Miracle-dz/UniFAR-FTUs huggingface dataset:
Augmenting Decompiler Output with Learned Variable Names and Types

A common tool used by security professionals for reverse-engineering binaries found in the wild is the decompiler. A decompiler attempts to reverse compilation, transforming a binary to a higher-level language such as C. High-level languages ease reasoning about programs by providing useful abstractions such as loops, typed variables, and comments, but these abstractions are lost during compilation. Decompilers are able to deterministically reconstruct structural properties of code, but comments, variable names, and custom variable types are technically impossible to recover. In this paper we present DIRTY (DecompIled variable ReTYper), a novel technique for improving the quality of decompiler output that automatically generates meaningful variable names and types. Empirical evaluation on a novel dataset of C code mined from GitHub shows that DIRTY outperforms prior work approaches by a sizable margin, recovering the original names written by developers 66.4% of the time and the original types 75.8% of the time.
Contextual Mood Analysis with Knowledge Graph Representation for Hindi Song Lyrics in Devanagari Script

Lyrics play a significant role in conveying the song's mood and are information to understand and interpret music communication. Conventional natural language processing approaches use translation of the Hindi text into English for analysis. This approach is not suitable for lyrics as it is likely to lose the inherent intended contextual meaning. Thus, the need was identified to develop a system for Devanagari text analysis. The data set of 300 song lyrics with equal distribution in five different moods is used for the experimentation. The proposed system performs contextual mood analysis of Hindi song lyrics in Devanagari text format. The contextual analysis is stored as a knowledge base, updated using an incremental learning approach with new data. Contextual knowledge graph with moods and associated important contextual terms provides the graphical representation of the lyric data set used. The testing results show 64% accuracy for the mood prediction. This work can be easily extended to applications related to Hindi literary work such as summarization, indexing, contextual retrieval, context-based classification and grouping of documents.
Deepfake Representation with Multilinear Regression

Generative neural network architectures such as GANs, may be used to generate synthetic instances to compensate for the lack of real data. However, they may be employed to create media that may cause social, political or economical upheaval. One emerging media is"Deepfake". Techniques that can discriminate between such media is indispensable. In this paper, we propose a modified multilinear (tensor) method, a combination of linear and multilinear regressions for representing fake and real data. We test our approach by representing Deepfakes with our modified multilinear (tensor) approach and perform SVM classification with encouraging results.
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction

In this paper, we propose HF2-VAD, a Hybrid framework that integrates Flow reconstruction and Frame prediction seamlessly to handle Video Anomaly Detection. Firstly, we design the network of ML-MemAE-SC (Multi-Level Memory modules in an Autoencoder with Skip Connections) to memorize normal patterns for optical flow reconstruction so that abnormal events can be sensitively identified with larger flow reconstruction errors. More importantly, conditioned on the reconstructed flows, we then employ a Conditional Variational Autoencoder (CVAE), which captures the high correlation between video frame and optical flow, to predict the next frame given several previous frames. By CVAE, the quality of flow reconstruction essentially influences that of frame prediction. Therefore, poorly reconstructed optical flows of abnormal events further deteriorate the quality of the final predicted future frame, making the anomalies more detectable. Experimental results demonstrate the effectiveness of the proposed method. Code is available at https://github.com/LiUzHiAn/hf2vad.
An authentication model based on cryptography

In this paper we proposed an authentication technique based on the user cards, to improve the authentication process in systems that allows remote access for the users, and raise the security rate during an exchange of their messages. in this technique the server performs two functions, the first function, register the users, and give him user ID, PIN code, and user private card contains secrecy information, which is used to encrypt user messages by using two kinds of encryption symmetric using RC4-Pr and asymmetric using RSA encryption. , the second function, distribute the user's public card if the user demand that, in which the user sends the own authentication code with their own user ID and recipient user ID to the authentication check, and then the server sends the user public card to the recipient user, thus the sender user can send the messages to recipient user without back to the server again. We attained confidentiality using RC4-Pr and RSA encryption and message authentication, user signature, and mutual secret key by using RSA encryption. in this paper we also implement the proposal in [1] RC4-pr algorithm which is modified to improve the key weakness of basic RC4.
Free‐will arbitrary time consensus protocols with diffusive coupling

In this article, free‐will arbitrary time consensus protocols are proposed for multi‐agent systems with single‐ and double‐ (possibly higher‐order) integrator dynamics, respectively, and with (possibly switching) connected interaction graphs and bounded matched disturbances. Under the proposed consensus laws, an average consensus is achieved in a free‐will arbitrary prespecified time regardless of the initial conditions or any other design parameters. Further, the proposed consensus laws for the case with no disturbances are smooth, and they are distributed in the sense that information is only communicated locally between neighboring agents. Finally, simulation results are also provided to illustrate the theoretical results and, an application to arbitrary prespecified time formation control of mobile agents is also presented.
The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation

