Miracle-dz/UniFAR-FTUs huggingface dataset:
Manufacture of a New Opaque Predicate for Java Programs

Most of developers protect their Java programs by code obfuscation,and opaque predicates act as tools for obfuscating control flow logic embedded within code.A new method of using congruent equation to construct a family of opaque predicates for Java programs is presented.Additionally,key and Diophantine equation is first exploited for estimating output of opaque predicates.The opaque predicates are trapdoor one-way being more resilient,stealthier and cheaper.The opaque predicates are dynamic and secure against all usual types of static attacks and dynamic attacks,and also secure against cryptanalysis.
A Theory of Stabilizing Algebraic Algorithms

The paper is about stabilizing algorithms. To capture the flavor in this abstract, we describe key results and techniques without the accompanying hypotheses. The first result is that given an algorithm A one can automatically generate a sequence of algorithms fAigi which converges to A. Moreover fAigi stabilizes A in the sense that if the input I is approximated by a sequence of inputs fIjgj then limi limj Ai(Ij) = A(I). The key underlying technique is to enlarge the data set on which A operates. This is done by exchanging coecients in the original data set with pairs consisting of a coecient
SPT-BASED LOCAL LANDMARK SCHEME FOR SHORTEST DISTANCE CALCULATION IN NETWORK ROUTING

Shortest distance query is mostly used in large scale networks to achieve fastest routing speed in a network using link state protocols such as OSPF and IS-IS. The Landmark embedding approach is mostly used to get results of such kind of queries. In this approach, set of graph nodes needs to be selected as landmarks and distance from those graph nodes is calculated as an embedding. The precomputed distance from those landmark nodes is calculated as part of the answer shortest distance query using old approach. This approach doesn't give accurate and error free results. Depending on the factors related to landmark embedding new approach have been proposed to solve these problem as query dependent local landmark scheme. In this approach a local landmarks with respect to queried nodes are identified and shortest distance is calculated which is more accurate than traditional approaches. Based on this theory, better solution is provided which is Shortest Path Tree based on Local Landmark scheme to achieve fastest routing speed in large network.
Uncertain Data Classification Using Decision Tree Classification Tool With Probability Density Function Modeling Technique

Classical decision tree classifiers are constructed using certain or point data only. But in many real life applications inherently data is always uncertain. Attribute or value uncertainty is inherently associated with data values during data collection process. Attributes in the training data sets are of two types - numerical (continuous) and categorical (discrete) attributes. Data uncertainty exists in both numerical and categorical attributes. Data uncertainty in numerical attributes means range of values and data uncertainty in categorical attributes means set or collection of values. In this paper we propose a method for handling data uncertainty in numerical attributes. One of the simplest and easiest methods of handling data uncertainty in numerical attributes is finding the mean or average or representative value of the set of original values of each value of an attribute. With data uncertainty the value of an attribute is usually represented by a set of values. Decision tree classification accuracy is much improved when attribute values are represented by sets of values rather than one single representative value. Probability density function with equal probabilities is one effective data uncertainty modelling technique to represent each value of an attribute as a set of values. Here the main assumption is that actual values provided in the training data sets are averaged or representative values of originally collected values through data collection process. For each representative value of each numerical attribute in the training data set, approximated values corresponding to the originally collected values are generated by using probability density function with equal probabilities and these newly generated sets of values are used in constructing a new decision tree classifier.
Clothing and Fashion

CLOTHING and FASHION   Do you like the clothes you are wearing now? Why? / Why not?  What did you wear yesterday?  Do you like to watch fashion shows on TV? Why? / Why not?  Do you enjoy shopping for new clothes? Why? / Why not?  Where do you usually buy your clothes?  Compare clothes in your country with those of another country.  Why do men wear neckties? Are they a good idea?  Would you like to own a clothing store? Why? / Why not?  Do you spend a lot of money on clothes? Why? / Why not?  Does clothing look cute on animals? Why? / Why not?  Do you ever wear jewelry? Why? / Why not?  Do you ever wear a hat? Why? / Why not?  Do you like to wear T-shirts? Why? / Why not?  Do you plan to go shopping for clothes soon? If ‘yes’, where?  How was fashion different 100 years ago?  How will fashion be different 100 years in the future?
The Rotational Structure of Molecules In the Quantum He4 and He3 Liquids

