Miracle-dz/UniFAR-FTUs huggingface dataset:
Less is More: Supporting Developers in Vulnerability Detection during Code Review

Reviewing source code from a security perspective has proven to be a difficult task. Indeed, previous research has shown that developers often miss even popular and easy-to-detect vulnerabilities during code review. Initial evidence suggests that a significant cause may lie in the reviewers' mental attitude and common practices. In this study, we investigate whether and how explicitly asking developers to focus on security during a code review affects the detection of vulnerabilities. Furthermore, we evaluate the effect of providing a security checklist to guide the security review. To this aim, we conduct an online experiment with 150 participants, of which 71% report to have three or more years of professional development experience. Our results show that simply asking reviewers to focus on security during the code review increases eight times the probability of vulnerability detection. The presence of a security checklist does not significantly improve the outcome further, even when the checklist is tailored to the change under review and the existing vulnerabilities in the change. These results provide evidence supporting the mental attitude hypothesis and call for further work on security checklists' effectiveness and design. Preprint: https://arxiv.org/abs/2202.04586 Data and materials: https://doi.org/10.5281/zenodo.6026291
The Origin of the Unfocused XMM-Newton Background, Its Variability, and Lessons Learned for ATHENA

We analyzed the unexposed to the sky (outFOV) region of the MOS2 detector on board XMM-Newton covering 15 yr of data amounting to 255 Ms. We show convincing evidence that the origin of the unfocused background in XMM-Newton is due to energetic protons, electrons, and hard X-ray photons. Galactic cosmic rays are the main contributors as shown by the tight correlation (2.6% of total scatter) with the 1 GeV proton data of the SOHO EPHIN detector. Tight correlations are found with a proxy of the Chandra background rate, revealing the common source of background for detectors in similar orbits, and with the data of the EPIC Radiation Monitor, only when excluding Solar energetic particle events. The entrance to the outer electron belts is associated with a sudden increase in the outFOV MOS2 rate and a spectral change. These facts support the fact that MeV electrons can generate an unfocused background signal. The correlation between MOS2 outFOV data and the SOHO EPHIN data reveals a term constant in time and isotropic, similar to the one found in the study of the pn data. The most plausible origin of this component is hard unfocused X-ray photons of the cosmic X-ray background Compton scattering in the detector as supported by the strength of the signal in the two detectors with different thicknesses. Based on this physical understanding, a particle radiation monitor on board the Advanced Telescope for High Energy Astrophysics has been proposed and it is currently under study. It will be able to track different species with the necessary accuracy and precision to guarantee the challenging requirement of 2% reproducibility of the background.
Privacy-preserving Generative Framework Against Membership Inference Attacks

Artificial intelligence and machine learning have been integrated into all aspects of our lives and the privacy of personal data has attracted more and more attention. Since the generation of the model needs to extract the effective information of the training data, the model has the risk of leaking the privacy of the training data. Membership inference attacks can measure the model leakage of source data to a certain degree. In this paper, we design a privacy-preserving generative framework against membership inference attacks, through the information extraction and data generation capabilities of the generative model variational autoencoder (VAE) to generate synthetic data that meets the needs of differential privacy. Instead of adding noise to the model output or tampering with the training process of the target model, we directly process the original data. We first map the source data to the latent space through the VAE model to get the latent code, then perform noise process satisfying metric privacy on the latent code, and finally use the VAE model to reconstruct the synthetic data. Our experimental evaluation demonstrates that the machine learning model trained with newly generated synthetic data can effectively resist membership inference attacks and still maintain high utility.
Generalized quaternionic free rotational Dirac equation and spinor solutions in the electromagnetic field

Starting with the quaternionic Minkowski space–time and its four-vector representation, a rotational analogue of the quaternionic Dirac equation in the electromagnetic field is developed, which includes not only the energy solutions but also the angular momentum solutions for rotating Dirac fermions. The striking feature of the quaternionic approach is that it depicts a unified representation of energy–momentum in a single framework as the space–time. Furthermore, we establish the generalized Schrodinger–Pauli-like equation associated with generalized electric and magnetic dipole energies that corresponds to their dipole moments. As such, the present analysis deals with the invariant behavior of the extended quaternionic rotational Dirac equation under Lorentz–Poincaré, Gauge, duality, and CPT invariance.
Global Optimization of Data Pipelines in Heterogeneous Cloud Environments