Accurate detection and segmentation of marine debris is important for keeping the water bodies clean. This paper presents a novel dataset for marine debris segmentation collected using a Forward Looking Sonar (FLS). The dataset consists of 1868 FLS images captured using ARIS Explorer 3000 sensor. The objects used to produce this dataset contain typical house-hold marine debris and distractor marine objects (tires, hooks, valves,etc), divided in 11 classes plus a background class. Performance of state of the art semantic segmentation architectures with a variety of encoders have been analyzed on this dataset and presented as baseline results. Since the images are grayscale, no pre-trained weights have been used. Comparisons are made using Intersection over Union (IoU). The best performing model is Unet with ResNet34 backbone at 0.7481 mIoU.
Interpolation between noncommutative martingale Hardy and BMO spaces: the case 
$\textbf {0

Abstract Let $\mathcal {M}$ be a semifinite von Nemann algebra equipped with an increasing filtration $(\mathcal {M}_n)_{n\geq 1}$ of (semifinite) von Neumann subalgebras of $\mathcal {M}$ . For $0<p <\infty $ , let $\mathsf {h}_p^c(\mathcal {M})$ denote the noncommutative column conditioned martingale Hardy space and $\mathsf {bmo}^c(\mathcal {M})$ denote the column “little” martingale BMO space associated with the filtration $(\mathcal {M}_n)_{n\geq 1}$ . We prove the following real interpolation identity: if $0<p <\infty $ and $0<\theta <1$ , then for $1/r=(1-\theta )/p$ , $$ \begin{align*} \big(\mathsf{h}_p^c(\mathcal{M}), \mathsf{bmo}^c(\mathcal{M})\big)_{\theta, r}=\mathsf{h}_{r}^c(\mathcal{M}), \end{align*} $$ with equivalent quasi norms. For the case of complex interpolation, we obtain that if $0<p<q<\infty $ and $0<\theta <1$ , then for $1/r =(1-\theta )/p +\theta /q$ , $$ \begin{align*} \big[\mathsf{h}_p^c(\mathcal{M}), \mathsf{h}_q^c(\mathcal{M})\big]_{\theta}=\mathsf{h}_{r}^c(\mathcal{M}) \end{align*} $$ with equivalent quasi norms. These extend previously known results from $p\geq 1$ to the full range $0<p<\infty $ . Other related spaces such as spaces of adapted sequences and Junge’s noncommutative conditioned $L_p$ -spaces are also shown to form interpolation scale for the full range $0<p<\infty $ when either the real method or the complex method is used. Our method of proof is based on a new algebraic atomic decomposition for Orlicz space version of Junge’s noncommutative conditioned $L_p$ -spaces. We apply these results to derive various inequalities for martingales in noncommutative symmetric quasi-Banach spaces.
Generating Cyber Threat Intelligence to Discover Potential Security Threats Using Classification and Topic Modeling

Due to the variety of cyber-attacks or threats, the cybersecurity community enhances the traditional security control mechanisms to an advanced level so that automated tools can encounter potential security threats. Very recently, Cyber Threat Intelligence (CTI) has been presented as one of the proactive and robust mechanisms because of its automated cybersecurity threat prediction. Generally, CTI collects and analyses data from various sources e.g., online security forums, social media where cyber enthusiasts, analysts, even cybercriminals discuss cyber or computer security-related topics and discovers potential threats based on the analysis. As the manual analysis of every such discussion (posts on online platforms) is time-consuming, inefficient, and susceptible to errors, CTI as an automated tool can perform uniquely to detect cyber threats. In this paper, we identify and explore relevant CTI from hacker forums utilizing different supervised (classification) and unsupervised learning (topic modeling) techniques. To this end, we collect data from a real hacker forum and constructed two datasets: a binary dataset and a multi-class dataset. We then apply several classifiers along with deep neural network-based classifiers and use them on the datasets to compare their performances. We also employ the classifiers on a labeled leaked dataset as our ground truth. We further explore the datasets using unsupervised techniques. For this purpose, we leverage two topic modeling algorithms namely Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF).
C*-algebraic Schur product theorem, P\'{o}lya-Szeg\H{o}-Rudin question and Novak's conjecture

