OpenRaiser/Intern-Atlas huggingface dataset:
Automating the Discovery of Partial Differential Equations in Dynamical Systems
Venue: arXiv cs.LG 2024
Year: 2024

Identifying partial differential equations (PDEs) from data is crucial for understanding the governing mechanisms of natural phenomena, yet it remains a challenging task. We present an extension to the ARGOS framework, ARGOS-RAL, which leverages sparse regression with the recurrent adaptive lasso to identify PDEs from limited prior knowledge automatically. Our method automates calculating partial derivatives, constructing a candidate library, and estimating a sparse model. We rigorously evaluate the performance of ARGOS-RAL in identifying canonical PDEs under various noise levels and sample sizes, demonstrating its robustness in handling noisy and non-uniformly distributed data. We also test the algorithm's performance on datasets consisting solely of random noise to simulate scenarios with severely compromised data quality. Our results show that ARGOS-RAL effectively and reliably identifies the underlying PDEs from data, outperforming the sequential threshold ridge regression method in most cases. We highlight the potential of combining statistical methods, machine learning, and dynamical systems theory to automatically discover governing equations from collected data, streamlining the scientific modeling process.
Canonical Decision Diagrams Modulo Theories
Venue: arXiv cs.LO 2024
Year: 2024

Decision diagrams (DDs) are powerful tools to represent effectively propositional formulas, which are largely used in many domains, in particular in formal verification and in knowledge compilation. Some forms of DDs (e.g., OBDDs, SDDs) are canonical, that is, (under given conditions on the atom list) they univocally represent equivalence classes of formulas. Given the limited expressiveness of propositional logic, a few attempts to leverage DDs to SMT level have been presented in the literature. Unfortunately, these techniques still suffer from some limitations: most procedures are theory-specific; some produce theory DDs (T-DDs) which do not univocally represent T-valid formulas or T-inconsistent formulas; none of these techniques provably produces theory-canonical T-DDs, which (under given conditions on the T-atom list) univocally represent T-equivalence classes of formulas. Also, these procedures are not easy to implement, and very few implementations are actually available. In this paper, we present a novel very-general technique to leverage DDs to SMT level, which has several advantages: it is very easy to implement on top of an AllSMT solver and a DD package, which are used as blackboxes; it works for every form of DDs and every theory, or combination thereof, supported by the AllSMT solver; it produces theory-canonical T-DDs if the propositional DD is canonical. We have implemented a prototype tool for both T-OBDDs and T-SDDs on top of OBDD and SDD packages and the MathSAT SMT solver. Some preliminary empirical evaluation supports the effectiveness of the approach.
The Impact of Social Environment and Interaction Focus on User Experience and Social Acceptability of an Augmented Reality Game
Venue: arXiv cs.HC 2024
Year: 2024

One of the most promising technologies inside the Extended Reality (XR) spectrum is Augmented Reality. This technology is already in people's pockets regarding Mobile Augmented Reality with their smartphones. The scientific community still needs answers about how humans could and should interact in environments where perceived stimuli are different from fully physical or digital circumstances. Moreover, it is still being determined if people accept these new technologies in different social environments and interaction settings or if some obstacles could exist. This paper explores the impact of the Social Environment and the Focus of social interaction on users while playing a location-based augmented reality game, measuring it with user experience and social acceptance indicators. An empirical study in a within-subject fashion was performed in different social environments and under different settings of social interaction focus with N = 28 participants compiling self-reported questionnaires after playing a Scavenger Hunt in Augmented Reality. The measures from two different Social Environments (Crowded vs. Uncrowded) resulted in statistically relevant mean differences with indicators from the Social Acceptability dimension. Moreover, the analyses show statistically relevant differences between the variances from different degrees of Social Interaction Focus with Overall Social Presence, Perceived Psychological Engagement, Perceived Attentional Engagement, and Perceived Emotional Contagion. The results suggest that a location-based AR game played in different social environments and settings can influence the user experience's social dimension. Therefore, they should be carefully considered while designing immersive technological experiences in public spaces involving social interactions between players.
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey
Venue: arXiv cs.CV 2024
Year: 2024

This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a diverse test set containing a variety of 4K images ranging from digital art to gaming and photography. The images are compressed using the modern AVIF codec, instead of JPEG. All the proposed methods improve PSNR fidelity over Lanczos interpolation, and process images under 10ms. Out of the 160 participants, 25 teams submitted their code and models. The solutions present novel designs tailored for memory-efficiency and runtime on edge devices. This survey describes the best solutions for real-time SR of compressed high-resolution images.
Cost-Driven Data Replication with Predictions
Venue: arXiv cs.DS 2024
Year: 2024

