Miracle-dz/UniFAR-FTUs huggingface dataset:
Luci: Loader-based Dynamic Software Updates for Off-the-shelf Shared Objects

Shared libraries indisputably facilitate software development but also significantly increase the attack surface, and when using multiple libraries, frequent patches for vulnerabilities are to be expected. However, such a bugfix commonly requires restarting all services depending on the compromised library, which causes downtimes and unavailability of services. This can be prevented by dynamic software updating, but existing approaches are often costly and incur additional maintenance due to necessary source or infrastructure modifications. With L UCI , we present a lightweight linker/loader technique to unobtrusively and automatically update shared libraries during runtime by exploiting the indirection mechanisms of position-independent code, hence avoiding severe runtime overhead. L UCI further adds no additional requirements, such as adjusting the source or interfering with the build chain, as it fully adapts to today’s build and package-update mechanisms of common Linux distributions. We demonstrate our approach on popular libraries (like Expat and libxcrypt ) using off-the-shelf (i.e., unmodified) binaries from Debian and Ubuntu packages, being able to update the majority of releases without the necessity of a process restart.
A New Interpretation of the Higgs Vacuum Potential Energy Based on a Planckion Composite Model for the Higgs

: We present a new interpretation of the Higgs field as a composite particle made up of a positive, with, a negative mass Planck particle. According to the Winterberg hypothesis, space, i.e., the vacuum, consists of both positive and negative physical massive particles, which he called planckions, interacting through strong superfluid forces. In our composite model for the Higgs boson, there is an intrinsic length scale associated with the vacuum, different from the one introduced by Winterberg, where, when the vacuum is in a perfectly balanced state, the number density of positive Planck particles equals the number density of negative Planck particles. Due to the mass compensating effect, the vacuum thus appears massless, charge-less, without pressure, energy density, or entropy. However, a situation can arise where there is an effective mass density imbalance due to the two species of Planck particle not matching in terms of populations, within their respective excited energy states. This does not require the physical addition or removal of either positive or negative Planck particles, within a given region of space, as originally thought. Ordinary matter, dark matter, and dark energy can thus be given a new interpretation as residual vacuum energies within the context of a greater vacuum, where the populations of the positive and negative energy states exactly balance. In the present epoch, it is estimated that the dark energy number density imbalance amounts to, (𝑛 + ̅̅̅̅ − 𝑛 − ̅̅̅̅) 𝛬 = 8.52 𝐸 − 3 , per cubic meter, when cosmic distance scales in excess of, 100 𝑀𝑝𝑐 , are considered. Compared to a strictly balanced vacuum, where we estimate that the positive, and the negative Planck number density, is of the order, 7.85 𝐸54 particles per cubic meter, the above is a very small perturbation. This slight imbalance, we argue, would dramatically alleviate, if not altogether eliminate, the long standing cosmological constant problem.
On regular copying languages

This paper proposes a formal model of regular languages enriched with unbounded copying. We augment finite-state machinery with the ability to recognize copied strings by adding an unbounded memory buffer with a restricted form of first-in-first-out storage. The newly introduced computational device, finite-state buffered machines (FS-BMs), characterizes the class of regular languages and languages de-rived from them through a primitive copying operation. We name this language class regular copying languages (RCLs). We prove a pumping lemma and examine the closure properties of this language class. As suggested by previous literature (Gazdar and Pullum 1985, p.278), regular copying languages should approach the correct characteriza-tion of natural language word sets.
Wasabi: A Conceptual Model for Trustworthy AI

—The expansion of AI into our lives and livelihoods makes clear that we must develop AI to be ethical and trustworthy. We propose Wasabi, a novel conceptual model for trustworthy AI based on an adaptation of the well-known ability-benevolence-integrity model of trust to trustworthiness. Current approaches to trustworthy AI propose lists of desirable properties, including fairness, explainability, and accountability. However, these properties inadequately cover the criteria of ability, benevolence, and integrity, and the resulting incompleteness hurts trustworthiness even when these properties are met. We examine case law as evidence for concepts underlying trustworthiness. Legal cases represent boundary conditions that were vigorously contested by lawyers and carefully deliberated on by juries. Thus, they capture important details and tradeoffs absent in shallower analyses. From each case, we identify lessons for AI. We close with directions for future investigation.
ANGELIC II: An Improved Methodology for Representing Legal Domain Knowledge

