Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing One with Many - Solving Binary2source Function Matching Under Function Inlining

Binary2source function matching is a fundamental task for many security applications, including Software Component Analysis (SCA). “1-to-1” mechanism has been applied in existing binary2source matching works, in which one binary function is matched against one source function. However, we discovered that such mapping could be “1-to-n” (one query binary function maps multiple source functions), due to the existence of function inlining . To help conduct binary2source function matching under function inlining, we propose a method named O2NMatcher to generate Source Function Sets (SFSs) as the matching target for binary functions with inlining. We first propose a model named ECOCCJ48 for inlined call site prediction. To train this model, we leverage the compilable OSS to generate a dataset with labeled call sites (inlined or not), extract several features from the call sites, and design a compiler-opt-based multi-label classifier by inspecting the inlining correlations between different compilations. Then, we use this model to predict the labels of call sites in the uncompilable OSS projects without compilation and obtain the labeled function call graphs of these projects. Next, we regard the construction of SFSs as a sub-tree generation problem and design root node selection and edge extension rules to construct SFSs automatically. Finally, these SFSs will be added to the corpus of source functions and compared with binary functions with inlining. We conduct several experiments to evaluate the effectiveness of O2NMatcher and results show our method increases the performance of existing works by 6% and exceeds all the state-of-the-art works. 1
Annotating Privacy Policies in the Sharing Economy

Applications (apps) of the Digital Sharing Economy (DSE), such as Uber, Airbnb, and TaskRabbit, have become a main enabler of economic growth and shared prosperity in modern-day societies. However, the complex exchange of goods, services, and data that takes place over these apps frequently puts their end-users' privacy at risk. Privacy policies of DSE apps are provided to disclose how private user data is being collected and handled. However, in reality, such policies are verbose and difficult to understand, leaving DSE users vulnerable to privacy intrusive practices. To address these concerns, in this paper, we propose an automated approach for annotating privacy policies in the DSE market. Our approach identifies data collection claims in these policies and maps them to the quality features of their apps. Visual and textual annotations are then used to further explain and justify these claims. The proposed approach is evaluated with 18 DSE app users. The results show that annotating privacy policies can significantly enhance their comprehensibility to the average DSE user. Our findings are intended to help DSE app developers to draft more comprehensible privacy policies as well as help their end-users to make more informed decisions in one of the fastest growing software ecosystems in the world.
InGVIO: A Consistent Invariant Filter for Fast and High-Accuracy GNSS-Visual-Inertial Odometry

Combining Global Navigation Satellite System (GNSS) with visual and inertial sensors can give smooth pose estimation without drifting. The fusion system gradually degrades to Visual-Inertial Odometry (VIO) with the number of satellites decreasing, which guarantees robust global navigation in GNSS unfriendly environments. In this letter, we propose an open-sourced invariant filter-based platform, InGVIO, to tightly fuse monocular/stereo visual-inertial measurements, along with raw data from GNSS. InGVIO gives highly competitive results in terms of computational load compared to current graph-based algorithms, meanwhile possessing the same or even better level of accuracy. Thanks to our proposed marginalization strategies, the baseline for triangulation is large although only a few cloned poses are kept. Moreover, we define the infinitesimal symmetries of the system and exploit the various structures of its symmetry group, being different from the total symmetries of the VIO case, which elegantly gives results for the pattern of degenerate motions and the structure of unobservable subspaces. We prove that the properly-chosen invariant error is still compatible with all possible symmetry group structures of InGVIO and has intrinsic consistency properties. Besides, InGVIO has strictly linear error propagation without linearization error. InGVIO is tested on both open datasets and our proposed fixed-wing datasets with variable levels of difficulty and various numbers of satellites. The latter datasets, to the best of our knowledge, are the first datasets open-sourced to the community on a fixed-wing aircraft with raw GNSS.
Dictionary-Assisted Supervised Contrastive Learning

Text analysis in the social sciences often involves using specialized dictionaries to reason with abstract concepts, such as perceptions about the economy or abuse on social media. These dictionaries allow researchers to impart domain knowledge and note subtle usages of words relating to a concept(s) of interest. We introduce the dictionary-assisted supervised contrastive learning (DASCL) objective, allowing researchers to leverage specialized dictionaries when fine-tuning pretrained language models. The text is first keyword simplified: a common, fixed token replaces any word in the corpus that appears in the dictionary(ies) relevant to the concept of interest. During fine-tuning, a supervised contrastive objective draws closer the embeddings of the original and keyword-simplified texts of the same class while pushing further apart the embeddings of different classes. The keyword-simplified texts of the same class are more textually similar than their original text counterparts, which additionally draws the embeddings of the same class closer together. Combining DASCL and cross-entropy improves classification performance metrics in few-shot learning settings and social science applications compared to using cross-entropy alone and alternative contrastive and data augmentation methods.
Reconstruction of compressed spectral imaging based on global structure and spectral correlation

