Miracle-dz/UniFAR-FTUs huggingface dataset:
Zero-Value Filtering for Accelerating Non-Profiled Side-Channel Attack on Incomplete NTT-Based Implementations of Lattice-Based Cryptography

Lattice-based cryptographic schemes such as Crystals-Kyber and Dilithium are post-quantum algorithms selected to be standardized by NIST as they are considered to be secure against quantum computing attacks. The multiplication in polynomial rings is the most time-consuming operation in many lattice-based cryptographic schemes, which is also subject to side-channel attacks. While NTT-based polynomial multiplication is almost a norm in a wide range of implementations, a relatively new method, incomplete NTT is preferred to accelerate lattice-based cryptography, especially on some computing platforms that feature special instructions. In this paper, we present a novel, efficient and non-profiled power/EM side-channel attack targeting polynomial multiplication based on the incomplete NTT algorithm. We apply the attack on the Crystals-Dilithium signature algorithm and Crystals-Kyber KEM. We demonstrate that the method accelerates attack run-time when compared to the existing approaches. While a conventional non-profiled side-channel attack tests a much larger hypothesis set because it needs to predict two coefficients of secret polynomials together, we propose a much faster zero-value filtering attack (ZV-FA), which reduces the size of the hypothesis set by targeting the coefficients individually. We also propose an effective and efficient validation and correction technique employing the inverse NTT to estimate and modify the mispredicted coefficients. Our experimental results show that we can achieve a speed-up of ${1915}\times $ over brute-force.
Non parametric regression analysis for longitudinal data with time-depending autoregressive error process

ABSTRACT This paper considers a non parametric longitudinal model, where the within-subject correlation structure is represented by a time-depending autoregressive error process. An initial estimator without taking into account the within-subject correlation is obtained to fit the time-depending autoregressive error process. With the initial estimator, we construct a two-stage local linear estimator of the mean function. According to the asymptotic normality of the initial and two-stage estimators, it is discovered that the two-stage estimator has a smaller asymptotic variance. The simulation results show us that the two-stage estimation has some good properties. The analysis of a data set demonstrates its application.
A CONSTRAINED CONVEX SPLITTING SCHEME FOR THE VECTOR-VALUED CAHN–HILLIARD EQUATION

A BSTRACT . In contrast to the well-developed convex splitting schemes for gradient ﬂows of two-component system, there were few efforts on applying the convex splitting idea to gradient ﬂows of multi-component system, such as the vector-valued Cahn–Hilliard (vCH) equation. In the case of the vCH equation, one need to consider not only the convex splitting idea but also a speciﬁc method to manage the partition of unity constraint to design an unconditionally energy stable scheme. In this paper, we propose a constrained Convex Splitting (cCS) scheme for the vCH equation, which is based on a convex splitting of the energy functional for the vCH equation under the constraint. We show analytically that the cCS scheme is mass conserving and unconditionally uniquely solvable. And it satisﬁes the constraint at the next time level for any time step thus is unconditionally energy stable. Numerical experiments are presented demonstrating the accuracy, energy stability, and efﬁciency of the proposed cCS scheme.
False positives are common in single-station template 3 matching 4

Template matching has become a cornerstone technique of observational seismol- 10 ogy. By taking known events, and scanning them against a continuous record, new events smaller 11 than the signal-to-noise ratio can be found, substantially improving the magnitude of complete- 12 ness of earthquake catalogues. Template matching is normally used in an array setting, however 13 as we move into the era of planetary seismology, we are likely to apply template matching for very 14 small arrays or even single stations. Given the high impact of planetary seismology studies on our 15 understanding of the structure and dynamics of non-Earth bodies, it is important to assess the reli- 16 abilityoftemplatematchinginthesmall-nsetting. Towardsthisgoal,weestimatealowerboundon 17 therateoffalsepositivesforsingle-stationtemplatematchingbyexaminingthebehaviourofcorre- 18 lations of totally uncorrelated white noise. We find that, for typical processing regimes and match 19 thresholds, false positives are likely quite common. We must therefore be exceptionally careful 20 when considering the output of template matching in the small-n setting. 21
Narrative Origin Classiﬁcation of Israeli-Palestinian Conﬂict Texts