It is shown that the drastic distinction in the rotational structure of molecules in the He4 and He3 liquids observed in [1] is due to the difference in the spectral density of excitations regardless of the hydrodynamic properties. The large width of rotational levels in the He3 is determined by particle-hole excitations whereas the small density of phonon excitations results only in the small broadening of levels in the He4.
Effets régularisants de semi-groupes non linéaires dans des espaces de Banach

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The convex hull of random balls

While the convex hull of n d-dimensional balls is not a polytope, it does have an underlying combinatorial structure similar to that of a polytope. In the worst case, its combinatorial complexity can be of order Ω(n [d/2]). The thrust of this work is to show that its complexity is typically much smaller, and that it can therefore be constructed more quickly on average than in the worst case. To this end, four models of the random d-ball are developed, and the expected combinatorial complexity of the convex hull of n independent random d-balls is investigated for each. As n grows without bound, these expectations are O(1), O(n (d–1)/(d+4)), O(1) (for d = 2 only), and O(n (d–1)/(d+3)).
Composite metric method for time series similarity measurement based on symbolic aggregate approximation

Key point-based Symbolic Aggregate approximation(SAX) improving algorithm(KP_SAX) uses key points to measure point distance of time series based on SAX,which can measure the similarity of time series more effectively.However,it is too short of information about the patterns of time series to measure the similarity of time series reasonably.To overcome the defects,a composite metric method of time series similarity measurement based on SAX was proposed.The method synthesized both point distance measurement and pattern distance measurement.First,key points were used to further subdivide the Piecewise Aggregate Approximation(PAA) segments into several sub-segments,and then a triple including the information about the two kinds of distance measurement was used to represent each sub-segment.Finally a composite metric formula was used to measure the similarity between two time series.The calculation results can reflect the difference between two time series more effectively.The experimental results show that the proposed method is only 0.96% lower than KP_SAX algorithm in time efficiency.However,it is superior to the KP_SAX algorithm and the traditional SAX algorithm in differentiating between two time series.
On Model-Based Clustering, Classification, and Discriminant Analysis

The use of mixture models for clustering and classification has burgeoned into an important subfield of multivariate analysis. These approaches have been around for a half-century or so, with significant activity in the area over the past decade. The primary focus of this paper is to review work in model-based clustering, classification, and discriminant analysis, with particular attention being paid to two tech- niques that can be implemented using respective R packages. Parameter estimation and model selection are also discussed. The paper concludes with a summary, discussion, and some thoughts on future work.
Conjunctive Regular Path Queries under Injective Semantics

We introduce injective semantics for Conjunctive Regular Path Queries (CRPQs), and study their fundamental properties. We identify two such semantics: atom-injective and query-injective semantics, both defined in terms of injective homomorphisms. These semantics are natural generalizations of the well-studied class of RPQs under simple-path semantics to the class of CRPQs. We study their evaluation and containment problems, providing useful characterizations for them, and we pinpoint the complexities of these problems. Perhaps surprisingly, we show that containment for CRPQs becomes undecidable for atom-injective semantics, and PSPACE-complete for query-injective semantics, in contrast to the known EXPSPACE-completeness result for the standard semantics. The techniques used differ significantly from the ones known for the standard semantics, and new tools tailored to injective semantics are needed. We complete the picture of complexity by investigating, for each semantics, the containment problem for the main subclasses of CRPQs, namely Conjunctive Queries and CRPQs with finite languages.
ShapeClipper: Scalable 3D Shape Learning from Single-View Images via Geometric and CLIP-Based Consistency

We present ShapeClipper, a novel method that reconstructs 3D object shapes from real-world single-view RGB images. Instead of relying on laborious 3D, multi-view or camera pose annotation, ShapeClipper learns shape reconstruction from a set of single-view segmented images. The key idea is to facilitate shape learning via CLIP-based shape consistency, where we encourage objects with similar CLIP encodings to share similar shapes. We also leverage off-the-shelf normals as an additional geometric constraint so the model can learn better bottom-up reasoning of detailed surface geometry. These two novel consistency constraints, when used to regularize our model, improve its ability to learn both global shape structure and local geometric details. We evaluate our method over three challenging real-world datasets, Pix3D, Pascal3D+, and Open-Images, where we achieve superior performance over state-of-the-art methods.11project website at: https://zixuanh.com/projects/shapeclipper.html
Total variation bound for Hadwiger's functional using Stein's method