Striking result of Vyb´ıral [51] says that Schur product of positive matrices is bounded below by the size of the matrix and the row sums of Schur product. Vyb´ıral used this result to prove the Novak’s conjecture. In this paper, we deﬁne Schur product of matrices over ar- bitrary C*-algebras and derive the results of Schur and Vyb´ıral. As an application, we state C*-algebraic version of Novak’s conjecture and solve it for commutative unital C*-algebras. We formulate P´olya-Szeg˝o-Rudin question for the C*-algebraic Schur product of positive matrices.
A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation

Few-shot segmentation focuses on the generalization of models to segment unseen object with limited annotated samples. However, existing approaches still face two main challenges. First, a huge feature distinction between support and query images a causes knowledge transferring barrier, which harms the segmentation performance. Second, limited support prototypes cannot adequately represent features of support objects, hard to guide high-quality query segmentation. To deal with the above two issues, we propose a self-distillation embedded supervised affinity attention model to improve the performance of few-shot segmentation task. Specifically, the self-distillation guided prototype module uses self-distillation to align the features of support and query. The supervised affinity attention module generates a high-quality query attention map to provide sufficient object information. Extensive experiments prove that our model significantly improves the performance compared to existing methods. Comprehensive ablation experiments and visualization studies also show the significant effect of our method on the few-shot segmentation task. On COCO- $20^{i}$ data set, we achieve new state-of-the-art results. Training code and pretrained models are available at https://github.com/cv516Buaa/SD-AANet.
Optimal and Efficient Algorithms for General Mixable Losses against Switching Oracles

We investigate the problem of online learning, which has gained significant attention in recent years due to its applicability in a wide range of fields from machine learning to game theory. Specifically, we study the online optimization of mixable loss functions in a dynamic environment. We introduce online mixture schemes that asymptotically achieves the performance of the best dynamic estimation sequence of the switching oracle with optimal regret redundancies. The best dynamic estimation sequence that we compete against is selected in hindsight with full observation of the loss functions and is allowed to select different optimal estimations in different time intervals (segments). We propose two mixtures in our work. Firstly, we propose a tractable polynomial time complexity algorithm that can achieve the optimal redundancy of the intractable brute force approach. Secondly, we propose an efficient logarithmic time complexity algorithm that can achieve the optimal redundancy up to a constant multiplicity gap. Our results are guaranteed to hold in a strong deterministic sense in an individual sequence manner.
Receding Horizon Iterative Learning Control for Continuously Operated Systems

This paper presents an iterative learning control (ILC) scheme for continuously operated repetitive systems for which no initial condition reset exists. To accomplish this, we develop a lifted system representation that accounts for the effect of the initial conditions on dynamics and projects the dynamics over multiple future iterations. Additionally, we develop an economic cost function and update law that considers the performance over multiple iterations in the future, thus allowing for the prediction horizon to be larger than just the next iteration. Convergence of the iteration varying initial condition and applied input are proven and demonstrated using a simulated servopositioning system test case.
A Two-Layer Near-Optimal Strategy for Substation Constraint Management via Home Batteries

Within electrical distribution networks, substation constraint management requires that aggregated power demand from residential users is kept within suitable bounds. Efficiency of substation constraint management can be measured as the reduction of constraint violations w.r. t. unmanaged demand. Home batteries hold the promise of enabling efficient and user-oblivious substation constraint management. Centralized control of home batteries would achieve optimal efficiency. However, it is hardly acceptable by users, since service providers (e.g., utilities or aggregators) would directly control batteries at user premises. Unfortunately, devising efficient hierarchical control strategies, thus overcoming the above problem, is far from easy. In this article, we present a novel two-layer control strategy for home batteries that avoids direct control of home devices by the service provider and at the same time yields near-optimal substation constraint management efficiency. Our simulation results on field data from 62 households in Denmark show that the substation constraint management efficiency achieved with our approach is at least 82% of the one obtained with a theoretical optimal centralized strategy.
A ug 2 02 1 Multipole representation of a generic gravitational lens

We consider gravitational lensing by a generic extended mass distribution. We represent the static external gravitational field of the lens as a potential via an infinite set of symmetric trace free (STF) moments. This leads to a formulation of the diffraction integral where the gravitational phase shift has the most general form. We use this integral to describe the optical properties of extended gravitational lenses. We discuss the possibility of determining the physical characteristics of the lens including its shape, orientation and composition via gravitational lensing. To do that, we consider STF multipole moments for several well-known solids with uniform density. We discuss the caustics formed by the point spread function (PSF) of such lenses, and also the view seen by an imaging telescope placed in the strong interference region of the lens. We show that at each STF order, all the bodies produce similar caustics that are different only by their magnitudes and orientations. Furthermore, there is ambiguity in determining the shape of the lens and its mass distribution if only a limited number of moments are used in the model. This result justifies the development of more comprehensive lens models that contain a greater number of multipole moments. At the same time, inclusion of higher multipole moments leads to somewhat limited improvements as their contributions are suppressed by corresponding powers of the small parameter (R/b), where R characterizes the body’s physical size and b is the impact parameter, resulting in a weaker signature from those multipole moments in the PSF. Thus, in realistic observations there will always be some ambiguity in the optical properties of a generic lens, unless the properties of the lens can be determined independently, as in the case of the solar gravitational lens (SGL). Our results are novel and offer new insight into gravitational lensing by realistic astrophysical systems.
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution

