Miracle-dz/UniFAR-FTUs huggingface dataset:
IOTA: A Framework for Analyzing System-Level Security of IoTs

Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses only focus on a subset of all the essential components, such as device firmware or communication protocols, and ignore IoT systems' interactive nature, resulting in limited attack detection capabilities. In this work, we propose Iota, a logic programming-based framework to perform system-level security analysis for IoT systems. Iota generates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces. In building Iota, we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. We also identify and model physical dependencies between different devices as they are unique to IoT systems and are employed by adversaries to launch complicated attacks. In addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions. Iota automatically translates vulnerabilities, exploits, and device dependencies to Prolog clauses and invokes MulVAL to construct attack graphs. To evaluate vulnerabilities' system-wide impact, we propose two metrics based on the attack graph, which provide guidance on fortifying IoT systems. Evaluation on 127 IoT CVEs (Common Vulnerabilities and Exposures) shows that IOTA's exploit modeling module achieves over 80% accuracy in predicting vulnerabilities' preconditions and effects. We apply Iota to 37 synthetic smart home IoT systems based on real-world IoT apps and devices. Experimental results show that our framework is effective and highly efficient. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices.
Towards Micro-video Thumbnail Selection via a Multi-label Visual-semantic Embedding Model

The thumbnail, as the first sight of a micro-video, plays a pivotal role in attracting users to click and watch. While in the real scenario, the more the thumbnails satisfy the users, the more likely the micro-videos will be clicked. In this paper, we aim to select the thumbnail of a given micro-video that meets most users` interests. Towards this end, we present a multi-label visual-semantic embedding model to estimate the similarity between the pair of each frame and the popular topics that users are interested in. In this model, the visual and textual information is embedded into a shared semantic space, whereby the similarity can be measured directly, even the unseen words. Moreover, to compare the frame to all words from the popular topics, we devise an attention embedding space associated with the semantic-attention projection. With the help of these two embedding spaces, the popularity score of a frame, which is defined by the sum of similarity scores over the corresponding visual information and popular topic pairs, is achieved. Ultimately, we fuse the visual representation score and the popularity score of each frame to select the attractive thumbnail for the given micro-video. Extensive experiments conducted on a real-world dataset have well-verified that our model significantly outperforms several state-of-the-art baselines.
On (Global) Unique Continuation Properties of the Fractional Discrete Laplacian

We study various qualitative and quantitative (global) unique continuation properties for the fractional discrete Laplacian. We show that while the fractional Laplacian enjoys striking rigidity properties in the form of (global) unique continuation properties, the fractional discrete Laplacian does not enjoy these in general. While discretization thus counteracts the strong rigidity properties of the continuum fractional Laplacian, by discussing quantitative forms of unique continuation, we illustrate that these properties can be recovered if exponentially small (in the lattice size) correction terms are added. This in particular allows us to deduce uniform stability properties for a discrete, linear inverse problem for the fractional Laplacian. We complement these observations with a transference principle and the discussion of these properties on the discrete torus.
Energy-Efficient Dynamic-Subarray With Fixed True-Time-Delay Design for Terahertz Wideband Hybrid Beamforming

Hybrid beamforming for Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) systems is a promising technology for 6G space-air-ground integrated networks, which can overcome huge propagation loss and offer unprecedented data rates. With ultra-wide bandwidth and ultra-large-scale antennas array in THz band, the beam squint becomes one of the critical problems which could reduce the array gain and degrade the data rate substantially. However, the traditional phase-shifters-based hybrid beamforming architectures cannot tackle this issue due to the frequency-flat property of the phase shifters. In this paper, to combat the beam squint while keeping high energy efficiency, a novel dynamic-subarray with fixed true-time-delay (DS-FTTD) architecture is proposed. Compared to the existing studies which use the complicated adjustable TTDs, the DS-FTTD architecture has lower power consumption and hardware complexity, thanks to the low-cost FTTDs. Furthermore, a low-complexity row-decomposition (RD) algorithm is proposed to design hybrid beamforming matrices for the DS-FTTD architecture. Extensive simulation results show that, by using the RD algorithm, the DS-FTTD architecture achieves near-optimal array gain and significantly higher energy efficiency than the existing architectures. Moreover, the spectral efficiency of DS-FTTD architecture with the RD algorithm is robust to the imperfect channel state information.
Bayesian Linear Bandits for Large-Scale Recommender Systems

