Miracle-dz/UniFAR-FTUs huggingface dataset:
Time series modeling for IDS alert management

Intrusion detection systems create large amounts of alerts. Significant part of these alerts can be seen as background noise of an operational information system, and its quantity typically overwhelms the user. In this paper we have three points to make. First, we present our findings regarding the causes of this noise. Second, we provide some reasoning why one would like to keep an eye on the noise despite the large number of alerts. Finally, one approach for monitoring the noise with reasonable user load is proposed. The approach is based on modeling regularities in alert flows with classical time series methods. We present experimentations and results obtained using real world data.
Nuclear Translocation of Phosphorylated STAT3 Is Essential for Vascular Endothelial Growth Factor-induced Human Dermal Microvascular Endothelial Cell Migration and Tube Formation*

Vascular endothelial growth factor (VEGF) is a potent, multifunctional, endothelial-cell-specific growth factor. It stimulates proliferation and migration of endothelial cells. Characterization of intracellular signal transduction after VEGF and VEGF receptor (VEGFR) interaction has demonstrated the involvement of the mitogen-activated protein kinase pathway. However, several studies indicated that signal transducers and activators of transcription (STAT) is another important pathway downstream of VEGF/VEGFR interaction. Therefore, we studied the role of STAT3 in the migration and tube formation of the human dermal microvascular endothelial cells (HDMEC). HDMEC expressed phosphorylated forms of STAT1, STAT3, and STAT5, and a marked increase of phosphorylated STAT3 in the nuclear fraction after addition of VEGF was observed by Western blot and immunohistochemical staining. To verify the functional implication of STAT3 phosphorylation in HDMEC migration, we introduced a dominant-negative STAT3 using adenovirus vector system. Dominant-negative STAT3 abolished the VEGF-induced nuclear translocation of phosphorylated STAT3 and inhibited HDMEC migration completely. Dominant-negative STAT3 also suppressed VEGF-induced HDMEC tube formation on Matrigel and on collagen gel. These data demonstrate that STAT3 and its phosphorylation are involved in the downstream pathway of VEGF/VEGFR interaction and regulate VEGF-induced HDMEC migration and tube formation.
Fast Methods for Kernel-Based Text Analysis

Kernel-based learning (e.g., Support Vector Machines) has been successfully applied to many hard problems in Natural Language Processing (NLP). In NLP, although feature combinations are crucial to improving performance, they are heuristically selected. Kernel methods change this situation. The merit of the kernel methods is that effective feature combination is implicitly expanded without loss of generality and increasing the computational costs. Kernel-based text analysis shows an excellent performance in terms in accuracy; however, these methods are usually too slow to apply to large-scale text analysis. In this paper, we extend a Basket Mining algorithm to convert a kernel-based classifier into a simple and fast linear classifier. Experimental results on English BaseNP Chunking, Japanese Word Segmentation and Japanese Dependency Parsing show that our new classifiers are about 30 to 300 times faster than the standard kernel-based classifiers.
Predictors of preconceptional folic acid or multivitamin supplement use: a cross-sectional study of Danish pregnancy planners

Purpose Compliance with the Danish preconceptional folic acid (FA) recommendation – a daily supplement of 400 μg – is reported to be poor. Uncertainty remains, however, about the prevalence of compliers and health-related predictors of compliance in the preconceptional period. Methods We used self-reported baseline data from 5383 women, aged 18–40 years, enrolled in an Internet-based prospective cohort study of Danish pregnancy planners during 2007–2011. We estimated the prevalence proportions of FA or multivitamin (MV) use in relation to selected sociodemographic, lifestyle, reproductive, and medical characteristics. Multivariate binomial regression was used to obtain prevalence proportion differences with 95% confidence intervals for each level of study predictors, adjusted for all other predictors. Results Overall, 7.7% of women used FA supplements, 20.4% used MV supplements, 34.0% used both, 1.5% used other single vitamins or minerals, and 36.4% did not use any dietary supplements. The prevalence of FA or MV supplement use was higher among older women, women with higher education and income, and women with healthy lifestyle factors such as being a nonsmoker, nondrinker, physically active, maintaining a normal body mass index and having regular pap smears. Greater intercourse frequency and a history of spontaneous abortion were also positively associated with FA or MV supplement use. We found no clear association between use of FA or MV supplements and a diagnosis of hypertension, diabetes, thyroid disease, pelvic inflammatory disease, or chlamydia. Conclusion A large proportion of pregnancy planners do not use FA or MV supplements. Pregnancy planners with generally risky lifestyle behaviors are less likely to comply with the FA recommendation.
Diffusion tensor tractography of the arcuate fasciculus in patients with brain tumors: Comparison between deterministic and probabilistic models.

