Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistically Inferring Attack Ramifications Using Temporal Dependence Network

There is an increasing need of assessing and mitigating the effects of successful attacks. Uncovering malicious and contaminated objects in an attacked computing system is referred to as identification of attack ramifications. Previous methods identify the attack ramifications by directly tracking information flows (or dependences) from the intrusion root (i.e., the entry point of an attack). They face challenges such as undetermined intrusion root and dependence explosion. In this paper, we present a novel, light-weight method capable of identifying attack ramifications without the knowledge of intrusion root and less subject to dependency explosion. The method utilizes a probabilistic reasoning approach to fuse evidence derived from a subset of objects whose security states are known. It first splits the lifetime of an object into consecutive time slices (object-slices) to profile how the security state of this object changes over time. Then, a temporal dependence network (TDN) is constructed from system call traces to correlate object-slices according to information flows between them. Based on that, a Bayesian network (BN) model is built to characterize the uncertainties of infection propagations in the TDN. Finally, the method adopts loopy belief propagation on the BN model to infer the security state of an object. We evaluate the proposed method using a large data set of 389 attacks launched by the real-world malware samples including sophisticated ones such as Stuxnet. Extensive experiments demonstrate that our method is able to identify attack ramifications with a 97.47% precision at 97.21% recall without the knowledge of intrusion root.
Evidence for depletion of heavy silicon isotopes at comet 67P/Churyumov-Gerasimenko

Context. The Rosetta Orbiter Spectrometer for Ion and Neutral Analysis (ROSINA) was designed to measure the composition of the gas in the coma of comet 67P/Churyumov-Gerasimenko, the target of the European Space Agency's Rosetta mission. In addition to the volatiles, ROSINA measured refractories sputtered off the comet by the interaction of solar wind protons with the surface of the comet. Aims. The origin of different solar system materials is still heavily debated. Isotopic ratios can be used to distinguish between different reservoirs and investigate processes occurring during the formation of the solar system. Methods. ROSINA consisted of two mass spectrometers and a pressure sensor. In the ROSINA Double Focusing Mass Spectrometer (DFMS), the neutral gas of cometary origin was ionized and then deflected in an electric and a magnetic field that separated the ions based on their mass-to-charge ratio. The DFMS had a high mass resolution, dynamic range, and sensitivity that allowed detection of rare species and the known major volatiles. Results. We measured the relative abundance of all three stable silicon isotopes with the ROSINA instrument on board the Rosetta spacecraft. Furthermore, we measured $^{13}$C/$^{12}$C in C$_2$H$_4$, C$_2$H$_5$, and CO. The DFMS in situ measurements indicate that the average silicon isotopic composition shows depletion in the heavy isotopes $^{29}$Si and $^{30}$Si with respect to $^{28}$Si and solar abundances, while $^{13}$C to $^{12}$C is analytically indistinguishable from bulk planetary and meteorite compositions. Although the origin of the deficiency of the heavy silicon isotopes cannot be explained unambiguously, we discuss mechanisms that could have contributed to the measured depletion of the isotopes $^{29}$Si and $^{30}$Si.
HYBRID CLOUDS

Worldwide globalization processes as well as rapid development of information and communication technologies (ICT) significantly determine modern business operations in each and every organization. The basic concept of mass customization as a new trend is to increase the variety of individually tailored products/services to meet customer needs without a large increase in production costs. It requires a highly flexible production technology though. Developing such technologies can be expensive and time-consuming. Clouds enable delivery of mass customized services/information in the “Data to Information to Knowledge“ chain. The aim of this paper is to introduce the way how the mid-market can utilize Public Cloud computing in conjunction with a secure Private Clouds and further more to propose a framework for mass customization and its collaboration in Clouds.
Spatiotemporal dynamics of cattle behavior and resource selection patterns on East African rangelands: evidence from GPS-tracking

