Miracle-dz/UniFAR-FTUs huggingface dataset:
DroidRL: Reinforcement Learning Driven Feature Selection for Android Malware Detection

Due to the completely open-source nature of Android, the exploitable vulnerability of malware attacks is increasing. Machine learning has led to a great evolution in Android malware detection in recent years, which is typically applied in the classiﬁcation phase. Applying neural networks to feature selection phase is another topic worth investigating, while the correlation between features could be ignored in some traditional ranking-based feature selection algorithms. And it is time-consuming for exploring all possible valid feature subsets when processing a large number of Android features using a wrapper-based approach. Attempting to tackle the problem of computational expense and the vulnerability of the syntax integrity of multi-source data, this paper proposed the DroidRL framework. The main idea is to apply Double DQN(DDQN) algorithm to select a subset of features that can be eﬀectively used for malware classiﬁcation. To select a better subset of features over a larger range, the exploration-exploitation policy is applied in the model training phase. The RNN is used as the decision network of DDQN to give the framework the ability to sequentially select features. Word embedding is applied for feature representation to enhance the framework’s ability to ﬁnd the semantic relevance of features. On one hand, The framework’s feature selection exhibits high performance without any human intervention. On the other hand, the proposed framework can easily be ported to other feature selection tasks with some minor changes. The experiment results compared with diﬀerent classiﬁers, decision networks, and related work showed a signiﬁcant eﬀect using the Random Forest classiﬁer, reaching 95.6% accuracy with only 24 features selected.
Preserving Nonnegativity in Discontinuous Galerkin Approximations to Scalar Transport via Truncation and Mass Aware Rescaling (TMAR)

AbstractAn accurate nonnegativity preserving limiter is presented for use with discontinuous Galerkin (DG) discretizations of scalar advection equations. The nonnegativity of the tracer field is preserved through the application of a mass conservative limiter that truncates negatives within each element and linearly rescales the resulting DG polynomials to preserve element-mean mass. As a preliminary step, the DG fluxes through each side of the element are limited in a manner similar to flux-corrected transport to ensure that the element-mean mass remains nonnegative during each individual stage of the time integration. In this paper, it is proven that such a truncation and mass aware rescaling (TMAR) does not change the order of accuracy of the underlying unlimited DG approximation. Numerical tests with two-dimensional deforming flows confirm that the method remains accurate and efficient while preserving nonnegativity. In comparison to some popular previous approaches, TMAR limiting is particularly well.
Complex best $r$-term approximations almost always exist in finite dimensions

We show that in finite-dimensional nonlinear approximations, the best $r$-term approximant of a function $f$ almost always exists over $\mathbb{C}$ but that the same is not true over $\mathbb{R}$, i.e., the infimum $\inf_{f_1,\dots,f_r \in Y} \lVert f - f_1 - \dots - f_r \rVert$ is almost always attainable by complex-valued functions $f_1,\dots, f_r$ in $Y$, a set of functions that have some desired structures. Our result extends to functions that possess special properties like symmetry or skew-symmetry under permutations of arguments. For the case where $Y$ is the set of separable functions, the problem becomes that of best rank-$r$ tensor approximations. We show that over $\mathbb{C}$, any tensor almost always has a unique best rank-$r$ approximation. This extends to other notions of tensor ranks such as symmetric rank and alternating rank, to best $r$-block-terms approximations, and to best approximations by tensor networks. When applied to sparse-plus-low-rank approximations, we obtain that for any given $r$ and $k$, a general tensor has a unique best approximation by a sum of a rank-$r$ tensor and a $k$-sparse tensor with a fixed sparsity pattern; this arises in, for example, estimation of covariance matrices of a Gaussian hidden variable model with $k$ observed variables conditionally independent given $r$ hidden variables. The existential (but not the uniqueness) part of our result also applies to best approximations by a sum of a rank-$r$ tensor and a $k$-sparse tensor with no fixed sparsity pattern, as well as to tensor completion problems.
New Approximation Algorithms for Maximum Asymmetric Traveling Salesman and Shortest Superstring

In the maximum asymmetric traveling salesman problem (Max ATSP) we are given a complete directed graph with nonnegative weights on the edges and we wish to compute a traveling salesman tour of maximum weight. In this paper we give a fast combinatorial $\frac{7}{10}$-approximation algorithm for Max ATSP. It is based on techniques of {\em eliminating} and {\em diluting} problematic subgraphs with the aid of {\it half-edges} and a method of edge coloring. (A {\it half-edge} of edge $(u,v)$ is informally speaking"either a head or a tail of $(u,v)$". ) A novel technique of {\em diluting} a problematic subgraph $S$ consists in a seeming reduction of its weight, which allows its better handling. The current best approximation algorithms for Max ATSP, achieving the approximation guarantee of $\frac 23$, are due to Kaplan, Lewenstein, Shafrir, Sviridenko (2003) and Elbassioni, Paluch, van Zuylen (2012). Using a result by Mucha, which states that an $\alpha$-approximation algorithm for Max ATSP implies a $(2+\frac{11(1-\alpha)}{9-2\alpha})$-approximation algorithm for the shortest superstring problem (SSP), we obtain also a $(2 \frac{33}{76} \approx 2,434)$-approximation algorithm for SSP, beating the previously best known (having an approximation factor equal to $2 \frac{11}{23} \approx 2,4782$.)
Robust Directional Modulation Design for Secrecy Rate Maximization in Multiuser Networks

