Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Fault Injection Attacks with Runtime Verification

Fault injections are increasingly used to attack/test secure applications. In this paper, we define formal models of runtime monitors that can detect fault injections that result in test inversion attacks and arbitrary jumps in the control flow. Runtime verification monitors offer several advantages. The code implementing a monitor is small compared to the entire application code. Monitors have a formal semantics; and we prove that they effectively detect attacks. Each monitor is a module dedicated to detecting an attack and can be deployed as needed to secure the application. A monitor can run separately from the application or it can be weaved inside the application. Our monitors have been validated by detecting simulated attacks on a program that verifies a user PIN.
System Architecture for Real-Time Surface Inspection Using Multiple UAVs

This paper presents a real-time control system for surface inspection using multiple unmanned aerial vehicles (UAVs). The UAVs are coordinated in a specific formation to collect data of the inspecting objects. The communication platform for data transmission is based on the Internet of things (IoT). In the proposed architecture, the UAV formation is established via angle-encoded particle swarm optimization to generate an inspecting path and redistribute it to each UAV where communication links are embedded with an IoT board for network and data processing capabilities. The data collected are transmitted in real time through the network to remote computational units. To detect potential damage or defects, an online image-processing technique is proposed and implemented based on histograms. Extensive simulation, experiments, and comparisons have been conducted to verify the validity and performance of the proposed system.
Single and bilayer graphene on the topological insulator 
Bi2Se3
: Electronic and spin-orbit properties from first principles

We present a detailed study of the electronic and spin-orbit properties of single and bilayer graphene in proximity to the topological insulator Bi$_2$Se$_3$. Our approach is based on first-principles calculations, combined with symmetry derived model Hamiltonians that capture the low-energy band properties. We consider single and bilayer graphene on 1--3 quintuple layers of Bi$_2$Se$_3$ and extract orbital and proximity induced spin-orbit coupling (SOC) parameters. We find that graphene gets significantly hole doped (350 meV), but the linear dispersion is preserved. The proximity induced SOC parameters are about 1 meV in magnitude, and are of valley-Zeeman type. The induced SOC depends weakly on the number of quintuple layers of Bi$_2$Se$_3$. We also study the effect of a transverse electric field, that is applied across heterostructures of single and bilayer graphene above 1 quintuple layer of Bi$_2$Se$_3$. Our results show that band offsets, as well as proximity induced SOC parameters can be tuned by the field. Most interesting is the case of bilayer graphene, in which the band gap, originating from the intrinsic dipole of the heterostructure, can be closed and reopened again, with inverted band character. The switching of the strong proximity SOC from the conduction to the valence band realizes a spin-orbit valve. Additonally, we find a giant increase of the proximity induced SOC of about 200\%, when we decrease the interlayer distance between graphene and Bi$_2$Se$_3$ by only 10\%. Finally, for a different substrate material Bi$_2$Te$_2$Se, band offsets are significantly different, with the graphene Dirac point located at the Fermi level, while the induced SOC strength stays similar in magnitude compared to the Bi$_2$Se$_3$ substrate.
All-Instances Restricted Chase Termination

The chase procedure is a fundamental algorithmic tool in database theory with a variety of applications. A key problem concerning the chase procedure is all-instances termination: for a given set of tuple-generating dependencies (TGDs), is it the case that the chase terminates for every input database? In view of the fact that this problem is undecidable, it is natural to ask whether known well-behaved classes of TGDs ensure decidability. We consider here the main paradigms that led to robust TGD-based formalisms, that is, guardedness and stickiness. Although all-instances termination is well-understood for the oblivious chase, the more subtle case of the restricted (a.k.a. the standard) chase is rather unexplored. We show that all-instances restricted chase termination for guarded/sticky single-head TGDs is decidable in elementary time.
Signature of Stochastic Acceleration and Cooling Processes in an Outburst Phase of the TeV Blazar ON 231

We present a detailed spectral and temporal study of the intermediate-type blazar ON 231 during the TeV outburst phase in 2008 June with observations performed by Swift and XMM-Newton. The X-ray flux of the source, which was significantly dominated by the soft photons (below 3–4 keV), varies between 27% and 38% on day timescales, while mild variations were observed in the optical/UV emissions. We found a maximum soft lag of ∼1 hr between the UV and soft X-ray band, which can be understood if the magnetic field of the emitting region is ∼5.6 δ−1/3 G. The 0.6–10 keV spectra can be well represented by a broken power-law model, which indicates the presence of both synchrotron and inverse Compton components in the studied X-ray regime. The synchrotron part of the spectral energy distributions (SEDs) constructed with simultaneous optical/UV and X-ray data follows a log-parabolic shape. A time-resolved spectral analysis shows that the break energy varies significantly between 2.4 and 7.3 keV with the changing flux state of the source, and the similar variations of the spectral slopes of the two components support the synchrotron self-Compton scenario. The synchrotron tail, following a log-parabolic function, shows that the peak frequency (νp) varies by two orders of magnitude (∼1014–1016 Hz) during the event. A significantly positive Ep–β relation is observed from both SED and time-resolved spectral analyses. The most feasible scenario for the observed trend during the flaring event could be associated with a magnetic-field-driven stochastic process evolving toward an equilibrium energy level.
How many groups? A statistical methodology for data-driven partitioning of infectious disease incidence into age-groups