The purpose of this paper is to provide a definitive, up-to-date account of a methodology has that been proven successful for representing and reasoning about legal domains. The ANGELIC (ADF for kNowledGe Encapsulation of Legal Information for Cases) methodology was originally developed to exploit then recent developments in knowledge representation techniques that lend themselves well to capturing factor-based reasoning about legal cases. The methodology is situated firmly within the tradition of research in AI and Law that aims to build systems that are knowledge rich in terms of the domain expertise that is emulated within the systems. When the methodology was first introduced, it was demonstrated on academic examples, but it was subsequently used in and evaluated on a variety of real world domains for external clients. This set of evaluation exercises yielded a variety of learning points as the methodology was applied to different legal domains with their own particular features. These learning points, and the extensions to the methodology that follow from them, urge a consolidation exercise to provide an updated version of the methodology that reflects how it has matured over time. This paper represents a milestone in the development of the methodology in that it presents the ANGELIC II Domain Model, along with a description of its constituent parts, and demonstrates its application through a case study in a key evaluation domain.
vWitness: Certifying Web Page Interactions with Computer Vision

Web servers service client requests, some of which might cause the web server to perform security-sensitive operations (e.g. money transfer, voting). An attacker may thus forge or maliciously manipulate such requests by compromising a web client. Unfortunately, a web server has no way of knowing whether the client from which it receives a request has been compromised or not-current “best practice” defenses such as user authentication or network encryption cannot aid a server as they all assume web client integrity. To address this shortcoming, we propose vWitness, which “witnesses” the interactions of a user with a web page and certifies whether they match a specification provided by the web server, enabling the web server to know that the web request is user-intended. The main challenge that vWitness overcomes is that even benign clients introduce unpredictable variations in the way they render web pages. vWitness differentiates between these benign variations and malicious manipulation using computer vision, allowing it to certify to the web server that 1) the web page user interface is properly displayed 2) observed user interactions are used to construct the web request. Our vWitness prototype achieves compatibility with modern web pages, is resilient to adversarial example attacks and is accurate and performant-vWitness achieves 99.97% accuracy and adds 197ms of overhead to the entire interaction session in the average case.
Faster Detours in Undirected Graphs

The $k$-Detour problem is a basic path-finding problem: given a graph $G$ on $n$ vertices, with specified nodes $s$ and $t$, and a positive integer $k$, the goal is to determine if $G$ has an $st$-path of length exactly $\text{dist}(s, t) + k$, where $\text{dist}(s, t)$ is the length of a shortest path from $s$ to $t$. The $k$-Detour problem is NP-hard when $k$ is part of the input, so researchers have sought efficient parameterized algorithms for this task, running in $f(k)\text{poly}(n)$ time, for $f$ as slow-growing as possible. We present faster algorithms for $k$-Detour in undirected graphs, running in $1.853^k \text{poly}(n)$ randomized and $4.082^k \text{poly}(n)$ deterministic time. The previous fastest algorithms for this problem took $2.746^k \text{poly}(n)$ randomized and $6.523^k \text{poly}(n)$ deterministic time [Bez\'akov\'a-Curticapean-Dell-Fomin, ICALP 2017]. Our algorithms use the fact that detecting a path of a given length in an undirected graph is easier if we are promised that the path belongs to what we call a"bipartitioned"subgraph, where the nodes are split into two parts and the path must satisfy constraints on those parts. Previously, this idea was used to obtain the fastest known algorithm for finding paths of length $k$ in undirected graphs [Bj\"orklund-Husfeldt-Kaski-Koivisto, JCSS 2017]. Our work has direct implications for the $k$-Longest Detour problem: in this problem, we are given the same input as in $k$-Detour, but are now tasked with determining if $G$ has an $st$-path of length at least $\text{dist}(s, t) + k. $ Our results for k-Detour imply that we can solve $k$-Longest Detour in $3.432^k \text{poly}(n)$ randomized and $16.661^k \text{poly}(n)$ deterministic time. The previous fastest algorithms for this problem took $7.539^k \text{poly}(n)$ randomized and $42.549^k \text{poly}(n)$ deterministic time [Fomin et al., STACS 2022].
Object Recognition System on a Tactile Device for Visually Impaired

People with visual impairments face numerous challenges when interacting with their environment. Our objective is to develop a device that facilitates communication between individuals with visual impairments and their surroundings. The device will convert visual information into auditory feedback, enabling users to understand their environment in a way that suits their sensory needs. Initially, an object detection model is selected from existing machine learning models based on its accuracy and cost considerations, including time and power consumption. The chosen model is then implemented on a Raspberry Pi, which is connected to a specifically designed tactile device. When the device is touched at a specific position, it provides an audio signal that communicates the identification of the object present in the scene at that corresponding position to the visually impaired individual. Conducted tests have demonstrated the effectiveness of this device in scene understanding, encompassing static or dynamic objects, as well as screen contents such as TVs, computers, and mobile phones.
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs

Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact many KGs contain two fundamentally different views: high-level ontology-view concepts and fine-grained instance-view entities. They usually embed all nodes as vectors in one latent space. However, a single geometric representation fails to capture the structural differences between two views and lacks probabilistic semantics towards concepts' granularity. We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations. We model concepts with box embeddings, which learn the hierarchy structure and complex relations such as overlap and disjoint among them. Box volumes can be interpreted as concepts' granularity. Different from concepts, we model entities as vectors. To bridge the gap between concept box embeddings and entity vector embeddings, we propose a novel vector-to-box distance metric and learn both embeddings jointly. Experiments on both the public DBpedia KG and a newly-created industrial KG showed the effectiveness of Concept2Box.
Inflation in simple one-loop effective potentials of perturbative quantum gravity

We study inflation in scalar-tensor perturbative quantum gravity driven by a one-loop effective potential. We consider effective potentials generated by three models. The first model describes a single scalar field with a non-vanishing mass. The second model describes a massless scalar field with non-minimal coupling to the Einstein tensor. The third model generalises the Coleman-Weinberg model for the gravitational case. The first model can be consistent with the observational data for $N\sim 70$ e-foldings. The second model can be consistent with the observational data for $N \sim 40$ e-foldings. We did not find parameters that make the generalised Coleman-Weinberg model consistent with the observational data. We discuss the implications of these results and ways to improve them with other terms of effective action.
Another Proof of the Generalized Tutte--Berge Formula for $f$-Bounded Subgraphs

Given a nonnegative integer weight $f(v)$ for each vertex $v$ in a multigraph $G$, an {\it $f$-bounded subgraph} of $G$ is a multigraph $H$ contained in $G$ such that $d_H(v)\le f(v)$ for all $v\in V(G)$. Using Tutte's $f$-Factor Theorem, we give a new proof of the min-max relation for the maximum size of an $f$-bounded subgraph of $G$. When $f(v)=1$ for all $v$, the formula reduces to the classical Tutte--Berge Formula for the maximum size of a matching.
Social Impressions of the NAO Robot and its Impact on Physiology

The social applications of robots possess intrinsic challenges with respect to social paradigms and heterogeneity of different groups. These challenges can be in the form of social acceptability, anthropomorphism, likeability, past experiences with robots etc. In this paper, we have considered a group of neurotypical adults to describe how different voices and motion types of the NAO robot can have effect on the perceived safety, anthropomorphism, likeability, animacy, and perceived intelligence of the robot. In addition, prior robot experience has also been taken into consideration to perform this analysis using a one-way Analysis of Variance (ANOVA). Further, we also demonstrate that these different modalities instigate different physiological responses in the person. This classification has been done using two different deep learning approaches, 1) Convolutional Neural Network (CNN), and 2) Gramian Angular Fields on the Blood Volume Pulse (BVP) data recorded. Both of these approaches achieve better than chance accuracy (> 25%) for a 4 class classification.
Infrared spectra of solid indene pure and in water ice: implications for observed IR absorptions in TMC-1

Experimental and theoretical infrared spectra, between 4000 and 500 cm−1 (2.5–20 μm), and infrared band strengths of two solid phases of indene, amorphous, and crystalline, are given for the first time. The samples were generated via vapour deposition under high vacuum conditions on a cold surface. Density functional theory was employed for the calculations of the infrared (IR) spectra. For lack of previous information, a monoclinic symmetry is suggested for the theoretical crystalline phase of indene, based on the comparison of the calculated and experimental IR spectra. Assignments, based on the calculations, are given for the main indene IR absorptions. The infrared spectra of highly diluted mixtures of indene in amorphous solid water at 10 K are also provided, evidencing that the indene spectrum is not much altered by the water ice environment. These data are expected to be useful for the search of this species in the solid phase in astrophysical environments with the JWST. With the band strengths obtained in this work, and applying a simple literature model, we find that indene could represent at most 2–5 per cent of the intensity of a weak absorption feature at 3.3 μm recently reported for Elias 16. A column density of (1.5–0.6) × 1016 cm−2 is estimated for indene in the ice mantles of TMC-1. It would correspond to ≈ (2–0.8) × 10−2 of cosmic carbon, which is probably too high for a single small hydrocarbon.
Hybrid NOMA for STAR-RIS Enhanced Communication

