Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Impact of Interface Vulnerabilities in Compartmentalized Software

Least-privilege separation decomposes applications into compartments limited to accessing only what they need. When compartmentalizing existing software, many approaches neglect securing the new inter-compartment interfaces, although what used to be a function call from/to a trusted component is now potentially a targeted attack from a malicious compartment. This results in an entire class of security bugs: Compartment Interface Vulnerabilities (CIVs). This paper provides an in-depth study of CIVs. We taxonomize these issues and show that they affect all known compartmentalization approaches. We propose ConfFuzz, an in-memory fuzzer specialized to detect CIVs at possible compartment boundaries. We apply ConfFuzz to a set of 25 popular applications and 36 possible compartment APIs, to uncover a wide data-set of 629 vulnerabilities. We systematically study these issues, and extract numerous insights on the prevalence of CIVs, their causes, impact, and the complexity to address them. We stress the critical importance of CIVs in compartmentalization approaches, demonstrating an attack to extract isolated keys in OpenSSL and uncovering a decade-old vulnerability in sudo. We show, among others, that not all interfaces are affected in the same way, that API size is uncorrelated with CIV prevalence, and that addressing interface vulnerabilities goes beyond writing simple checks. We conclude the paper with guidelines for CIV-aware compartment interface design, and appeal for more research towards systematic CIV detection and mitigation.
Finite-State Channels With Feedback and State Known at the Encoder

We consider finite-state channels (FSCs) with feedback and state information known causally at the encoder. This setting is quite general and includes: a memoryless channel with i.i. d. state (the Shannon strategy), Markovian states that include look-ahead (LA) access to the state and energy harvesting. We characterize the feedback capacity of the general setting as the directed information between auxiliary random variables with memory to the channel outputs. We also propose two methods for computing the feedback capacity: (i) formulating an infinite-horizon average-reward dynamic program; and (ii) a single-letter lower bound based on auxiliary directed graphs called $Q$ -graphs. We demonstrate our computation methods on three examples. In the first example, we introduce a channel with LA and establish a closed-form, analytic lower bound on its feedback capacity. Furthermore, we extend the channel with general parameters, and derive numerical lower bounds for each parameter. In the second example, we show that the mentioned methods achieve the feedback capacity of known unifilar FSCs such as the Ising channel. Finally, in the last example, we generalize the Ising channel such that the state is stochastically dependent on the input, and investigate its feedback capacity.
Refined Edge Usage of Graph Neural Networks for Edge Prediction

Graph Neural Networks (GNNs), originally proposed for node classification, have also motivated many recent works on edge prediction (a.k.a., link prediction). However, existing methods lack elaborate design regarding the distinctions between two tasks that have been frequently overlooked: (i) edges only constitute the topology in the node classification task but can be used as both the topology and the supervisions (i.e., labels) in the edge prediction task; (ii) the node classification makes prediction over each individual node, while the edge prediction is determinated by each pair of nodes. To this end, we propose a novel edge prediction paradigm named Edge-aware Message PassIng neuRal nEtworks (EMPIRE). Concretely, we first introduce an edge splitting technique to specify use of each edge where each edge is solely used as either the topology or the supervision (named as topology edge or supervision edge). We then develop a new message passing mechanism that generates the messages to source nodes (through topology edges) being aware of target nodes (through supervision edges). In order to emphasize the differences between pairs connected by supervision edges and pairs unconnected, we further weight the messages to highlight the relative ones that can reflect the differences. In addition, we design a novel negative node-pair sampling trick that efficiently samples 'hard' negative instances in the supervision instances, and can significantly improve the performance. Experimental results verify that the proposed method can significantly outperform existing state-of-the-art models regarding the edge prediction task on multiple homogeneous and heterogeneous graph datasets.
Diagnosis of COVID-19 based on Chest Radiography

The Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in early December 2019 and now becoming a pandemic. When COVID-19 patients undergo radiography examination, radiologists can observe the present of radiographic abnormalities from their chest X-ray (CXR) images. In this study, a deep convolutional neural network (CNN) model was proposed to aid radiologists in diagnosing COVID-19 patients. First, this work conducted a comparative study on the performance of modified VGG-16, ResNet-50 and DenseNet-121 to classify CXR images into normal, COVID-19 and viral pneumonia. Then, the impact of image augmentation on the classification results was evaluated. The publicly available COVID-19 Radiography Database was used throughout this study. After comparison, ResNet-50 achieved the highest accuracy with 95.88%. Next, after training ResNet-50 with rotation, translation, horizontal flip, intensity shift and zoom augmented dataset, the accuracy dropped to 80.95%. Furthermore, an ablation study on the effect of image augmentation on the classification results found that the combinations of rotation and intensity shift augmentation methods obtained an accuracy higher than baseline, which is 96.14%. Finally, ResNet-50 with rotation and intensity shift augmentations performed the best and was proposed as the final classification model in this work. These findings demonstrated that the proposed classification model can provide a promising result for COVID-19 diagnosis.
Mining Architectural Information: A Systematic Mapping Study