ABSTRACT Characterizing cattle behavior is crucial to inferring fine-scale resource selection patterns and improving rangeland management. However, our understanding of cattle behavior and resource selection on the extensive African rangelands suffers from a lack of quantitative, continuous and inter-seasonal monitoring of cattle movement. Based on integration of GPS-tracking and field observations, this study links cattle behavioral types with statistical parameters of movement, analyzes spatiotemporal dynamics of behavior and predicts resource selection patterns in Borana Zone of southern Ethiopia. We find that different cattle behavioral types were associated with distinct ranges of movement velocity. Distribution of identified cattle behavior varied substantially within the day and along the distance gradient from camp locations. Vegetation greenness, topography, study site, herding strategy and season were dominant factors influencing foraging areas selection by cattle. Research findings suggested that extensive herding through camp relocation can promote forage uptake while reducing energy spent on traveling. Future modeling of cattle resource selection needs to be based on longer-term GPS-tracking data and incorporate additional social, environmental, institutional and cultural factors to better interpret the complexity associated with cattle behavior in extensive grazing systems.
Multi-biomarker disease activity score as a predictor of disease relapse in patients with rheumatoid arthritis stopping TNF inhibitor treatment

Objective Successfully stopping or reducing treatment for patients with rheumatoid arthritis (RA) in low disease activity (LDA) may improve cost-effectiveness of care. We evaluated the multi-biomarker disease activity (MBDA) score as a predictor of disease relapse after discontinuation of TNF inhibitor (TNFi) treatment. Methods 439 RA patients who were randomized to stop TNFi treatment in the POET study were analyzed post-hoc. Three indicators of disease relapse were assessed over 12 months: 1) restarting TNFi treatment, 2) escalation of any DMARD therapy and 3) physician-reported flare. MBDA score was assessed at baseline. Associations between MBDA score and disease relapse were examined using univariate analysis and multivariate logistic regression. Results At baseline, 50.1%, 35.3% and 14.6% of patients had low (<30), moderate (30−44) or high (>44) MBDA scores. Within 12 months, 49.9% of patients had restarted TNFi medication, 59.0% had escalation of any DMARD and 57.2% had ≥1 physician-reported flare. MBDA score was associated with each indicator of relapse. At least one indicator of relapse was observed in 59.5%, 68.4% and 81.3% of patients with low, moderate or high MBDA scores, respectively (P = 0.004). Adjusted for baseline DAS28-ESR, disease duration, BMI and erosions, high MBDA scores were associated with increased risk for restarting TNFi treatment (OR = 1.85, 95% CI 1.00–3.40), DMARD escalation (OR = 1.99, 95% CI 1.01–3.94) and physician-reported flare (OR = 2.00, 95% 1.06–3.77). Conclusion For RA patients with stable LDA who stopped TNFi, a high baseline MBDA score was independently predictive of disease relapse within 12 months. The MBDA score may be useful for identifying patients at risk of relapse after TNFi discontinuation.
Named Entity Recognition for Kannada using Gazetteers list with Conditional Random Fields

Named Entities (NEs) that exist in the sentences are essential to build Natural Language Processing (NLP) applications for Information Extraction (IE) from large corpora. However, generating a large corpus is challenging for resource poor languages, such as Kannada. Further, there is no annotated corpus available online. The challenges faced in annotating NEs with pre-defined classes are: It is morphologically joined with other words and the spelling variations are more frequent for Kannada words. Sentence structure varies according to morphology, parts of speech (pos) and chunking of a language. These parameters differ from one language to another. To address these challenges, a novel application system is proposed to identify NEs in Kannada using a large corpus of 73,676 tokens. The Named Entity Recognition (NER) system consist of a robust pos tagger and Noun Phrase (NP) chunker developed for generic data. Five gazetteer lists were created from many orthographic patterns for each word. Context information such as previous two words, next two words, word morphology and gazetteer lists were added to feature lists. An unigram-bigram template was designed and incorporated into Conditional Random Fields (CRFs) to generate conditional feature functions. The proposed system resulted in 86.85% and 71.01% f-measure for gold test data and newspaper data respectively.
Graph-Regularized Fast and Robust Principal Component Analysis for Hyperspectral Band Selection

