Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware

Malware allegedly developed by nation-states, also known as advanced persistent threats (APT), are becoming more common. The task of attributing an APT to a specific nation-state or classifying it to the correct APT family is challenging for several reasons. First, each nation-state has more than a single cyber unit that develops such malware, rendering traditional authorship attribution algorithms useless. Furthermore, the dataset of such available APTs is still extremely small. Finally, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. In this paper, we use a deep neural network (DNN) as a classifier for nation-state APT attribution. We record the dynamic behavior of the APT when run in a sandbox and use it as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself. We also use the same raw features for APT family classification. Finally, we use the feature abstractions learned by the APT family classifier to solve the attribution problem. Using a test set of 1000 Chinese and Russian developed APTs, we achieved an accuracy rate of 98.6%
Performance comparison of classifiers for differentiation among obstructive lung diseases based on features of texture analysis at HRCT

The performance of classification algorithms for differentiating among obstructive lung diseases based on features from texture analysis using HRCT (High Resolution Computerized Tomography) images was compared. HRCT can provide accurate information for the detection of various obstructive lung diseases, including centrilobular emphysema, panlobular emphysema and bronchiolitis obliterans. Features on HRCT images can be subtle, however, particularly in the early stages of disease, and image-based diagnosis is subject to inter-observer variation. To automate the diagnosis and improve the accuracy, we compared three types of automated classification systems, naïve Bayesian classifier, ANN (Artificial Neural Net) and SVM (Support Vector Machine), based on their ability to differentiate among normal lung and three types of obstructive lung diseases. To assess the performance and cross-validation of these three classifiers, 5 folding methods with 5 randomly chosen groups were used. For a more robust result, each validation was repeated 100 times. SVM showed the best performance, with 86.5% overall sensitivity, significantly different from the other classifiers (one way ANOVA, p<0.01). We address the characteristics of each classifier affecting performance and the issue of which classifier is the most suitable for clinical applications, and propose an appropriate method to choose the best classifier and determine its optimal parameters for optimal disease discrimination. These results can be applied to classifiers for differentiation of other diseases.
How much does a word weigh? Weighting word embeddings for word sense induction

The paper describes our participation in the first shared task on word sense induction and disambiguation for the Russian language RUSSE'2018 (Panchenko et al., 2018). For each of several dozens of ambiguous words, the participants were asked to group text fragments containing it according to the senses of this word, which were not provided beforehand, therefore the "induction" part of the task. For instance, a word "bank" and a set of text fragments (also known as "contexts") in which this word occurs, e.g. "bank is a financial institution that accepts deposits" and "river bank is a slope beside a body of water" were given. A participant was asked to cluster such contexts in the unknown in advance number of clusters corresponding to, in this case, the "company" and the "area" senses of the word "bank". The organizers proposed three evaluation datasets of varying complexity and text genres based respectively on texts of Wikipedia, Web pages, and a dictionary of the Russian language. We present two experiments: a positive and a negative one, based respectively on clustering of contexts represented as a weighted average of word embeddings and on machine translation using two state-of-the-art production neural machine translation systems. Our team showed the second best result on two datasets and the third best result on the remaining one dataset among 18 participating teams. We managed to substantially outperform competitive state-of-the-art baselines from the previous years based on sense embeddings.
Decaying dark matter: the case for a deep X-ray observation of Draco

Recent studies of M31, the Galactic Centre (GC), and galaxy clusters have made tentative detections of an X-ray line at similar to 3.5 keV that could be produced by decaying dark matter. We use high-resolution simulations of the Aquarius project to predict the likely amplitude of the X-ray decay flux observed in the GC relative to that observed in M31, and also of the GC relative to other parts of the Milky Way halo and to dwarf spheroidal galaxies. We show that the reported detections from M31 and the GC are compatible with each other, and with upper limits arising from high galactic latitude observations, and imply a decay time tau similar to 10(28) s. We argue that this interpretation can be tested with deep observations of dwarf spheroidal galaxies: in 95 per cent of our mock observations, a 1.3 Ms pointed observation of Draco with XMM-Newton will enable us to discover or rule out at the 3 sigma level an X-ray feature from dark matter decay at 3.5 keV, for decay times tau < 0.8 x 10(28) s.
Global carbon tetrachloride distributions obtained from the Atmospheric Chemistry Experiment (ACE)