The Israeli-Palestinian conﬂict is one of the most controver- sial in history. Not surprisingly, historic and contemporary literature on the topic tends to be polarized. Drawing inspiration from work in political ideology and hyperpartisan news detection, we collect two new datasets of history book excerpts and newspaper articles regarding the Israeli-Palestinian conﬂict and train sequence classiﬁers to predict whether a text is written by an Israeli or Palestinian source. Moreover, we ﬁnd that data augmentation techniques improve performance, allowing our best model to detect narrative origin with an F1 score of 85.1% for history book excerpts and 91.9% for news- paper articles. Analysis of indicative phrases discovered by our models corroborate historian insight regarding the con- ﬂict.
Post-Doctoral Research Visit

either in analysis or in motion synthesis. OS3. 5 to 10 subjects with reduced mobility of their upper limb (score<60 at Pedi-ASES14) will be recruited via the network of clinics in Rennes (Pôle Saint Hélier in particular, which has agreed in principle to validate the use case) to evaluate the benefits of the optimal settings proposed by the abacus, compared to the classic configuration of the arm. These experiments will then allow us to determine the effectiveness of the abacus in proposing a personalized optimization of assistance arm settings. We will investigate seven tasks (school, food and play) that will be selected through collaboration with occupational therapists and clinicians
Argot as a Trust Signal: Slang, Jargon & Reputation on a Large Cybercrime Forum

We apply signalling theory to a cybercrime forum to explore how argot (slang and jargon) is used to signal trust in untrustworthy environments. We develop an argot detection tool, using word embeddings from forum and non-forum datasets, which are aligned using training annotations. Compared with prior work, our approach improves performance, with an increase in the F1 and accuracy scores. Using the detected argot to create per-user variables, we find a negative correlation between the use of argot and reputation votes. We explore the trajectories of groups of forum members to observe how the use of argot and user reputation in the forum varies over time. Our findings indicate forum users are using argot to overcome the cold start problem, a conundrum faced by new users to social networks with ranking systems and marketplaces with feedback systems. A significant group of long-standing users is characterised by high levels of argot in their early forum postings. This decreases once reputation metrics increase. This particular trajectory group are amongst the highest-rated long-term members.
Geometric Diffusions for Reconstruction of Cell Differentiation Dynamics

The temporal order of differentiating cells is intrinsically encoded in their single-cell expression profiles. In this dissertation an adaptation of geometric diffusions is proposed to robustly estimate this order. The developed method makes it possible to reconstruct the developmental progression of cells and identify transient or metastable states, branching decisions and differentiation endpoints.
High-precision attitude angle measuring system based on Talbot interferometry

A traditional tracking device obtain the attitude angle by analyzing the spots position on photodetector. However, the attainable angular measurement accuracy depends on the field of view (FOV), number of pixels of the photodetector and the centroiding algorithm. In this paper, we present a high-precision attitude angle measuring system based on Talbot interferometry using cross-gratings and four wedge plates, which can acquire the real-time change of incident angle along two axis. The specific structure of the system is introduced, and the formula for calculating the relative angle is derived. The tracking accuracy is analyzed to be better than 0.2 arcsecond, which is dependent on the grating period, the distance between the two gratings and the gray scale of image. The Simulation results show that the RMS error of relative angle is better than 0.1 arcsecond both in x and y direction.
Instabilities and dynamics of elastic rods in the presence of movable constraints

A new trend in the mechanical design of devices for advanced technologies, such as soft robotics and micro/nano mechanics, is the exploitation of structures undergoing large deflections, in an attempt of achieving superior performances. Within this framework, non-linear modelling becomes a fundamental tool for the design of compliant structures and deformable mechanism. Two structural systems are investigated, both based on the planar elastica and subject to movable and configurational constraints. These two structures disclose unforeseen behaviours when the values of the parameters defining the models are varied. The first structural system is an elastic rod constrained by a slowly rotating clamp, while the other end is loaded with a lumped mass weight. When this weight is lower than that corresponding to buckling, the edge of the rod describes a closed curve, behaving as an elastica compass. Differently, when the load is higher than that of buckling, a release of elastic energy is observed, leading to a snapback of the structure, so that the rod realizes an elastica catapult. The clamp in the above described structure is replaced by a frictionless and fixed sliding sleeve in the second system considered in this thesis. The rod is subject to a sudden release from the underformed configuration, providing dynamic effects on the system. By means of the variational approach, the presence of a configurational force at the exit of the sliding sleeve is proven within the dynamical setting, extending previous results restricted to the quasi-static assumption. The configurational force is found to strongly affect the dynamics of the structure. In particular, two different behaviours are observed, in which the rod may either completely penetrate in ("injection") or be expelled from ("ejection") the sliding sleeve. In both the above problems, the theoretical predictions are corroborated through the experimental validation on physical models, which have been ad hoc invented and designed. A new insight is obtained in the design of flexible devices, paving the way to applications in soft robotics.
APPLICATION OF THE BIFOCUSING METHOD IN MICROWAVE IMAGING WITHOUT BACKGROUND INFORMATION

