Miracle-dz/UniFAR-FTUs huggingface dataset:
Android Malware Detection via Graph Representation Learning

With the widespread usage of Android smartphones in our daily lives, the Android platform has become an attractive target for malware authors. 'ere is an urgent need for developing an automatic malware detection approach to prevent the spread of malware. 'e low code coverage and poor efficiency of the dynamic analysis limit the large-scale deployment of malware detection methods based on dynamic features. 'erefore, researchers have proposed a plethora of detection approaches based on abundant static features to provide efficient malware detection. 'is paper explores the direction of Android malware detection based on graph representation learning. Without complex feature graph construction, we propose a new Android malware detection approach based on lightweight static analysis via the graph neural network (GNN). Instead of directly extracting Application Programming Interface (API) call information, we further analyze the source code of Android applications to extract high-level semantic information, which increases the barrier of evading detection. Particularly, we construct approximate call graphs from function invocation relationships within an Android application to represent this application and further extract intrafunction attributes, including required permission, security level, and Smali instructions’ semantic information via Word2Vec, to form the node attributes within graph structures. 'en, we use the graph neural network to generate a vector representation of the application, and then malware detection is performed on this representation space. We conduct experiments on real-world application samples. 'e experimental results demonstrate that our approach implements high effective malware detection and outperforms state-of-the-art detection approaches.
Learning Value Functions in Deep Policy Gradients using Residual Variance

Policy gradient algorithms have proven to be successful in diverse decision making and control tasks. However, these methods suffer from high sample complexity and instability issues. In this paper, we address these challenges by providing a different approach for training the critic in the actor-critic framework. Our work builds on recent studies indicating that traditional actor-critic algorithms do not succeed in fitting the true value function, calling for the need to identify a better objective for the critic. In our method, the critic uses a new state-value (resp. state-actionvalue) function approximation that learns the value of the states (resp. state-action pairs) relative to their mean value rather than the absolute value as in conventional actor-critic. We prove the theoretical consistency of the new gradient estimator and observe dramatic empirical improvement across a variety of continuous control tasks and algorithms. Furthermore, we validate our method in tasks with sparse rewards, where we provide experimental evidence and theoretical insights.
Bayesian Context Aggregation for Neural Processes

Formulating scalable probabilistic regression models with reliable uncertainty estimates has been a long-standing challenge in machine learning research. Recently, casting probabilistic regression as a multi-task learning problem in terms of conditional latent variable (CLV) models such as the Neural Process (NP) has shown promising results. In this paper, we focus on context aggregation, a central component of such architectures, which fuses information from multiple context data points. So far, this aggregation operation has been treated separately from the inference of a latent representation of the target function in CLV models. Our key contribution is to combine these steps into one holistic mechanism by phrasing context aggregation as a Bayesian inference problem. The resulting Bayesian Aggregation (BA) mechanism enables principled handling of task ambiguity, which is key for efficiently processing context information. We demonstrate on a range of challenging experiments that BA consistently improves upon the performance of traditional mean aggregation while remaining computationally efficient and fully compatible with existing NP-based models.
The removal of excreted bacteria and viruses in deep waste stabilization ponds in Northeast Brazil

The removal of excreted bacteria (faecal coliforms, faecal streptococci, Clostridium perfringens, total and sorbitol-fermenting bifidobacteria, salmonellae and thermophilic campylobacters) and viruses (enterovirus and rotavirus) in a series of deep anaerobic, facultative and maturation ponds (depth range: 2.8 - 3.4 m), with an overall retention time of 21 days and a mean mid-depth temperature of 27°C, was studied. Thermophilic campylobacters, bifidobacteria and salmonellae were not detected after 11, 16 and 21 days' retention respectively. Faecal coliforms, faecal streptcocci and Cl. perfringens were reduced by 4, 4 and 2 orders of magnitude respectively, and enteroviruses arid rotaviruses both by 3 orders. The results indicate that pathogen removal in deep ponds is similar to that in ponds of normal depth.
Protecting DNNs from Theft using an Ensemble of Diverse Models

Several recent works have demonstrated highly effective model stealing (MS) attacks on Deep Neural Networks (DNNs) in black-box settings, even when the training data is unavailable. These attacks typically use some form of Out of Distribution (OOD) data to query the target model and use the predictions obtained to train a clone model. Such a clone model learns to approximate the decision boundary of the target model, achieving high accuracy on in-distribution examples. We propose Ensemble of Diverse Models (EDM) to defend against such MS attacks. EDM is made up of models that are trained to produce dissimilar predictions for OOD inputs. By using a different member of the ensemble to service different queries, our defense produces predictions that are highly discontinuous in the input space for the adversary’s OOD queries. Such discontinuities cause the clone model trained on these predictions to have poor generalization on in-distribution examples. Our evaluations on several image classification tasks demonstrate that EDM defense can severely degrade the accuracy of clone models (up to 39.7%). Our defense has minimal impact on the target accuracy, negligible computational costs during inference, and is compatible with existing defenses for MS attacks.
Cross-Attentional Audio-Visual Fusion for Weakly-Supervised Action Localization