In this paper, a hybrid non-orthogonal multiple access (NOMA) framework for the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhanced cell-edge communication is investigated. Specifically, one transmitted user and one reflected user are paired as one NOMA-pair, while multiple NOMA-pairs are served via time division multiple access (TDMA). The objective is to maximize the minimum downlink rate by jointly optimizing the user pairing, decoding order, passive beamforming, power and time allocation. A novel two-layer iterative algorithm is proposed to solve the highly coupled problem. Simulation results show that: 1) the proposed framework outperforms the conventional reflecting-only-RIS-based and the OMA-based frameworks; 2) the beamforming design and power allocation dominate the achieved performance; 3) increasing the number of passive elements and shortening the distance between BS and STAR-RIS are two effective ways to further improve the performance.
3HWC J0631+107/LHAASO J0631+1040: A TeV Halo Powered by the Pulsar J0631+1036?

PSR J0631+1036 is a middle-aged pulsar with properties similar to those of the nearby Geminga pulsar. It is bright in γ-rays, and has been noted as the only source possibly associated with the TeV source 3HWC J0631+107 (also the LHAASO J0631+1040). For understanding the nature of the TeV source, we analyze the GeV γ-ray data obtained with the Large Area Telescope on board the Fermi Gamma-ray Space Telescope for the source region. We are able to remove the pulsar’s emission from the region from timing analysis, and find that the region is rather clean without possible GeV γ-ray emission present as the counterpart to the TeV source. By comparing this pulsar to Geminga and considering the spectral feature of the TeV source, we argue that it is likely the TeV halo powered by the pulsar.
Expert-Driven Validation of Set-Based Data Mining Results

This dissertation addresses the problem of dealing with large numbers of set-based patterns, such as association rules and itemsets, discovered by data mining algorithms. Since many discovered patterns may be spurious, irrelevant, or trivial, one of the main problems is how to validate them, e.g., how to separate the “good” rules from the “bad. ” Many researchers have advocated the explicit involvement of a human expert in the validation process. However, scalability becomes an issue when large numbers of patterns are discovered, since the expert cannot perform the validation on a pattern-by-pattern basis in a reasonable period of time. To address this problem, this dissertation describes a new expert-driven approach to set-based pattern validation. The proposed validation approach is based on validation sequences, i.e., we rely on the expert's ability to iteratively apply various validation operators that can validate multiple patterns at a time, thus making the expert-based validation feasible. We identified the class of scalable set predicates called cardinality predicates and demonstrated how these predicates can be effectively used in the validation process, i.e., as a basis for validation operators. We examined various properties of cardinality predicates, including their expressiveness. We also have developed and implemented the set validation language (SVL) that can be used for manual specification of cardinality predicates by a domain expert. In addition, we have proposed and developed a scalable algorithm for set and rule grouping that can be used to generate cardinality predicates automatically. The dissertation also explores various theoretical properties of sequences of validation operators and facilitates a better understanding of the validation process. We have also addressed the problem of finding optimal validation sequences and have shown that certain formulations of this problem are NP-complete. In addition, we provided some heuristics for addressing this problem. Finally, we have tested our rule validation approach on several real-life applications, including personalization and bioinformatics applications.
Robust finite element methods and solvers for the Biot-Brinkman equations in vorticity form

In this article, we propose a new formulation and a suitable finite element method for the steady coupling of viscous flow in deformable porous media using divergence‐conforming filtration fluxes. The proposed method is based on the use of parameter‐weighted spaces, which allows for a more accurate and robust analysis of the continuous and discrete problems. Furthermore, we conduct a solvability analysis of the proposed method and derive optimal error estimates in appropriate norms. These error estimates are shown to be robust in a variety of regimes, including the case of large Lamé parameters and small permeability and storativity coefficients. To illustrate the effectiveness of the proposed method, we provide a few representative numerical examples, including convergence verification and testing of robustness of block‐diagonal preconditioners with respect to model parameters.
Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators

High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control. However, the complexities of part geometries make it challenging for current models to maintain high accuracy across a wide range of geometries. Additionally, many models report a low mean square error (MSE) across the entire domain (part). However, in each time step, most areas of the domain do not experience significant changes in temperature, except for the heat-affected zones near recent depositions. Therefore, the MSE-based fidelity measurement of the models may be overestimated. This paper presents a data-driven model that uses Fourier Neural Operator to capture the local temperature evolution during the additive manufacturing process. In addition, the authors propose to evaluate the model using the R2 metric, which provides a relative measure of the model’s performance compared to using mean temperature as a prediction. The model was tested on numerical simulations based on the Discontinuous Galerkin Finite Element Method for the Direct Energy Deposition process, and the results demonstrate that the model achieves high fidelity as measured by R2 and maintains generalizability to geometries that were not included in the training process.