This paper studies an online replication problem for distributed data access. The goal is to dynamically create and delete data copies in a multi-server system as time passes to minimize the total storage and network cost of serving access requests. We study the problem in the emergent learning-augmented setting, assuming simple binary predictions about inter-request times at individual servers. We develop an online algorithm and prove that it is ($\frac{5+α}{3}$)-consistent (competitiveness under perfect predictions) and ($1 + \frac{1}α$)-robust (competitiveness under terrible predictions), where $α\in (0, 1]$ is a hyper-parameter representing the level of distrust in the predictions. We also study the impact of mispredictions on the competitive ratio of the proposed algorithm and adapt it to achieve a bounded robustness while retaining its consistency. We further establish a lower bound of $\frac{3}{2}$ on the consistency of any deterministic learning-augmented algorithm. Experimental evaluations are carried out to evaluate our algorithms using real data access traces.
360SFUDA++: Towards Source-free UDA for Panoramic Segmentation by Learning Reliable Category Prototypes
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we address the challenging source-free unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation, given only a pinhole image pre-trained model (i.e., source) and unlabeled panoramic images (i.e., target). Tackling this problem is non-trivial due to three critical challenges: 1) semantic mismatches from the distinct Field-of-View (FoV) between domains, 2) style discrepancies inherent in the UDA problem, and 3) inevitable distortion of the panoramic images. To tackle these problems, we propose 360SFUDA++ that effectively extracts knowledge from the source pinhole model with only unlabeled panoramic images and transfers the reliable knowledge to the target panoramic domain. Specifically, we first utilize Tangent Projection (TP) as it has less distortion and meanwhile slits the equirectangular projection (ERP) to patches with fixed FoV projection (FFP) to mimic the pinhole images. Both projections are shown effective in extracting knowledge from the source model. However, as the distinct projections make it less possible to directly transfer knowledge between domains, we then propose Reliable Panoramic Prototype Adaptation Module (RP2AM) to transfer knowledge at both prediction and prototype levels. RP$^2$AM selects the confident knowledge and integrates panoramic prototypes for reliable knowledge adaptation. Moreover, we introduce Cross-projection Dual Attention Module (CDAM), which better aligns the spatial and channel characteristics across projections at the feature level between domains. Both knowledge extraction and transfer processes are synchronously updated to reach the best performance. Extensive experiments on the synthetic and real-world benchmarks, including outdoor and indoor scenarios, demonstrate that our 360SFUDA++ achieves significantly better performance than prior SFUDA methods.
Exploring the Dynamics of Data Transmission in 5G Networks: A Conceptual Analysis
Venue: arXiv cs.NI 2024
Year: 2024

This conceptual analysis examines the dynamics of data transmission in 5G networks. It addresses various aspects of sending data from cameras and LiDARs installed on a remote-controlled ferry to a land-based control center. The range of topics includes all stages of video and LiDAR data processing from acquisition and encoding to final decoding, all aspects of their transmission and reception via the WebRTC protocol, and all possible types of network problems such as handovers or congestion that could affect the quality of experience for end-users. A series of experiments were conducted to evaluate the key aspects of the data transmission. These include simulation-based reproducible runs and real-world experiments conducted using open-source solutions we developed: "Gymir5G" - an OMNeT++-based 5G simulation and "GstWebRTCApp" - a GStreamer-based application for adaptive control of media streams over the WebRTC protocol. One of the goals of this study is to formulate the bandwidth and latency requirements for reliable real-time communication and to estimate their approximate values. This goal was achieved through simulation-based experiments involving docking maneuvers in the Bay of Kiel, Germany. The final latency for the entire data processing pipeline was also estimated during the real tests. In addition, a series of simulation-based experiments showed the impact of key WebRTC features and demonstrated the effectiveness of the WebRTC protocol, while the conducted video codec comparison showed that the hardware-accelerated H.264 codec is the best. Finally, the research addresses the topic of adaptive communication, where the traditional congestion avoidance and deep reinforcement learning approaches were analyzed. The comparison in a sandbox scenario shows that the AI-based solution outperforms the WebRTC baseline GCC algorithm in terms of data rates, latency, and packet loss.
Energy Efficient Service Placement for IoT Networks
Venue: arXiv cs.NI 2024
Year: 2024