Temporally localizing actions in videos is one of the key components for video understanding. Learning from weakly-labeled data is seen as a potential solu-tion towards avoiding expensive frame-level annotations. Different from other works which only depend on visual-modality, we propose to learn richer audio-visual representation for weakly-supervised action localization. First, we propose a multi-stage cross-attention mechanism to collaboratively fuse audio and visual features, which preserves the intra-modal characteristics. Second, to model both foreground and background frames, we construct an open-max classiﬁer which treats the background class as an open-set. Third, for precise action localization, we design consistency losses to enforce temporal continuity for the action-class prediction, and also help with foreground-prediction reliability. Extensive experiments on two publicly available video-datasets (AVE and ActivityNet1.2) show that the proposed method effectively fuses audio and visual modalities, and achieves the state-of-the-art results for weakly-supervised action localization.
Filtered Inner Product Projection for Crosslingual Embedding Alignment

Due to widespread interest in machine translation and transfer learning, there are numerous algorithms for mapping multiple embeddings to a shared representation space. Recently, these algorithms have been studied in the setting of bilingual lexicon induction where one seeks to align the embeddings of a source and a target language such that translated word pairs lie close to one another in a common representation space. In this paper, we propose a method, Filtered Inner Product Projection (FIPP), for mapping embeddings to a common representation space. As semantic shifts are pervasive across languages and domains, FIPP first identifies the common geometric structure in both embeddings and then, only on the common structure, aligns the Gram matrices of these embeddings. FIPP aligns embeddings to isomorphic vector spaces even when the source and target embeddings are of differing dimensionalities. Additionally, FIPP provides computational benefits in ease of implementation and is faster to compute than current approaches. Following the baselines in Glavaš et al. (2019), we evaluate FIPP in the context of bilingual lexicon induction and downstream language tasks. We show that FIPP outperforms existing methods on the XLING (5K) BLI dataset and the XLING (1K) BLI dataset, when using a self-learning approach, while also providing robust performance across downstream tasks.
Event Detection in Twitter Using Multi Timing Chained Windows

Twitter is a popular microblogging and social networking service. Twitter posts are continuously generated and well suited for knowledge discovery us-ing diﬀerent data mining techniques. We present a novel near real-time approach for processing tweets and detecting events. The proposed method, Multi Timing Chained Windows (MTCW), is independent of the language of the tweets. The MTCW deﬁnes several Timing Windows and links them to each other like a chain. Indeed, in this chain, the input of the larger window will be the output of the smaller previous one. Using MTCW, the events can be detected over a few minutes. To evaluate this idea, the required dataset has been collected using the Twitter API. The results of evaluations show the accuracy and the eﬀectiveness of our approach compared with other state-of-the-art methods in the event detection in Twitter.
Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning

Dancing to music is one of human's innate abilities since ancient times. In machine learning research, however, synthesizing dance movements from music is a challenging problem. Recently, researchers synthesize human motion sequences through autoregressive models like recurrent neural network (RNN). Such an approach often generates short sequences due to an accumulation of prediction errors that are fed back into the neural network. This problem becomes even more severe in the long motion sequence generation. Besides, the consistency between dance and music in terms of style, rhythm and beat is yet to be taken into account during modeling. In this paper, we formalize the music-conditioned dance generation as a sequence-to-sequence learning problem and devise a novel seq2seq architecture to efficiently process long sequences of music features and capture the fine-grained correspondence between music and dance. Furthermore, we propose a novel curriculum learning strategy to alleviate error accumulation of autoregressive models in long motion sequence generation, which gently changes the training process from a fully guided teacher-forcing scheme using the previous ground-truth movements, towards a less guided autoregressive scheme mostly using the generated movements instead. Extensive experiments show that our approach significantly outperforms the existing state-of-the-arts on automatic metrics and human evaluation. We also make a demo video to demonstrate the superior performance of our proposed approach at https://www.youtube. com/watch? v=lmE20MEheZ8.
Probabilistic Memory Model for Visual Images Categorization

During the past decades, numerous memory models have been proposed, which focused mainly on how spoken words are studied, whereas models on how visual images are studied are still limited. In this study, we propose a probabilistic memory model (PMM) for visual images categorization which is able to mimic the workings of the human brain during the image storage and retrieval. First, in the learning phase, the visual images are represented by the feature vectors extracted with convolutional neural network (CNN) and each feature component is assumed to conform to a Gaussian distribution and may be incompletely copied with a certain probability or randomly produced in accordance to an exponential distribution. Then, in the test phase, the likelihood ratio between the test image and each studied image is calculated based on the probabilistic inference theory, and an odd value in favor of an old item over a new one is obtained based on all likelihood values. Finally, if the odd value is above a certain threshold, the Bayesian decision rule is applied for image classiﬁcation. Experimental results on two benchmark image datasets demonstrate that the proposed PMM can perform well on categorization tasks for both studied and non-studied images.
The metabolizable energy and net energy values of corn meal when fed exclusively and in combination with alfalfa hay.

