Miracle-dz/UniFAR-FTUs huggingface dataset:
Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning

We propose a novel method to detect and visualize malware through image classification. The executable binaries are represented as grayscale images obtained from the count of N-grams (N=2) of bytes in the Discrete Cosine Transform (DCT) domain and a neural network is trained for malware detection. A shallow neural network is trained for classification, and its accuracy is compared with deep-network architectures such as ResNet that are trained using transfer learning. Neither dis-assembly nor behavioral analysis of malware is required for these methods. Motivated by the visual similarity of these images for different malware families, we compare our deep neural network models with standard image features like GIST descriptors to evaluate the performance. A joint feature measure is proposed to combine different features using error analysis to get an accurate ensemble model for improved classification performance. A new dataset called MaleX which contains around 1 million malware and benign Windows executable samples is created for large-scale malware detection and classification experiments. Experimental results are quite promising with 96% binary classification accuracy on MaleX. The proposed model is also able to generalize well on larger unseen malware samples and the results compare favorably with state-of-the-art static analysis-based malware detection algorithms.
“Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space

Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. Users often use figurative language, such as sarcasm, either as persuasive devices or to attack the opponent by an ad hominem argument. To further our understanding of the role of sarcasm in shaping the disagreement space, we present a thorough experimental setup using a corpus annotated with both argumentative moves (agree/disagree) and sarcasm. We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e.g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures). We demonstrate that modeling sarcasm improves the argumentative relation classification task (agree/disagree/none) in all setups.
Fractionalized quantum criticality in spin-orbital liquids from field theory beyond the leading order

Two-dimensional spin-orbital magnets with strong exchange frustration have recently been predicted to facilitate the realization of a quantum critical point in the Gross-Neveu-SO(3) universality class. In contrast to previously known Gross-Neveu-type universality classes, this quantum critical point separates a Dirac semimetal and a long-range-ordered phase, in which the fermion spectrum is only partially gapped out. Here, we characterize the quantum critical behavior of the Gross-Neveu-SO(3) universality class by employing three complementary field-theoretical techniques beyond their leading orders. We compute the correlation-length exponent $\nu$, the order-parameter anomalous dimension $\eta_\phi$, and the fermion anomalous dimension $\eta_\psi$ using a three-loop $\epsilon$ expansion around the upper critical space-time dimension of four, a second-order large-$N$ expansion (with the fermion anomalous dimension obtained even at the third order), as well as a functional renormalization group approach in the improved local potential approximation. For the physically relevant case of $N=3$ flavors of two-component Dirac fermions in 2+1 space-time dimensions, we obtain the estimates $1/\nu = 1.03(15)$, $\eta_\phi = 0.42(7)$, and $\eta_\psi = 0.180(10)$ from averaging over the results of the different techniques, with the displayed uncertainty representing the degree of consistency among the three methods.
PolyLM: Learning about Polysemy through Language Modeling

To avoid the “meaning conflation deficiency” of word embeddings, a number of models have aimed to embed individual word senses. These methods at one time performed well on tasks such as word sense induction (WSI), but they have since been overtaken by task-specific techniques which exploit contextualized embeddings. However, sense embeddings and contextualization need not be mutually exclusive. We introduce PolyLM, a method which formulates the task of learning sense embeddings as a language modeling problem, allowing contextualization techniques to be applied. PolyLM is based on two underlying assumptions about word senses: firstly, that the probability of a word occurring in a given context is equal to the sum of the probabilities of its individual senses occurring; and secondly, that for a given occurrence of a word, one of its senses tends to be much more plausible in the context than the others. We evaluate PolyLM on WSI, showing that it performs considerably better than previous sense embedding techniques, and matches the current state-of-the-art specialized WSI method despite having six times fewer parameters. Code and pre-trained models are available at https://github.com/AlanAnsell/PolyLM.
Redshift evolution of the H2/H i mass ratio in galaxies