Let $K$ be a convex body in $\mathbb{R}^d$. Let $X_K$ be a $d$-dimensional random vector distributed according to the Hadwiger-Wills density $\mu_K$ associated with $K$, defined as $\mu_K(x)=ce^{-\pi {\rm dist}^2(x,K)}$, $x\in \mathbb{R}^d$. Finally, let the information content $H_K$ be defined as $H_K={\rm dist}^2(X_K,K)$. The goal of this paper is to study the fluctuations of $H_K$ around its expectation as the dimension $d$ go to infinity. Relying on Stein's method and Brascamp-Lieb inequality, we compute an explicit bound for the total variation distance between $H_K$ and its Gaussian counterpart.
Semiclassical analysis of generic codimension 3 crossings

We study a family of solutions to a system of two equations which display a codimension 3 generic crossing: the crossing set is a symplectic submanifold of the phase space. We consider initial data which are bounded families in L 2 . We obtain a quantitative description of the energy transfer above the crossing in terms of two-scale Wigner measures. We prove first that this transfer concerns the part of the energy localized at finite distance, with respect to the scale h, of the sets of all the ingoing classical trajectories. Provided a generalization of two-scale Wigner measures to the case of symplectic submanifolds, we split the Wigner measure of families of solutions into a countable sum of positive Radon measures. The energy transfer at the crossing is described by Landau-Zener formula for the terms of this decomposition.
Contact Models in Robotics: a Comparative Analysis

Physics simulation is ubiquitous in robotics. Whether in model-based approaches (e.g., trajectory optimization), or model-free algorithms (e.g., reinforcement learning), physics simulators are a central component of modern control pipelines in robotics. Over the past decades, several robotic simulators have been developed, each with dedicated contact modeling assumptions and algorithmic solutions. In this article, we survey the main contact models and the associated numerical methods commonly used in robotics for simulating advanced robot motions involving contact interactions. In particular, we recall the physical laws underlying contacts and friction (i.e., Signorini condition, Coulomb's law, and the maximum dissipation principle), and how they are transcribed in current simulators. For each physics engine, we expose their inherent physical relaxations along with their limitations due to the numerical techniques employed. Based on our study, we propose theoretically grounded quantitative criteria on which we build benchmarks assessing both the physical and computational aspects of simulation. We support our work with an open-source and efficient C++ implementation of the existing algorithmic variations. Our results demonstrate that some approximations or algorithms commonly used in robotics can severely widen the reality gap and impact target applications. We hope this work will help motivate the development of new contact models, contact solvers, and robotic simulators in general, at the root of recent progress in motion generation in robotics.
FollowMe: Vehicle Behaviour Prediction in Autonomous Vehicle Settings

An ego vehicle following a virtual lead vehicle planned route is an essential component when autonomous and non-autonomous vehicles interact. Yet, there is a question about the driver's ability to follow the planned lead vehicle route. Thus, predicting the trajectory of the ego vehicle route given a lead vehicle route is of interest. We introduce a new dataset, the FollowMe dataset, which offers a motion and behavior prediction problem by answering the latter question of the driver's ability to follow a lead vehicle. We also introduce a deep spatio-temporal graph model FollowMe-STGCNN as a baseline for the dataset. In our experiments and analysis, we show the design benefits of FollowMe-STGCNN in capturing the interactions that lie within the dataset. We contrast the performance of FollowMe-STGCNN with prior motion prediction models showing the need to have a different design mechanism to address the lead vehicle following settings.
Understanding Overfitting in Adversarial Training via Kernel Regression

Adversarial training and data augmentation with noise are widely adopted techniques to enhance the performance of neural networks. This paper investigates adversarial training and data augmentation with noise in the context of regularized regression in a reproducing kernel Hilbert space (RKHS). We establish the limiting formula for these techniques as the attack and noise size, as well as the regularization parameter, tend to zero. Based on this limiting formula, we analyze specific scenarios and demonstrate that, without appropriate regularization, these two methods may have larger generalization error and Lipschitz constant than standard kernel regression. However, by selecting the appropriate regularization parameter, these two methods can outperform standard kernel regression and achieve smaller generalization error and Lipschitz constant. These findings support the empirical observations that adversarial training can lead to overfitting, and appropriate regularization methods, such as early stopping, can alleviate this issue.
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey

Machine learning-based wearable human activity recognition (WHAR) models enable the development of various smart and connected community applications such as sleep pattern monitoring, medication reminders, cognitive health assessment, sports analytics, etc. However, the widespread adoption of these WHAR models is impeded by their degraded performance in the presence of data distribution heterogeneities caused by the sensor placement at different body positions, inherent biases and heterogeneities across devices, and personal and environmental diversities. Various traditional machine learning algorithms and transfer learning techniques have been proposed in the literature to address the underpinning challenges of handling such data heterogeneities. Domain adaptation is one such transfer learning techniques that has gained significant popularity in recent literature. In this paper, we survey the recent progress of domain adaptation techniques in the Inertial Measurement Unit (IMU)-based human activity recognition area, discuss potential future directions.
Revisiting K2-233 spectroscopic time-series with multidimensional Gaussian Processes