PURPOSE The purpose of this study was to compare the deterministic and probabilistic tracking methods of diffusion tensor white matter fiber tractography in patients with brain tumors. MATERIALS AND METHODS We identified 29 patients with left brain tumors <2 cm from the arcuate fasciculus who underwent pre-operative language fMRI and DTI. The arcuate fasciculus was reconstructed using a deterministic Fiber Assignment by Continuous Tracking (FACT) algorithm and a probabilistic method based on an extended Monte Carlo Random Walk algorithm. Tracking was controlled using two ROIs corresponding to Broca's and Wernicke's areas. Tracts in tumoraffected hemispheres were examined for extension between Broca's and Wernicke's areas, anterior-posterior length and volume, and compared with the normal contralateral tracts. RESULTS Probabilistic tracts displayed more complete anterior extension to Broca's area than did FACT tracts on the tumor-affected and normal sides (p < 0.0001). The median length ratio for tumor: normal sides was greater for probabilistic tracts than FACT tracts (p < 0.0001). The median tract volume ratio for tumor: normal sides was also greater for probabilistic tracts than FACT tracts (p = 0.01). CONCLUSION Probabilistic tractography reconstructs the arcuate fasciculus more completely and performs better through areas of tumor and/or edema. The FACT algorithm tends to underestimate the anterior-most fibers of the arcuate fasciculus, which are crossed by primary motor fibers.
Perceptual and conceptual cueing in implicit and explicit retrieval.

Subjects saw or heard words in a list (e.g. limerick) and then took two successive tests. The first was a yes/no recognition test in which auditory/visual modality of test words was manipulated orthogonally to the study modality. The second test varied with experimental conditions: subjects produced words to either perceptual (fragment) cues (l- -e-ick) or conceptual cues (What name is given to a lighthearted five-line poem? ), under either explicit or implicit retrieval instructions. The major findings were: (a) that regardless of the type of retrieval cue (perceptual or conceptual) the degree of dependency between recognition and cued recall was greater than that between recognition and implicit retrieval; and (b) that modality shifts adversely affected perceptually cued explicit and implicit retrieval, whereas they had no effect either on conceptually cued retrieval or on recognition. These results suggest that the memory system subserving, and the processes involved in, conceptual priming differ from those underlying recognition and perceptual priming.
Sequence Length , Area Cost and Non-Target Defect Coverage Tradeoffs in Deterministic Logic BIST

For the first time, we study the coverage of non-target defects for Deterministic Logic BIST (DLBIST) architecture. We consider several DLBIST implementation options that result in test sequences of different lengths. Resistive brid ging faults are used as a surrogate of non-target defects. Experi mental data obtained for largest ISCAS benchmarks suggests that, although DLBIST always guarantees complete stuck-at coverage, test sequence length does influence the non-targe t defect detection capabilities. For circuits with a large fr action of random-pattern resistant faults, the embedded dete rministic patterns as well as a sufficient amount of random patterns are both demonstrated to be essential for non-targ e defect detection. It turns out, moreover, that area cost is lower for DLBIST solutions with longer test sequences, due to additional degrees of freedom for the embedding procedure and a lower number of faults undetected by pseudorandom patterns. This implies that DLBIST is particularly effective in covering non-target defects.
Trend Decomposition, informativeness and co-integration of futures market price discovery

