Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Unified Cyber Threat Intelligence Entity Recognition Method Based on Multiple Features

The frequent occurrence of APT attacks targeting industrial control systems has made the knowledge graph of CTI increasingly crucial for recognizing and analyzing APT attacks. However, the majority of CTI exists in the form of unstructured natural language content, which needs NER techniques to extract the threat entities within it. Nevertheless, NER research for CTI still has the problems of Out-of-Vocabulary (OOV), discontinuous entity recognition and unbalanced data distribution. Therefore, in order to extract the threat entities in CTI more comprehensively and accurately, this paper proposes a unified threat entity recognition model based on multiple features. Firstly, the model enhances the semantic feature by integrating the character feature, the POS feature and the position feature which mitigates the OOV problems. Next, introduces the method of recognizing discontinuous entities based on word-word relation classification into the NER for CTI. Finally, incorporates the Focal Loss algorithm, reducing the impact of non-entity data on the loss function through the weighting factor and the modulating factor. The experimental results on the CTI dataset DNRTI demonstrate that compared with the existing threat entity recognition methods, the F1 value of the present model for recognizing threat entities reaches 89.41%, which is an improvement of 0.48%, and it is able to effectively recognize discontinuous entities.
The NINJA project: Making Java Work for High Performance Numerical Computing

When Java(TM) was first introduced, there was a perception (properly founded at the time) that its many benefits came at a significant performance cost. In few areas were the performance deficiencies of Java so blatant as in numerical computing. Our own measurements, with second-generation Java virtual machines, showed differences in performance of up to one hundred-fold relative to C or Fortran. The initial experiences with such poor performance caused many developers of high performance numerical applications to reject Java out-of-hand as a platform for their applications. Despite the rapid progress that has been made in the past few years, the performance of commercially available Java platforms is not yet on par with state-of-the-art Fortran and C compilers. Programs using complex arithmetic exhibit particularly bad performance. Furthermore, current Java platforms are incapable of automatically applying important optimizations for numerical code, such as loop transformations and automatic parallelization [12]. Nevertheless, our thesis is that there are no technical barriers to high performance computing in Java. To prove this thesis, we have developed a prototype Java environment, called Numerically INtensive JAva (NINJA), which has demonstrated Fortran-like performance can be obtained by Java on a variety of problems. We have successfully addressed issues such as dense and irregular matrix computations, calculations with complex numbers, automatic loop transformations, and automatic parallelization. Moreover, our techniques are straightforward to implement, and allow reuse of existing optimization components already deployed by software vendors for other languages [9], lowering the economic barriers to Java’s acceptance. The next challenge for numerically intensive computing in Java is convincing developers and managers in this domain that Java’s benefits can be obtained with performance comparable with highly tuned Fortran and C. This treatise contributes to overcoming that challenge. Once it is accepted that Java performance is only an artifact of particular implementations of Java, and that there are no technical barriers to Java achieving excellent numerical performance, our techniques will allow vendors and researchers to quickly deliver high performance Java platforms to program developers.
The place of place in geographical IR

Let us deﬁne geographical IR (GIR) as the activity whosepurpose is to retrieve information in a geographically-awareway. In other words, considering the geographical dimensionas special. GIR presupposes two things:• the possibility to associate to (possibly retrieve from)the collection geographical information• the existence (or the possibility of creation) of semanticrepositories that allow geographical reasoning, hence-forth called geo-ontologies.The most common kind of collections for GIR so far arethe Web and other document collections, which are mainlytextual. This paper is concerned with the non-trivial rela-tionship between reference to place in natural language (NL)and common GIR assumptions. There are two main ways inwhich NL texts and GIR meet: in the attempt to derive orpopulate geo-ontologies from text itself, and in the attemptto label Web pages with what is called geo-scopes, derivingthese from clues in the pages themselves.We will survey brieﬂy the two, noting in passing that bothapproaches are bottom-up in the sense that they look atthe texts, but the second makes use of a prior informationsource, a geo-ontology, which is typically top-down (see Geo-Net-PT01 in Table 1).
Immigrants' Ethnic Identification and Political Involvement in the Face of Discrimination: A Longitudinal Study of the German Case