Understanding age-group dynamics of infectious diseases is a fundamental issue for both scientific study and policymaking. Age-structure epidemic models were developed in order to study and improve our understanding of these dynamics. By fitting the models to incidence data of real outbreaks one can infer estimates of key epidemiological parameters. However, estimation of the transmission in an age-structured populations requires first to define the age-groups of interest. Misspecification in representing the heterogeneity in the age-dependent transmission rates can potentially lead to biased estimation of parameters. We develop the first statistical, data-driven methodology for deciding on the best partition of incidence data into age-groups. The method employs a top-down hierarchical clustering algorithm, with a metric distance built for maximizing mathematical identifiability of the transmission matrix, and a stopping criteria based on significance testing. The methodology is tested using simulations showing good statistical properties. The methodology is then applied to influenza incidence data of 14 seasons in order to extract the significant age-group clusters in each season.
Fair Byzantine Agreements for Blockchains

The Byzantine general problem is the core problem that consensus algorithms are trying to solve, which is at the heart of the design of blockchains. As a result, we have seen numerous proposals of consensus algorithms in recent years, trying to improve the level of decentralization, performance, and security of blockchains. In our opinion, there are two most challenging issues when we consider the design of such algorithms in the context of powering blockchains in practice. First, the outcome of a consensus algorithm usually depends on the underlying incentive model, so each participant should have an equal probability of receiving rewards for its work. Secondly, the protocol should be able to resist network failures, such as cloud services shutdown, while maintaining high performance otherwise. We address these two critical issues in this paper. First, we propose a new metric, called fair validity, for measuring the performance of Byzantine agreements. Intuitively, fair validity provides a lower bound for the probability of acceptances of honest nodes’ proposals. This is a strong notion of fairness, and we argue that it is crucial for the success of a blockchain in practice. We then show that any Byzantine agreement could not achieve fair validity in an asynchronous network, so we will focus on synchronous protocols. This leads to our second contribution: we propose a fair, responsive, and partition-resilient Byzantine agreement protocol able to tolerate up to 1/3 corruptions. As we will show in the paper, our protocol achieves fair validity and is responsive in the sense that the termination time only depends on actual network delay, as opposed to arbitrary, pre-determined time-bound. Furthermore, our proposal is partition-resilient. Last but not least, experimental results show that our Byzantine agreement protocol outperforms a wide variety of state-of-art synchronous protocols, combining the best from both theoretic and practical worlds.
Calibrating Head-Coupled Virtual Reality Systems

Head-tracking virtual environments are difficult to implement because of the need to calibrate such systems accurately, as well as the difficulty in computing the correct off-axis image for a given eye location. The situation is further complicated by the use of multiple screens, the need to change the calibration for different users, and the desire to write portable software which can be reused on different hardware with varying screen configurations. This thesis presents a solution to these problems, allowing greatly simplified development of head-tracking software. By making use of the head-tracking sensors built into the environment, we can quickly and accurately calibrate not only userspecific measurements, such as eye-positions, but also system measurements, such as the size and locations of display screens. A method of doing this calibration is developed, as well as a software library which will read a system configuration and integrate with OpenGL to compute correct off-axis projections for a user’s viewing position. The calibration makes use of a novel “sighting” technique which has the great advantage of accurately finding the true rotational centre of a user’s eyes. To complement this, the software library includes functions which predict the optical centre of a user’s eye based on a given gaze point. As a demonstration of both the calibration method and the utility library, a hardware rendering application is discussed. This application performs the real-time rendering of view-dependent LaFortune reflectance functions in graphics hardware. As with all view-dependent lighting methods, both the viewing angle and position of the light are taken into account while rendering. Head-coupling allows the system to use the user’s true viewing direction in the lighting computation, and the position of the virtual light is controlled by a 3D sensor in the user’s hand. The method in which the view-dependent lighting model is implemented in hardware is explained, as well as possible improvements. Throughout, the Polhemus FASTRAK is used as the tracking system, though all the results are easily applicable to any six degree-of-freedom tracking system.
Quantum geometric information flows and emergent gravity

