Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on threat intelligence detection model based on deep neural network

Threat Intelligence is the knowledge set and operational advice of a series of evidences including vulnerabilities, threats, characteristics and behaviors obtained through big data, distributed system or other specific collection methods. It can restore the network attacks that have happened and predict the future possible attacks, and provide reference for users to make decisions. Help users avoid or minimize losses caused by network attacks. However, the existing technologies cannot respond in time and defend in advance to threat behaviors in the network environment as a whole, and cannot simultaneously take into account the prediction efficiency and accuracy of threat prediction. Aiming at the deficiency of existing technologies, this paper builds a security defense model of industrial control system based on threat intelligence. Firstly, credible threat intelligence is extracted through the quality assessment model of deep neural network algorithm. Secondly, high-quality threat intelligence is extracted through the self-defined matching principle, and contextual data is extracted to analyze the attack intention and predict the attack behavior. Finally, by constructing an attack and defense game model based on the attacker and the defender, the mixed strategy Nash equilibrium is used to predict the attack behavior based on non-high quality threat intelligence. Through a series of experiments, the model has a good predictive effect in the industrial control system.
Understanding Consumer perception towards the usage of natural gas as Compressed Natural Gas (CNG) and Piped Natural Gas (PNG)

Natural gas is one of the cleanest and most environment-friendly fuels compared to all other alternative fuels. It can potentially be the next-generation fuel in India, but there is still limited use of natural gas. The paper aims to understand consumer perception and identify the main driving factors influencing consumers to use natural gas as a fuel source. A survey was conducted to understand consumers’ perceptions, and a sample size of 105 individuals was tested for various hypotheses. Most of the responses from the individuals are from Delhi, followed by Uttar Pradesh and Chhattisgarh. The data collected was analyzed by IBM SPSS software through factor analysis followed by regression. The result showed that implementing policies is one of the highest influential factors in accelerating the use of natural gas. Further, it was analyzed that affordability, infrastructure, environmental benefits, and performance of natural gas are the factors on which policies should be focused to increase the use of natural gas as Compressed natural gas (CNG) and Piped natural gas (PNG).
Pitman-closeness of preliminary test and some classical estimators based on records from two-parameter exponential distribution

In this paper, we study the performance of estimators of parameters of two-parameter exponential distribution based on upper records. The generalized likelihood ratio (GLR) test was used to generate preliminary test estimator (PTE) for both parameters. We have compared the proposed estimator with maximum likelihood (ML) and unbiased estimators (UE) under mean-squared error (MSE) and Pitman measure of closeness (PMC). Analytical as well as graphical methods are used to show the range of parameter in which PTE performs better than ML and UE. Results demonstrate that in the case of that prior information is not too far from its real value, the PTE is superior in compare with ML and UE based on both MSE and PMC criteria. The results of the paper will be useful in estimation with record data in life testing experiments.
SCF-FDPS: A Fast N-body Code for Simulating Disk–Halo Systems

A fast N-body code has been developed for simulating a stellar disk embedded in a live dark matter halo. In generating its Poisson solver, a self-consistent field (SCF) code that inherently possesses perfect scalability is incorporated into a tree code that is parallelized using a library termed Framework for Developing Particle Simulators (FDPS). Thus, the code developed here is called SCF-FDPS. This code has realized the speedup of a conventional tree code by applying an SCF method not only to the calculation of the self-gravity of the halo but also to that of the gravitational interactions between the disk and halo particles. Consequently, in the SCF-FDPS code, a tree algorithm is applied only to calculate the self-gravity of the disk. On a many-core parallel computer, the SCF-FDPS code has performed at least 3 times (in one case, nearly an order of magnitude) faster than an extremely tuned tree code on it, if the numbers of disk and halo particles are, respectively, fixed for both codes. In addition, the SCF-FDPS code shows that the central processing unit cost scales almost linearly with the total number of particles and almost inversely with the number of cores. We find that the time evolution of a disk–halo system simulated with the SCF-FDPS code is, in large measure, similar to that obtained using the tree code. We suggest how the present code can be extended to cope with a wide variety of disk-galaxy simulations.
THE TWO-TIME GREEN'S FUNCTIONS IN THE METHOD OF BRIEF DESCRIPTION

Summary. On the basis of method N. Bogolyubov about brief description of the non-equilibrium states asymptotic of Green’s function is investigated in the hydrodynamic approaching for the degenerated systems of Boze-particles with weak co-operation. Lowfrequency asymptotic of Green’s normal functions in approaching of ideal and viscid liquid is calculated.
Off-Road Navigation of Legged Robots Using Linear Transfer Operators

