Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Relational Learning for Link Prediction

Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat” file format, but rather involve complex relationships among objects. In this paper, we propose the application of our methodology for Statistical Relational Learning to building link prediction models. We propose an integrated approach to building regression models from data stored in relational databases in which potential predictors are generated by structured search of the space of queries to the database, and then tested for inclusion in a logistic regression. We present experimental results for the task of predicting citations made in scientific literature using relational data taken from CiteSeer. This data includes the citation graph, authorship and publication venues of papers, as well as their word content.
A fast interest point detection algorithm

An interest point detection scheme is presented that is comparable in quality to existing methods, but can be performed much faster. The detection is based on a straightforward color analysis at a coarse granularity. A 3x3 grid of squares is centered on the candidate point, so that the candidate point corresponds to the middle square. If the color of the center region is inhomogeneous with all of the surrounding regions, the point is labeled as interesting. A point will also be labeled as interesting if a minority of the surrounding squares are homogeneous, and arranged in an appropriate pattern. Testing confirms that this detection scheme is much faster than the state-of-the-art. It is also repeatable, even under different viewing conditions. The detector is robust with respect to changes in viewpoint, lighting, zoom, and to a certain extent, rotation.
Analysis of a Multivariate Public Key Cryptosystem and Its Application in Software Copy Protection

We analysed and solved possible singularity for an improved MFE multivariate public key (Medium Field Multivariate Public Key Encryption) and studied the use of it in software copy protection. We used our new MFE multivariate public key cryptosystem to design an algorithm of software registration, in which a given plaintext can result in multi-cipher-text. The breaking is hard because the ciphertext is variable. The ability to withstand algebraic attacks is enhanced. The dependence of registration string on the fingerprints of machine prevents any registration string from being shared by multiple machines.
Current Research in Risk-aware Business Process Management - Overview, Comparison, and Gap Analysis

The management of risks in business processes has been a subject of active research in the past few years. Many benefits can potentially be obtained by integrating the two traditionally-separated fields of risk management and business process management, including the ability to minimize risks in business processes (by design) and to mitigate risks at run time. In the past few years, an increasing amount of research aimed at delivering such an integrated system has been proposed. However, these research efforts vary in terms of their scope, goals, and functionality. Through systematic collection and evaluation of relevant literature, this paper compares and classifies current approaches in the area of risk-aware business process management in order to identify and explain relevant research gaps. The process through which relevant literature is collected, filtered, and evaluated is also detailed.
Can We Group Storage? Statistical Techniques to Identify Predictive Groupings in Storage System Accesses

Storing large amounts of data for different users has become the new normal in a modern distributed cloud storage environment. Storing data successfully requires a balance of availability, reliability, cost, and performance. Typically, systems design for this balance with minimal information about the data that will pass through them. We propose a series of methods to derive groupings from data that have predictive value, informing layout decisions for data on disk. Unlike previous grouping work, we focus on dynamically identifying groupings in data that can be gathered from active systems in real time with minimal impact using spatiotemporal locality. We outline several techniques we have developed and discuss how we select particular techniques for particular workloads and application domains. Our statistical and machine-learning-based grouping algorithms answer questions such as “What can a grouping be based on? ” and “Is a given grouping meaningful for a given application? ” We design our models to be flexible and require minimal domain information so that our results are as broadly applicable as possible. We intend for this work to provide a launchpad for future specialized system design using groupings in combination with caching policies and architectural distinctions such as tiered storage to create the next generation of scalable storage systems.
Promoting early diagnosis of hemodynamic instability during simulated hemorrhage with the use of a real-time decision-assist algorithm

BACKGROUND This study aimed to test the hypothesis that the addition of a real-time decision-assist machine learning algorithm by emergency medical system personnel could shorten the time needed to identify an unstable patient during a hemorrhage profile as compared with vital sign information alone. METHODS Fifty emergency medical team-paramedics from a large, urban fire department participated as subjects. Subjects viewed a monitor screen on two occasions as follows: (1) display of standard vital signs alone and (2) with the addition of an index (Compensatory Reserve Index) associated with estimated central blood volume status. The subjects were asked to push a computer key at any point in the sequence they believed the patient had become unstable based on information provided by the monitor screen. The average difference in time to identify hemodynamic instability between experimental and control groups was assessed by paired, two-tailed t test and reported with 95% confidence intervals (95% CI). RESULTS The mean (SD) amount of time required to identify an unstable patient was 18.3 (4.1) minutes (95% CI, 17.2–19.4 minutes) without the algorithm and 10.7 (4.2) minutes (95% CI, 9.5–11.9 minutes) with the algorithm (p < 0.001). CONCLUSION In a simulated patient encounter involving uncontrolled hemorrhage, the use of a monitor that estimates central blood volume loss was associated with early identification of impending hemodynamic instability. Physiologic monitors capable of early identification and estimation of the physiologic capacity to compensate for blood loss during hemorrhage may enable optimal guidance for hypotensive resuscitation. They may also help identify casualties benefitting from forward administration of plasma, antifibrinolytics and procoagulants in a remote damage-control resuscitation model.
Robust H∞ Control for Discrete Time-Delay Fuzzy Systems