In recent years, there has been a significant expansion in the Internet of Things (IoT), with a growing number of devices being connected to the internet. This has led to an increase in data collection and analysis as well as the development of new technologies and applications. The rise of IoT has also brought about new challenges, such as security concerns and energy efficiency. This study investigates a layered IoT architecture that combines fog and cloud computing, aiming to assess the impact of service placement on energy efficiency. Through simulations, we analyse energy use across Access Fog, Metro Fog, and Cloud Data Centre layers for different IoT request volumes. Findings indicate that Access Fog is optimal for single requests, while Metro Fog efficiently manages higher demands from multiple devices. The study emphasizes the need for adaptive service deployment, responsive to network load variations, to improve energy efficiency. Hence, we propose the implementation of dynamic service placement strategies within Internet of Things (IoT) environments.
Vision-based robot manipulation of transparent liquid containers in a laboratory setting
Venue: arXiv cs.RO 2024
Year: 2024

Laboratory processes involving small volumes of solutions and active ingredients are often performed manually due to challenges in automation, such as high initial costs, semi-structured environments and protocol variability. In this work, we develop a flexible and cost-effective approach to address this gap by introducing a vision-based system for liquid volume estimation and a simulation-driven pouring method particularly designed for containers with small openings. We evaluate both components individually, followed by an applied real-world integration of cell culture automation using a UR5 robotic arm. Our work is fully reproducible: we share our code at at \url{https://github.com/DaniSchober/LabLiquidVision} and the newly introduced dataset LabLiquidVolume is available at https://data.dtu.dk/articles/dataset/LabLiquidVision/25103102.
Application of Long-Short Term Memory and Convolutional Neural Networks for Real-Time Bridge Scour Prediction
Venue: arXiv cs.LG 2024
Year: 2024

Scour around bridge piers is a critical challenge for infrastructures around the world. In the absence of analytical models and due to the complexity of the scour process, it is difficult for current empirical methods to achieve accurate predictions. In this paper, we exploit the power of deep learning algorithms to forecast the scour depth variations around bridge piers based on historical sensor monitoring data, including riverbed elevation, flow elevation, and flow velocity. We investigated the performance of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models for real-time scour forecasting using data collected from bridges in Alaska and Oregon from 2006 to 2021. The LSTM models achieved mean absolute error (MAE) ranging from 0.1m to 0.5m for predicting bed level variations a week in advance, showing a reasonable performance. The Fully Convolutional Network (FCN) variant of CNN outperformed other CNN configurations, showing a comparable performance to LSTMs with significantly lower computational costs. We explored various innovative random-search heuristics for hyperparameter tuning and model optimisation which resulted in reduced computational cost compared to grid-search method. The impact of different combinations of sensor features on scour prediction showed the significance of the historical time series of scour for predicting upcoming events. Overall, this study provides a greater understanding of the potential of Deep Learning algorithms for real-time scour prediction and early warning for bridges with distinct geology, geomorphology and flow characteristics.
Neural Interaction Energy for Multi-Agent Trajectory Prediction
Venue: arXiv cs.AI 2024
Year: 2024

Maintaining temporal stability is crucial in multi-agent trajectory prediction. Insufficient regularization to uphold this stability often results in fluctuations in kinematic states, leading to inconsistent predictions and the amplification of errors. In this study, we introduce a framework called Multi-Agent Trajectory prediction via neural interaction Energy (MATE). This framework assesses the interactive motion of agents by employing neural interaction energy, which captures the dynamics of interactions and illustrates their influence on the future trajectories of agents. To bolster temporal stability, we introduce two constraints: inter-agent interaction constraint and intra-agent motion constraint. These constraints work together to ensure temporal stability at both the system and agent levels, effectively mitigating prediction fluctuations inherent in multi-agent systems. Comparative evaluations against previous methods on four diverse datasets highlight the superior prediction accuracy and generalization capabilities of our model.
AudioScenic: Audio-Driven Video Scene Editing
Venue: arXiv cs.CV 2024
Year: 2024