This paper presents the implementation of off-road navigation on legged robots using convex optimization through linear transfer operators. Given a traversability measure that captures the off-road environment, we lift the navigation problem into the density space using the Perron-Frobenius (P-F) operator. This allows the problem formulation to be represented as a convex optimization. Due to the operator acting on an infinite-dimensional density space, we use data collected from the terrain to get a finite-dimension approximation of the convex optimization. Results of the optimal trajectory for off-road navigation are compared with a standard iterative planner, where we show how our convex optimization generates a more traversable path for the legged robot compared to the suboptimal iterative planner.
FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation

This paper presents FUSegNet, a new model for foot ulcer segmentation in diabetes patients, which uses the pre-trained EfficientNet-b7 as a backbone to address the issue of limited training samples. A modified spatial and channel squeeze-and-excitation (scSE) module called parallel scSE or P-scSE is proposed that combines additive and max-out scSE. A new arrangement is introduced for the module by fusing it in the middle of each decoder stage. As the top decoder stage carries a limited number of feature maps, max-out scSE is bypassed there to form a shorted P-scSE. A set of augmentations, comprising geometric, morphological, and intensity-based augmentations, is applied before feeding the data into the network. The proposed model is first evaluated on a publicly available chronic wound dataset where it achieves a data-based dice score of 92.70%, which is the highest score among the reported approaches. The model outperforms other scSE-based UNet models in terms of Pratt's figure of merits (PFOM) scores in most categories, which evaluates the accuracy of edge localization. The model is then tested in the MICCAI 2021 FUSeg challenge, where a variation of FUSegNet called x-FUSegNet is submitted. The x-FUSegNet model, which takes the average of outputs obtained by FUSegNet using 5-fold cross-validation, achieves a dice score of 89.23%, placing it at the top of the FUSeg Challenge leaderboard. The source code for the model is available on https://github.com/mrinal054/FUSegNet.
Madvex: Instrumentation-based Adversarial Attacks on Machine Learning Malware Detection

WebAssembly (Wasm) is a low-level binary format for web applications, which has found widespread adoption due to its improved performance and compatibility with existing software. However, the popularity of Wasm has also led to its exploitation for malicious purposes, such as cryptojacking, where malicious actors use a victim's computing resources to mine cryptocurrencies without their consent. To counteract this threat, machine learning-based detection methods aiming to identify cryptojacking activities within Wasm code have emerged. It is well-known that neural networks are susceptible to adversarial attacks, where inputs to a classifier are perturbed with minimal changes that result in a crass misclassification. While applying changes in image classification is easy, manipulating binaries in an automated fashion to evade malware classification without changing functionality is non-trivial. In this work, we propose a new approach to include adversarial examples in the code section of binaries via instrumentation. The introduced gadgets allow for the inclusion of arbitrary bytes, enabling efficient adversarial attacks that reliably bypass state-of-the-art machine learning classifiers such as the CNN-based Minos recently proposed at NDSS 2021. We analyze the cost and reliability of instrumentation-based adversarial example generation and show that the approach works reliably at minimal size and performance overheads.
Unified Model Learning for Various Neural Machine Translation

Existing neural machine translation (NMT) studies mainly focus on developing dataset-specific models based on data from different tasks (e.g., document translation and chat translation). Although the dataset-specific models have achieved impressive performance, it is cumbersome as each dataset demands a model to be designed, trained, and stored. In this work, we aim to unify these translation tasks into a more general setting. Specifically, we propose a ``versatile'' model, i.e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks, and can translate well in multiple settings simultaneously, and theoretically it can be as many as possible. Through unified learning, UMLNMT is able to jointly train across multiple tasks, implementing intelligent on-demand translation. On seven widely-used translation tasks, including sentence translation, document translation, and chat translation, our UMLNMT results in substantial improvements over dataset-specific models with significantly reduced model deployment costs. Furthermore, UMLNMT can achieve competitive or better performance than state-of-the-art dataset-specific methods. Human evaluation and in-depth analysis also demonstrate the superiority of our approach on generating diverse and high-quality translations. Additionally, we provide a new genre translation dataset about famous aphorisms with 186k Chinese->English sentence pairs.
Deep Learning Aided Beamforming for Downlink Non Orthogonal Multiple Access Systems