Mining Software Repositories (MSR) has become an essential activity in software development. Mining architectural information to support architecting activities, such as architecture understanding, has received significant attention in recent years. However, there is a lack of clarity on what literature on mining architectural information is available. Consequently, this may create difficulty for practitioners to understand and adopt the state-of-the-art research results, such as what approaches should be adopted to mine what architectural information in order to support architecting activities. It also hinders researchers from being aware of the challenges and remedies for the identified research gaps. We aim to identify, analyze, and synthesize the literature on mining architectural information in terms of architectural information and sources mined, architecting activities supported, approaches and tools used, and challenges faced. An SMS has been conducted on the literature published between January 2006 and December 2022. Of the 104 primary studies selected, 7 categories of architectural information have been mined, among which architectural description is the most mined architectural information; 11 categories of sources have been leveraged for mining architectural information, among which version control system is the most popular source; 11 architecting activities can be supported by the mined architectural information, among which architecture understanding is the most supported activity; 95 approaches and 56 tools were proposed and employed in mining architectural information; and 4 types of challenges in mining architectural information were identified. This SMS provides researchers with future directions and help practitioners be aware of what approaches and tools can be used to mine what architectural information from what sources to support various architecting activities.
Bernoulli honeywords

Decoy passwords, or"honeywords,"planted in a credential database can alert a site to its breach if ever submitted in a login attempt. To be effective, some honeywords must appear at least as likely to be user-chosen passwords as the real ones, and honeywords must be very difficult to guess without having breached the database, to prevent false breach alarms. These goals have proved elusive, however, for heuristic honeyword generation algorithms. In this paper we explore an alternative strategy in which the defender treats honeyword selection as a Bernoulli process in which each possible password (except the user-chosen one) is selected as a honeyword independently with some fixed probability. We show how Bernoulli honeywords can be integrated into two existing system designs for leveraging honeywords: one based on a honeychecker that stores the secret index of the user-chosen password in the list of account passwords, and another that does not leverage secret state at all. We show that Bernoulli honeywords enable analytic derivation of false breach-detection probabilities irrespective of what information the attacker gathers about the sites' users; that their true and false breach-detection probabilities demonstrate compelling efficacy; and that Bernoulli honeywords can even enable performance improvements in modern honeyword system designs.
Quaternion Backpropagation

Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for optimization are calculated as the sum of the partial derivatives with respect to the real and imaginary parts. However, we can show that product- and chain-rule does not hold with this approach. We solve this by employing the GHRCalculus and derive quaternion backpropagation based on this. Furthermore, we experimentally prove the functionality of the derived quaternion backpropagation.
MicroRes: Versatile Resilience Profiling in Microservices via Degradation Dissemination Indexing

Microservice resilience, the ability of microservices to recover from failures and continue providing reliable and responsive services, is crucial for cloud vendors. However, the current practice relies on manually configured rules specific to a certain microservice system, resulting in labor-intensity and flexibility issues, given the large scale and high dynamics of microservices. A more labor-efficient and versatile solution is desired. Our insight is that resilient deployment can effectively prevent the dissemination of degradation from system performance metrics to user-aware metrics, and the latter directly affects service quality. In other words, failures in a non-resilient deployment can impact both types of metrics, leading to user dissatisfaction. With this in mind, we propose MicroRes, the first versatile resilience profiling framework for microservices via degradation dissemination indexing. MicroRes first injects failures into microservices and collects available monitoring metrics. Then, it ranks the metrics according to their contributions to the overall service degradation. It produces a resilience index by how much the degradation is disseminated from system performance metrics to user-aware metrics. Higher degradation dissemination indicates lower resilience. We evaluate MicroRes on two open-source and one industrial microservice system. The experiments show MicroRes' efficient and effective resilience profiling of microservices. We also showcase MicroRes' practical usage in production.
Quantum enhanced probing of multilayered samples

Quantum sensing exploits quantum phenomena to enhance the detection and estimation of classical parameters of physical systems and biological entities, particularly so as to overcome the inefficiencies of its classical counterparts. A particularly promising approach within quantum sensing is Quantum Optical Coherence Tomography which relies on non-classical light sources to reconstruct the internal structure of multilayered materials. Compared to traditional classical probing, Quantum Optical Coherence Tomography provides enhanced-resolution images and is unaffected by even-order dispersion. One of the main limitations of this technique lies in the appearance of artifacts and echoes, i.e. fake structures that appear in the coincidence interferogram, which hinder the retrieval of information required for tomography scans. Here, by utilizing a full theoretical model, in combination with a fast genetic algorithm to post-process the data, we successfully extract the morphology of complex multilayered samples and thoroughly distinguish real interfaces, artifacts, and echoes. We test the effectiveness of the model and algorithm by comparing its predictions to experimentally-generated interferograms through the controlled variation of the pump wavelength. Our results could potentially lead to the development of practical high-resolution probing of complex structures and non-invasive scanning of photo-degradable materials for biomedical imaging/sensing, clinical applications, and materials science.
Optical control of collective states in 1D ordered atomic chains beyond the linear regime