In this paper, a convolutional sparse coding method based on global structure characteristics and spectral correlation is proposed for the reconstruction of compressive spectral images. The spectral data is regarded as the convolution sum of the convolution kernel and the corresponding coefficients, using the convolution kernel operates the global image information, preserving the structure information of the spectral image in the spatial dimension. To take full exploration of the constraints between spectra, the coefficients corresponding to the convolution kernel are constrained by the L_(2,1)norm to improve spectral accuracy. And, to solve the problem that convolutional sparse coding is insensitive to low frequency, the global total-variation (TV) constraint is added to estimate the low-frequency components. It not only ensures the effective estimation of the low-frequency but also transforms the convolutional sparse coding into a de-noising process, which makes the reconstructing process simpler. Simulations show that compared with the current mainstream optimization methods, the proposed method can improve the reconstruction quality by up to 4 dB in PSNR and 10% in SSIM, and has a great improvement in the details of the reconstructed image.
Generalizability of Functional Forms for Interatomic Potential Models Discovered by Symbolic Regression

In recent years there has been great progress in the use of machine learning algorithms to develop interatomic potential models. Machine-learned potential models are typically orders of magnitude faster than density functional theory but also orders of magnitude slower than physics-derived models such as the embedded atom method. In our previous work, we used symbolic regression to develop fast, accurate and transferrable interatomic potential models for copper with novel functional forms that resemble those of the embedded atom method. To determine the extent to which the success of these forms was specific to copper, here we explore the generalizability of these models to other face-centered cubic transition metals and analyze their out-of-sample performance on several material properties. We found that these forms work particularly well on elements that are chemically similar to copper. When compared to optimized Sutton-Chen models, which have similar complexity, the functional forms discovered using symbolic regression perform better across all elements considered except gold where they have a similar performance. They perform similarly to a moderately more complex embedded atom form on properties on which they were trained, and they are more accurate on average on other properties. We attribute this improved generalized accuracy to the relative simplicity of the models discovered using symbolic regression. The genetic programming models are found to outperform other models from the literature about 50% of the time in a variety of property predictions, with about 1/10th the model complexity on average. We discuss the implications of these results to the broader application of symbolic regression to the development of new potentials and highlight how models discovered for one element can be used to seed new searches for different elements.
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations

Sim-to-real transfer trains RL agents in the simulated environments and then deploys them in the real world. Sim-to-real transfer has been widely used in practice because it is often cheaper, safer and much faster to collect samples in simulation than in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest.
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants

Deep learning-based adversarial malware detectors have yielded promising results in detecting never-before-seen malware executables without relying on expensive dynamic behavior analysis and sandbox. Despite their abilities, these detectors have been shown to be vulnerable to adversarial malware variants - meticulously modified, functionality-preserving versions of original malware executables generated by machine learning. Due to the nature of these adversarial modifications, these adversarial methods often use a single view of malware executables (i.e., the binary/hexadecimal view) to generate adversarial malware variants. This provides an opportunity for the defenders (i.e., malware detectors) to detect the adversarial variants by utilizing more than one view of a malware file (e.g., source code view in addition to the binary view). The rationale behind this idea is that while the adversary focuses on the binary view, certain characteristics of the malware file in the source code view remain untouched which leads to the detection of the adversarial malware variants. To capitalize on this opportunity, we propose Adversarially Robust Multiview Malware Defense (ARMD), a novel multi-view learning framework to improve the robustness of DL-based malware detectors against adversarial variants. Our experiments on three renowned open-source deep learning-based malware detectors across six common malware categories show that ARMD is able to improve the adversarial robustness by up to seven times on these malware detectors.
A shot in the Dark (Ages): a faint galaxy at z = 9 . 76 con-ﬁrmed with JWST