In this paper we present an attempt to estimate the redshift evolution of the molecular to neutral gas mass ratio within galaxies (at fixed stellar mass). For a sample of five nearby grand design spirals located on the Main Sequence (MS) of star forming galaxies, we exploit maps at 500 pc resolution of stellar mass and star formation rate (M★ and SFR). For the same cells, we also have estimates of the neutral (MHI) and molecular (MH2 ) gas masses. To compute the redshift evolution we exploit two relations: i) one between the molecular-to-neutral mass ratio and the total gas mass (Mgas), whose scatter shows a strong dependence with the distance from the spatially resolved MS, and ii) the one between log(MH2/M★) and log(MHI/M★). For both methods, we find that MH2 /MHI within the optical radius slightly decreases with redshift, contrary to common expectations of galaxies becoming progressively more dominated by molecular hydrogen at high redshifts. We discuss possible implications of this trend on our understanding of the internal working of high redshift galaxies.
Global-Local Propagation Network for RGB-D Semantic Segmentation

Depth information matters in RGB-D semantic segmentation task for providing additional geometric information to color images. Most existing methods exploit a multi-stage fusion strategy to propagate depth feature to the RGB branch. However, at the very deep stage, the propagation in a simple element-wise addition manner can not fully utilize the depth information. We propose Global-Local propagation network (GLPNet) to solve this problem. Specifically, a local context fusion module(L-CFM) is introduced to dynamically align both modalities before element-wise fusion, and a global context fusion module(G-CFM) is introduced to propagate the depth information to the RGB branch by jointly modeling the multi-modal global context features. Extensive experiments demonstrate the effectiveness and complementarity of the proposed fusion modules. Embedding two fusion modules into a two-stream encoder-decoder structure, our GLPNet achieves new state-of-the-art performance on two challenging indoor scene segmentation datasets, i.e., NYU-Depth v2 and SUN-RGBD dataset.
$\alpha$-Information-theoretic Privacy Watchdog and Optimal Privatization Scheme

This paper proposes an <tex>$\alpha$</tex>-lift measure for data privacy and determines the optimal privatization scheme that minimizes the <tex>$\alpha$</tex>-lift in the watchdog method. To release useful data <tex>$X$</tex> that is correlated with sensitive data <tex>$S$</tex>, the ratio of the posterior belief to the prior belief on sensitive data with respect to the useful data is called ‘lift’, which quantifies privacy risk. The <tex>$\alpha$</tex>-lift denoted by <tex>$\ell_{\alpha}(x)$</tex> is proposed as the <tex>$L_{\alpha}$</tex>-norm of the lift for a given realization <tex>$x$</tex>. This is a tunable measure: when <tex>$\alpha < \infty$</tex>, each lift is weighted by its likelihood of appearing in the dataset (w.r.t. the marginal probability <tex>$p(s)$</tex>); for <tex>$\alpha=\infty,\ \alpha$</tex>-lift reduces to the existing maximum lift. To generate the sanitized data <tex>$Y$</tex>, we adopt the privacy watchdog method using <tex>$\alpha$</tex>-lift: obtain realizations of useful data such that the <tex>$\alpha$</tex>-lift is greater than a threshold <tex>$e^{\epsilon}$</tex>; apply a randomization mechanism to these ‘high-risk’ realizations, while all other realizations of <tex>$X$</tex> are published directly. For the resulting <tex>$\alpha$</tex>-lift denoted by <tex>$\ell_{\alpha}(y)$</tex>, it is shown that the Sibson mutual information <tex>$I_{\alpha}^{S}(S;Y)$</tex> is proportional to <tex>$\mathbb{E}[\ell_{\alpha}(y)]$</tex>. We further define a stronger privacy measure denoted <tex>$\overline{I}_{\alpha}^{S}(S;Y)$</tex> using the worst-case <tex>$\alpha$</tex>-lift: <tex>$\bar{I}_{\alpha}^{S}(S;Y)\propto\max\nolimits_{y}\ell_{\alpha}(y)$</tex>. We prove that the optimal watchdog randomization that minimizes both <tex>$I_{\alpha}^{S}(S;Y)$</tex> and <tex>$\overline{I}_{\alpha}^{S}(S;Y)$</tex> is <tex>$X$</tex>-invariant. Numerical experiments show that <tex>$\alpha$</tex>-lift can provide flexibility in the privacy-utility tradeoff.
SIMULTANEOUS ESTIMATION OF LEVOSALBUTAMOL SULPHATE AND BECLOMETHASONE DIPROPIONATE IN COMBINED ROTACAP DOSAGE FORM BY RP-HPLC METHOD