Based on directional modulation (DM), robust beamforming matrix design for sum secrecy rate maximization is investigated in this article in multiuser systems. First, in case of random estimation error of direction angles toward eavesdroppers, a Von Mises distribution-sum secrecy rate maximization (VMD-SSRM) method is proposed to maximize the sum secrecy rate. Semidefinite relaxation and first-order approximation based on Taylor expansion are employed to solve the optimization problem in VMD-SSRM. Then in order to optimize the sum secrecy rate in the case of the worst estimation errors of direction angles, a maximum angle estimation error-SSRM (MAEE-SSRM) method is proposed. The optimization problem is constructed based on the upper and lower bounds of the estimated eavesdropping channel related coefficient and then solved by the change of the variable. Simulation results show that our two proposed methods have better sum secrecy rates than the state-of-the-art methods. Furthermore, when the estimation error of direction angle toward each eavesdropper is random but within a certain range, the secrecy performance of our VMD-SSRM method is better than that of our MAEE-SSRM method. On contrary, when the direction angle estimation error reaches its maximum, our MAEE-SSRM method performs better.
Assessing photoplethysmographic imaging performance beyond facial perfusion analysis

Photoplethysmographic imaging (PPGI) systems are relatively new non-contact biophotonic diffuse reflectance systems able to assess arterial pulsations through transient changes in light-tissue interaction. Many PPGI studies have focused on extracting heart rate from the face or hand. Though PPGI systems can be used for widefield imaging of any anatomical area, whole-body investigations are lacking. Here, using a novel PPGI system, coded hemodynamic imaging (CHI), we explored and analyzed the pulsatility at major arterial locations across the whole body, including the neck (carotid artery), arm/wrist (brachial, radial and ulnar arteries), and leg/feet (popliteal and tibial arteries). CHI was positioned 1.5 m from the participant, and diffuse reactance from a broadband tungsten-halogen illumination was filtered using 850{1000 nm bandpass filter for deep tissue penetration. Images were acquired over a highly varying 24-participant sample (11/13 female/male, age 28.7±12.4 years, BMI 25.5±5.2 kg/m2), and a preliminary case study was performed. B-mode ultrasound images were acquired to validate observations through planar arterial characteristics.
A Class of Exact Solutions of the Navier-Stokes Equations in Three-Dimensions

A few basic, intuitive, properties of the Navier-Stokes system of equations for incompressible ﬂuid ﬂows are discussed in this paper. We present a rephrased interpretation of the Navier-Stokes equation. A spatially periodic solution of the velocity and pressure ﬁelds spanning the entire unbounded domain in three dimensions is then derived for a given smooth solenoidal initial velocity vector ﬁeld. In this solution all three velocity components depend non-trivially on all three Cartesian coordinate directions.
Au particle formation on the electron beam induced membrane

Recently the single molecules such as protein and deoxyribonucleic acid (DNA) have been successfully characterized by using a portable solidstate nanopore (MinION) with an electrical detection technique. However, there have been several reports about the high error rates of the fabricated nanopore device, possibly due to an electrical double layer formed inside the pore channel. The current DNA sequencing technology utilized is based on the optical detection method. In order to utilize the current optical detection technique, we will present the formation of the Au nano-pore with Au particle under the various electron beam irradiations. In order to provide the diffusion of Au atoms, a 2 keV electron beam irradiation has been performed During electron beam irradiations by using field emission scanning electron microscopy (FESEM), Au and C atoms would diffuse together and form the binary mixture membrane. Initially, the Au atoms diffused in the membrane are smaller than 1 nm, below the detection limit of the transmission electron microscopy (TEM), so that we are unable to observe the Au atoms in the formed membrane. However, after several months later, the Au atoms became larger and larger with expense of the smaller particles: Ostwald ripening. Furthermore, we also observe the Au crystalline lattice structure on the binary Au-C membrane. The formed Au crystalline lattice structures were constantly changing during electron beam imaging process due to Spinodal decomposition; the unstable thermodynamic system of Au-C binary membrane. The fabricated Au nanopore with an Au nanoparticle can be utilized as a single molecule nanobio sensor.
Model-Aided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks for Wireless System Optimization

