OpenRaiser/Intern-Atlas huggingface dataset:
Development of parallel programs on shared data-structures -- Revised version
Venue: arXiv cs.FL 2024
Year: 2024

A syntax-directed formal system for the development of totally correct programs with respect to an unfair shared-state parallel while-language is proposed. The system can be understood as a compositional reformulation of the Owicki/Gries method for verification of parallel programs. Auxiliary variables are used both as a specification tool to eliminate undesirable implementations, and as a verification tool to make it possible to prove that an already finished program satisfies a particular specification. Auxiliary variables may be of any sort, and it is up to the user to define the auxiliary structure he prefers. Moreover, the auxiliary structure is only a part of the logic. This means that auxiliary variables do not have to be implemented as if they were ordinary programming variables. The system is proved sound and relatively complete with respect to an operational semantics and employed to develop three nontrivial algorithms: the Dining-Philosophers, the Bubble-Lattice-Sort and the Set-Partition algorithms. Finally, a related method for the development of (possibly nonterminating) programs with respect to four properties is described. This approach is then used to develop Dekker's algorithm.
Self-Balanced R-CNN for Instance Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Current state-of-the-art two-stage models on instance segmentation task suffer from several types of imbalances. In this paper, we address the Intersection over the Union (IoU) distribution imbalance of positive input Regions of Interest (RoIs) during the training of the second stage. Our Self-Balanced R-CNN (SBR-CNN), an evolved version of the Hybrid Task Cascade (HTC) model, brings brand new loop mechanisms of bounding box and mask refinements. With an improved Generic RoI Extraction (GRoIE), we also address the feature-level imbalance at the Feature Pyramid Network (FPN) level, originated by a non-uniform integration between low- and high-level features from the backbone layers. In addition, the redesign of the architecture heads toward a fully convolutional approach with FCC further reduces the number of parameters and obtains more clues to the connection between the task to solve and the layers used. Moreover, our SBR-CNN model shows the same or even better improvements if adopted in conjunction with other state-of-the-art models. In fact, with a lightweight ResNet-50 as backbone, evaluated on COCO minival 2017 dataset, our model reaches 45.3% and 41.5% AP for object detection and instance segmentation, with 12 epochs and without extra tricks. The code is available at https://github.com/IMPLabUniPr/mmdetection/tree/sbr_cnn
Evolutionary Large Language Models for Hardware Security: A Comparative Survey
Venue: arXiv cs.CR 2024
Year: 2024

Automating hardware (HW) security vulnerability detection and mitigation during the design phase is imperative for two reasons: (i) It must be before chip fabrication, as post-fabrication fixes can be costly or even impractical; (ii) The size and complexity of modern HW raise concerns about unknown vulnerabilities compromising CIA triad. While Large Language Models (LLMs) can revolutionize both HW design and testing processes, within the semiconductor context, LLMs can be harnessed to automatically rectify security-relevant vulnerabilities inherent in HW designs. This study explores the seeds of LLM integration in register transfer level (RTL) designs, focusing on their capacity for autonomously resolving security-related vulnerabilities. The analysis involves comparing methodologies, assessing scalability, interpretability, and identifying future research directions. Potential areas for exploration include developing specialized LLM architectures for HW security tasks and enhancing model performance with domain-specific knowledge, leading to reliable automated security measurement and risk mitigation associated with HW vulnerabilities.
Multilayer Correlation Clustering
Venue: arXiv cs.DS 2024
Year: 2024

In this paper, we establish Multilayer Correlation Clustering, a novel generalization of Correlation Clustering (Bansal et al., FOCS '02) to the multilayer setting. In this model, we are given a series of inputs of Correlation Clustering (called layers) over the common set $V$. The goal is then to find a clustering of $V$ that minimizes the $\ell_p$-norm ($p\geq 1$) of the disagreements vector, which is defined as the vector (with dimension equal to the number of layers), each element of which represents the disagreements of the clustering on the corresponding layer. For this generalization, we first design an $O(L\log n)$-approximation algorithm, where $L$ is the number of layers, based on the well-known region growing technique. We then study an important special case of our problem, namely the problem with the probability constraint. For this case, we first give an $(α+2)$-approximation algorithm, where $α$ is any possible approximation ratio for the single-layer counterpart. For instance, we can take $α=2.5$ in general (Ailon et al., JACM '08) and $α=1.73+ε$ for the unweighted case (Cohen-Addad et al., FOCS '23). Furthermore, we design a $4$-approximation algorithm, which improves the above approximation ratio of $α+2=4.5$ for the general probability-constraint case. Computational experiments using real-world datasets demonstrate the effectiveness of our proposed algorithms.
Multimodal Semantic-Aware Automatic Colorization with Diffusion Prior
Venue: arXiv cs.CV 2024
Year: 2024