A BSTRACT . In this study, we consider the application of the bifocusing method (BFM) for identifying the locations and shapes of small anomalies from scattering parameter data when the exact values of background permittivity and conductivity are unknown. To this end, an imaging function using numerical focusing operator is introduced and its mathematical structure is revealed by establishing a relationship with an infinite series of Bessel functions, antenna arrangements, and anomaly properties. On the basis of the revealed structure, we demonstrate why inaccurate location and size of anomalies were retrieved via the BFM. Some simulation results are illustrated using synthetic data polluted by random noise to support the theoretical result.
Covariance Changepoint Detection for Time Series Data

This paper investigates the ability of the covariance changepoint detection (CCPD) method introduced by Galeano and Peña to detect and explain changes in covariance [2]. The algorithm is tested on two case studies that use time series data from air quality index (AQI) sensors that experience positive covariance shifts due to wildfire outbreaks. These experiments demonstrate that, when applied to lower-dimensional problems, the CCPD algorithm pro-vides highly accurate and timely covariance changepoint detection. However, the high computational complexity of the algorithm de-mands further research to increase its efficiency. Until then, the CCPD method is only a viable alternative to existing univariate methods in lower-dimensional settings where variance changes are more effectively explained by detecting underlying changes in covariance.
Comparing the Effectiveness of Support Vector Machines with Fuzzy, Neuro-fuzzy and Genetic Programming Approaches in Result Prediction of Football Games

A soft computing method for result prediction of football games based on machine learning techniques such as support vector machines is proposed in the article. The model is taking into account the following features of football teams: difference of infirmity factors; difference of dynamics profile; difference of ranks; host factor; personal score of the teams. Testing shows that the proposed model achieves a satisfactoiy estimation of the actual game outcomes. The current work concludes with the recommendation of support vector machines technique as a powerful approach, for the creation of result prediction models of diverse sport championships.
Geometric Analysis of Matrix Sensing over Graphs

In this work, we consider the problem of matrix sensing over graphs (MSoG). As a general case of matrix completion and matrix sensing problems, the MSoG problem has not been analyzed in the literature and the existing results cannot be directly applied to the MSoG problem. This work provides the first theoretical results on the optimization landscape of the MSoG problem. More specifically, we propose a new condition, named the Ω -RIP condition, to characterize the optimization complexity of the problem. In addition, with an improved regularizer of the incoherence, we prove that the strict saddle property holds for the MSoG problem with high probability under the incoherence condition and the Ω -RIP condition, which guarantees the polynomial-time global convergence of saddle-avoiding methods. Compared with state-of-the-art results, the bounds in this work are tight up to a constant. Besides the theoretical guarantees, we numerically illustrate the close relation between the Ω -RIP condition and the optimization complexity.
Shrinking Against Sentiment: Exploiting Latent Asset Demand in Portfolio Selection

We examine how sentiment-driven demand, a key component of latent asset demand, can be used to build mean-variance portfolios. We decompose these portfolios into an equally weighted component and an arbitrage component that captures the asset mispricing unexplained by the equally weighted component. Our approach shrinks mean-variance portfolios toward the equally weighted component when investor sentiment is low, i.e., shrinks against sentiment, reducing estimation risk and imposing a tighter bound on the amount of asset mispricing the arbitrage component exploits. The significant economic gains offered by our approach highlight the importance of considering latent demand in building robust investment strategies.
3D Reconstruction of Building Morphology from Façade Drawings using Transformed-based Mono-depth Estimation

In this paper we introduce a transformer-based system that reconstructs a 3D building model from line drawing of building facades. Specifically, we generate the dataset of drawings from 3D geometric models of houses and extract the rendered lateral views associated with the relative depth map. The dataset is used to fine-tune the Dense Prediction Transformer model to learn the depth maps of singular views. Each depth map predicted from the transformer model is combined with the relative lateral view and converted into a point cloud. Using plane detection and rigid transformations of the points, we implement a recombination mechanism that aims to correctly reconstruct the building geometry. We develop a hybrid recombination system that combines the predicted depth maps and the orthogonal projection in the 3D space of the point clouds to correct imprecisions of the point conversion. Finally, we regenerate the mesh corresponding to the recovered point cloud using a patch-based Delaunay triangulation.
Two Step Semantic Phrase Construction from Images