A new, simple, specific, sensitive, rapid, accurate and precise RP-HPLC method was developed for the simultaneous estimation of Levosalbutamol sulphate (LS) and Beclomethasone dipropionate (BD) in bulk and pharmaceutical formulations. They were chromatographed on a reverse phase C18 column (25cm x 4.6mm, 5µ) in a mobile phase consisting of methanol and water in the ratio 90:10 v/v. The mobile phase was pumped at a flow rate of 1.0 mL/min with detection at 320 nm. The detector response was linear in the concentration of 20-100 μg/mL and 10-50 μg/mL for LS and BD respectively. The limit of detection and limit of quantitation was found to be 0.5178 and 0.2195, for LS respectively and 1.5691 and 0.6652 for BD respectively. The intra and inter day variation was found to be less than 1%. The proposed method is simple, fast, accurate, precise and reproducible hence, it can be applied for routine quality control analysis of Levosalbutamol sulphate and Beclomethasone dipropionate in bulk and pharmaceutical formulations.
Cloud-based traffic data fusion for situation evaluation of handover scenarios

Upcoming vehicles introduce functions at the level of conditional automation where a driver no longer must supervise the system but must be able to take over the driving function when the system request it. This leads to the situation that the driver does not concentrate on the road but is reading mails for example. In this case, the driver is not able to take over the driving function immediately because she must first orient herself in the current traffic situation. In an urban scenario a situation that an automated vehicle is not able to steer further can arise quickly. To find suitable handover situations, data from traffic infrastructure systems, vehicles, and drivers is fused in a cloud-based situation to provide the hole traffic environment as base for the decision when the driving function should be transferred best and possibly even before a critical situation arises.
Towards glass-box CNNs

With the substantial performance of neural networks in sensitive fields increases the need for interpretable deep learning models. Major challenge is to uncover the multiscale and distributed representation hidden inside the basket mappings of the deep neural networks. Researchers have been trying to comprehend it through visual analysis of features, mathematical structures, or other data-driven approaches. Here, we work on implementation invariances of CNN-based representations and present an analytical binary prototype that provides useful insights for large scale real-life applications. We begin by unfolding conventional CNN and then repack it with a more transparent representation. Inspired by the attainment of neural networks, we choose to present our findings as a three-layer model. First is a representation layer that encompasses both the class information (group invariant) and symmetric transformations (group equivariant) of input images. Through these transformations, we decrease intra-class distance and increase the inter-class distance. It is then passed through a dimension reduction layer followed by a classifier. The proposed representation is compared with the equivariance of AlexNet (CNN) internal representation for better dissemination of simulation results. We foresee following immediate advantages of this toy version: i) contributes pre-processing of data to increase the feature or class separability in large scale problems, ii) helps designing neural architecture to improve the classification performance in multi-class problems, and iii) helps building interpretable CNN through scalable functional blocks.
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN

Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images. However, 2D CNN only considers the spatial information and ignores the spectral information whereas 3D CNN jointly exploits spatial-spectral information at a high computational cost. Therefore, this work proposed a lightweight CNN (3D followed by 2D-CNN) model which significantly reduces the computational cost by distributing spatial-spectral feature extraction across a lighter model alongside a preprocessing that has been carried out to improve the classification results. Five benchmark Hyperspectral datasets (i.e., SalinasA, Salinas, Indian Pines, Pavia University, Pavia Center, and Botswana) are used for experimental evaluation. The experimental results show that the proposed pipeline outperformed in terms of generalization performance, statistical significance, and computational complexity, as compared to the state-of-the-art 2D/3D CNN models except commonly used computationally expensive design choices.
Joint Estimation of Location and Scatter in Complex Elliptical Distributions: A robust semiparametric and computationally efficient $R$-estimator of the shape matrix

The joint estimation of the location vector and the shape matrix of a set of independent and identically Complex Elliptically Symmetric (CES) distributed observations is investigated from both the theoretical and computational viewpoints. This joint estimation problem is framed in the original context of semiparametric models allowing us to handle the (generally unknown) density generator as an \textit{infinite-dimensional} nuisance parameter. In the first part of the paper, a computationally efficient and memory saving implementation of the robust and semiparmaetric efficient $R$-estimator for shape matrices is derived. Building upon this result, in the second part, a joint estimator, relying on the Tyler's $M$-estimator of location and on the $R$-estimator of shape matrix, is proposed and its Mean Squared Error (MSE) performance compared with the Semiparametric Cram\'{e}r-Rao Bound (CSCRB).
Toward Personalized Affect-Aware Socially Assistive Robot Tutors in Long-Term Interventions for Children with Autism

Affect-aware socially assistive robotics (SAR) has shown great potential for augmenting interventions for children with autism spectrum disorders (ASD). However, current SAR cannot yet perceive the unique and diverse set of atypical cognitive-affective behaviors from children with ASD in an automatic and personalized fashion in long-term (multi-session) real-world interactions. To bridge this gap, this work designed and validated personalized models of arousal and valence for children with ASD using a multi-session in-home dataset of SAR interventions. By training machine learning (ML) algorithms with supervised domain adaptation (s-DA), the personalized models were able to trade off between the limited individual data and the more abundant less personal data pooled from other study participants. We evaluated the effects of personalization on a long-term multimodal dataset consisting of 4 children with ASD with a total of 19 sessions, and derived inter-rater reliability (IR) scores for binary arousal (IR = 83%) and valence (IR = 81%) labels between human annotators. Our results show that personalized Gradient Boosted Decision Trees (XGBoost) models with s-DA outperformed two non-personalized individualized and generic model baselines not only on the weighted average of all sessions, but also statistically (p<.05) across individual sessions. This work paves the way for the development of personalized autonomous SAR systems tailored toward individuals with atypical cognitive-affective and socio-emotional needs.
A MeerKAT view of pre-processing in the Fornax A group

We present MeerKAT neutral hydrogen (H I) observations of the Fornax A group, which is likely falling into the Fornax cluster for the first time. Our H I image is sensitive to 1.4 × 1019 atoms cm−2 over 44.1 km s−1, where we detect H I in 10 galaxies and a total of (1.12 ± 0.02) × 109 M⊙ of H I in the intra-group medium (IGM). We search for signs of pre-processing in the 12 group galaxies with confirmed optical redshifts that reside within the sensitivity limit of our H I image. There are 9 galaxies that show evidence of pre-processing and we classify each galaxy into their respective pre-processing category, according to their H I morphology and gas (atomic and molecular) scaling relations. Galaxies that have not yet experienced pre-processing have extended H I discs and a high H I content with a H2-to-H I ratio that is an order of magnitude lower than the median for their stellar mass. Galaxies that are currently being pre-processed display H I tails, truncated H I discs with typical gas fractions, and H2-to-H I ratios. Galaxies in the advanced stages of pre-processing are the most H I deficient. If there is any H I, they have lost their outer H I disc and efficiently converted their H I to H2, resulting in H2-to-H I ratios that are an order of magnitude higher than the median for their stellar mass. The central, massive galaxy in our group (NGC 1316) underwent a 10:1 merger ∼2 Gyr ago and ejected 6.6−11.2 × 108 M⊙ of H I, which we detect as clouds and streams in the IGM, some of which form coherent structures up to ∼220 kpc in length. We also detect giant (∼100 kpc) ionised hydrogen (Hα) filaments in the IGM, likely from cool gas being removed (and subsequently ionised) from an in-falling satellite. The Hα filaments are situated within the hot halo of NGC 1316 and there are localised regions that contain H I. We speculate that the Hα and multiphase gas is supported by magnetic pressure (possibly assisted by the NGC 1316 AGN), such that the hot gas can condense and form H I that survives in the hot halo for cosmological timescales.
Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU/UK legislation where text similarity has limitations

