance with only one training step. Synchronous joint optimization is utilized in an end-to-end manner between two components, thus not being affected by the local optimal solution of the individual task. Besides, a comprehensive optimization is established in the gradient matrix between the shared parameters for both tasks. It can converge to an optimal point with fusion detection weights. We extensively test it on several public datasets, demonstrating superior performance on not only visually appealing fusion but also favorable detection performance (e.g., 6.6% mAP50:95) over other state-of-the-art approaches.
Improving Real-Time Omnidirectional 3D Multi-Person Human Pose Estimation with People Matching and Unsupervised 2D-3D Lifting

Current human pose estimation systems focus on retrieving an accurate 3D global estimate of a single person. Therefore, this paper presents one of the first 3D multi-person human pose estimation systems that is able to work in real-time and is also able to handle basic forms of occlusion. First, we adjust an off-the-shelf 2D detector and an unsupervised 2D-3D lifting model for use with a 360° panoramic camera and mm Wave radar sensors. We then introduce several contributions, including camera and radar calibrations, and the improved matching of people within the image and radar space. The system addresses both the depth and scale ambiguity problems by employing a lightweight 2D-3D pose lifting algorithm that is able to work in real-time while exhibiting accurate performance in both indoor and outdoor environments which offers both an affordable and scalable solution. Notably, our system's time complexity remains nearly constant irrespective of the number of detected individuals, achieving a frame rate of approximately 7–8 fps on a laptop with a commercial-grade GPU.
On sampling diluted Spin Glasses using Glauber dynamics

Spin-glasses are Gibbs distributions that have been studied in CS for many decades. Recently, they have gained renewed attention as they emerge naturally in learning, inference, optimisation etc. We consider the Edwards-Anderson (EA) spin-glass distribution at inverse temperature $\beta$ when the underlying graph is an instance of $G(n,d/n)$. This is the random graph on $n$ vertices where each edge appears independently with probability $d/n$ and $d=\Theta(1)$. We study the problem of approximate sampling from this distribution using Glauber dynamics. For a range of $\beta$ that depends on $d$ and for typical instances of the EA model on $G(n,d/n)$, we show that the corresponding Glauber dynamics exhibits mixing time $O(n^{2+\frac{3}{\log^2 d}})$. The range of $\beta$ for which we obtain our rapid-mixing results correspond to the expected influence being $<1/d$; we conjecture that this is the best possible. Unlike the mean-field spin-glasses, where the problem has been studied before, the diluted case has not. We utilise the well-known path-coupling technique. In the standard Glauber dynamics on $G(n,d/n)$, one has to deal with the so-called effect of high degree vertices. Here, rather than considering degrees, it is more natural to use a different measure on the vertices called aggregate influence. We build on the block-construction approach proposed by [Dyer et al. 2006] to circumvent the problem of high-degree vertices. Specifically, we first establish rapid mixing for an appropriately defined block-dynamics. We design this dynamics such that vertices of large aggregate influence are placed deep inside their blocks. Then, we obtain rapid mixing for the Glauber dynamics utilising a comparison argument.
Enhancing Trust in Autonomous Agents: An Architecture for Accountability and Explainability through Blockchain and Large Language Models

The deployment of autonomous agents in environments involving human interaction has increasingly raised security concerns. Consequently, understanding the circumstances behind an event becomes critical, requiring the development of capabilities to justify their behaviors to non-expert users. Such explanations are essential in enhancing trustworthiness and safety, acting as a preventive measure against failures, errors, and misunderstandings. Additionally, they contribute to improving communication, bridging the gap between the agent and the user, thereby improving the effectiveness of their interactions. This work presents an accountability and explainability architecture implemented for ROS-based mobile robots. The proposed solution consists of two main components. Firstly, a black box-like element to provide accountability, featuring anti-tampering properties achieved through blockchain technology. Secondly, a component in charge of generating natural language explanations by harnessing the capabilities of Large Language Models (LLMs) over the data contained within the previously mentioned black box. The study evaluates the performance of our solution in three different scenarios, each involving autonomous agent navigation functionalities. This evaluation includes a thorough examination of accountability and explainability metrics, demonstrating the effectiveness of our approach in using accountable data from robot actions to obtain coherent, accurate and understandable explanations, even when facing challenges inherent in the use of autonomous agents in real-world scenarios.