Audio-driven visual scene editing endeavors to manipulate the visual background while leaving the foreground content unchanged, according to the given audio signals. Unlike current efforts focusing primarily on image editing, audio-driven video scene editing has not been extensively addressed. In this paper, we introduce AudioScenic, an audio-driven framework designed for video scene editing. AudioScenic integrates audio semantics into the visual scene through a temporal-aware audio semantic injection process. As our focus is on background editing, we further introduce a SceneMasker module, which maintains the integrity of the foreground content during the editing process. AudioScenic exploits the inherent properties of audio, namely, audio magnitude and frequency, to guide the editing process, aiming to control the temporal dynamics and enhance the temporal consistency. First, we present an audio Magnitude Modulator module that adjusts the temporal dynamics of the scene in response to changes in audio magnitude, enhancing the visual dynamics. Second, the audio Frequency Fuser module is designed to ensure temporal consistency by aligning the frequency of the audio with the dynamics of the video scenes, thus improving the overall temporal coherence of the edited videos. These integrated features enable AudioScenic to not only enhance visual diversity but also maintain temporal consistency throughout the video. We present a new metric named temporal score for more comprehensive validation of temporal consistency. We demonstrate substantial advancements of AudioScenic over competing methods on DAVIS and Audioset datasets.
Introducing Systems Thinking as a Framework for Teaching and Assessing Threat Modeling Competency
Venue: arXiv cs.CR 2024
Year: 2024

Computing systems face diverse and substantial cybersecurity threats. To mitigate these cybersecurity threats, software engineers need to be competent in the skill of threat modeling. In industry and academia, there are many frameworks for teaching threat modeling, but our analysis of these frameworks suggests that (1) these approaches tend to be focused on component-level analysis rather than educating students to reason holistically about a system's cybersecurity, and (2) there is no rubric for assessing a student's threat modeling competency. To address these concerns, we propose using systems thinking in conjunction with popular and industry-standard threat modeling frameworks like STRIDE for teaching and assessing threat modeling competency. Prior studies suggest a holistic approach, like systems thinking, can help understand and mitigate cybersecurity threats. Thus, we developed and piloted two novel rubrics - one for assessing STRIDE threat modeling performance and the other for assessing systems thinking performance while conducting STRIDE. To conduct this study, we piloted the two rubrics mentioned above to assess threat model artifacts of students enrolled in an upper-level software engineering course at Purdue University in Fall 2021, Spring 2023, and Fall 2023. Students who had both systems thinking and STRIDE instruction identified and attempted to mitigate component-level as well as systems-level threats. Students with only STRIDE instruction tended to focus on identifying and mitigating component-level threats and discounted system-level threats. We contribute to engineering education by: (1) describing a new rubric for assessing threat modeling based on systems thinking; (2) identifying trends and blindspots in students' threat modeling approach; and (3) envisioning the benefits of integrating systems thinking in threat modeling teaching and assessment.
Privacy-Preserving Statistical Data Generation: Application to Sepsis Detection
Venue: arXiv cs.LG 2024
Year: 2024

The biomedical field is among the sectors most impacted by the increasing regulation of Artificial Intelligence (AI) and data protection legislation, given the sensitivity of patient information. However, the rise of synthetic data generation methods offers a promising opportunity for data-driven technologies. In this study, we propose a statistical approach for synthetic data generation applicable in classification problems. We assess the utility and privacy implications of synthetic data generated by Kernel Density Estimator and K-Nearest Neighbors sampling (KDE-KNN) within a real-world context, specifically focusing on its application in sepsis detection. The detection of sepsis is a critical challenge in clinical practice due to its rapid progression and potentially life-threatening consequences. Moreover, we emphasize the benefits of KDE-KNN compared to current synthetic data generation methodologies. Additionally, our study examines the effects of incorporating synthetic data into model training procedures. This investigation provides valuable insights into the effectiveness of synthetic data generation techniques in mitigating regulatory constraints within the biomedical field.
Explanations in Everyday Software Systems: Towards a Taxonomy for Explainability Needs
Venue: arXiv cs.SE 2024
Year: 2024

Modern software systems are becoming increasingly complex and opaque. The integration of explanations within software has shown the potential to address this opacity and can make the system more understandable to end-users. As a result, explainability has gained much traction as a non-functional requirement of complex systems. Understanding what type of system requires what types of explanations is necessary to facilitate the inclusion of explainability in early software design processes. In order to specify explainability requirements, an explainability taxonomy that applies to a variety of different software types is needed. In this paper, we present the results of an online survey with 84 participants. We asked the participants to state their questions and confusions concerning their three most recently used software systems and elicited both explicit and implicit explainability needs from their statements. These needs were coded by three researchers. In total, we identified and classified 315 explainability needs from the survey answers. Drawing from a large pool of explainability needs and our coding procedure, we present two major contributions of this work: 1) a taxonomy for explainability needs in everyday software systems and 2) an overview of how the need for explanations differs between different types of software systems.