Using data from the German Socio-Economic Panel (GSOEP) 1993–2006 as well as longitudinal modelling techniques, the present paper contributes to the growing body of literature on ethnic identity and its effects on Immigrants' social integration by examining the role of various forms of labour Immigrants' ethnic sense of belonging and cognitive involvement in politics. Theoretically, the paper draws on interdisciplinary integration models, social psychological theories of social identity as well as theoretical frameworks that delineate the politicisation of collective identity and especially the role of dual identification. Applying ‘hybrid’ models that combine the virtues of both fixed and random effects models, the statistical analysis confirms that dual identification—immigrant's identification with both the ethnic in-group and the national community simultaneously—is positively related to labour Immigrants' political interest, conditional on the perception of discrimination on behalf of the ethnic origin. Secondly, the longitudinal analyses show some indication that the effect is more pronounced among Turkish immigrants as well as that the mechanisms behind the cognitive politicising process of ethnic identity types differ by ethnic group. In summary, the paper recognizes the value of a multiple-, instead of a one-sided inclusion in emotional terms for Immigrants' cognitive political involvement.
Adaptive Filtering Based on Time-Averaged MSE for Cyclostationary Signals

Adaptive filters are commonly used in many signal processing and communications systems. In many practical digital communications scenarios, including, for example, interference-limited wireless and wireline communications, as well as narrowband power line communications, the considered signals are jointly cyclostationary. Yet, most works on adaptive filtering of cyclostationary signals used ad hoc application of adaptive algorithms designed for stationary signals, e.g., the least-mean-squares (LMS). It is known that these algorithms may not converge for jointly cyclostationary signals. In this paper, we rigorously study the optimal adaptive filtering of jointly cyclostationary signals. We first identify the relevant objective as the time-averaged mean-squared error criterion (TA-MSE), and obtain an adaptive algorithm as the stochastic approximation of the TA-MSE minimizer. When the considered signals are jointly stationary, the algorithm specializes to the standard LMS algorithm. We provide a comprehensive transient and steady-state performance analysis without imposing a specific distribution on the considered signals, and derive conditions for convergence and stability. The algorithm, which we call time-averaged LMS, is applied to practical scenarios in a simulations study, and an excellent agreement between the theoretical and the empirical performance is observed.
Graphical presentation of data in non‐invasive algorithms for cardiac output estimation

References 1 Maharaj CH, O’Croinin D, Curley G, Harte BH, Laffey JG. Acomparison of tracheal intubation using the Airtraq or the Macintosh laryngoscope in routine airway management:a randomized controlled clinical trial. Anaesthesia 2006; 61: 1093–9. 2 Maharaj CH, Higgins BD, Harte BH, Laffey JG. Evaluation of intubation using the Airtraq or Macintosh laryngoscope by anaesthetists in easy and simulated difficult laryngoscopy – a manikin study. Anaesthesia 2006; 61: 469–77.
Test Review: Hresko, W., Schlieve, P., Herron, S., Swain, C., & Sherbenau, R. (2003). Comprehensive Mathematical Abilities Test (CMAT). Austin, TX: PRO-ED

its lightweight, condensed size contributes to its convenience and utility in school settings. Administration of the core subtests requires approximately 40 minutes to 1 hour, contingent upon the age, skill level, and processing speed of the student. The CMAT is not a timed test, and subtests do not have specified time limits. Calculators are permitted with the exception of the Addition, Subtraction, Multiplication, and Division subtests. The directions for administration are relatively clear and concise. Basals, ceilings, and entry points are clearly delineated in the test manual. For practitioners with experience in assessment, administration of the items
Unsupervised training of HMMs with variable number of mixture components per state

In this work automatic methods for determining the number of gaussians per state in a set of Hidden Markov Models are studied. Four different mix-up criteria are proposed to decide how to increase the size of the states. These criteria, derived from Maximum Likelihood scores, are focused to increase the discrimination between states obtaining different number of gaussians per state. We compare these proposed methods with the common approach where the number of density functions used in every state is equal and pre-fixed by the designer. Experimental results demonstrate that performance can be maintained while reducing the total number of density functions by 17% (from 2046 down to 1705). These results are obtained in a flexible large vocabulary isolated word recognizer using context dependent models.
ON WHY “EMOTIONAL INTELLIGENCE” WILL NOT PREDICT LEADERSHIP EFFECTIVENESS BEYOND IQ OR THE “BIG FIVE”: AN EXTENSION AND REJOINDER

Emotional intelligence (EI) has been embraced by many practitioners and academicians without clear empirical support for the construct. In this rejoinder and extension of an earlier comment, I highlight the importance of using methodologically defensible scientific criteria for conducting or evaluating research. I review literature demonstrating that EI models are beset with problems concerning their validity and show that support for the EI construct may be based more on tangential speculation than on empirical findings. Although I find some common positions with EI researchers such as Prati et al., I underline contradictions and inconsistencies which may cast doubt on the necessity of EI for understanding and predicting leadership effectiveness.
Wireless Body Area Network (WBAN)