Abstract. The first study of the global atmospheric distribution of carbon tetrachloride (CCl4), as a function of altitude and latitude, was performed using solar occultation measurements obtained by the Atmospheric Chemistry Experiment (ACE) mission using Fourier transform spectroscopy. A total of 8703 profile measurements were taken in the upper troposphere and lower stratosphere between February 2004 and August 2007. The zonal distribution of carbon tetrachloride displays a slight hemispheric asymmetry and decreasing concentration with increasing altitude at all latitudes. Maximum carbon tetrachloride concentrations are situated below 10 km in altitude with VMR (Volume Mixing Ratio) values of 100–130 ppt (parts per trillion). The highest concentrations are located about the Equator and at mid-latitudes, particularly for latitudes in heavily industrialised regions (20–45° N), with values declining towards the poles. Global distributions obtained from ACE were compared with predictions from three chemistry transport models showing good agreement in terms of the vertical gradient despite an overall offset. The ACE dataset gives unique global and temporal coverage of carbon tetrachloride and its transport through the atmosphere. An estimated lifetime for carbon tetrachloride of 34±5 years was determined through correlation with CFC-11.
A new look upon the Meese-Rogoff puzzle based on Support Vector Regression

While the seminal of Meese and Rogoff states that no model can consistently outperform the Random Walk, the application of machine learning and Kernel methods have resulted good results in terms of out-of-sample prediction, making them prominent research agendas in exchange rate forecasting. This paper analyzed the prediction of the spot exchange rate of 10 currency pairs using Support Vector Regression (SVR) based on a fundamentalist model composed of 13 explanatory variables. 9 different Kernel functions were teste and the predictions were compared to the Random Walk benchmark and evaluated for directional accuracy rate and error performance metrics RMSE (Root Mean Square Error) and MAE (Mean Absolute Error). The statistical significance of the explanatory power gain of SVR models over the Random Walk was checked by applying White’s Reality Check Test. The results show that SVR models achieved satisfactory predictive performance, with many of the proposed models showing strong statistical significance of predictive superiority. Furthermore, the results showed that mainstream Kernel functions commonly used in the scientific literature failed to outperform the Random Walk, indicating a possible gap in the state of art of machine learning methods applications to exchange rates forecasting, which can provide new insights regarding the Meese-Rogoff puzzle and the predictability of exchange rates.
The Dynamics of Coral-Algal Interactions in Space and Time on the Southern Great Barrier Reef

Globally, tropical coral reefs are being degraded by human activities, and as a result, reef-building corals have declined while macroalgae have increased. Recent work has focused on measuring macroalgal abundance in response to anthropogenic stressors. To accurately evaluate the effects of human impacts, however, it is necessary to understand the effects of natural processes on reef condition. To better understand how coral reef communities are influenced by natural processes, we investigated how spatial and seasonal changes in environmental conditions (temperature and PAR) influence benthic community structure, and the composition and frequency of coral-algal interactions across eight distinct zones and over a 23-month period at Heron reef on the southern Great Barrier Reef. Hard coral cover and macroalgal density showed distinct spatio-temporal variations, both within and between zones. Broad hard coral cover was significantly higher at the reef slope sites compared to the lagoon and was not significantly influenced by season. The composition and biomass of macroalgae increased in spring and declined in summer, with maximum macroalgal abundance corresponding with average temperatures of between 22 and 24°C and average 24 h PAR of 300-500 μmol qanta m s. Changes in macroalgal biomass further influenced the composition and frequency of coral-algal interactions, however the incidence of coral-algal contact was best explained by coral cover. The results presented here emphasize that natural levels of macroalgae and coral-algal interactions are context-specific, and vary not only with-in zones, but in somewhat predictable seasonal cycles. Further, these results emphasize that the frequency of coral-algal interactions is dependent on hard coral, not just macroalgal cover, and an increase in coral-algal interactions does not necessarily translate to degradation of coral reefs.
Intervention Effect Rates as a Path to Research Relevance: Information Systems Security Example

In the current information systems security (ISS) research, new theory contributions are especially valued. This research typically reflects the following formula: Suggest a new theory (or set of constructs) of ISS and show that it is empirically supported, then suggest another new theory (or set of constructs with some linkages) and show that it is empirically supported, and so on. Despite the merits of this approach, it leaves out many important scientific aspects. For example, after more than 30 years of ISS research, (1) we know little about the conditions and situations to which new theories (or constructs) do not apply; (2) we do not know which new theories are more effective than others in solving an ISS problem; and (3) we have not demonstrated that our best research, or new theoretical contributions, can beat industry best practices or practitioners’ intuitive approaches. We suggest that ISS research be examined in terms of long-term research programs comprising four levels: metalevel research, basic research, applied research, and postintervention research. The ultimate success of such programs does not entail new theories, “contextualized theories,” or adding IT artifacts to theories; rather, it hinges on the question of which program can demonstrate the best intervention effect rate for a given ISS problem. The lack of demonstrated intervention effectiveness (e.g., by showing treatment effect rates) is one important inhibitor that may prevent ISS research from achieving relevance in practice. Without reporting such evidence, ISS research cannot overpower the folklore, fads, or industry “best practices” that often guide operations. With such treatment effect rates, evidence-based practice may become more justifiable. We believe that our ideas also can be applied to information systems research in general.
Local Tomography of Large Networks Under the Low-Observability Regime