Colorizing grayscale images offers an engaging visual experience. Existing automatic colorization methods often fail to generate satisfactory results due to incorrect semantic colors and unsaturated colors. In this work, we propose an automatic colorization pipeline to overcome these challenges. We leverage the extraordinary generative ability of the diffusion prior to synthesize color with plausible semantics. To overcome the artifacts introduced by the diffusion prior, we apply the luminance conditional guidance. Moreover, we adopt multimodal high-level semantic priors to help the model understand the image content and deliver saturated colors. Besides, a luminance-aware decoder is designed to restore details and enhance overall visual quality. The proposed pipeline synthesizes saturated colors while maintaining plausible semantics. Experiments indicate that our proposed method considers both diversity and fidelity, surpassing previous methods in terms of perceptual realism and gain most human preference.
CBRW: A Novel Approach for Cancelable Biometric Template Generation based on
Venue: arXiv cs.CV 2024
Year: 2024

Cancelable Biometric is a challenging research field in which security of an original biometric image is ensured by transforming the original biometric into another irreversible domain. Several approaches have been suggested in literature for generating cancelable biometric templates. In this paper, two novel and simple cancelable biometric template generation methods based on Random Walk (CBRW) have been proposed. By employing random walk and other steps given in the proposed two algorithms viz. CBRW-BitXOR and CBRW-BitCMP, the original biometric is transformed into a cancellable template. The performance of the proposed methods is compared with other state-of-the-art methods. Experiments have been performed on eight publicly available gray and color datasets i.e. CP (ear) (gray and color), UTIRIS (iris) (gray and color), ORL (face) (gray), IIT Delhi (iris) (gray and color), and AR (face) (color). Performance of the generated templates is measured in terms of Correlation Coefficient (Cr), Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), Mean Absolute Error (MAE), Number of Pixel Change Rate (NPCR), and Unified Average Changing Intensity (UACI). By experimental results, it has been proved that proposed methods are superior than other state-of-the-art methods in qualitative as well as quantitative analysis. Furthermore, CBRW performs better on both gray as well as color images.
JITScanner: Just-in-Time Executable Page Check in the Linux Operating System
Venue: arXiv cs.CR 2024
Year: 2024

Modern malware poses a severe threat to cybersecurity, continually evolving in sophistication. To combat this threat, researchers and security professionals continuously explore advanced techniques for malware detection and analysis. Dynamic analysis, a prevalent approach, offers advantages over static analysis by enabling observation of runtime behavior and detecting obfuscated or encrypted code used to evade detection. However, executing programs within a controlled environment can be resource-intensive, often necessitating compromises, such as limiting sandboxing to an initial period. In our article, we propose an alternative method for dynamic executable analysis: examining the presence of malicious signatures within executable virtual pages precisely when their current content, including any updates over time, is accessed for instruction fetching. Our solution, named JITScanner, is developed as a Linux-oriented package built upon a Loadable Kernel Module (LKM). It integrates a user-level component that communicates efficiently with the LKM using scalable multi-processor/core technology. JITScanner's effectiveness in detecting malware programs and its minimal intrusion in normal runtime scenarios have been extensively tested, with the experiment results detailed in this article. These experiments affirm the viability of our approach, showcasing JITScanner's capability to effectively identify malware while minimizing runtime overhead.
Redefining Safety for Autonomous Vehicles
Venue: arXiv cs.RO 2024
Year: 2024

Existing definitions and associated conceptual frameworks for computer-based system safety should be revisited in light of real-world experiences from deploying autonomous vehicles. Current terminology used by industry safety standards emphasizes mitigation of risk from specifically identified hazards, and carries assumptions based on human-supervised vehicle operation. Operation without a human driver dramatically increases the scope of safety concerns, especially due to operation in an open world environment, a requirement to self-enforce operational limits, participation in an ad hoc sociotechnical system of systems, and a requirement to conform to both legal and ethical constraints. Existing standards and terminology only partially address these new challenges. We propose updated definitions for core system safety concepts that encompass these additional considerations as a starting point for evolving safe-ty approaches to address these additional safety challenges. These results might additionally inform framing safety terminology for other autonomous system applications.
Predicting SSH keys in Open SSH Memory dumps
Venue: arXiv cs.CR 2024
Year: 2024