—Potentially, taking advantage of available side information boosts the performance of recommender systems; nevertheless, with the rise of big data, the side information has often several dimensions. Hence, it is imperative to develop decision-making algorithms that can cope with such a high-dimensional context in real-time. That is especially challenging when the decision-maker has a variety of items to recommend. In this paper, we build upon the linear contextual multi-armed bandit framework to address this problem. We develop a decision-making policy for a linear bandit problem with high-dimensional context vectors and several arms. Our policy employs Thompson sampling and feeds it with reduced context vectors, where the dimensionality reduction follows by random projection. Our proposed recommender system follows this policy to learn online the item preferences of users while keeping its runtime as low as possible. We prove a regret bound that scales as a factor of the reduced dimension instead of the original one. For numerical evaluation, we use our algorithm to build a recommender system and apply it to real-world datasets. The theoretical and numerical results demonstrate the effectiveness of our proposed algorithm compared to the state-of-the-art in terms of computational complexity and regret performance.
Inferring potential landscapes from noisy trajectories

While particle trajectories encode information on their governing potentials, potentials can be challenging to robustly extract from trajectories. Measurement errors may corrupt a particle’s position, and sparse sampling of the potential limits data in higher-energy regions such as barriers. We develop a Bayesian method to infer potentials of arbitrary shape alongside measurement noise. As an alternative to Gaussian process priors over potentials, we introduce structured kernel interpolation to the Natural Sciences which allows us to extend our analysis to large data sets. Our method is validated on 1D and 2D experimental trajectories for particles in a feedback trap.
Towards Loosely-Coupling Knowledge Graph Embeddings and Ontology-based Reasoning

Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of inferring missing information from knowledge graphs, is a widely used task in many applications, such as product recommendation and question answering. The state-of-the-art approaches of knowledge graph embeddings and/or rule mining and reasoning are data-driven and, thus, solely based on the information the input knowledge graph contains. This leads to unsatisfactory prediction results which make such solutions inapplicable to crucial domains such as healthcare. To further enhance the accuracy of knowledge graph completion we propose to loosely-couple the data-driven power of knowledge graph embeddings with domain-specific reasoning stemming from experts or entailment regimes (e.g., OWL2). In this way, we not only enhance the prediction accuracy with domain knowledge that may not be included in the input knowledge graph but also allow users to plugin their own knowledge graph embedding and reasoning method. Our initial results show that we enhance the MRR accuracy of vanilla knowledge graph embeddings by up to 3x and outperform hybrid solutions that combine knowledge graph embeddings with rule mining and reasoning up to 3.5x MRR.
Momentum recoil in the relativistic two-body problem: Higher-order tails

In the description of the relativistic two-body interaction, together with the eﬀects of energy and angular momentum losses due to the emission of gravitational radiation, one has to take into account also the loss of linear momentum, which is responsible for the recoil of the center-of-mass of the system. We compute higher-order tail (i.e., tail-of-tail and tail-squared) contributions to the linear momentum ﬂux for a nonspinning binary system either along hyperboliclike or ellipticlike orbits. The corresponding orbital averages are evaluated at their leading post-Newtonian approximation, using harmonic coordinates and working in the Fourier domain. The ﬁnal expressions are given in a large-eccentricity (or large-angular momentum) expansion along hyperboliclike orbits and in a small-eccentricity expansion along ellipticlike orbits. We thus complete a previous analysis focusing on both energy and angular momentum losses [Phys. Rev. D 104 , no.10, 104020 (2021)], providing brick-type results which will be useful, e.g., in the high-accurate determination of the radiated impulses of the two bodies undergoing a scattering process.
Formulating Connectedness in Security-Constrained Optimal Transmission Switching Problems

This paper focuses on the issue of network connectedness (NC) in security-constrained optimal transmission switching problems, which is complicated by branch contingencies and corrective line switching. Two criteria are firstly proposed with the principle of preserving NC as much as possible within reasonable limits. By extending the electrical flow based NC constraints, a proposition is derived to associate different cases of NC with the optimum of a linear program, yielding the mathematical formulation of the NC criteria. By Karush-Kuhn-Tucker conditions, this formulation is further transformed into a tractable version which can be incorporated with existing SCOTS models without affecting the applicability of original solution approaches. Finally, case studies on various networks and SCOTS models demonstrate the efficacy of the proposed approach.
Evaluation of Runtime Monitoring for UAV Emergency Landing