This article studies the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. We focus on the large-scale network setting with the additional constraint of partial observations, where only a small fraction of the agents can be feasibly observed. The goal is to infer the underlying subnetwork of interactions and we refer to this problem as local tomography. In order to study the large-scale setting, we adopt a proper stochastic formulation where the unobserved part of the network is modeled as an Erdős-Rényi random graph, while the observable subnetwork is left arbitrary. The main result of this work is to establish that, under this setting, local tomography is actually possible with high probability, provided that certain conditions on the network model are met (such as stability and symmetry of the network combination matrix). Remarkably, such conclusion is established under the low-observability regime, where the cardinality of the observable subnetwork is fixed, while the size of the overall network scales to infinity.
The acquisition of the active transitive construction in English: A detailed case study

In this study, we test a number of predictions concerning children’s knowledge of the transitive Subject-Verb-Object (SVO) construction between two and three years on one child (Thomas) for whom we have densely collected data. The data show that the earliest SVO utterances reflect earlier use of those same verbs, and that verbs acquired before 2;7 show an earlier move towards adultlike levels of use in the SVO construction and in object argument complexity than later acquired verbs. There is not a close relation with the input in the types of subject and object referents used, nor a close adherence to Preferred Argument Structure (PAS) before 2;7, but both early and late acquired verbs show a simultaneous move towards PAS patterns in selection of referent type at 2;9. The event semantics underpinning early transitive utterances do not straightforwardly fit prototype (high or inalienable) notions of transitivity, but rather may reflect sensitivity to animacy and intentionality in a way that mir rors the input. We conclude that children’s knowledge of the transitive construction continues to undergo significant development between 2;0 and 3;0, reflecting the gradual abstraction and integration of the SVO and VO constructions, verb semantics, discourse pragmatics, and the interactions between these factors. These factors are considered in the context of a prototype for the transitive construction.
AutoAugment: Learning Augmentation Policies from Data

Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars.
Visual Working Memory Cannot Trade Quantity for Quality

Two main models have been proposed to describe how visual working memory (WM) allocates its capacity: the slot-model and the continuous resource-model. The purpose of the current study was to test a direct prediction of the resource model suggesting that WM can trade-off between the quantity and quality of the encoded information. Previous research reported equivocal results, with studies that failed to find such a trade-off and other studies that reported a trade-off. Following the design of previous studies, in Experiment 1 we replicated this trade-off, by presenting the memory array for 1200 ms. Experiment 2 failed to observe a trade-off between quantity and quality using a memory array interval of 300 ms (a standard interval for visual WM). Experiment 3 again failed to find this trade-off, when reinstating the 1200 ms memory array interval but adding an articulatory suppression manipulation. We argue that while participants can trade quantity for quality, this pattern depends on verbal encoding and transfer to long-term memory processes that were possible to perform only during the long retention interval. When these processes were eliminated, the trade-off disappeared. Thus, we didn’t find any evidence that the trade-off between quantity for quality can occur within visual WM.
Physico-Chemical Properties of MgGa Mixed Oxides and Reconstructed Layered Double Hydroxides and Their Performance in Aldol Condensation of Furfural and Acetone