We investigate the problem of optimal beamformer design for the downlink of Multi Input Single Output (MISO) Non-Orthogonal Multiple Access (NOMA). In more detail, focusing on the two-user scenario, we first derive a closed from expression for the Bit Error Rate (BER) experienced by both user. Using the derived expression, in an effort to introduce fairness in our system design, we introduce the problem of optimal, with respect to minimizing the maximum of the BER values experienced by the two users, beamforming and propose a Machine Learning (ML) based solution for this problem. Finally, we conduct simulations which allow us to verify that our proposed algorithm outperforms other existing benchmarks as well as that in a variety of cases, it may result to BER performance close to the one obtained by the use of time consuming constrained optimization methods, such as to solve the given optimization problem.
Recording, noise, and servo characteristics of patterned thin film media

In this paper, we present a combined experimental and micromagnetic simulation study of the recording, noise and position error signal characteristics in patterned thin film media. The medium is patterned with a focused ion beam etching technique on typical present thin film disks. It is found the transition noise is completely absent in the patterned thin film media. Comparing with continuous thin film media, the medium noise is reduced to the level of noise at saturation remnant state and is no longer density-dependent. With the elimination of transition noise, the signal-to-medium-noise ratio will be sufficient at very narrow track widths without further reduction of grain size. A slight increase of intergranular exchange coupling will have little impact on medium noise, but enhance thermal magnetic stability of the medium. Down track orientation of the crystalline easy axes will further improve the recording performances. Position error signal patterns in patterned media can be more versatile and significantly more effective and efficient.
Combining Combination Properties: An Analysis of Stable Infiniteness, Convexity, and Politeness

We make two contributions to the study of theory combination in satisfiability modulo theories. The first is a table of examples for the combinations of the most common model-theoretic properties in theory combination, namely stable infiniteness, smoothness, convexity, finite witnessability, and strong finite witnessability (and therefore politeness and strong politeness as well). All of our examples are sharp, in the sense that we also offer proofs that no theories are available within simpler signatures. This table significantly progresses the current understanding of the various properties and their interactions. The most remarkable example in this table is of a theory over a single sort that is polite but not strongly polite (the existence of such a theory was only known until now for two-sorted signatures). The second contribution is a new combination theorem showing that in order to apply polite theory combination, it is sufficient for one theory to be stably infinite and strongly finitely witnessable, thus showing that smoothness is not a critical property in this combination method. This result has the potential to greatly simplify the process of showing which theories can be used in polite combination, as showing stable infiniteness is considerably simpler than showing smoothness.
Existence and approximation of densities of chord length- and cross section area distributions

In various stereological problems an $n$-dimensional convex body is intersected with an $(n-1)$-dimensional Isotropic Uniformly Random (IUR) hyperplane. In this paper the cumulative distribution function associated with the $(n-1)$-dimensional volume of such a random section is studied. This distribution is also known as chord length distribution and cross section area distribution in the planar and spatial case respectively. For various classes of convex bodies it is shown that these distribution functions are absolutely continuous with respect to Lebesgue measure. A Monte Carlo simulation scheme is proposed for approximating the corresponding probability density functions.
Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator

Multimodal-driven talking face generation refers to animating a portrait with the given pose, expression, and gaze transferred from the driving image and video, or estimated from the text and audio. However, existing methods ignore the potential of text modal, and their generators mainly follow the source-oriented feature rearrange paradigm coupled with unstable GAN frameworks. In this work, we first represent the emotion in the text prompt, which could inherit rich semantics from the CLIP, allowing flexible and generalized emotion control. We further reorganize these tasks as the target-oriented texture transfer and adopt the Diffusion Models. More specifically, given a textured face as the source and the rendered face projected from the desired 3DMM coefficients as the target, our proposed Texture-Geometry-aware Diffusion Model decomposes the complex transfer problem into multi-conditional denoising process, where a Texture Attention-based module accurately models the correspondences between appearance and geometry cues contained in source and target conditions, and incorporate extra implicit information for high-fidelity talking face generation. Additionally, TGDM can be gracefully tailored for face swapping. We derive a novel paradigm free of unstable seesaw-style optimization, resulting in simple, stable, and effective training and inference schemes. Extensive experiments demonstrate the superiority of our method.
Separability and Non-Determinizability of WSTS