In this paper Beveridge-Nelson Decomposition is used to examine trend content of soybean purchase price of oil plant in northeast China and soybean future prices of DCE and CBOT, in order to uncover full information in soybean's price discovery process and avoid contemporaneous correlation. It was found that DCE price was consistently more imformative about the fundamental price of soybean because of its lower noise level. Co-integration analysis and error correction model proved that there is a long-term equilibrium among three sample series; however, no evidence shows co-integration between two futures prices. Both DCE price and CBOT price are Granger Causes of domestic spot price. Price discovery process in DCE is independent of CBOT while price in CBOT sharing information from DCE, due to current situation in Chinese soybean spot market.
Robustness of the Learning with Errors Assumption

Starting with the work of Ishai-Sahai-Wagner and Micali-Reyzin, a new goal has been set within the theory of cryptography community, to design cryptographic primitives that are secure against large classes of side-channel attacks. Recently, many works have focused on designing various cryptographic primitives that are robust (retain security) even when the secret key is "leaky", under various intractability assumptions. In this work we propose to take a step back and ask a more basic question: which of our cryptographic assumptions (rather than cryptographic schemes) are robust in presence of leakage of their underlying secrets? Our main result is that the hardness of the learning with error (LWE) problem implies its hardness with leaky secrets. More generally, we show that the standard LWE assumption implies that LWE is secure even if the secret is taken from an arbitrary distribution with sufficient entropy, and even in the presence of hard-to-invert auxiliary inputs. We exhibit various applications of this result. 1. Under the standard LWE assumption, we construct a symmetric-key encryption scheme that is robust to secret key leakage, and more generally maintains security even if the secret key is taken from an arbitrary distribution with sufficient entropy (and even in the presence of hard-to-invert auxiliary inputs). 2. Under the standard LWE assumption, we construct a (weak) obfuscator for the class of point functions with multi-bit output. We note that in most schemes that are known to be robust to leakage, the parameters of the scheme depend on the maximum leakage the system can tolerate, and hence the efficiency degrades with the maximum anticipated leakage, even if no leakage occurs at all! In contrast, the fact that we rely on a robust assumption allows us to construct a single symmetric-key encryption scheme, with parameters that are independent of the anticipated leakage, that is robust to any leakage (as long as the secret key has sufficient entropy left over). Namely, for any k
Is the Tendency to Engage in Entrepreneurship Genetic?

We used quantitative genetics techniques to compare the entrepreneurial activity of 870 pairs of monozygotic (MZ) and 857 pairs of same-sex dizygotic (DZ) twins from the United Kingdom. We ran model-fitting analyses to estimate the genetic, shared environmental and nonshared environmental effects on the propensity of people to become entrepreneurs. We found relatively high heritabilities for entrepreneurship across different operationalizations of the phenomenon, with little effect of family environment and upbringing. Our findings suggest the importance of considering genetic factors in explanations for why people engage in entrepreneurial activity.
Implementing Dramatization as an Effective Storytelling Method To Increase Comprehension.

A study examined two different methods used to increase story comprehension. These methods were implemented using two kindergarten classes from the Secaucus New Jersey Public School District during a 4-week instructional period. The students from one classroom listened to stories proceeded by dramatization. Retelling techniques used were role playing, puppet theater, flannelboard, and pantomiming. The other sample had the same stories orally read to them, however, these were followed by teacher instruction and an art activity related to the story. Both groups were then given reduced photocopies of the main parts of the story and asked to put these sequencing strips in correct order by gluing them onto construction paper. Results indicated that those students who used dramatization had greater comprehension. Findings suggest (by a significant 21.35-point difference) that children who reenact a story become more emotionally involved and therefore more interested. (Contains a table of data and 18 references; a list of related literature is appended.) (Author/CR) ******************************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * ******************************************************************************** IMPLEMENTING DRAMATIZATION AS AN EFFECTIVE STORYTELLING METHOD TO INCREASE COMPREHENSION
USE OF SEMANTIC SIMILARITY AND WEB USAGE MINING TO ALLEVIATE THE DRAWBACKS OF USER-BASED COLLABORATIVE FILTERING RECOMMENDER SYSTEMS USE