Driven by the need to develop efficient atom-photon interfaces, recent efforts have proposed replacing cavities by large arrays of cold atoms that can support subradiant or superradiant collective states. In practice, subradiant states are decoupled from radiation, which constitutes a hurdle to most applications. In this work, we study theoretically a protocol that bypasses this limit using a one dimensional (1D) chain composed of N three-level atoms in a V-shaped configuration. Throughout the protocol, the chain behaves as a time-varying metamaterial: enabling absorption, storage and on-demand emission in a spectrally and spatially controlled mode. Taking into account the quantum nature of atoms, we establish the boundary between the linear regime and the nonlinear regime. In the nonlinear regime, we demonstrate that doubly-excited states can be coherently transferred from superradiant to subradiant states, opening the way to the optical characterization of their entanglement.
A Level-Depth Correspondence between Verlinde Rings and Subfactors

We establish a correspondence between the levels of Verlinde rings and the depths of subfactors. Given the $l$-level Verlinde ring $R_l(G)$ of a simple compact Lie group $G$, the tensor products of fundamental representations give us the inclusion of a pair of $\text{II}_1$ factors $N\subset M$. For the depth $d$ of $N\subset M$, we first prove $d=l$ for type $A_n,C_n$ and $B_2$. More generally, the depth $d$ is shown to satisfy $\beta\cdot l\leq d\leq l$ with $\beta\in (0,1)$, where $\beta$ is uniquely determined by the simple type of $G$. We also show that the simple $N$-$N$-bimodules contained in $L^2(M)$ generate the Verlinde ring $R_l(G)$ as its fusion category.
Polar modes and isospectrality of Ellis-Bronnikov wormholes

We consider polar perturbations of static Ellis-Bronnikov wormholes and derive the coupled set of perturbation equations for the gravitational and the scalar field. For massless wormholes the perturbations decouple, and we obtain two identical master equations for the scalar and gravitational modes, which moreover agree with the master equation for the axial modes. Consequently there is isospectrality with threefold degenerate modes. For a finite mass of the background wormhole solutions, the equations are coupled. We then obtain two distinct branches of polar quasinormal modes for a given multipole number l, associated with the presence of the two types of fields. We calculate the quasi-normal mode frequencies and decay rates for the branches with l=2, 3 and 4. For a given l the real frequencies of the two branchesget the closer, the higher the multipole number gets.
Conditional Quasi-Monte Carlo with Constrained Active Subspaces

Conditional Monte Carlo or pre-integration is a powerful tool for reducing variance and improving the regularity of integrands when using Monte Carlo and quasi-Monte Carlo (QMC) methods. To select the variable to pre-integrate, one must consider both the variable's importance and the tractability of the conditional expectation. For integrals over a Gaussian distribution, any linear combination of variables can potentially be pre-integrated. Liu and Owen (2022) propose to select the linear combination based on an active subspace decomposition of the integrand. However, pre-integrating the selected direction might be intractable. In this work, we address this issue by finding the active subspace subject to constraints such that pre-integration can be easily carried out. The proposed algorithm also provides a computationally-efficient alternative to dimension reduction for pre-integrated functions. The method is applied to examples from computational finance, density estimation, and computational chemistry, and is shown to achieve smaller errors than previous methods.
Visualizing Information Bottleneck through Variational Inference

The Information Bottleneck theory provides a theoretical and computational framework for finding approximate minimum sufficient statistics. Analysis of the Stochastic Gradient Descent (SGD) training of a neural network on a toy problem has shown the existence of two phases, fitting and compression. In this work, we analyze the SGD training process of a Deep Neural Network on MNIST classification and confirm the existence of two phases of SGD training. We also propose a setup for estimating the mutual information for a Deep Neural Network through Variational Inference.
Matrix Balancing Based Interior Point Methods for Point Set Matching Problems