MgGa layered double hydroxides (Mg/Ga = 2–4) were synthesized and used for the preparation of MgGa mixed oxides and reconstructed hydrotalcites. The properties of the prepared materials were examined by physico-chemical methods (XRD, TGA, NH3-TPD, CO2-TPD, SEM, and DRIFT) and tested in aldol condensation of furfural and acetone. The as-prepared phase-pure MgGa samples possessed hydrotalcite structure, and their calcination resulted in mixed oxides with MgO structure with a small admixture phase characterized by a reflection at 2θ ≈ 36.0°. The interaction of MgGa mixed oxides with pure water resulted in reconstruction of the HTC structure already after 15 s of the rehydration with maximum crystallinity achieved after 60 s. TGA-MS experiments proved a substantial decrease in carbonates in all rehydrated samples compared with their as-prepared counterparts. This allowed suggesting presence of interlayer hydroxyls in the samples. Acido-basic properties of MgGa mixed oxides determined by TPD technique did not correlate with Mg/Ga ratio which was explained by the specific distribution of Ga atoms on the external surface of the samples. CO2-TPD method was also used to evaluate the basic properties of the reconstructed MgGa samples. In these experiments, an intensive peak at T = 450°C on CO2-TPD curve was attributed to the decomposition of carbonates newly formed by CO2 interaction with interlayer carbonates rather than to CO2 desorption from basic sites. Accordingly, CO2-TPD method quantitatively characterized the interlayer hydroxyls only indirectly. Furfural conversion on reconstructed MgGa materials was much larger compared with MgGa mixed oxides confirming that Brønsted basic sites in MgGa catalysts, like MgAl catalysts, were active in the reaction. Mg/Ga ratio in mixed oxides influenced product selectivity which was explained by the difference in textural properties of the samples. In contrast, Mg/Ga ratio in reconstructed catalysts had practically no effect on the composition of reaction products suggesting that the basic sites in these catalysts acted similarly in aldol condensation of acetone with furfural. It was concluded that the properties of MgGa samples resembled in a great extent those of MgAl hydrotalcite-based materials and demonstrated their potential as catalysts for base-catalyzed reactions.
Thermodynamics of Alkanolamine + Water System 1

1 Madhusree Kundu # and 2 S.S. Bandyopadhyay Department of Chemical Engineering, Birla Institute of Technology and Science (BITS), Pilani, Rajasthan-333031 2 Separation Science Laboratory, Cryogenic Engineering Centre, IIT, Kharagpur-721302 Correspondence : Dr. Madhusree Kundu, Department of Chemical Engineering, Birla Institute of Technology and Science (BITS), Pilani, Rajasthan-333031, E-mail: mkundu@bits-pilani.ac. in
Reducing phosphorus in swine effluent with aluminum chloride treatment during lagoon cleanout.

Phosphorus (P) runoff from agricultural lands fertilized with swine manure can be a significant environmental issue. The objectives of this study were to evaluate the effect of aluminum chloride (AlCl3) applications to a swine lagoon during total clean out on P concentrations in manure and runoff water. There were four treatments: 1) untreated manure; 2) manure treated with AlCl3 in-situ; 3) manure treated with AlCl3 post collection; and 4) manure treated with AlCl3 plus lime post collection. Manure applications were equivalent to 112 kg P ha-1. Rainfall simulators were used to provide a 5-cm h-1 storm event 1, 8, 15, 17, and 24 days after application. All AlCl3 amendments significantly reduced manure soluble reactive P (SRP) concentrations. The AlCl3/lime treatment was the most effective, reducing manure SRP concentrations by 69% compared to the untreated manure. In runoff water, SRP loads were significantly higher from plots fertilized with the untreated manure. Cumulative SRP loads were 30% lower from plots fertilized with manure treated with AlCl3 in-situ than the untreated manure. Cumulative P losses increased as SRP application rates increased. Treatment of manure with AlCl3 in-situ can significantly decrease manure SRP concentrations and subsequently reduce P loss from fields receiving manure applications.
Evaluating Pro- and Re-Active Driving Behavior by Means of the EEG

Traffic safety essentially depends on the drivers’ alertness and vigilance, especially in monotonous or demanding driving situations. Brain oscillatory EEG activity offers insight into a drivers’ mental state and has therefore attracted much attention in the past. However, EEG measures do not only vary with internal factors like attentional engagement and vigilance but might also interact with external factors like time on task, task demands, or the degree to which a traffic situation is predictable. In order to identify EEG parameters for cognitive mechanisms involved in tasks of high and low controllability, the present study investigated the interaction of time on task, task load, and cognitive controllability in simulated driving scenarios, using an either re-active or pro-active driving task. Participants performed a lane-keeping task, half of them compensating varying levels of crosswind (re-active task), and the other half driving along a winding road (pro-active task). Both driving tasks were adjusted with respect to difficulty. The analysis of oscillatory EEG parameters showed an increase in total power (1–30 Hz) with time on task, with decreasing task load, and in the re-active compared to the pro-active task. Furthermore, the relative power in Alpha band increased with decreasing task load and time on task, while relative Theta power showed the opposite pattern. Moreover, relative Alpha power was also higher in the re-active, than pro-active, driving situation, an effect that even increased with time on task. The results demonstrate that the controllability of a driving situation has a similar effect on oscillatory EEG activity like time on task and task load.