Modern production data processing and machine learning pipelines on the cloud are critical components for many cloud-based companies. These pipelines are typically composed of complex workflows represented by directed acyclic graphs (DAGs). Cloud environments are attractive to these workflows due to the wide range of choice with heterogeneous instances and prices that can provide the flexibility for different cost-performance needs. However, this flexibility also leads to the complexity of selecting the right resource configuration (e.g., instance type, resource demands) for each task in the DAG, while simultaneously scheduling the tasks with the selected resources to reach the optimal end-to-end performance and cost. These two decisions are often codependent resulting in an NP-hard scheduling optimization bottleneck. Existing solutions only focus solely on either problem and ignore the co-effect on the end-to-end optimum. We propose AGORA, a scheduler that considers both task-level resource allocation and execution for DAG workflows as a whole in heterogeneous cloud environments. AGORA first (1) studies the characteristics of the tasks from prior runs and gives predictions on resource configurations, and (2) automatically finds the best configuration with its corresponding schedules for the entire workflow with a cost-performance objective. We evaluate AGORA in a heterogeneous Amazon Web Services (AWS) cloud environment with multi-tenant workflows served by Airflow and demonstrate a performance improvement up to 45% and cost reduction up to 77% compared to state-of-the-art schedulers. In addition, we apply AGORA to a real-world production trace from Alibaba and show cost reduction of 65% and DAG completion time reduction of 57%.
Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search

Cooperative trajectory planning methods for automated vehicles can solve traffic scenarios that require a high degree of cooperation between traffic participants. However, for cooperative systems to integrate into human-centered traffic, the automated systems must behave human-like so that humans can anticipate the system’s decisions. While Reinforcement Learning has made remarkable progress in solving the decision-making part, it is non-trivial to parameterize a reward model that yields predictable actions. This work employs feature-based Maximum Entropy Inverse Reinforcement Learning combined with Monte Carlo Tree Search to learn reward models that maximize the likelihood of recorded multi-agent cooperative expert trajectories. The evaluation demonstrates that the approach can recover a reasonable reward model that mimics the expert and performs similarly to a manually tuned baseline reward model.
Multi-level Latent Space Structuring for Generative Control

Truncation is widely used in generative models for improving the quality of the generated samples, at the expense of reducing their diversity. We propose to leverage the StyleGAN generative architecture to devise a new truncation technique, based on a decomposition of the latent space into clusters, enabling customized truncation to be performed at multiple semantic levels. We do so by learning to re-generate W-space, the extended intermediate latent space of StyleGAN, using a learnable mixture of Gaussians, while simultaneously training a classifier to identify, for each latent vector, the cluster that it belongs to. The resulting truncation scheme is more faithful to the original untruncated samples and allows a better trade-off between quality and diversity. We compare our method to other truncation approaches for StyleGAN, both qualitatively and quantitatively.
Conditional Uniformity and Hawkes Processes

Classic results show that the Hawkes self-exciting point process can be viewed as a collection of temporal clusters, in which exogenously generated initial events give rise to endogenously driven descendant events. This perspective provides the distribution of a cluster’s size through a natural connection to branching processes, but this is irrespective of time. Insight into the chronology of a Hawkes process cluster has been much more elusive. Here, we employ this cluster perspective and a novel adaptation of the random time change theorem to establish an analog of the conditional uniformity property enjoyed by Poisson processes. Conditional on the number of epochs in a cluster, we show that the transformed times are jointly uniform within a particular convex polytope. Furthermore, we find that this polytope leads to a surprising connection between these continuous state clusters and parking functions, discrete objects central in enumerative combinatorics and closely related to Dyck paths on the lattice. In particular, we show that uniformly random parking functions constitute hidden spines within Hawkes process clusters. This yields a decomposition that is valuable both methodologically and practically, which we demonstrate through application to the popular Markovian Hawkes model and proposal of a flexible and efficient simulation algorithm.
CATs++: Boosting Cost Aggregation With Convolutions and Transformers

Cost aggregation is a process in image matching tasks that aims to disambiguate the noisy matching scores. Existing methods generally tackle this by hand-crafted or CNN-based methods, which either lack robustness to severe deformations or inherit the limitation of CNNs that fail to discriminate incorrect matches due to limited receptive fields and inadaptability. In this paper, we introduce Cost Aggregation with Transformers (CATs) to tackle this by exploring global consensus among initial correlation map with the help of some architectural designs that allow us to benefit from global receptive fields of self-attention mechanism. To this end, we include appearance affinity modeling, which helps to disambiguate the noisy initial correlation maps. Furthermore, we introduce some techniques, including multi-level aggregation to exploit rich semantics prevalent at different feature levels and swapping self-attention to obtain reciprocal matching scores to act as a regularization. Although CATs can attain competitive performance, it may face some limitations, i.e., high computational costs, which may restrict its applicability only at limited resolution and hurt performance. To overcome this, we propose CATs++, an extension of CATs. Concretely, we introduce early convolutions prior to cost aggregation with a transformer to control the number of tokens and inject some convolutional inductive bias, then propose a novel transformer architecture for both efficient and effective cost aggregation, which results in apparent performance boost and cost reduction. With the reduced costs, we are able to compose our network with a hierarchical structure to process higher-resolution inputs. We show that the proposed method with these integrated outperforms the previous state-of-the-art methods by large margins. Codes and pretrained weights are available at: https://ku-cvlab.github. io/CATs-PlusPlus-Project-Page/.
A survey on privacy impacts of digital investigation