The classic Monte Carlo path tracing can achieve high quality rendering at the cost of heavy computation. Recent works make use of deep neural networks to accelerate this process, by improving either low-resolution or fewer-sample rendering with super-resolution or denoising neural networks in post-processing. However, denoising and super-resolution have only been considered separately in previous work. We show in this work that Monte Carlo path tracing can be further accelerated by joint super-resolution and denoising (SRD) in post-processing. This new type of joint filtering allows only a low-resolution and fewer-sample (thus noisy) image to be rendered by path tracing, which is then fed into a deep neural network to produce a high-resolution and clean image. The main contribution of this work is a new end-to-end network architecture, specifically designed for the SRD task. It contains two cascaded stages with shared components. We discover that denoising and super-resolution require very different receptive fields, a key insight that leads to the introduction of deformable convolution into the network design. Extensive experiments show that the proposed method outperforms previous methods and their variants adopted for the SRD task.
Adapting to Unseen Vendor Domains for MRI Lesion Segmentation

One of the key limitations in machine learning models is poor performance on data that is out of the domain of the training distribution. This is especially true for image analysis in magnetic resonance (MR) imaging, as variations in hardware and software create non-standard intensities, contrasts, and noise distributions across scanners. Recently, image translation models have been proposed to augment data across domains to create synthetic data points. In this paper, we investigate the application an unsupervised image translation model to augment MR images from a source dataset to a target dataset. Specifically, we want to evaluate how well these models can create synthetic data points representative of the target dataset through image translation, and to see if a segmentation model trained these synthetic data points would approach the performance of a model trained directly on the target dataset. We consider three configurations of augmentation between datasets consisting of translation between images, between scanner vendors, and from labels to images. It was found that the segmentation models trained on synthetic data from labels to images configuration yielded the closest performance to the segmentation model trained directly on the target dataset. The Dice coeffcient score per each target vendor (GE, Siemens, Philips) for training on synthetic data was 0.63, 0.64, and 0.58, compared to training directly on target dataset was 0.65, 0.72, and 0.61.
Adaptive optimal estimation of irregular mean and covariance functions

We propose straightforward nonparametric estimators for the mean and the covariance functions of functional data. Our setup covers a wide range of practical situations. The random trajectories are, not necessarily differentiable, have unknown regularity, and are measured with error at discrete design points. The measurement error could be heteroscedastic. The design points could be either randomly drawn or common for all curves. The definition of our nonparametric estimators depends on the local regularity of the stochastic process generating the functional data. We first propose a simple estimator of this local regularity which takes strength from the replication and regularization features of functional data. Next, we use the “smoothing first, then estimate” approach for the mean and the covariance functions. The new nonparametric estimators achieve optimal rates of convergence. They can be applied with both sparsely or densely sampled curves, are easy to calculate and to update, and perform well in simulations. Simulations built upon a real data example on household power consumption illustrate the effectiveness of the new approach.
Investigating Bias In Automatic Toxic Comment Detection: An Empirical Study

With surge in online platforms, there has been an upsurge in the user engagement on these platforms via comments and reactions. A large portion of such textual comments are abusive, rude and offensive to the audience. With machine learning systems in-place to check such comments coming onto platform, biases present in the training data gets passed onto the classifier leading to discrimination against a set of classes, religion and gender. In this work, we evaluate different classifiers and feature to estimate the bias in these classifiers along with their performance on downstream task of toxicity classification. Results show that improvement in performance of automatic toxic comment detection models is positively correlated to mitigating biases in these models. In our work, LSTM with attention mechanism proved to be a better modelling strategy than a CNN model. Further analysis shows that fasttext embeddings is marginally preferable than glove embeddings on training models for toxicity comment detection. Deeper analysis reveals the findings that such automatic models are particularly biased to specific identity groups even though the model has a high AUC score. Finally, in effort to mitigate bias in toxicity detection models, a multi-task setup trained with auxiliary task of toxicity sub-types proved to be useful leading to upto 0.26% (6% relative) gain in AUC scores.