Point sets matching problems can be handled by optimal transport. The mechanism behind it is that optimal transport recovers the point-to-point correspondence associated with the least curl deformation. Optimal transport is a special form of linear programming with dense constraints. Linear programming can be handled by interior point methods, provided that the involved ill-conditioned Hessians can be computed accurately. During the decade, matrix balancing has been employed to compute optimal transport under entropy regularization approaches. The solution quality in the interior point method relies on two ingredients: the accuracy of matrix balancing and the boundedness of the dual vector. To achieve high accurate matrix balancing, we employ Newton methods to implement matrix balancing of a sequence of matrices along one central path. In this work, we apply sparse support constraints to matrix-balancing based interior point methods, in which the sparse set fulfilling total support is iteratively updated to truncate the domain of the transport plan. Total support condition is one crucial condition, which guarantees the existence of matrix balancing as well as the boundedness of the dual vector.
Cohomology and crossed modules extension of Hom-Leibniz-Rinehart algebras

In this paper, we introduce the concept of crossed module for Hom-Leibniz-Rinehart algebras. We study the cohomology and extension theory of Hom-Leibniz-Rinehart algebras. It is proved that there is one-to-one correspondence between equivalence classes of abelian extensions of Hom-Leibniz-Rinehart algebras and the elements of second cohomology group. Furthermore, we prove that there is a natural map from α -crossed modules extension of Hom-Leibniz-Rinehart algebras to the third cohomology group of Hom-Leibniz-Rinehart algebras.
U NDERSTANDING THE C OMPLEXITY

Reinforcement learning (RL) problems can be challenging without well-shaped rewards. Prior work on provably efﬁcient RL methods generally proposes to address this issue with dedicated exploration strategies. However, another way to tackle this challenge is to reformulate it as a multi-task RL problem, where the task space contains not only the challenging task of interest but also easier tasks that implicitly function as a curriculum. Such a reformulation opens up the possibility of running existing multi-task RL methods as a more efﬁcient alternative to solving a single challenging task from scratch. In this work, we provide a theoretical framework that reformulates a single-task RL problem as a multi-task RL problem deﬁned by a curriculum. Under mild regularity conditions on the curriculum, we show that sequentially solving each task in the multi-task RL problem is more computationally efﬁcient than solving the original single-task problem, without any explicit exploration bonuses or other exploration strategies. We also show that our theoretical insights can be translated into an effective practical learning algorithm that can accelerate curriculum learning on simulated robotic tasks.
The oral cancer microbiome contains tumor space–specific and clinicopathology-specific bacteria

The crosstalk between the oral microbiome and oral cancer has yet to be characterized. This study recruited 218 patients for clinicopathological data analysis. Multiple types of specimens were collected from 27 patients for 16S rRNA gene sequencing, including 26 saliva, 16 swabs from the surface of tumor tissues, 16 adjacent normal tissues, 22 tumor outer tissue, 22 tumor inner tissues, and 10 lymph nodes. Clinicopathological data showed that the pathogenic bacteria could be frequently detected in the oral cavity of oral cancer patients, which was positively related to diabetes, later T stage of the tumor, and the presence of cervical lymphatic metastasis. Sequencing data revealed that compared with adjacent normal tissues, the microbiome of outer tumor tissues had a greater alpha diversity, with a larger proportion of Fusobacterium, Prevotella, and Porphyromonas, while a smaller proportion of Streptococcus. The space-specific microbiome, comparing outer tumor tissues with inner tumor tissues, suggested minor differences in diversity. However, Fusobacterium, Neisseria, Porphyromonas, and Alloprevotella were more abundant in outer tumor tissues, while Prevotella, Selenomonas, and Parvimonas were enriched in inner tumor tissues. Clinicopathology-specific microbiome analysis found that the diversity was markedly different between negative and positive extranodal extensions, whereas the diversity between different T-stages and N-stages was slightly different. Gemella and Bacillales were enriched in T1/T2-stage patients and the non-lymphatic metastasis group, while Spirochaetae and Flavobacteriia were enriched in the extranodal extension negative group. Taken together, high-throughput DNA sequencing in combination with clinicopathological features facilitated us to characterize special patterns of oral tumor microbiome in different disease developmental stages.
Robustness of randomisation tests as alternative analysis methods for repeated measures design

Abstract Randomisation tests (R-tests) are regularly proposed as an alternative method of hypothesis testing when assumptions of classical statistical methods are violated in data analysis. In this paper, the robustness in terms of the type-I-error and the power of the R-test were evaluated and compared with that of the F-test in the analysis of a single factor repeated measures design. The study took into account normal and non-normal data (skewed: exponential, lognormal, Chi-squared, and Weibull distributions), the presence and lack of outliers, and a situation in which the sphericity assumption was met or not under varied sample sizes and number of treatments. The Monte Carlo approach was used in the simulation study. The results showed that when the data were normal, the R-test was approximately as sensitive and robust as the F-test, while being more sensitive than the F-test when data had skewed distributions. The R-test was more sensitive and robust than the F-test in the presence of an outlier. When the sphericity assumption was met, both the R-test and the F-test were approximately equally sensitive, whereas the R-test was more sensitive and robust than the F-test when the sphericity assumption was not met.