Major scandals in corporate history have urged the need for regulatory compliance, where organizations need to ensure that their controls (processes) comply with relevant laws, regulations, and policies. However, keeping track of the constantly changing legislation is difficult, thus organizations are increasingly adopting Regulatory Technology (RegTech) to facilitate the process. To this end, we introduce regulatory information retrieval (REG-IR), an application of document-to-document information retrieval (DOC2DOC IR), where the query is an entire document making the task more challenging than traditional IR where the queries are short. Furthermore, we compile and release two datasets based on the relationships between EU directives and UK legislation. We experiment on these datasets using a typical two-step pipeline approach comprising a pre-fetcher and a neural re-ranker. Experimenting with various pre-fetchers from BM25 to k nearest neighbors over representations from several BERT models, we show that fine-tuning a BERT model on an in-domain classification task produces the best representations for IR. We also show that neural re-rankers under-perform due to contradicting supervision, i.e., similar query-document pairs with opposite labels. Thus, they are biased towards the pre-fetcher’s score. Interestingly, applying a date filter further improves the performance, showcasing the importance of the time dimension.
Consistent Mesh Colors for Multi-View Reconstructed 3D Scenes

We address the issue of creating consistent mesh texture maps captured from scenes without color calibration. We find that the method for aggregation of the multiple views is crucial for creating spatially consistent meshes without the need to explicitly optimize for spatial consistency. We compute a color prior from the cross-correlation of observable view faces and the faces per view to identify an optimal per-face color. We then use this color in a re-weighting ratio for the best-view texture, which is identified by prior mesh texturing work, to create a spatial consistent texture map. Despite our method not explicitly handling spatial consistency, our results show qualitatively more consistent results than other state-of-the-art techniques while being computationally more efficient. We evaluate on prior datasets and additionally Matterport3D showing qualitative improvements.
Few-Shot Semantic Parsing for New Predicates

In this work, we investigate the problems of semantic parsing in a few-shot learning setting. In this setting, we are provided with k utterance-logical form pairs per new predicate. The state-of-the-art neural semantic parsers achieve less than 25% accuracy on benchmark datasets when k = 1. To tackle this problem, we proposed to i) apply a designated meta-learning method to train the model; ii) regularize attention scores with alignment statistics; iii) apply a smoothing technique in pretraining. As a result, our method consistently outperforms all the baselines in both one and two-shot settings.
B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows

Human Activity Recognition (HAR), based on machine and deep learning algorithms is considered one of the most promising technologies to monitor professional and daily life activities for different categories of people (e.g., athletes, elderly, kids, employers) in order to provide a variety of services related, for example to well-being, empowering of technical performances, prevention of risky situation, and educational purposes. However, the analysis of the effectiveness and the efficiency of HAR methodologies suffers from the lack of a standard workflow, which might represent the baseline for the estimation of the quality of the developed pattern recognition models. This makes the comparison among different approaches a challenging task. In addition, researchers can make mistakes that, when not detected, definitely affect the achieved results. To mitigate such issues, this paper proposes an open-source automatic and highly configurable framework, named B-HAR, for the definition, standardization, and development of a baseline framework in order to evaluate and compare HAR methodologies. It implements the most popular data processing methods for data preparation and the most commonly used machine and deep learning pattern recognition models.