There is a recent separability result for the languages of well-structured transition systems (WSTS) that is surprisingly general: disjoint WSTS languages are always separated by a regular language. The result assumes that one of the languages is accepted by a deterministic WSTS, and it is not known whether this assumption is needed. There are two ways to get rid of the assumption, none of which has led to conclusions so far: (i) show that WSTS can be determinized or (ii) generalize the separability result to non-deterministic WSTS languages. Our contribution is to show that (i) does not work but (ii) does. As for (i), we give a non-deterministic WSTS language that we prove cannot be accepted by a deterministic WSTS. The proof relies on a novel characterization of the languages accepted by deterministic WSTS. As for (ii), we show how to find finitely represented inductive invariants without having the tool of ideal decompositions at hand. Instead, we work with closures under converging sequences. Our results hold for upward- and downward-compatible WSTS.
SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning

We propose SimSC, a remarkably simple framework, to address the problem of semantic matching only based on the feature backbone. We discover that when fine-tuning ImageNet pre-trained backbone on the semantic matching task, L2 normalization of the feature map, a standard procedure in feature matching, produces an overly smooth matching distribution and significantly hinders the fine-tuning process. By setting an appropriate temperature to the softmax, this over-smoothness can be alleviated and the quality of features can be substantially improved. We employ a learning module to predict the optimal temperature for fine-tuning feature backbones. This module is trained together with the backbone and the temperature is updated online. We evaluate our method on three public datasets and demonstrate that we can achieve accuracy on par with state-of-the-art methods under the same backbone without using a learned matching head. Our method is versatile and works on various types of backbones. We show that the accuracy of our framework can be easily improved by coupling it with more powerful backbones.
Ground-to-UAV Integrated Network: Low Latency Communication over Interference Channel

We present a novel and first-of-its-kind information-theoretic framework for the key design consideration and implementation of a ground-to-UAV (G2U) communication network to minimize end-to-end transmission delay in the presence of interference. The proposed framework is useful as it describes the minimum transmission latency for an uplink ground-to-UAV communication must satisfy while achieving a given level of reliability. To characterize the transmission delay, we utilize Fano's inequality and derive the tight upper bound for the capacity for the G2U uplink channel in the presence of interference, noise, and potential jamming. Subsequently, given the reliability constraint, the error exponent is obtained for the given channel. Furthermore, a relay UAV in the dual-hop relay mode, with amplify-and-forward (AF) protocol, is considered, for which we jointly obtain the optimal positions of the relay and the receiver UAVs in the presence of interference. Interestingly, in our study, we find that for both the point-to-point and relayed links, increasing the transmit power may not always be an optimal solution for delay minimization problems. Moreover, we prove that there exists an optimal height that minimizes the end-to-end transmission delay in the presence of interference. The proposed framework can be used in practice by a network controller as a system parameters selection criteria, where among a set of parameters, the parameters leading to the lowest transmission latency can be incorporated into the transmission. The based analysis further set the baseline assessment when applying Command and Control (C2) standards to mission-critical G2U and UAV-to-UAV(U2U) services.
Moment Based Thresholding in Binary Classification

Binary classification is the process of assigning one of two different labels or classes to each of data patterns. One of simplest methods for classifying data patterns is thresholding and specifying best threshold value is a major issue in these algorithms. This paper proposes a thresholding algorithm in which threshold value is determined by using third order momentum values. Perfornmance of the algorithm is evaluated based on the classification accuracy and compared with well known MiniMax algorithm. Results reveal that the proposed algorithm achieves higher accuracy with respect to the MiniMax.
Distribution of Chores with Information Asymmetry

Fair distribution of indivisible tasks with non-positive valuations (aka chores) has given rise to a large body of work in recent years. A popular approximate fairness notion is envy-freeness up to one item (EF1), which requires that any pairwise envy can be eliminated by the removal of a single item. While an EF1 and Pareto optimal (PO) allocation of goods always exists and can be computed via several well-known algorithms, even the existence of such solutions for chores remains open, to date. We take an epistemic approach utilizing information asymmetry by introducing dubious chores -- items that inflict no cost on receiving agents, but are perceived costly by others. On a technical level, dubious chores provide a more fine-grained approximation of envy-freeness -- compared to relaxations such as EF1 -- which enables progress towards addressing open problems on the existence and computation of EF1 and PO. In particular, we show that finding allocations with optimal number of dubious chores is computationally hard even for highly restricted classes of valuations. Nonetheless, we prove the existence of envy-free and PO allocations for $n$ agents with only $2n-2$ dubious chores and strengthen it to $n-1$ dubious chores in four special classes of valuations. Our experimental analysis demonstrate that baseline algorithms only require a relatively small number of dubious chores to achieve envy-freeness in practice.