Wireless Body Area Network (WBAN) has been a key element in e-health to monitor bodies. This technology enables new applications under the umbrella of different domains, including the medical field, the entertainment and ambient intelligence areas. This survey paper places substantial emphasis on the concept and key features of the WBAN technology. First, the WBAN concept is introduced and a review of key applications facilitated by this networking technology is provided. The study then explores a wide variety of communication standards and methods deployed in this technology. Due to the sensitivity and criticality of the data carried and handled by WBAN, fault tolerance is a critical issue and widely discussed in this paper. Hence, this survey investigates thoroughly the reliability and fault tolerance paradigms suggested for WBANs. Open research and challenging issues pertaining to fault tolerance, coexistence and interference management and power consumption are also discussed along with some suggested trends in these aspects.
Citizenship, Nationality and Migration in Europe

Introduction. David Cesarani and Mary Fulbrook Part One: The European Context 1. The legal framework regulating citizenship of the EU Elspeth Guild 2. Changing citizenship in Europe: Remarks on postnational membership and the nation state Yasemin Soysal Part Two: Citizenship, Nationality and the Construction of Identity 3. The changing character of citizenship and nationality in Britain David Cesarani 4. The revenge of civil society: state, nation and society in France Max Silverman 5. Germany for the Germans? Citizenship and nationality in a divided nation Mary Fulbrook 6. Italy Enrico Pugliese Part Three: The Politicisation of 'Difference' 7. The spice of life? Ethnic difference, politics and culture in modern Britain Tony Kushner 8. France Patrick Weil 9. Migration, refugees and ethnic plurality as issues of public and political debates in (West) Germany Karen Schonwalder 10. The northern league in Italy: Changing friends and foe, and its political opportunity structure Carlo Ruzza and Oliver Schmidtke Conclusion. David Cesarani and Mary Fulbrook
MC3: A cloud caching strategy for next generation virtual content distribution networks

With the advent of network functions visualization and software defined networking, cloud content distribution network (CDN) providers can auto-scale their virtual CDN appliances in order to meet changing demands for commercial and user generated content services in a cost and energy efficient manner. However, existing caching policies, constrained to work with dedicated CDN resources and designed to maximize local cache hit rates, do not exploit the elasticity of virtualized cloud environments to adaptively guarantee service requirements with minimum cost. In this paper, we design and evaluate MC3 (MinCostCloudCache), an adaptive distributed caching strategy whose fundamental goal is to guarantee content service requirements while minimizing the use and associated cost of the shared physical infrastructure. MC3 estimates the global benefit of caching an object at a network node using only locally available information. The caching benefit is flexible and adaptive to the particular content service requirements, and is aware of the behavior of neighbor network caches, creating effective cache cooperation using only local information. Through simulation, we show how MC3 not only reduces the experienced average delay with respect to existing caching policies, but it also uses significantly less storage and transport resources, leading to increased revenues and reduced operational costs.
Coloring that reveals high-dimensional structures in data

Introduces a method for assigning colors to displays of cluster structures of high-dimensional data, such that the perceptual differences of the colors reflect the distances in the original data space as faithfully as possible. The cluster structure is first discovered with a self-organizing map (SOM), and then a new nonlinear projection method is applied to map the cluster structure into the CIELab color space. The projection method preserves best the local data distances that are the most important ones, while the global order is still discernible from the colors, too. This allows the method to conform flexibly to the available color space. The output space of the projection need not necessarily be the color space, however. Projections onto, say, two dimensions can be visualized as well.
Ontology based disambiguation of semantic relations between noun phrases

In our preliminary studies we are trying to investigate the possibilities of using a combination of machine learning, ontologyes and an annotated corpus in order to identify semantic relation between noun phrases, e.g. to able to identify that in “vitamin K deficiency causes increased tendency to bleed”, there is a causes relation between the lack of K vitamin and the tendency to bleed. The purpose of this work is to extend the current analysis of the OntoQuery project to include not only the noun phrases themselves, but also the relations that holds between them. In this paper we present our corpus, the relations we are trying to learn, and the challenges in designing the problem as a machine learning task. We hope we will be able to present empirical results soon.
ANSI-C Bounded Model Checker User Manual

We describe a tool that formally verifies ANSI-C programs. The tool implements a technique called Bounded Model Checking (BMC). In BMC, the transition relation for a complex state machine and its specification are jointly unwound to obtain a Boolean formula, which is then checked for satisfiability by using a SAT procedure. The tool supports all ANSI-C integer operators and all pointer constructs allowed by the ANSIC standard, including dynamic memory allocation, pointer arithmetic, and pointer type casts. This research was sponsored by the Semiconductor Research Corporation (SRC) under contract no. 99-TJ-684, the National Science Foundation (NSF) under grant no. CCR-9803774, the Office of Naval Research (ONR), the Naval Research Laboratory (NRL) under contract no. N00014-01-1-0796, and by the Defense Advanced Research Projects Agency and the Army Research Office (ARO) under contract no. DAAD19-01-1-0485. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of SRC, NSF, ONR, NRL, DOD, ARO, or the U.S. government.
Detecting spam comments posted in micro-blogs using the self-extensible spam dictionary