The purpose of this investigation, which was conducted in the fall of 1929, was to determine whether the energy value of a ration is equal to the sum of the separately determined energy values of its components ; or, in other words, whether the energy value of a feeding stuff is affected by the combination in which it is fed with other feeding stuffs. So far as the writers are aware, all existing values for metaboUzable energy and net energy of concentrates for herbivorous animals have been determined indirectly, that is, by difference between such values for mixed rations (roughage plus concentrate) and for rations of roughage alone, this procedure involving the assumption that the nutritive value of the roughage in the mixed ration is the same as when it is fed alone. It is important, therefore, to learn the extent of any such error as may result from this indirect procedure, and to determine the possibihty of more direct measurements of energy values. The only directly determined values of the digestibihty of a concentrate, of which the writers are aware, are those of Beach ^ on the digestibihty of corn meal. Beach fed two dry, farrow cows exclusively on corn meal for a period of 130 days, and determined the digestibihty of the meal from the feces collected during the last week of this interval. In other periods with the same animals he also determined the digestibihty of hay fed by itself. The main conclusion drawn from these experiments was that the digestible nutrients of the corn meal were utihzed by the animals more efficiently than the digestible nutrients of the hay, and that the exclusive feeding of meal produced no unfavorable effects. The data of Beach on the digestibility of corn meal are of especial interest since they throw hght on the question as to whether ruminants can efficiently utihze concentrated feeds without the addition of roughages. They did not, however, provide a basis for direct comparison of the utiHzation of corn meal when fed alone and when fed in combination with roughage. Such a comparison was made possible in the present experiments. The experimental subjects were the same two steers. Nos. 17 and 85, that were used in the preceding fasting study ^, and were approximately fifteen to seventeen months old at the time of these experiments.
GM 2 Calc : Precise MSSM prediction for ( g − 2 ) of the muon

We present GM2Calc, a public C++ program for the calculation of MSSM contributions to the anomalous magnetic moment of the muon, (g − 2)μ. The code computes (g − 2)μ precisely, by taking into account the latest two-loop corrections and by performing the calcu l tion in a physical on-shell renormalization scheme. In particular the program includes a tan β resummation so that it is valid for arbitrarily high values of tan β, as well as fermion/sfermion-loop corrections which lead t o non-decoupling effects from heavy squarks. GM2Calc can be run with a standard SLHA input file, in ternally converting the input into onshell parameters. Alternatively, input parameters may be s pecified directly in this on-shell scheme. In both cases the input file allows one to switch on/off individual co ntributions to study their relative impact. This paper also provides typical usage examples not only in conju nction with spectrum generators and plotting programs but also as C++ subroutines linked to other program s.
Host/Guest Interactions in a Sepiolite-Based Maya Blue Pigment: A Spectroscopic Study

Maya Blue pigment forms through heating-induced encapsula- tion and bonding of indigo in microporous clays, namely, palygorskite or sepiolite.Stabilizinghost/guest interactionsinasepiolite-basedMayaBluewere investigated by means of vibrational and NMR spectroscopies and proved to be H-bonds formed between indigo reactive groups and the clay structural OH2. Indigo sorption inhibits sepiolite structural folding induced by partial OH2 loss with temperature rise, but despite this, no direct Mg/indigo bonding was detected. Encapsulation is favored by breaking of intermolecular bonds, allow- ing dye diffusion inside the clay microtunnels. H-bond formation involves both CdO/NH indigo groups, its molecule undergoing distortion and partial oxidation to dehydroindigo. Both the strength and number of sepiolite/indigo interactions are less marked than those occurring in a palygorskite-based composite. In the wider sepiolite channels, H-bonds can form only on one side of the guest molecule, whereas both sides are involved in palygorskite. Halving in bond number undermines the stability of the resulting adduct.
J an 2 00 5 Study of the collapse of granular columns using DEM numerical simulation