We investigate classical and quantum geometric information flow theories (respectively, GIFs and QGIFs) when the geometric flow evolution and field equations for nonholonomic Einstein systems, NES, are derived from Perelman–Lyapunov type entropic type functionals. There are defined canonical (nonholonomic, i.e. with non-integrable distributions) geometric variables which allow a general decoupling and integration of nonlinear systems of equations describing GIFs and QGIFs and (for self-similar geometric flows) Ricci soliton type configurations. Our approach is more general and different from the ideas and methods used for special classes of solutions with the area–hypersurface entropy, and related holographic and dual gauge–gravity models, involving the concepts and generalizations of the Bekenstein–Hawking entropy and black hole thermodynamics. Formulating the theory of QGIFs and GIFs (in particular, encoding models with flow evolution of NES), a set of fundamental geometric, probability, and quantum mechanical, QM, concepts are developed for nonholonomic Lorentz spacetime manifolds. There are analysed the most important properties (inequalities) for emergent NES, their geometric flow evolution and QGIF versions of the von Neumann, relative and conditional entropy, mutual information, (modified) entanglement and Rényi entropy. This paper belongs to a series of partner works on the theory and applications of GIFs and QGIFs, entanglement and geometry of relativistic and quantum mechanical systems, emergent / entropic and modified gravity theories, MGTs, W-entropy and associated thermodynamics for generalized stationary and cosmological solutions in MGTs etc.
Discriminative stream‐weight training for mandarin audio‐visual speech recognition

Abstract In a large vocabulary audio‐visual speech recognition system, to efficiently improve the robustness of the system and reduce the word error rate, two discriminative stream‐weight training methods are provided. The state‐dependent stream weights are trained based on lattice rescoring by the minimum phone error and boosted maximum mutual information using the extended Baum Welch algorithm respectively. Experimental results show considerable error reductions have been achieved by the proposed methods over those using global stream weights. It is also shown that these methods provide better results than the minimum classification error based stream weight training methods.
Predicting Customer Call Intent by Analyzing Phone Call Transcripts based on CNN for Multi-Class Classification

Auto dealerships receive thousands of calls daily from customers who are interested in sales, service, vendors and jobseekers. With so many calls, it is very important for auto dealers to understand the intent of these calls to provide positive customer experiences that ensure customer satisfaction, deep customer engagement to boost sales and revenue, and optimum allocation of agents or customer service representatives across the business. In this paper, we define the problem of customer phone call intent as a multi-class classification problem stemming from the large database of recorded phone call transcripts. To solve this problem, we develop a convolutional neural network (CNN)-based supervised learning model to classify the customer calls into four intent categories: sales, service, vendor and jobseeker. Experimental results show that with the thrust of our scalable data labeling method to provide sufficient training data, the CNN-based predictive model performs very well on long text classification according to the quantitative metrics of F1-Score, precision, recall, and accuracy.
Control and imaging of molecular quantum states with second-scale coherence

Cold molecules provide a platform for a variety of scientific applications such as quantum computation, simulation, cold chemistry, and searches for physics beyond the Standard Model. Mastering quantum state control and measurement of diverse molecular species is critical for enabling these applications. However, state control and readout are difficult for the majority of molecular species due to the lack of optical cycling transitions. Here we demonstrate internal state cooling and orientation-selective photofragment imaging in a spin precession measurement with multi-second coherence, allowing us to achieve the quantum projection noise (QPN) limit in a large ensemble of trapped molecular ions. We realize this scheme for both HfF+ and ThF+ --- molecules chosen for their sensitivity to beyond Standard Model physics rather than their amenability to state control and readout.
Intrinsic Motivation Driven Intuitive Physics Learning using Deep Reinforcement Learning with Intrinsic Reward Normalization

At an early age, human infants are able to learn and build a model of the world very quickly by constantly observing and interacting with objects around them. One of the most fundamental intuitions human infants acquire is intuitive physics. Human infants learn and develop these models, which later serve as prior knowledge for further learning. Inspired by such behaviors exhibited by human infants, we introduce a graphical physics network integrated with deep reinforcement learning. Specifically, we introduce an intrinsic reward normalization method that allows our agent to efficiently choose actions that can improve its intuitive physics model the most. Using a 3D physics engine, we show that our graphical physics network is able to infer object's positions and velocities very effectively, and our deep reinforcement learning network encourages an agent to improve its model by making it continuously interact with objects only using intrinsic motivation. We experiment our model in both stationary and non-stationary state problems and show benefits of our approach in terms of the number of different actions the agent performs and the accuracy of agent's intuition model. Videos are at this https URL
fbSAT: Automatic Inference of Minimal Finite-State Models of Function Blocks Using SAT Solver