A fast and robust principal component analysis on Laplacian graph (FRPCALG) method is proposed to select bands of hyperspectral imagery (HSI). The FRPCALG assumes that a clean band matrix lies in a unified manifold subspace with low-rank and clustering properties, whereas sparse noise does not lie in the same subspace. It estimates the clean low-rank approximation of the original HSI band matrix while uncovering the clustering structure of all bands. Specifically, a structured random projection is adopted to reduce the high spatial dimensionality of the original data for computational cost saving, and then a Laplacian graph (LG) term is regularized into the regular robust principal component analysis (RPCA) to formulate the FRPCALG model for the submatrix of bands to be selected. The RPCA term ensures the clean and low-rank approximation of original data, and the LG term guarantees the clustering quality of a low-rank matrix in the low-dimensional manifold subspace. The alternating direction method of multipliers’ algorithm is utilized to optimize the convex program of the FRPCALG. The K-means algorithm is to group all columns of submatrix into clusters, and corresponding bands closest to their cluster centroids finally constitute the desired band subset. Experimental results show that FRPCALG outperforms state-of-the-art methods with lower computational cost. A moderate regularization parameter $\lambda $ and a small $\mu $ could guarantee satisfying the classification accuracy of FRPCALG, and a small projected dimension greatly reduces the computational cost and does not affect the classification performance. Therefore, the FRPCALG can be an alternative method for hyperspectral band selection.
A Simple, Efficient, Parallelizable Algorithm for Approximated Nearest Neighbors

The use of the join operator in metric spaces leads to what is known as a similarity join, where objects of two datasets are paired if they are somehow similar. We propose an heuristic that solves the 1-NN selfsimilarity join, that is, a similarity join of a dataset with itself, that brings together each element with its nearest neighbor within the same dataset. Solving the problem using a simple brute-force algorithm requires O(n) distance calculations, since it requires to compare every element against all others. We propose a simple divide-and-conquer algorithm that gives an approximated solution for the self-similarity join that computes only O(n 3 2 ) distances. We show how the algorithm can be easily modified in order to improve the precision up to 31% (i.e., the percentage of correctly found 1-NNs) and such that 79% of the results are within the 10-NN, with no significant extra distance computations. We present how the algorithm can be executed in parallel and prove that using Θ( √ n) processors, the total execution takes linear time. We end discussing ways in which the algorithm can be improved in the future.
An Infinite Deep Boltzmann Machine

The deep Boltzmann machine (DBM) is a powerful "deep" probabilistic model which learns a hierarchical representation of the data. However, choosing the size of each hidden layer of a DBM is difficult as the proper size of the model varies according to different tasks. Choosing a proper model size is a essential model selection problem for latent variable graphical models. This paper provides a new variant of DBM, called the infinite deep Boltzmann machine (iDBM), which can freely change the number of hidden units participating in the energy function of each layer. A greedy training method is proposed to pre-train our model, after which the size of each layer is fixed, and the model is transferred into an ordinary DBM. Experimental results on MNIST and CalTech101 Silhouettes indicate that iDBM can learn a generative and discriminative model as good as the original DBM, and has successfully eliminated the requirement of model selection for hidden layer sizes of DBMs.
Nonlocal Similarity Modeling and Deep CNN Gradient Prior for Super Resolution

This letter presents a novel super-resolution (SR) method via nonlocal similarity modeling and deep convolutional neural network (CNN) gradient prior (GP). Specifically, on the one hand, the group similarity reliability (GSR) strategy is proposed for improving the adaptive high-dimensional nonlocal total variation (AHNLTV) model [statistical prior, GSR-based AHNLTV (GA)], which captures the structures of the underlying high-resolution (HR) image via the image itself. On the other hand, the GP is learned by using the deep CNN (learned prior), which predicts the gradients from external images. Finally, the GA–GP approach is proposed by incorporating the two complementary priors. The results show that GA–GP achieves better performance than other state-of-the-art SR methods.
My Approach = Your Apparatus?

Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers. These applications use cross-collection topic modeling for the exploration, clustering, and comparison of large sets of documents, such as digital libraries. However, topic modeling on documents from different collections is challenging because of domain-specific vocabulary. We present a cross-collection topic model combined with automatic domain term extraction and phrase segmentation. This model distinguishes collection-specific and collection-independent words based on information entropy and reveals commonalities and differences of multiple text collections. We evaluate our model on patents, scientific papers, newspaper articles, forum posts, and Wikipedia articles. In comparison to state-of-the-art cross-collection topic modeling, our model achieves up to 13% higher topic coherence, up to 4% lower perplexity, and up to 31% higher document classification accuracy. More importantly, our approach is the first topic model that ensures disjunct general and specific word distributions, resulting in clear-cut topic representations.
Bioactivity guided isolation and characterization of the phytoconstituents from the Tridax procumbens

Tridax procumbens L. , Asteraceae, has been extensively used in Ayurvedic system of medicine for various ailments. Previous studies on the extracts of T. procumbens revealed remarkable immunomodulatory activity of TPEIF (T. procumbens ethanol insoluble fraction) extract. The dried methanol extract of T. procumbens was dissolved in distilled water, and then fractioned by re-extracting with chloroform, ethyl acetate and n-butanol subsequently. Immunomodulatory activities of these fractions were determined in vivo. The amounts of total phenolic compounds were also determined. Ethyl acetate and n-butanol fractions showed the significant immunomodulary activity. However, the ethyl acetate fraction exhibited the highest total phenolic content. Therefore, ethyl acetate fraction was subjected to further separation by chromatographic methods. Two phytochemicals SA-3 and SA-4 were obtained by repeated purification in sufficient amount to screen them for the immunomodulatory activity by the in vivo models i.e. neutrophil adhesion and delayed type hypersensitivity. In addition, the n-butanol fraction was subjected to silica gel column chromatography (CC); SA-6 was isolated from it. Mice were treated with two doses of SA-3, SA-4 and SA-6 (2 and 4 mg/kg) for fifteen days. Immune responses to T-dependent antigen SRBCs were observed using parameters like DTH and Neutrophil adhesion. Overall, SA-4 and SA-6 showed dose relative immunostimulatory effect on in vivo immune functions in mice. From these results, it can be suggested that these compounds may be used as potential immunostimulators. The structures of isolated phytochemicals were determined by UV, IR, NMR, and MS spectroscopic methods.
A Fast Content-Based Spam Filtering Algorithm with Fuzzy-SVM and K-means

Nowadays, spam is pervasive in the mailbox, and not only caused a waste of network resources, but also brings a lot of trouble to people's daily life. How to filter spam quickly and accurately is a challenge we are facing. For handling this challenge, this paper proposes a fast content-based spam filtering algorithm with fuzzy-SVM and k-means. First, K-means clustering algorithm is used to compress data with retain most of the effective information. Then, fuzzy support vector machine is used to train classification model, in order to deal with uncertain factors better. The experimental results show that this algorithms could improve the spam filtering speed and filtering accuracy rate effectively.
Calcium silicate as a graft material for bone fractures: a systematic review

Objective The goal of this review was to determine whether calcium silicate (wollastonite) as a bone graft material is a viable alternative to autogenous bone or whether the evidence base for its use is weak. Methods In this systematic review, electronic databases (MEDLINE/PubMed and BVS) were searched for relevant articles in indexed journals. Articles published in a 10-year period were identified (n = 48). After initial selection, 17 articles were assessed for eligibility; subsequently, seven articles were excluded and 10 articles were included. Results Among the studies included, 20% emphasized the importance of randomization, which adds reliability to the study, minimizing the risk of bias. High variability was observed in the material used, such as additives, amounts, dosage, and chemical alterations, rendering direct comparison among these studies impossible. The experimental periods varied considerably; one of the studies did not include statistical analysis, weakening the evaluation. Nonetheless, the true potential of wollastonite as a graft material conducive to new bone formation was reported in all studies. Conclusion The results support the use of wollastonite as a bone graft material. The initial research question was answered despite the significant variability observed among these preclinical studies, which hindered the precision of this analysis.
Locator® attachment system for implant overdentures: a systematic review.