One of the most famous methods for recommendation is user-based Collaborative Filtering (CF). This system compares active user’s items rating with historical rating records of other users to find similar users and recommending items which seems interesting to these similar users and have not been rated by the active user. As a way of computing recommendations, the ultimate goal of the user-based collaborative filtering is recommending items with the high accuracy and coverage degree. Nevertheless, some famous limitations are obstacles to meet them. They are Scalability, Sparseness and new item problems. Scalability problem can be handled with the use of Data Mining techniques like clustering. However, use of this technique often leads to the lower recommendation accuracy. Nevertheless, two other problems still remain. Involving Semantic knowledge can increase the performance of recommendation in sparseness and New-Item Problem conditions as well. This paper presents a new approach to deal with the drawbacks of userbased CF systems for web pages recommendation by Combination of Semantic Knowledge with Web Usage Mining (WUM). Semantic knowledge of web pages are extracted and subsequently incorporated into the navigation patterns of each cluster which obtained from clustering the access sessions to get the Semantic Patterns of each cluster. The cluster with the most relevant semantic pattern is chosen with the comparison of semantic representation of the active user session with the semantic patterns and the proper web pages are recommended based on a switching recommendation engine. This engine recommends a list of appropriate recommendations. Results of the implementation of this hybrid web recommender system indicates that this combined approach yields better results in both accuracy and coverage metrics and also has a considerable capability to handle collaborative filtering recommender system for its typical shortcomings.
Pattern of self-medication with analgesics among Iranian University students in central Iran

Background: Self-medication is defined as the use of drugs for the treatment of self-diagnosed disorders. It is influenced by factors such as education, family, society, law, availability of drugs and exposure to advertisements. This study was performed to evaluate self-medication with analgesics and its pattern among different groups of Iranian University Students. Materials and Methods: A randomized, cross-sectional, multicenter study was conducted from December 2009 to February 2010. The target population of this study was 564 students out of 10,000 students attending four medical and non-medical science universities in Qom state. Data was analyzed using SPSS version 16, and analysis was conducted with descriptive analysis procedures. Results: 76.6% of the students had used analgesics in self-medication in the previous 3 months. The frequency of analgesic use in the study period was once in 19.2% of the participants, twice in 22.2%, three times in 16.3% and more than three times in 35.5% of the participants, although 6.8% of them were not sure when they were used. Of all the respondents, 49.8% reported headache as the problem. This was the most common problem, after which came Dysmenorrhea,headache and stomach ache. Bone and joint pains were other problems that led to the use of analgesics. The most commonly used source of information for self-medication with analgesics was advice from friends and family (54.7%), previously prescribed medications (30.1%), their medical knowledge (13.3%) and recommendation of a pharmacist (1.9%). Conclusion: Self-medication with analgesics is very high among Iranian students in Qom city. This could be an index for other parts of the Iranian community. Because the source of information about analgesics is inappropriate, we would recommend education courses about analgesics and self-medication on the radio and television for the entire population.
Implicit Aspect Indicator Extraction for Aspect based Opinion Mining

Aspect-based opinion mining aims to model relations between the polarity of a document and its opinion targets, or aspects. While explicit aspect extraction has been widely researched, limited work has been done on extracting implicit aspects. An implicit aspect is the opinion target that is not explicitly specified in the text. E.g., the sentence “This camera is sleek and very affordable” gives an opinion on the aspects appearance and price, as suggested by the words “sleek” and “affordable”; we call such words Implicit Aspect Indicators (IAI). In this paper, we propose a novel method for extracting such IAI using Conditional Random Fields and show that our method significantly outperforms existing approaches. As a part of this effort, we developed a corpus for IAI extraction by manually labeling IAI and their corresponding aspects in a well-known opinion-mining corpus. To the best of our knowledge, our corpus is the first publicly available resource that specifies implicit aspects along with their indicators.
PARAMETRICAL WORDS IN THE SENTIMENT LEXICON