Finite-state models are widely used in software engineering, especially in the development of control systems. In control applications, such models are often developed manually, which can make it difficult to keep them up to date. To simplify the maintenance process, an automatic approach can be used to infer models from behavior examples and temporal specification. As an example of a specific control systems development application, we focus on inferring finite-state models of function blocks (FBs) defined by the IEC 61499 international standard for distributed automation systems. In this paper, we propose a method for inferring FB models from behavior examples based on reduction to the Boolean satisfiability problem (SAT). Additionally, we take into account linear temporal properties using counterexample-guided synthesis. The developed tool, fbSAT, implementing the proposed method is evaluated in three case studies: inferring a finite-state controller for a Pick-and-Place manipulator, reconstructing randomly generated automata, and minimizing transition systems. In contrast to existing approaches, the suggested method is more efficient and produces finite-state models that are minimal in terms of both the number of states and the complexity of guard conditions.
Segway DRIVE Benchmark: Place Recognition and SLAM Data Collected by A Fleet of Delivery Robots

Visual place recognition and simultaneous localization and mapping (SLAM) have recently begun to be used in real-world autonomous navigation tasks like food delivery. Existing datasets for SLAM research are often not representative of in situ operations, leaving a gap between academic research and real-world deployment. In response, this paper presents the Segway DRIVE benchmark, a novel and challenging dataset suite collected by a fleet of Segway delivery robots. Each robot is equipped with a global-shutter fisheye camera, a consumer-grade IMU synced to the camera on chip, two low-cost wheel encoders, and a removable high-precision lidar for generating reference solutions. As they routinely carry out tasks in office buildings and shopping malls while collecting data, the dataset spanning a year is characterized by planar motions, moving pedestrians in scenes, and changing environment and lighting. Such factors typically pose severe challenges and may lead to failures for SLAM algorithms. Moreover, several metrics are proposed to evaluate metric place recognition algorithms. With these metrics, sample SLAM and metric place recognition methods were evaluated on this benchmark. The first release of our benchmark has hundreds of sequences, covering more than 50 km of indoor floors. More data will be added as the robot fleet continues to operate in real life. The benchmark is available at this http URL.
A Multi-Stage Clustering Framework for Automotive Radar Data

Radar sensors provide a unique method for executing environmental perception tasks towards autonomous driving. Especially their capability to perform well in adverse weather conditions often makes them superior to other sensors such as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of the commonly used detection data level is a major challenge for subsequent signal processing. Therefore, the data points are often merged in order to form larger entities from which more information can be gathered. The merging process is often implemented in form of a clustering algorithm. This article describes a novel approach for first filtering out static background data before applying a two-stage clustering approach. The two-stage clustering follows the same paradigm as the idea for data association itself: First, clustering what is ought to belong together in a low dimensional parameter space, then, extracting additional features from the newly created clusters in order to perform a final clustering step. Parameters are optimized for filtering and both clustering steps. All techniques are assessed both individually and as a whole in order to demonstrate their effectiveness. Final results indicate clear benefits of the first two methods and also the cluster merging process under specific circumstances.
Unsupervised cycle‐consistent deformation for shape matching

We propose a self‐supervised approach to deep surface deformation. Given a pair of shapes, our algorithm directly predicts a parametric transformation from one shape to the other respecting correspondences. Our insight is to use cycle‐consistency to define a notion of good correspondences in groups of objects and use it as a supervisory signal to train our network. Our method combines does not rely on a template, assume near isometric deformations or rely on point‐correspondence supervision. We demonstrate the efficacy of our approach by using it to transfer segmentation across shapes. We show, on Shapenet, that our approach is competitive with comparable state‐of‐the‐art methods when annotated training data is readily available, but outperforms them by a large margin in the few‐shot segmentation scenario.
Electroweak Physics with Polarized Electron Beams in a SuperKEKB Upgrade

Consideration is being given to upgrading the SuperKEKB e$^+$e$^-$ collider with polarized electron beams, which would open a new program of precision electroweak physics at a centre-of-mass energy of 10.58GeV, the mass of the $\Upsilon(4S)$. These measurements include $\sin^2\theta_W$ obtained via left-right asymmetry measurements of e$^+$e$^-$ transitions to pairs of electrons, muons, taus, charm and b-quarks. The precision obtainable at SuperKEKB will match that of the LEP/SLC world average and will thereby probe the neutral current couplings with unprecedented precision at a new energy scale sensitive to the running of the couplings. At SuperKEKB the measurements of the individual neutral current vector coupling constants to b-quarks and c-quarks and muons in particular will be substantially more precise than current world averages and the current 3$\sigma$ discrepancy between the SLC A$_{LR}$ measurements and LEP A$_{FB}^b$ measurements of $\sin^2\theta_W^{eff}$ will be addressed. This paper will include a discussion of the necessary upgrades to SuperKEKB. This program opens an exciting new window in searches for physics beyond the Standard Model.