The appearance of galaxies over the ﬁrst billion years after the Big Bang is believed to be responsible for the last dramatic change in the state of the Universe. Ultraviolet photons from galaxies within this time period - the Epoch of Reionization - ionized intergalactic Hydrogen, rendering the Universe transparent to UV radiation and ending the so-called cosmic Dark Ages, sometime after redshift z ∼ 8 . The majority of ionizing photons in the ﬁrst few hundred Myrs of cosmic history are thought to derive from galaxies signiﬁcantly fainter than the characteristic luminosity L ∗ 1,2 . These faint galaxies are thought to be surrounded by sufﬁcient neutral gas to prevent the escape of the Lyman- α photons that would allow conﬁrmation with current observatories. Here we demonstrate the power of the recently commissioned James Webb Space Telescope to transform our understanding of the sources of reionization, by reporting the ﬁrst spectroscopic conﬁrmation of a very low luminosity ( ∼ 0 . 05 L ∗ ) galaxy at z = 9 . 76 , observed 480 Myr after the Big Bang, via the detection of the Lyman-break and redward continuum with the NIRSpec and NIRCam instruments. The galaxy JD1 3 is gravitationally magniﬁed by a factor of µ ∼ 13 by the foreground cluster A2744. The power of JWST and lensing allows us to peer deeper than ever before into the cosmic Dark Ages, revealing the compact ( ∼ 150 pc) and complex morphology and physical properties of an ultrafaint galaxy ( M UV = − 17 . 45 ). We conducted James Webb Space Telescope ( JWST ) observations with NIRCam imaging and NIRSpec prism spectroscopy (DDT 2756; PI Chen) of the galaxy JD1, previously identi-4 (Ref. 5 and an update to Ref. 3 discussed in the methods section; blue and cyan curves respectively). The A and B multiple images of the z = 9 . 76 galaxy are highlighted by red circles. The inset represents a zoom-in on the brightest component (JD1), with the 0. ”2 × 1. ”2 NIRSpec slit (constructed using three MSA shutters) used for prism spectroscopy in our DDT program. Critical lines for a source at z ∼ 10 , the redshift indicated by the lens model, the photometry, and the spectroscopy, are in excellent mutual agreement – highlighting the robustness of the lens model – and fall between the two images consistent with their very similar brightness. In order to show the power of lensing to constrain the redshift of the source, we also show in orange line the critical line for a source at z = 2 . 34 5 . If JD1 were at this lower redshift the image positions and ﬂuxes would be inconsistent with the measurements.
TPU-MLIR: A Compiler For TPU Using MLIR

Multi-level intermediate representations (MLIR) show great promise for reducing the cost of building domain-specific compilers by providing a reusable and extensible compiler infrastructure. This work presents TPU-MLIR, an end-to-end compiler based on MLIR that deploys pre-trained neural network (NN) models to a custom ASIC called a Tensor Processing Unit (TPU). TPU-MLIR defines two new dialects to implement its functionality: 1. a Tensor operation (TOP) dialect that encodes the deep learning graph semantics and independent of the deep learning framework and 2. a TPU kernel dialect to provide a standard kernel computation on TPU. A NN model is translated to the TOP dialect and then lowered to the TPU dialect for different TPUs according to the chip's configuration. We demonstrate how to use the MLIR pass pipeline to organize and perform optimization on TPU to generate machine code. The paper also presents a verification procedure to ensure the correctness of each transform stage.
Identifying Diversity, Equity, Inclusion, and Accessibility (DEIA) Indicators for Transportation Systems using Social Media Data: The Case of New York City during Covid-19 Pandemic

The adoption of transportation policies that prioritized highway expansion over public transportation has disproportionately impacted minorities and low-income people by restricting their access to social and economic opportunities and thus resulting in residential segregation. Policymakers, transportation researchers, planners, and practitioners have started acknowledging the need to build a diverse, equitable, inclusive, and accessible (DEIA) transportation system. Traditionally, this has been done through survey-based approaches that are time-consuming and expensive. While there is recent attention on leveraging social media data in transportation, the literature is inconclusive regarding the use of social media data as a viable alternative to traditional sources to identify the latent DEIA indicators based on public reactions and perspectives on social media. This study utilized large-scale Twitter data covering eight counties around the New York City (NYC) area during the initial phase of the Covid-19 lockdown to address this research gap. Natural language processing techniques were used to identify transportation-related major DEIA issues for residents living around NYC by analyzing their relevant tweet conversations. The study revealed that citizens, who had negative sentiments toward the DEIA of their local transportation system, broadly discussed racism, income, unemployment, gender, ride dependency, transportation modes, and dependent groups. Analyzing the socio-demographic information based on census tracts, the study also observed that areas with a higher percentage of low-income, female, Hispanic, and Latino populations share more concerns about transportation DEIA on Twitter.
Growing Schr\"odinger's cat states by local unitary time evolution of product states