To certify UAV operations in populated areas, risk mitigation strategies - such as Emergency Landing (EL) - must be in place to account for potential failures. EL aims at reducing ground risk by finding safe landing areas using on-board sensors. The first contribution of this paper is to present a new EL approach, in line with safety requirements introduced in recent research. In particular, the proposed EL pipeline includes mechanisms to monitor learning based components during execution. This way, another contribution is to study the behavior of Machine Learning Runtime Monitoring (MLRM) approaches within the context of a real-world critical system. A new evaluation methodology is introduced, and applied to assess the practical safety benefits of three MLRM mechanisms. The proposed approach is compared to a default mitigation strategy (open a parachute when a failure is detected), and appears to be much safer.
Charged Swarmalator in a Magnetic Adiabatic Trap

In this letter, we provide a detailed numerical examination of the dynamics of a charged swarmalator in an external magnetic ﬁeld. We do so by adopting and then modifying the cyclically symmetric Thomas oscillator to study the dynamics of a charged particle in an external magnetic ﬁeld. These dynamical behaviours for weak and strong ﬁeld strength parameters fall under two categories; conservative and dissipative. For conservative regime the system shows a complex quasi-periodic attractor whose topology depends upon the initial condition at high ﬁeld strengths. On decreasing the ﬁeld strength parameter, there is a transition from adiabatic motion to chaos. In the dissipative regime, the system is chaotic for weak ﬁeld strength and weak damping but shows a limit cycle for high ﬁeld strengths. Such a behaviour mimics the dynamics of magneto-bacterial microrobots.
Lane Departure Warning and Prevention Systems in the U.S. Vehicle Fleet: Influence of Roadway Characteristics on Potential Safety Benefits

Road departure crashes account for one-tenth of all crashes but nearly one-third of all fatal crashes. Lane departure warning (LDW) and lane departure prevention (LDP) active safety systems could mitigate these crashes by warning the driver of a lane departure or automatically navigating the vehicle back into the lane. The objective of this study was to quantify the influence of certain roadway characteristics on the effectiveness of LDW and LDP systems within the U.S. vehicle fleet. This study used 478 real-world drift-out-of-lane road departure crashes and simulated them as if the vehicles had been equipped with LDW or LDP systems. The simulations were then repeated as if (a) all of the roadways had lane markings, (b) the roadway shoulders were expanded, and (c) lane markings were present and the shoulder widths were expanded. With the existing roadway infrastructure, LDW and LDP were found to potentially prevent 28% to 32% of U.S. road departure crashes and 21% to 28% of cases of serious driver injury. When lane markings were added to the roadways, LDW and LDP could prevent 32% to 36% of crashes and 27% to 31% of cases of serious driver injury. When only shoulder widths were expanded, LDW and LDP could prevent 50% to 54% of crashes and 44% to 48% of cases of serious driver injury. When lane markings were present and the shoulders were expanded, LDW and LDP could prevent 72% to 78% of crashes and 60% to 65% of cases of serious driver injury. The findings of this study highlight the important influence of roadway infrastructure on the performance of LDW and LDP.
Transport and emergent stratification in the equilibrated Eady model: the vortex-gas scaling regime

Abstract We numerically and theoretically investigate the Boussinesq Eady model, where a rapidly rotating density-stratified layer of fluid is subject to a meridional temperature gradient in thermal wind balance with a uniform vertically sheared zonal flow. Through a suite of numerical simulations, we show that the transport properties of the resulting turbulent flow are governed by quasigeostrophic (QG) dynamics in the rapidly rotating strongly stratified regime. The ‘vortex gas’ scaling predictions put forward in the context of the two-layer QG model carry over to this fully three-dimensional system: the functional dependence of the meridional flux on the control parameters is the same, the two adjustable parameters entering the theory taking slightly different values. In line with the QG prediction, the meridional heat flux is depth-independent. The vertical heat flux is such that turbulence transports buoyancy along isopycnals, except in narrow layers near the top and bottom boundaries, the thickness of which decreases as the diffusivities go to zero. The emergent (re)stratification is set by a simple balance between the vertical heat flux and diffusion along the vertical direction. Overall, this study demonstrates how the vortex-gas scaling theory can be adapted to quantitatively predict the magnitude and vertical structure of the meridional and vertical heat fluxes, and of the emergent stratification, without additional fitting parameters.
Hyper-Convolutions via Implicit Kernels for Medical Imaging