: With the growing technology, we all have experienced there is an extensive growth in the field of artificial intelligence and natural language processing. But what most attracted the attention of researchers is Image’s Feature Collection, which itself generate the captions of an image. Its application is wide and extensive as the traffic in social networking is increasing day by day. In this paper we have used CNN and LSTM which is special kind of RNN used to store data for a longer period of time firstly the input image is processed by a CNN and the output of that is given as an input to the RNN which generates textual descriptions, that’s what this paper summarizes. The dataset which we’ve used is flicker_8k containing the set of 8k images to train the model well enough. The different libraries are also involved in this project making like Keras, Numpy, etc. Finally, this paper underlines the advantages and shortcomings of this architecture as well.
Inaugural Workshop on Provably Safe and Beneficial AI (PSBAI)

The use of increasingly powerful AI systems for a broad range of purposes carries with it significant risks. These issues have been discussed from a variety of perspectives including technology, law, economics, medicine, sociology, politics, and ethics, and under a variety of names that indicate more specific concerns, such AI safety, AI ethics, responsible AI, beneficial AI, human-centered AI, AI alignment
A Case Study on Fuzzing Satellite Firmware

—Satellites perform critical functions of our modern digital infrastructure, such as providing communications, navigation, and earth observation services. Maintaining a satellite requires remote access, so securing that access is an essential aspect of developing and operating a satellite. While satellites have traditionally not been subjected to regular attacks, this might not hold in the future. Hence, securing satellite firmware—the software that controls the space segment of satellite missions— becomes increasingly relevant. In this work, we perform a case study of applying recent embedded firmware analysis techniques to satellite payload data handling systems. We explore whether F UZZWARE , a state-of-the-art firmware fuzz testing system, can be used to these firmware images. During this case study, we also describe and apply the process of manually optimizing F UZZWARE configurations for firmware targets, and measure the impact of different optimizations. Finally, we identify challenging aspects of fuzz testing satellite firmware and directions for future work to optimize fuzz testing performance in a fully automated manner. As part of our case study, we identified and responsibly disclosed 6 bugs in 3 satellite firmware images.
Evaluating the Robustness of Deep learning Models for NLP

The significance of deep neural networks (DNNs) has been well established through its success in a variety of tasks. However, recent studies have shown that DNNs are vulnerable to adversarial examples — inputs crafted by adding small perturbations to the original input. Such perturbations are almost imperceptible to humans but deceive DNNs thus raising major concerns about their utility in real world applications. Although, existing adversarial attack methods in NLP have achieved high success rate in attacking DNNs, they either require detailed information about the target model, training data or need a large amount of queries to generate attacks. Therefore, such attack methods are not realistic as it does not reflect the types of attacks that can be encountered in the real world and are less effective as attacks relying on model information and excessive queries can be easily defended against. In this thesis, we address the above mentioned drawbacks by proposing two realistic attack settings — hard label black box setting and limited query setting. Next, we propose two novel attack methods that crafts plausible and semantically similar adversarial examples in the above settings. The first attack method uses a population based optimization procedure to craft adversarial examples in the hard label black box setting. The second method is a query efficient method that leverages word attention scores and locality sensitive hashing to find important words for substitution in the limited query setting. We benchmark our results across the same search space used in prior attacks so as to ensure fair and consistent comparison. To improve the quality of generated adversarial examples, we propose an alternative method that uses masked language model to find candidate words for substitution by considering the information of both the original word and its surrounding context. We demonstrate the efficacy of each of our proposed approach by attacking NLP models for text classification and natural language inference task. In addition to that we use adversarial examples to evaluate the robustness and generalization of recent math word problem solvers. Our results showcase that DNNs for the above tasks are not robust as they can be deceived by our proposed attack methods in a highly restricted setting. We conduct human evaluation studies to verify the validity and quality of generated adversarial examples.
Solving Fractional Initial Value Problems by Using Hybrid Series

In this paper, we suggest a new method for solving fractional initial value problems of different fractional orders. We call it hybrid Series Solution (Fractional with power, Fractional with fractional, Fractional with power and fractional). The main difference between Caputo and Riemann – Liouville formulas for the fractional derivatives as mentioned. The paper focuses on finding the exact solution of Bagley-Torvik equation and other nonhomogeneous fractional differential equations, illustrated by some Theorems and examples.
INTERVENTION STRATEGIES FOR THE DYNAMICS OF POPULATION WITH OVEREATING BEHAVIOR

A BSTRACT . Disordered eating behaviors, such as overeating, are known to be contagious in the general population. The objective of our research is to find an optimal control strategy to reduce the social burden of unhealthy overeating behavior by establishing and analyzing a mathematical model for the social transmission dynamics of unhealthy overeating. We consider four compartments in the population: normal weight with normal eating behavior, normal weight with overeating behavior, overweight with normal eating behavior, and overweight with overeating behavior. Simulation results under various control scenarios show that integrated control measures may be necessary to reduce the growth rate of the overeating population.