As the digital landscape evolves, cybersecurity has become an indispensable focus of IT systems. Its ever-escalating challenges have amplified the importance of digital forensics, particularly in the analysis of heap dumps from main memory. In this context, the Secure Shell protocol (SSH) designed for encrypted communications, serves as both a safeguard and a potential veil for malicious activities. This research project focuses on predicting SSH keys in OpenSSH memory dumps, aiming to enhance protective measures against illicit access and enable the development of advanced security frameworks or tools like honeypots. This Masterarbeit is situated within the broader SmartVMI project, and seeks to build upon existing research on key prediction in OpenSSH heap dumps. Utilizing machine learning (ML) and deep learning models, the study aims to refine features for embedding techniques and explore innovative methods for effective key detection based on recent advancements in Knowledge Graph and ML. The objective is to accurately predict the presence and location of SSH keys within memory dumps. This work builds upon, and aims to enhance, the foundations laid by SSHkex and SmartKex, enriching both the methodology and the results of the original research while exploring the untapped potential of newly proposed approaches. The current thesis dives into memory graph modelization from raw binary heap dump files. Each memory graph can support a range of embeddings that can be used directly for model training, through the use of classic ML models and graph neural network. It offers an in-depth discussion on the current state-of-the-art in key prediction for OpenSSH memory dumps, research questions, experimental setups, programs development, results as well as discussing potential future directions.
To what extent are multiple pendulum systems viable in pseudo-random number generation?
Venue: arXiv cs.CR 2024
Year: 2024

This paper explores the development and viability of an alternative pseudorandom number generator (PRNG) that leverages the chaotic dynamics of multiple pendulum systems. Some traditional PRNGs, notably the one implemented in the Java.Random class, suffer from predictability which gives rise to exploitability. This study identifies these vulnerabilities and proposes a novel PRNG designed using ordinary differential equations, physics modeling, and chaos theory. The performance of the new PRNG is then tested against Java's standard PRNGs using the NIST Statistical Test Suite, which evaluates randomness through comprehensive statistical testing. Results indicate that the multiple pendulum-based PRNG not only offers enhanced security by generating less predictable number sequences but also demonstrates potential for efficiency improvements in applications requiring high levels of entropy. The findings suggest that integrating chaotic physics-based systems into PRNGs, such as the double-pendulum system tested in this study, could strengthen cryptographic practices and security protocols for applications that do not require the level of security created by true random number generators, which is useful in fields such as gaming.
AdaQAT: Adaptive Bit-Width Quantization-Aware Training
Venue: arXiv cs.LG 2024
Year: 2024

Large-scale deep neural networks (DNNs) have achieved remarkable success in many application scenarios. However, high computational complexity and energy costs of modern DNNs make their deployment on edge devices challenging. Model quantization is a common approach to deal with deployment constraints, but searching for optimized bit-widths can be challenging. In this work, we present Adaptive Bit-Width Quantization Aware Training (AdaQAT), a learning-based method that automatically optimizes weight and activation signal bit-widths during training for more efficient DNN inference. We use relaxed real-valued bit-widths that are updated using a gradient descent rule, but are otherwise discretized for all quantization operations. The result is a simple and flexible QAT approach for mixed-precision uniform quantization problems. Compared to other methods that are generally designed to be run on a pretrained network, AdaQAT works well in both training from scratch and fine-tuning scenarios.Initial results on the CIFAR-10 and ImageNet datasets using ResNet20 and ResNet18 models, respectively, indicate that our method is competitive with other state-of-the-art mixed-precision quantization approaches.
ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning
Venue: arXiv cs.CV 2024
Year: 2024

We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a $R^2$ score of 0.57 and our model for porosity localization produced an average IoU score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity "Digital Twins" based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.
Review of Data-centric Time Series Analysis from Sample, Feature, and Period
Venue: arXiv cs.LG 2024
Year: 2024

Data is essential to performing time series analysis utilizing machine learning approaches, whether for classic models or today's large language models. A good time-series dataset is advantageous for the model's accuracy, robustness, and convergence, as well as task outcomes and costs. The emergence of data-centric AI represents a shift in the landscape from model refinement to prioritizing data quality. Even though time-series data processing methods frequently come up in a wide range of research fields, it hasn't been well investigated as a specific topic. To fill the gap, in this paper, we systematically review different data-centric methods in time series analysis, covering a wide range of research topics. Based on the time-series data characteristics at sample, feature, and period, we propose a taxonomy for the reviewed data selection methods. In addition to discussing and summarizing their characteristics, benefits, and drawbacks targeting time-series data, we also introduce the challenges and opportunities by proposing recommendations, open problems, and possible research topics.
A Neural-Network-Based Approach for Loose-Fitting Clothing
Venue: arXiv cs.GR 2024
Year: 2024