We envisage many-body systems that can be described by quantum spin-chain Hamiltonians with a trivial separable eigenstate. For generic Hamiltonians, such a state represents a quantum scar. We show that, typically, a macroscopically-entangled state naturally grows after a single projective measurement of just one spin in the trivial eigenstate; moreover, we identify a condition under which what is growing is a"Schr\"odinger's cat state". Our analysis does not reveal any particular requirement for the entangled state to develop, provided that the trivial eigenstate does not minimise/maximise a local conservation law. We study two examples explicitly: systems described by generic Hamiltonians and a model that exhibits a $U(1)$ hidden symmetry. The latter can be reinterpreted as a 2-leg ladder in which the interactions along the legs are controlled by the local state on the other leg through transistor-like building blocks.
An f(R,T) Gravity Based FLRW Model and Observational Constraints

We attempt to construct a Friedmann-Lemaitre-Robertson-Walker(FLRW) cosmological model in $f(R, T)$ gravity which exhibits a phase transition from deceleration to acceleration at present. We take $f(R,T) = R + 2 \lambda T$, $\lambda$ being an arbitrary constant. In our model, the $\lambda$ parameter develops a negative pressure in the universe whose Equation of state is parameterized. The present values of model parameters such as density, Hubble, deceleration, Equation of state, and $\lambda$ are estimated statistically by using the Chi-Square test. For this, we have used three different types of observational data sets: the $46$ Hubble parameter data set, the SNeIa $715$ data sets of distance modulus, and the 66 Pantheon data set (the latest compilation of SNeIa 40 bined plus 26 high red shift apparent magnitude $m_b$ data set in the red shift ranges from $0.014 \leq z \leq 2.26 $). We have calculated the transitional red shift and time. The estimated results for the present values of various model parameters are found as per expectations and surveys. Interestingly, we get the present value of the density $\rho_0$, $\simeq 1.5 \rho_c $. The critical density is estimated as $\rho_c\simeq 1.88 ~ h_0^2~10^{-29}~gm/cm^3 $ in the literature. The higher value of the present density is attributed to the presence of some additional energies in the universe apart from baryon energy. We have examined the behavior of the pressure in our model. It is negative and produces acceleration in the universe. Its present value is obtained as $p_0 \simeq - 0.7 \rho_0$.
A Variational Inequality Model for Learning Neural Networks

Neural networks have become ubiquitous tools for solving signal and image processing problems, and they often outperform standard approaches. Nevertheless, training the layers of a neural network is a challenging task in many applications. The prevalent training procedure consists of minimizing highly non-convex objectives based on data sets of huge dimension. In this context, current methodologies are not guaranteed to produce global solutions. We present an alternative approach which foregoes the optimization framework and adopts a variational inequality formalism. The associated algorithm guarantees convergence of the iterates to a true solution of the variational inequality and it possesses an efficient block-iterative structure. A numerical application is presented.
Querying Incomplete Numerical Data: Between Certain and Possibile Answers

Queries with aggregation and arithmetic operations, as well as incomplete data, are common in real-world database, but we lack a good understanding of how they should interact. On the one hand, systems based on SQL provide ad-hoc rules for numerical nulls, on the other, theoretical research largely concentrates on the standard notions of certain and possible answers. In the presence of numerical attributes and aggregates, however, these answers are often meaningless, returning either too little or too much. Our goal is to define a principled framework for databases with numerical nulls and answering queries with arithmetic and aggregations over them. Towards this goal, we assume that missing values in numerical attributes are given by probability distributions associated with marked nulls. This yields a model of probabilistic bag databases in which tuples are not necessarily independent since nulls can repeat. We provide a general compositional framework for query answering and then concentrate on queries that resemble standard SQL with arithmetic and aggregation. We show that these queries are measurable, and their outputs have a finite representation. Moreover, since the classical forms of answers provide little information in the numerical setting, we look at the probability that numerical values in output tuples belong to specific intervals. Even though their exact computation is intractable, we show efficient approximation algorithms to compute such probabilities.
FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning

Multimodal tasks in the fashion domain have significant potential for e-commerce, but involve challenging vision-and-language learning problems—e.g., retrieving a fashion item given a reference image plus text feedback from a user. Prior works on multimodal fashion tasks have either been limited by the data in individual benchmarks, or have leveraged generic vision-and-language pre-training but have not taken advantage of the characteristics of fashion data. Additionally, these works have mainly been restricted to multimodal understanding tasks. To address these gaps, we make two key contributions. First, we propose a novel fashion-specific pre-training framework based on weakly-supervised triplets constructed from fashion image-text pairs. We show the triplet-based tasks are an effective addition to standard multimodal pre-training tasks. Second, we propose a flexible decoder-based model architecture capable of both fashion retrieval and captioning tasks. Together, our model design and pre-training approach are competitive on a diverse set of fashion tasks, including cross-modal retrieval, image retrieval with text feedback, image captioning, relative image captioning, and multimodal categorization.