The high popularity of Weibo has greatly enriched people's lives, allowing online users to share their feelings through posting comments. However, more and more spam comments are also being posted in users' blogs on this social media. In this paper, in order to effectively detect spam comments in Chinese micro-blogs, we introduce semantic analysis to construct a Self-Extensible Spam Dictionary which automatically expands itself when new words emerge on the micro-blogs frequently. The use of semantic analysis can provide us with additional features which are beneficial to detecting spam comments. A Proportion-Weight Filter (PWF) model is also proposed to detect two kinds of spam comments (AD and vulgar comments), by filtering the spam-weight and the spam-proportion of the Weibo comments based on our Self-Extensible Spam Dictionary criteria. Our experimental results demonstrate that when detecting a combination of both AD and vulgar spam comments, we can achieve an average detection accuracy of 87.9%. Particularly for AD spam comments detection, we can achieve an average accuracy of 96.2%, which is preferable compared to when using machine learning methods. The statistical analysis of the results verifies that our proposed methods can identify the spam comments effectively and to relatively high degrees of accuracy.
Sclerostin levels associated with inhibition of the Wnt/β-catenin signaling and reduced bone turnover in type 2 diabetes mellitus.

CONTEXT Patients with type 2 diabetes (T2DM) have low bone turnover, poor bone quality, and circulating levels of sclerostin significantly higher than non-T2DM controls. There are no data on the possible association of sclerostin with β-catenin, a key component of the Wnt/β-catenin canonical signaling. OBJECTIVES The aim of the study was to evaluate the circulating β-catenin levels in T2DM patients and to analyze their relationship with sclerostin and bone turnover markers. DESIGN This was a cross-sectional study. SETTING AND PATIENTS The study was conducted at a clinical research center. Forty T2DM postmenopausal women were studied and compared with 40 healthy controls. Bone status was assessed by dual-energy x-ray absorptiometry measurements (bone mineral density) and by measuring bone alkaline phosphatase and carboxy-terminal telopeptide of type 1 collagen. Sclerostin and β-catenin were evaluated by an immunoenzymetric assay. RESULTS Consistent with previous reports in T2DM subjects, we found sclerostin levels higher and bone turnover markers lower than controls. In our cohort of T2DM patients, β-catenin levels are significantly lower than in controls (median 1.22 pg/ml, 25th to 75th percentiles 0.50-2.80; and median 4.25 pg/ml, 25th to 75th percentiles 2.20-7.62, respectively; P=0.0002). β-Catenin correlated negatively with sclerostin (P<0.0001) and positively with bone alkaline phosphatase (P=0.0030) only in T2DM patients and negatively with age in both groups. Eight of the 40 T2DM patients had vertebral fractures. CONCLUSIONS These results show for the first time that T2DM patients have serum concentrations of β-catenin lower than controls. The negative association of β-catenin with sclerostin suggests a biological effect of increased sclerostin on the Wnt signaling, which appears impaired in T2DM.
THE EXTREME MICROLENSING EVENT OGLE-2007-BLG-224: TERRESTRIAL PARALLAX OBSERVATION OF A THICK-DISK BROWN DWARF

Parallax is the most fundamental technique for measuring distances to astronomical objects. Although terrestrial parallax was pioneered over 2000 years ago by Hipparchus (ca. 140 B.C.E.) to measure the distance to the Moon, the baseline of the Earth is so small that terrestrial parallax can generally only be applied to objects in the Solar System. However, there exists a class of extreme gravitational microlensing events in which the effects of terrestrial parallax can be readily detected and so permit the measurement of the distance, mass, and transverse velocity of the lens. Here we report observations of the first such extreme microlensing event OGLE-2007-BLG-224, from which we infer that the lens is a brown dwarf of mass M = 0.056 ± 0.004 M☉, with a distance of 525 ± 40 pc and a transverse velocity of 113 ± 21 km s−1. The velocity places the lens in the thick disk, making this the lowest-mass thick-disk brown dwarf detected so far. Follow-up observations may allow one to observe the light from the brown dwarf itself, thus serving as an important constraint for evolutionary models of these objects and potentially opening a new window on substellar objects. The low a priori probability of detecting a thick-disk brown dwarf in this event, when combined with additional evidence from other observations, suggests that old substellar objects may be more common than previously assumed.