OBJECTIVE To evaluate the success rate, complications, maintenance and patient satisfaction with implant-supported overdentures with the Locator® system, by means of a systematic review. MATERIALS AND METHODS PICO approach was used to formulate the clinical question. Research was conducted in primary (PubMed® and B-On®) and secondary (Cochrane®) information sources using different logical combination strategies of text words and MESH terms. Articles were selected according to research theme and scientific level evidence. RESULTS 55 articles were found. After reading the title and summary, and evaluating the article's level of scientific evidence, only ten were included for analysis. Eight studies were related to rehabilitations in the mandible and two were bi-maxillary. The analysis of the studies showed that complications and type of maintenance are primarily related to the loss of retention and the need to replace the nylon male component of the system. Patient's satisfaction was highlighted in five articles of this research. CONCLUSION The overall satisfaction rates of patients seem to indicate this system as a viable clinical option of prosthetic rehabilitation. Overdentures with the Locator® system appear to hold a good retention, either in the upper or lower jaw, but require frequent maintenance visits, due to complications observed in these prosthodontic rehabilitations.
Infection prevention and control: Who is the judge, you or the guidelines?

Objectives: The aim of this study was to explore the attitudes and behaviours of registered nurses and their colleagues around the adoption of standard precautions in order to determine strategies to promote adherence. Design: A qualitative exploratory descriptive design used interviews and focus group to collect data. Setting: Registered nurses and registered midwifes from a tertiary metropolitan hospital took part in the study. Participants: A voluntary sample of 29 adults was recruited from the Australian nursing (n = 25) and midwifery (n = 4) workforce. There were six men (mean age = 36.83 years; SD = 8.93) and 23 women (mean age = 41.36 years; SD = 10.25). Participants were recruited through advertisement on notice boards and emails from unit managers. Results: Thematic analysis revealed five themes but the focus here is on staff judgements which are against the guidelines. Participants indicated that where in their judgement the patient posed no risk and they judged themselves skilled in the procedure, they were justified in deviating from the guidelines. Some staff judgements appeared to be self-protecting, while others were irrational and inconsistent. Conclusions: Despite use of standard precautions being mandated, staff often deviated from them based on their own assessment of the situation or the patient. Any deviance from the guidelines is of concern but especially so when staff take it upon themselves to apply their own criteria or judgements. These results also suggest there may be some organisational inadequacies with regards to training and supervision of staff.
Epita at SemEval-2018 Task 1: Sentiment Analysis Using Transfer Learning Approach

In this paper we present our system for detecting valence task. The major issue was to apply a state-of-the-art system despite the small dataset provided: the system would quickly overfit. The main idea of our proposal is to use transfer learning, which allows to avoid learning from scratch. Indeed, we start to train a first model to predict if a tweet is positive, negative or neutral. For this we use an external dataset which is larger and similar to the target dataset. Then, the pre-trained model is re-used as the starting point to train a new model that classifies a tweet into one of the seven various levels of sentiment intensity. Our system, trained using transfer learning, achieves 0.776 and 0.763 respectively for Pearson correlation coefficient and weighted quadratic kappa metrics on the subtask evaluation dataset.
Differential Fault Analysis of Rectangle-80

We present various differential fault attack schemes for the RECTANGLE-80 and demonstrate how initially we started from a 80-bit fault to a single word fault scheme. This was mainly due to a differential vulnerability in the S-box of RECTANGLE as a result of which the exhaustive search space for the key reduces from 2 to 2. We have also presented a key schedule attack that is a variant of the single fault scheme, exploiting the same vulnerability and reduces the search space to 2. The paper concludes with the simulation results for the single word fault scheme followed by countermeasures.