Digital forensics is becoming more and more demanding as computers evolve into different platforms and penetrate different industries. The usage of computers in today’s society has covered in vast areas and are implemented in fields where the most sensitive information are stored and accessed for various operations. This paper will look into developmental trends of applications of digital forensics and security in various aspects within the span of 7 years based on critical review and statistical analysis, and then contribute the results of significant change and shift of focus in digital forensics development. Then, a discussion will be made on privacy concerns, whether the current development trend is paying more attention to privacy preservation or harsh forensic investigation techniques that usually result in compromised personal information.
Distributed D-core Decomposition over Large Directed Graphs

Given a directed graph G and integers k and l , a D-core is the maximal subgraph H ⊆ G such that for every vertex of H , its in-degree and out-degree are no smaller than k and l , respectively. For a directed graph G , the problem of D-core decomposition aims to compute the non-empty D-cores for all possible values of k and l. In the literature, several peeling-based algorithms have been proposed to handle D-core decomposition. However, the peeling-based algorithms that work in a sequential fashion and require global graph information during processing are mainly designed for centralized settings, which cannot handle large-scale graphs efficiently in distributed settings. Motivated by this, we study the distributed D-core decomposition problem in this paper. We start by defining a concept called anchored coreness , based on which we propose a new H-index-based algorithm for distributed D-core decomposition. Furthermore, we devise a novel concept, namely skyline coreness , and show that the D-core decomposition problem is equivalent to the computation of skyline corenesses for all vertices. We design an efficient D-index to compute the skyline corenesses distributedly. We implement the proposed algorithms under both vertex-centric and block-centric distributed graph processing frameworks. Moreover, we theoretically analyze the algorithm and message complexities. Extensive experiments on large real-world graphs with billions of edges demonstrate the efficiency of the proposed algorithms in terms of both the running time and communication overhead.
Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems

We analyze continuous-time models of accelerated gradient methods through deriving conservation laws in dilated coordinate systems. Namely, instead of analyzing the dynamics of $X(t)$, we analyze the dynamics of $W(t)=t^\alpha(X(t)-X_c)$ for some $\alpha$ and $X_c$ and derive a conserved quantity, analogous to physical energy, in this dilated coordinate system. Through this methodology, we recover many known continuous-time analyses in a streamlined manner and obtain novel continuous-time analyses for OGM-G, an acceleration mechanism for efficiently reducing gradient magnitude that is distinct from that of Nesterov. Finally, we show that a semi-second-order symplectic Euler discretization in the dilated coordinate system leads to an $\mathcal{O}(1/k^2)$ rate on the standard setup of smooth convex minimization, without any further assumptions such as infinite differentiability.
Neutrinos from Gamma-ray Bursts

Gamma-ray bursts (GRBs) are the most luminous electromagnetic burst in the Universe. They occur when a rapidly rotating massive star collapses or a binary neutron star merges. These events leave a newborn central compact object, either a black hole or neutron star, which launches relativistic jets that emit the luminous gamma-ray signals. These jets can accelerate non-thermal protons, which are expected to produce high-energy neutrinos via photohadronic interactions. This Chapter briefly summarizes the current physical picture of GRBs, and discusses neutrino emissions from GRBs, including both prompt and afterglow phases. Neutrinos from sub-classes of GRBs, including low-luminosity GRBs and short GRBs, are also discussed.
Implications of Seiberg-Witten Map on Type-I Superconductors

We incorporate the Seiberg-Witten map of noncommutative theory in the classical London theory of type-I superconductivity when an external magnetic field is applied. After defining the noncommutative Maxwell potentials, we derive the London equation for the supercurrent as a function of the noncommutative parameter, up to second order in gauge fields expansion. Keeping track of the effects of noncommutative gauge fields, we argue that noncommutative magnetic field effects can be cast in the permeability of the superconductor as an emergent property of the material, measurable in possible not-so-far ultra-high-energy experimental setups. Another consequence of the Seiberg-Witten map is the London penetration length that acquires corrections in the expansion. Also, we show that the flux quantization remains consistent relative to the commutative case. Our effective magnetic permeability and London penetration length reduce to the standard ones in the commutative limit.
Out-of-distribution and in-distribution posterior calibration using Kernel Density Polytopes