Since loose-fitting clothing contains dynamic modes that have proven to be difficult to predict via neural networks, we first illustrate how to coarsely approximate these modes with a real-time numerical algorithm specifically designed to mimic the most important ballistic features of a classical numerical simulation. Although there is some flexibility in the choice of the numerical algorithm used as a proxy for full simulation, it is essential that the stability and accuracy be independent from any time step restriction or similar requirements in order to facilitate real-time performance. In order to reduce the number of degrees of freedom that require approximations to their dynamics, we simulate rigid frames and use skinning to reconstruct a rough approximation to a desirable mesh; as one might expect, neural-network-based skinning seems to perform better than linear blend skinning in this scenario. Improved high frequency deformations are subsequently added to the skinned mesh via a quasistatic neural network (QNN). In contrast to recurrent neural networks that require a plethora of training data in order to adequately generalize to new examples, QNNs perform well with significantly less training data.
mlr3summary: Concise and interpretable summaries for machine learning models
Venue: arXiv cs.LG 2024
Year: 2024

This work introduces a novel R package for concise, informative summaries of machine learning models. We take inspiration from the summary function for (generalized) linear models in R, but extend it in several directions: First, our summary function is model-agnostic and provides a unified summary output also for non-parametric machine learning models; Second, the summary output is more extensive and customizable -- it comprises information on the dataset, model performance, model complexity, model's estimated feature importances, feature effects, and fairness metrics; Third, models are evaluated based on resampling strategies for unbiased estimates of model performances, feature importances, etc. Overall, the clear, structured output should help to enhance and expedite the model selection process, making it a helpful tool for practitioners and researchers alike.
Closing the gap: Optimizing Guidance and Control Networks through Neural ODEs
Venue: arXiv cs.LG 2024
Year: 2024

We improve the accuracy of Guidance & Control Networks (G&CNETs), trained to represent the optimal control policies of a time-optimal transfer and a mass-optimal landing, respectively. In both cases we leverage the dynamics of the spacecraft, described by Ordinary Differential Equations which incorporate a neural network on their right-hand side (Neural ODEs). Since the neural dynamics is differentiable, the ODEs sensitivities to the network parameters can be computed using the variational equations, thereby allowing to update the G&CNET parameters based on the observed dynamics. We start with a straightforward regression task, training the G&CNETs on datasets of optimal trajectories using behavioural cloning. These networks are then refined using the Neural ODE sensitivities by minimizing the error between the final states and the target states. We demonstrate that for the orbital transfer, the final error to the target can be reduced by 99% on a single trajectory and by 70% on a batch of 500 trajectories. For the landing problem the reduction in error is around 98-99% (position) and 40-44% (velocity). This step significantly enhances the accuracy of G&CNETs, which instills greater confidence in their reliability for operational use. We also compare our results to the popular Dataset Aggregation method (DaGGER) and allude to the strengths and weaknesses of both methods.
DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks
Venue: arXiv cs.LG 2024
Year: 2024

Repetitive Transcranial Magnetic Stimulation (rTMS) is a well-supported, evidence-based treatment for depression. However, patterns of response to this treatment are inconsistent. Emerging evidence suggests that artificial intelligence can predict rTMS treatment outcomes for most patients using fMRI connectivity features. While these models can reliably predict treatment outcomes for many patients for some underrepresented fMRI connectivity measures DNN models are unable to reliably predict treatment outcomes. As such we propose a novel method, Diversity Enhancing Conditional General Adversarial Network (DE-CGAN) for oversampling these underrepresented examples. DE-CGAN creates synthetic examples in difficult-to-classify regions by first identifying these data points and then creating conditioned synthetic examples to enhance data diversity. Through empirical experiments we show that a classification model trained using a diversity enhanced training set outperforms traditional data augmentation techniques and existing benchmark results. This work shows that increasing the diversity of a training dataset can improve classification model performance. Furthermore, this work provides evidence for the utility of synthetic patients providing larger more robust datasets for both AI researchers and psychiatrists to explore variable relationships.
Servicifying zk-SNARKs Execution for Verifiable Off-chain Computations
Venue: arXiv cs.SE 2024
Year: 2024

Zk-SNARKs help scale blockchains with Verifiable Off-chain Computations (VOC). zk-SNARK DSL toolkits are key when designing arithmetic circuits but fall short of automating the subsequent proof-generation step in an automated manner. We emphasize the need for portability, interoperability, and manageability in VOC-based solutions and introduce a Proving Service that is designed to provide a scalable and reusable solution for generating zk-SNARK proofs leveraging clouds.