In this paper, the main features of parametrical words within a sentiment lexicon are determined. The data for the research are client reviews in the Russian language taken from the bank client rating; the domain under study is bank service quality. The sentiment lexicon structure is presented; it includes two primary classes (positive and negative words) and three secondary classes (increments, polarity modifiers, and polarity anti- modifiers). This lexicon is used as the main tool for the sentiment analysis carried out by two methods: the Naive Bayes classifier and the REGEX algo- rithm. Parametrical words are referred to as the words denoting the value of some domain-specific parameter, e.g. the client's time consuming. To distinguish the main features of parametrical words, the parameters relevant for the bank service quality domain are determined. The revised lexicon struc- ture is proposed, with a new class (decrements) added. The results of the research demonstrate that parametrical words express implicit opinions, since parameters are not usually named directly in re- views. Only a small number of parametrical words can be ranged into the primary classes (positive or negative), but this ranging is domain-specific. It is the parameter that determines the domain specifici- ty of such words. Most parametrical words are ranged into the secondary classes, and this ranging can be considered universal. The parametrical words denoting the increase of a parameter should be ranged into the increment class, as they intensify positive or negative emotions. The parametrical words denoting the decrease of a parameter should be ranged into the decrement class, as they reduce positive or negative emotions. The evident progress on the way to the sentiment lexicon universalization can be achieved by classifying parametrical words within the sentiment lexicon.
On evidential combination rules for ensemble classifiers

Ensemble classifiers are known to generally perform better than each individual classifier of which they consist. One approach to classifier fusion is to apply Shaferpsilas theory of evidence. While most approaches have adopted Dempsterpsilas rule of combination, a multitude of combination rules have been proposed. A number of combination rules as well as two voting rules are compared when used in conjunction with a specific kind of ensemble classifier, known as random forests, w.r. t. accuracy, area under ROC curve and Brier score on 27 datasets. The empirical evaluation shows that the choice of combination rule can have a significant impact on the performance for a single dataset, but in general the evidential combination rules do not perform better than the voting rules for this particular ensemble design. Furthermore, among the evidential rules, the associative ones appear to have better performance than the non-associative.
Simulation Based Approach To Study The Interaction Of Scheduling And Routing On A Logistic Network

This research investigates the interaction of production scheduling and routing/transportation on a logistic network. Basic scheduling rules and existing rou ting/transportation alternatives were studied using a simulation model, with cost and customer service le vels as measures of performance. The simulation model, including network configuration, orders, service times, etc., was based on a “real world” electronic manufa cturer, where the production process requires comp onents to flow through three facilities. The two existing routing/transportation alternatives are ocean and air freight. The analysis of the results indicated that ce rtain combinations of scheduling and routing rules r esult in low manufacturing-logistic costs while maintain high levels of customer service.
Polychaete-like Pedundulatory Robotic Locomotion

The polychaete annelid marine worms propel themselves in a variety of challenging locomotion environments by a unique form of tail-to-head body undulations, combined with the synchronized action of numerous parapodial lateral appendages. This combined parapodial and undulatory mode of locomotion is termed pedundulatory in the present work. Robotic analogues of this type of locomotion are being studied, both in simulation, and via experiments with biomimetic robotic prototypes, which combine undulatory movements of their multi-link body with appropriately coordinated parapodial link oscillations. Extensive experimental studies of locomotion on sand demonstrate the potential of the pedundulatory robotic prototypes, especially their rich gait repertoire and their enhanced performance compared to robotic prototypes relying only on body undulations