Any reasonable machine learning (ML) model should not only interpolate efﬁciently in between the training samples provided (in-distribution region), but also approach the extrapolative or out-of-distribution (OOD) region without being overconﬁdent. Our experiment on human subjects justiﬁes the aforementioned properties for human intelligence as well. Many state-of-the-art algorithms have tried to ﬁx the overconﬁdence problem of ML models in the OOD region. However, in doing so, they have often impaired the in-distribution performance of the model. Our key insight is that ML models partition the feature space into polytopes and learn constant (random forests) or afﬁne (ReLU networks) functions over those polytopes. This leads to the OOD overconﬁdence problem for the polytopes which lie in the training data boundary and extend to inﬁnity. To resolve this issue, we propose kernel density meth-ods that ﬁt Gaussian kernel over the polytopes, which are learned using ML models. Speciﬁcally, we introduce two variants of kernel density polytopes: Kernel Density Forest (KDF) and Kernel Density Network (KDN) based on random forests and deep networks, respectively. Studies on various simulation settings show that both KDF and KDN achieve uniform conﬁdence over the classes in the OOD region while maintaining good in-distribution accuracy compared to that of their respective parent models.
STRUCTURE OF SOCIAL RELATIONS IN THE NOVEL THE MASTER AND MARGARITA BY M. A. BULGAKOV: A NETWORK ANALYSIS OF VERBAL COMMUNICATIONS

A network analysis of the structure of social relationships in one of the most popular Russian novels of the Soviet era The Master and Margarita by M. A. Bulgakov has been carried out. The structure of the novel is complex, i.e., there is ‘a story within a story’. Moreover, the real-world-characters and the other-world-characters are interacting in the novel. A complex and unusual structure of the character network is expected. Presum- ably, the network may be split in two subgraphs owing very diﬀerent properties. This complex and unusual composition makes the novel especially attractive for a network analysis. In our study, only paired verbal communications (conversations) between explicitly present and acting characters have been taken into account; frontal communications, monologues, oﬀ-stage characters as well as expected connections between characters have not been assessed. Based on a character pair verbal communication matrix, a graph has been constructed, the vertices of which are the characters of the novel, while the edges correspond to the connections between them. Taking only dialog into account leads to the result, that the character network can be described by an ordinary, rather than a directed graph. Since the activity of the dialogs was out of our intended scope, the edges have been given no weights. The largest connected component of the graph consists of 76 characters. Centralities, such as degree, betweenness, closeness, eigenvector, and assortativity coeﬃcient were computed to characterize the network. The assortativity coeﬃcient of the network under consideration is negative − 0 . 133, i.e., the network does not demonstrate the properties of a social network. The structure of the communities in the network was also analysed. In addition to the obvious large communities — the char- acters from the Yershalaim part of the novel and the characters of the Moscow part — the analysis also revealed a ﬁne structure in the Moscow component. Using the analysis of centralities, a group of main characters has been detected. The central characters of the novel are Bezdomny, Woland, Levi Matthew, Koroviev, Azazello, Behemoth, Bosoi, Warenukha, Master, and Margarita.
On Stability and Consensus of Signed Networks: A Self-loop Compensation Perspective

Positive semideﬁnite is not an inherent property of signed Laplacians, which renders the stability and consensus of multi-agent system on undirected signed networks intricate. Inspired by the correlation between diagonal dominance and spectrum of signed Laplacians, this paper proposes a self-loop compensation mechanism in the design of interaction protocol amongst agents and examines the stability/consensus of the compensated signed networks. It turns out that self-loop compensation acts as exerting a virtual leader on these agents that are incident to negative edges, steering whom towards origin. Analytical connections between self-loop compensation and the collective behavior of the compensated signed network are established. Necessary and/or suﬃcient conditions for predictable cluster consensus of signed networks via self-loop compensation are provided. The optimality of self-loop compensation is discussed. Further-more, we extend our results to directed signed networks where the symmetry of signed Laplacian is not free. Simulation examples are provided to demonstrate the theoretical results.
Private Adaptive Optimization with Side Information

Adaptive optimization methods have become the default solvers for many machine learning tasks. Unfortunately, the beneﬁts of adaptivity may degrade when training with differential privacy, as the noise added to ensure privacy reduces the ef-fectiveness of the adaptive preconditioner. To this end, we propose AdaDPS , a general framework that uses non-sensitive side information to precondition the gradients, allowing the effective use of adaptive methods in private settings. We formally show AdaDPS reduces the amount of noise needed to achieve similar privacy guarantees, thereby improving optimization performance. Empirically, we leverage simple and read-ily available side information to explore the performance of AdaDPS in practice, comparing to strong baselines in both centralized and federated settings. Our results show that AdaDPS improves accuracy by 7.7% (absolute) on average— yielding state-of-the-art privacy-utility trade-offs on large-scale text and image benchmarks.