Numerical simulations of the collapse and spreading of granular columns onto an horizontal plane using the Contact Dynamics method are presented. The final shape of the deposit seems to depend only on the aspect ratio a of the columns; these results are in good agreement with previous experimental work. In particular, the renormalised runout distance shows a power law dependence on the aspect ratio a, which is incompatible with a simple friction model. The dynamics of the collapse is shown to be mostly controlled by the free fall of the column. The energy dissipation at the base of the column can be described simply by a coefficient of restitution. Hence the energy available for the sideways flow is proportional to the initial potential energy E0. The dissipation process within the flow is well approximated by basal friction, contrary to the behaviour of the runout distance. The mass ejected sideways is showned to play a determining role in the spreading process. As a increases, the same fraction of initial potential energy E0 drives more mass against friction. This additional dissipation give a possible explanation for power-law dependence of the runout distance on a. Beyond the frictional properties of the material, we show that the flow characteristics strongly depend on the early dynamics of the collapse. We propose a new scaling for the runout distance that matches the data well, is compatible with a friction model, and provide a qualitative explanation to the column collapse phenomenology.
O ( N ) Iterative and O ( NlogN ) Fast Direct Volume Integral Equation Solvers with a Minimal-Rank H 2-Representation for Large-Scale 3-D Electrodynamic Analysis

Linear complexity iterative and log-linear complexity direct solvers are developed for the volume integral equation (VIE) based general large-scale electrodynamic analysis. The dense VIE system matrix is first represented by a new clusterbased multilevel low-rank representation. In this representation, all the admissible blocks associated with a single cluster are grouped together and represented by a single low-rank block, whose rank is minimized based on prescribed accuracy. From such an initial representation, an efficient algorithm is developed to generate a minimal-rank H-matrix representation. This representation facilitates faster computation, and ensures the same minimal rank’s growth rate with electrical size as evaluated from singular value decomposition. Taking into account the rank’s growth with electrical size, we develop linear-complexity H-matrix-based storage and matrix-vector multiplication, and thereby an O(N) iterative VIE solver regardless of electrical size. Moreover, we develop an O(NlogN) matrix inversion, and hence a fast O(NlogN) direct VIE solver for large-scale electrodynamic analysis. Both theoretical analysis and numerical simulations of large-scale 1-, 2and 3-D structures on a singlecore CPU, resulting in millions of unknowns, have demonstrated the low complexity and superior performance of the proposed VIE electrodynamic solvers.
Ju n 20 07 Curvature line parametrization from circle patterns

We study local and global approximations of smooth nets of curvature lines and smooth conjugate nets by respective discrete nets (circular nets and planar quadrilateral nets) with edges of order ǫ. It is shown that choosing the points of discrete nets on the smooth surface one can obtain the order ǫ2 approximation globally. Also a simple geometric construction for approximate determination of principal directions of smooth surfaces is given.
Association of Nucleotide Variants of GRHL3, IRF6, NAT2, SDC2, BCL3, and PVRL1 Genes with Nonsyndromic Cleft Lip With/Without Cleft Palate in Multigenerational Families: A Retrospective Study

Background: Several genes are associated with the etiology of cleft lip and palate (CLP) in different populations. Many nucleotide variants on genes such as GRHL3, IRF6, NAT2, SDC2, BCL3, and PVRL1 were reported in different populations, but not studied in multigenerational cases in the Indian population. Aim and Objective: The aim of this study is to evaluate whether nucleotide variants rs41268753, rs861020, rs1041983, rs1042381, rs2965169, and rs10790332 are involved in the etiology of nonsyndromic CLP (NSCLP) in multigenerational Indian families. Study Design: Retrospective genetic study. Materials and Methods: Based on inclusion and exclusion criteria, 20 multigenerational families with nonsyndromic cleft lip with or without cleft palate (NSCL/P) were selected. Blood samples from both affected and unaffected participants were collected as a source of genomic DNA. Six nucleotide variants on these genes were genotyped to test for the association with NSCL/P. Genotyping was performed with the MassArray method. Genotype distribution was used to calculate the Hardy–Weinberg equilibrium using PLINK, a whole-genome association analysis toolset. The allelic association was compared among cases and controls using Chi-square test as implemented in PLINK. P ≤ 0.05 indicates statistical differences between groups. Results: No significant associations were found between individual single-nucleotide polymorphisms and NSCL/P. The odds ratio was 1.531, 1.198, 0.8082, 1.418, 1, and 0.5929 for polymorphisms rs41268753, rs861020, rs1041983, rs1042381, rs2965169, and rs10790332, respectively. Conclusion: Our findings suggest that among the multigenerational families in our population, the high-risk nucleotide variants GRHL3 rs41268753, IRF6 rs861020, NAT2 rs1041983, SDC2 rs1042381, BCL3 rs2965169, and PVRL1 rs10790332 are not associated with increased risk of NSCL/P.