Deep learning based on artificial neural networks (ANNs) is a powerful machine-learning method that, in recent years, has been successfully used to realize tasks such as image classification, speech recognition, and language translation, among others, that are usually simple for human beings but extremely difficult for machines. This is one reason deep learning is considered one of the main enablers for realizing artificial intelligence (AI). The current methodology in deep learning consists of employing a data-driven approach to identify the best architecture of an ANN that allows input-output data pairs to be fitted. Once the ANN is trained, it is capable of responding to never-observed inputs by providing the optimum output based on past acquired knowledge. In this context, a recent trend in the deep-learning community complements purely data-driven approaches with prior information based on expert knowledge. In this article, we describe two methods that implement this strategy to optimize wireless communication networks. In addition, we provide numerical results to assess the performance of the proposed approaches compared with purely data-driven implementations.
Numerical simulation of photonic crystal fibers

Using a full modal vector model, the birefringence, dispersion, confinement loss, effective area, and mode field diameter in index-guiding photonic crystal fibers, also called holey fibers, are calculated. Through the real-model simulations, the polarization-dependent dispersion, confinement loss, effective area, and mode field diameter in actual fiber structures are numerically demonstrated.
Photoacoustic super-resolution microscopy using blind structured speckle illumination

We present an imaging method that uses the random optical speckle patterns that naturally emerge as light propagates through strongly scattering media as a structured illumination source for photoacoustic imaging. Our approach, termed blind structured illumination photoacoustic microscopy (BSIPAM), was inspired by recent work in fluorescence microscopy where super-resolution imaging was demonstrated using multiple unknown speckle illumination patterns. We extend this concept to the multiple scattering domain using photoacoustics (PA), with the speckle pattern serving to generate ultrasound. The optical speckle pattern that emerges as light propagates through diffuse media provides structured illumination to an object placed behind a scattering wall. The photoacoustic signal produced by such illumination is detected using a focused ultrasound transducer. We demonstrate through both simulation and experiment, that by acquiring multiple photoacoustic images, each produced by a different random and unknown speckle pattern, an image of an absorbing object can be reconstructed with a spatial resolution far exceeding that of the ultrasound transducer. We experimentally and numerically demonstrate a gain in resolution of more than a factor of two by using multiple speckle illuminations. The variations in the photoacoustic signals generated with random speckle patterns are utilized in BSIPAM using a novel reconstruction algorithm. Exploiting joint sparsity, this algorithm is capable of reconstructing the absorbing structure from measured PA signals with a resolution close to the speckle size.
Rethinking Multi-Modal Alignment in Video Question Answering from Feature and Sample Perspectives

Reasoning about causal and temporal event relations in videos is a new destination of Video Question Answering (VideoQA). The major stumbling block to achieve this purpose is the semantic gap between language and video since they are at different levels of abstraction. Existing efforts mainly focus on designing sophisticated architectures while utilizing frame-or object-level visual representations. In this paper, we reconsider the multi-modal alignment problem in VideoQA from feature and sample perspectives to achieve better performance. From the view of feature, we break down the video into trajectories and first leverage trajectory feature in VideoQA to enhance the alignment between two modalities. Moreover, we adopt a heterogeneous graph architecture and de-sign a hierarchical framework to align both trajectory-level and frame-level visual feature with language feature. In addition, we found that VideoQA models are largely dependent on language priors and always neglect visual-language interactions. Thus, two effective yet portable training augmentation strategies are designed to strengthen the cross-modal correspondence ability of our model from the view of sample. Extensive results show that our method outperforms all the state-of-the-art models on the challenging NExT-QA benchmark, which demonstrates the effectiveness of the proposed method.
Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework: Supplemental Material

In the DeepFashion dataset, there are 25,900 training images, 12,612 validation images and 14,218 test images, where we use query images as test images in the task of category classification. We first train our model on seen categories, and finetune the classifier on unseen categories for the task of category classification. For the task of image retrieval, we apply the features from the header to calculate the feature distances between a query image and gallery images. Note that there is no overlap in categories between seen and unseen data, and there is no overlap in images in train and test sets. ModelNet40 is a synthetic dataset of 3,183 CAD models from 40 object classes, and we split the data into 22 seen and 18 unseen categories. In the seen categories, the numbers of training, validation, and test images are 16,896, 4224, and 5,280, while in the unseen categories, the numbers of them are 13,662, 3,414, and 4,320. For the image retrieval task, the gallery dataset has 11,221 images and the query has 6,017. Since there is no official retrieval split, we split it similar to the validation/test ratio in DeepFashion In-Shop.
Appendix: Unseen Classes at a Later Time? No Problem