The convolutional neural network (CNN) is one of the most commonly used architectures for computer vision tasks. The key building block of a CNN is the convolutional kernel that aggregates information from the pixel neighborhood and shares weights across all pixels. A standard CNN's capacity, and thus its performance, is directly related to the number of learnable kernel weights, which is determined by the number of channels and the kernel size (support). In this paper, we present the \textit{hyper-convolution}, a novel building block that implicitly encodes the convolutional kernel using spatial coordinates. Hyper-convolutions decouple kernel size from the total number of learnable parameters, enabling a more flexible architecture design. We demonstrate in our experiments that replacing regular convolutions with hyper-convolutions can improve performance with less parameters, and increase robustness against noise. We provide our code here: \emph{https://github.com/tym002/Hyper-Convolution}
A framework for 3D traffic noise mapping using data from BIM and GIS integration

Abstract Traffic noise is a major health concern for people living in urban environments. Noise mapping can help evaluating the noise level for certain areas in a city. Traditionally, noise mapping is performed in 2D geographic information system (GIS). The use of 3D GIS is also emerging in noise mapping in recent years. However, the current noise-mapping platforms can only conduct noise evaluation for the outdoor environment and the indoor environment separately. In addition, related information about absorption coefficient and transmission loss (TL) in noise calculation is not properly retrieved and is often replaced with a single value. In this research, building information modelling (BIM) and 3D GIS are integrated in order to combine traffic noise evaluation in both outdoor environments and indoor environments in a single platform. In our developed BIM–GIS integration platform, the built environment is represented in a 3D GIS model that contains information at a high level of detail from BIM. With the integration with BIM, the 3D GIS model now has access to detailed indoor features such as interior walls and interior rooms. Noise evaluation could therefore be performed at a room level in the developed platform. Essential parameters such as absorption coefficient and TL can be extracted directly from BIM for noise calculation. The 3D GIS model is connected with detailed BIM so that any changes in the indoor and outdoor features can be reflected to each other. The Italian C.N. R model is modified and applied in the platform to conduct noise calculation. This paper presents the details for the development of the noise-mapping BIM–GIS platform based on ArcGIS. Two use cases were analysed to show the role of such platform in the decision-making process of both urban planning and interior design.
Sustained Control of Pyruvate Carboxylase by the Essential Second Messenger Cyclic di-AMP in Bacillus subtilis

If bacteria experience a starvation for potassium, by far the most abundant metal ion in every living cell, they have to activate high-affinity potassium transporters, switch off growth activities such as translation and transcription of many genes or replication, and redirect the metabolism in a way that the most essential functions of potassium can be taken over by metabolites. Importantly, potassium starvation triggers a need for glutamate-derived amino acids. ABSTRACT In Bacillus subtilis and other Gram-positive bacteria, cyclic di-AMP is an essential second messenger that signals potassium availability by binding to a variety of proteins. In some bacteria, c-di-AMP also binds to the pyruvate carboxylase to inhibit its activity. We have discovered that in B. subtilis the c-di-AMP target protein DarB, rather than c-di-AMP itself, specifically binds to pyruvate carboxylase both in vivo and in vitro. This interaction stimulates the activity of the enzyme, as demonstrated by in vitro enzyme assays and in vivo metabolite determinations. Both the interaction and the activation of enzyme activity require apo-DarB and are inhibited by c-di-AMP. Under conditions of potassium starvation and corresponding low c-di-AMP levels, the demand for citric acid cycle intermediates is increased. Apo-DarB helps to replenish the cycle by activating both pyruvate carboxylase gene expression and enzymatic activity via triggering the stringent response as a result of its interaction with the (p)ppGpp synthetase Rel and by direct interaction with the enzyme, respectively. IMPORTANCE If bacteria experience a starvation for potassium, by far the most abundant metal ion in every living cell, they have to activate high-affinity potassium transporters, switch off growth activities such as translation and transcription of many genes or replication, and redirect the metabolism in a way that the most essential functions of potassium can be taken over by metabolites. Importantly, potassium starvation triggers a need for glutamate-derived amino acids. In many bacteria, the responses to changing potassium availability are orchestrated by a nucleotide second messenger, cyclic di-AMP. c-di-AMP binds to factors involved directly in potassium homeostasis and to dedicated signal transduction proteins. Here, we demonstrate that in the Gram-positive model organism Bacillus subtilis, the c-di-AMP receptor protein DarB can bind to and, thus, activate pyruvate carboxylase, the enzyme responsible for replenishing the citric acid cycle. This interaction takes place under conditions of potassium starvation if DarB is present in the apo form and the cells are in need of glutamate. Thus, DarB links potassium availability to the control of central metabolism.