Detecting planetary signatures in radial velocity time-series of young stars is challenging due to their inherently strong stellar activity. However, it is possible to learn information about the properties of the stellar signal by using activity indicators measured from the same stellar spectra used to extract radial velocities. In this manuscript, we present a reanalysis of spectroscopic HARPS data of the young star K2-233, which hosts three transiting planets. We perform a multidimensional Gaussian Process regression on the radial velocity and the activity indicators to characterise the planetary Doppler signals. We demonstrate, for the first time on a real dataset, that the use of a multidimensional Gaussian Process can boost the precision with which we measure the planetary signals compared to a one-dimensional Gaussian Process applied to the radial velocities alone. We measure the semi-amplitudes of K2-233 b, c, and d as $1.31_{-0.74}^{+0.81}$ m s−1 , $1.81_{-0.67}^{+0.71}$ m s−1 and $2. 72_{-0.70}^{+0.66}$ m s−1 , which translates into planetary masses of $2.4_{-1.3}^{+1.5}$ , $4. 6_{-1.7}^{+1.8}$ , and $10. 3_{-2.6}^{+2.4}$M⊕ , respectively. These new mass measurements make K2-233 d a valuable target for transmission spectroscopy observations with JWST. K2-233 is the only young system with two detected inner planets below the radius valley and a third outer planet above it. This makes it an excellent target to perform comparative studies, to inform our theories of planet evolution, formation, migration, and atmospheric evolution.
SurfsUp: Learning Fluid Simulation for Novel Surfaces

Modeling the mechanics of fluid in complex scenes is vital to applications in design, graphics, and robotics. Learning-based methods provide fast and differentiable fluid simulators, however most prior work is unable to accurately model how fluids interact with genuinely novel surfaces not seen during training. We introduce SurfsUp, a framework that represents objects implicitly using signed distance functions (SDFs), rather than an explicit representation of meshes or particles. This continuous representation of geometry enables more accurate simulation of fluid-object interactions over long time periods while simultaneously making computation more efficient. Moreover, SurfsUp trained on simple shape primitives generalizes considerably out-of-distribution, even to complex real-world scenes and objects. Finally, we show we can invert our model to design simple objects to manipulate fluid flow.
Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs

Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi-relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta-paths, which significantly impact the quality of learned embeddings. In this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically explores meta-paths that involve multi-hop neighbors by aggregating multi-order adjacency matrices. The proposed model first constructs different orders of adjacency matrices from manually designed node connections. Next, AMOGCN fuses these various orders of adjacency matrices to create an intact multi-order adjacency matrix. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we employ a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN achieves superior semi-supervised classification performance compared with state-of-the-art competitors.
Repositioning Tiered HotSpot Execution Performance Relative to the Interpreter

Although the advantages of just-in-time compilation over traditional interpretive execution are widely recognised, there needs to be more current research investigating and repositioning the performance differences between these two execution models relative to contemporary workloads. Specifically, there is a need to examine the performance differences between Java Runtime Environment (JRE) Java Virtual Machine (JVM) tiered execution and JRE JVM interpretive execution relative to modern multicore architectures and modern concurrent and parallel benchmark workloads. This article aims to fill this research gap by presenting the results of a study that compares the performance of these two execution models under load from the Renaissance Benchmark Suite. This research is relevant to anyone interested in understanding the performance differences between just-in-time compiled code and interpretive execution. It provides a contemporary assessment of the interpretive JVM core, the entry and starting point for bytecode execution, relative to just-in-time tiered execution. The study considers factors such as the JRE version, the GNU GCC version used in the JRE build toolchain, and the garbage collector algorithm specified at runtime, and their impact on the performance difference envelope between interpretive and tiered execution. Our findings indicate that tiered execution is considerably more efficient than interpretive execution, and the performance gap has increased, ranging from 4 to 37 times more efficient. On average, tiered execution is approximately 15 times more efficient than interpretive execution. Additionally, the performance differences between interpretive and tiered execution are influenced by workload category, with narrower performance differences observed for web-based workloads and more significant differences for Functional and Scala-type workloads.