In this section, we provide a detailed description of the benchmark datasets used for evaluating our model in the static, dynamic and online CGZSL settings. Following the existing literature [6–9, 26] we assess our proposed model on five widely used benchmark datasets i.e AWA1, AWA2, CUB, SUN, aPY, which are traditionally used for zero-shot learning. (Zero-shot recognition datasets are used since we require access to semantic attributes for addressing the CGZSL problem [6–9, 26]) The Animals with Attributes dataset (AWA1 and AWA2) [11] consists of 50 classes of animals captured in diverse backgrounds. AWA1 consists of 30,475 images and AWA2 consists of 37,322 images. They are split into 40 seen classes and 10 unseen classes. The dataset also contains an 85-dimensional attribute vector for each class which is annotated by a human. Caltech UCSD Birds 200 (CUB) [28] dataset consists of 11,788 images of birds in total, each of which belongs to one of the 200 classes. In a standard generalized zero-shot learning (GZSL) setup, 150 of Dataset Attribute # Images Seen Class Unseen Class AWA1 85 30,475 40 10 AWA2 85 37,322 40 10 aPY 64 15,339 20 12 CUB 312 11,788 150 50 SUN 102 14,340 645 72
SmartThings Backend Cloud Groovy-Based Sandbox Permission ( Capability ) System Groovy-Based Sandbox [ Cmd / Attr ] [ Events ] SmartThings Hub SmartAppDevice Handler Smartphone

A number of Internet of Things (IoTs) platforms have emerged to enable various IoT apps developed by thirdparty developers to automate smart homes. Prior research mostly concerns the overprivilege problem in the permission model. Our work, however, reveals that even IoT apps that follow the principle of least privilege, when they interplay, can cause unique types of threats, named Cross-App Interference (CAI) threats. We describe and categorize the new threats, showing that unexpected automation, security and privacy issues may be caused by such threats, which cannot be handled by existing IoT security mechanisms. To address this problem, we present HOMEGUARD, a system for appified IoT platforms to detect and cope with CAI threats. A symbolic executor module is built to precisely extract the automation semantics from IoT apps. The semantics of different IoT apps are then considered collectively to evaluate their interplay and discover CAI threats systematically. A user interface is presented to users during IoT app installation, interpreting the discovered threats to help them make decisions. We evaluate HOMEGUARD via a proof-ofconcept implementation on Samsung’s SmartThings and discover many threat instances among apps in the SmartThings public repository. The evaluation shows that it is precise, effective and efficient.
Acta Botanica Brasilica

Th is work presents 19 lectotypifi cations and one epitype for the genus Passifl ora (Passifl oraceae sensu stricto) described by Frei José Mariano da Conceição Vellozo in Flora Fluminensis. Th e original illustrations are designated as lectotypes. Passifl ora obtusa is here proposed to be a synonym of P. porophylla.
Visual Tracking using Kernel Projected Measurement and Log-Polar Transformation

Visual Servoing is generally contained of control and feature tracking. Study of previous methods shows that no attempt has been made to optimize these two parts together. In kernel based visual servoing method, the main objective is to combine and optimize these two parts together and to make an entire control loop. This main target is accomplished by using Lyapanov theory. A Lyapanov candidate function is formed based on kernel definition such that the Lyapanov stability can be verified. The implementation is done in four degrees of freedom and Fourier transform is used for decomposition of the rotation and scale directions from 2D translation. In the present study, a new method in scale and rotation correction is presented. Log-Polar Transform is used instead of Fourier transform for these two degrees of freedom. Tracking in four degrees of freedom is synthesized to show the visual tracking of an unmarked object. Comparison between Log-Polar transform and Fourier transform shows the advantages of the presented method. KBVS based on Log-Polar transform proposed in this paper, because of its robustness, speed and featureless properties.
Hyperlink-induced Pre-training for Passage Retrieval of Open-domain Question Answering

To alleviate the data scarcity problem in train-001 ing question answering systems, recent works 002 propose additional intermediate pre-training 003 for dense passage retrieval (DPR). However, 004 there still remains a large discrepancy be-005 tween the provided upstream signals and 006 the downstream question-passage relevance, 007 which leads to less improvement. To bridge 008 this gap, we propose the H yper L ink-induced 009 P re-training (HLP), a method to pre-train the 010 dense retriever with the text relevance induced 011 by hyperlink-based topology within Web doc-012 uments. We demonstrate that the hyperlink-013 based structures of dual-link and co-mention 014 can provide effective relevance signals for 015 large-scale pre-training that better facilitate 016 downstream passage retrieval. We investigate 017 the effectiveness of our approach across a wide 018 range of open-domain QA datasets under zero-019 shot, few-shot, multi-hop, and out-of-domain 020 scenarios. The experiments show our HLP out-021 performs the BM25 by up to 7 points as well as 022 other pre-training methods by up to 30 points 023 in terms of top-20 retrieval accuracy under the 024 zero-shot scenario. Furthermore, HLP signiﬁ-025 cantly outperforms other pre-training methods 026 under the other scenarios. 027