This paper is concerned with the robust H<sub>∞</sub> control problem of norm bounded uncertain T-S fuzzy systems with state delay. First, by using a new basis-dependent Lyapunov-Krasovskii function, a sufficient condition is derived on H<sub>∞</sub> -disturbance attenuation, both system stability and prescribed H<sub>∞</sub> performance are required to be achieved. Then, based on the criterion, a robust H<sub>∞</sub> controller is developed in term of linear matrix inequality (LMI). The development represents a step towards reducing the conservatism of previous design methods. Finally, a design example illustrates the effectiveness of the proposed method.
Divergent Effects of Arsenic on NF-κB Signaling in Different Cells or Tissues: A Systematic Review and Meta-Analysis

Arsenic is ubiquitously present in human lives, including in the environment and organisms, and has divergent effects between different cells and tissues and between different exposure times and doses. These observed effects have been attributed to the nuclear transcription factor kappa B(NF-κB) signaling pathway. Herein, a meta-analysis was performed by independently searching databases including the Cochrane Library, PubMed, Springer, Embase, and China National Knowledge Infrastructure, to analyze effects of arsenic exposure on NF-κB signaling. Compared to controls, in the exposed group, p-IκB levels were found to be 8.13-fold higher (95% CI, 2.40–13.85; Z = 2.78; p = 0.005), IκB levels were 16.19-fold lower (95% CI, −27.44–−4.94; Z = 2.78; p = 0.005), and NF-κBp65 levels were 0.77-fold higher (95% CI, 0.13–1.42; Z = 2.34; p = 0.02) for normal cells and tissue, while NF-κBp65 levels were 4.90-fold lower (95% CI, −8.49–1.31; Z = 2.62; p = 0.009), NF-κB activity was 2.45-fold lower (95% CI, −3.66–1.25; Z = 4.00; p < 0.0001), and DNA-binding activity of NF-κB was 9.75-fold lower (95% CI, −18.66–4.54; Z = 2.15; p = 0.03) for abnormal cells and tissue. Short exposure to high arsenic doses activated the NF-κB signaling pathway, while long exposure to low arsenic doses suppressed NF-κB signaling pathway activation. These findings may provide a theoretical basis for injurious and therapeutic mechanisms of divergent effects of arsenic.
Variation in Inflammatory Cytokine/Growth-Factor Genes and Mammographic Density in Premenopausal Women Aged 50–55

Background Mammographic density (MD) has been found to be an independent risk factor for breast cancer. Although data from twin studies suggest that MD has a strong genetic component, the exact genes involved remain to be identified. Alterations in stromal composition and the number of epithelial cells are the most predominant histopathological determinants of mammographic density. Interactions between the breast stroma and epithelium are critically important in the maturation and development of the mammary gland and the cross-talk between these cells are mediated by paracrine growth factors and cytokines. The potential impact of genetic variation in growth factors and cytokines on MD is largely unknown. Methods We investigated the association between 89 single nucleotide polymorphisms (SNPs) in 7 cytokine/growth-factor genes (FGFR2, IGFBP1, IGFBP3, TGFB1, TNF, VEGF, IL6) and percent MD in 301 premenopausal women (aged 50 to 55 years) participating in the Norwegian Breast Cancer Screening Program. We evaluated the suggestive associations in 216 premenopausal Singapore Chinese Women of the same age. Results We found statistically significant associations between 9 tagging SNPs in the IL6 gene and MD in Norwegian women; the effect ranged from 3–5% in MD per variant allele (p-values = 0.02 to 0.0002). One SNP in the IL6 (rs10242595) significantly influenced MD in Singapore Chinese women. Conclusion Genetic variations in IL6 may be associated with MD and therefore may be an indicator of breast cancer risk in premenopausal women.
Integrating Spline Curves in Road Constraint Object Tracking

Including road map information in the tracking of ground moving objects is a challenging problem. While many self localizing algorithms base on a modelling in global Cartesian coordinates a few approaches prefer a modelling in a local map coordinate frame. Throughout this contribution both strategies are compared theoretically and in numerical simulations. To achieve robust tracking results current onboard sensor information is utilized in the tracking framework. Commonly available on cars or trains are absolute position information, estimated e.g. by a GPS unit, and relative velocity measurements, e.g. measured by an odometer. To integrate road map knowledge with this sensor information time- and measurement-update equations are derived for both modelling strategies. Roads or tracks are often composed by a sequence of geometric primitives. Approximating this progression of geometric elements with smooth piecewise defined polynomials yields an accurate model, which can easily be integrated in the tracking framework. General preconditions using curves for tracking purposes are presented. In particular, cubic Hermite spline curves are chosen and implemented into the tracking framework.
Ontology Design for Online News Analysis

News reporting events without people’s review is usually very short, so Vector Space Model (VSM) used for representing long text-based documents is not suitable for describing news. In addition, VSM can not represent basic questions in news: who, what, when, where. A new kind of text model is needed to manipulate news. In this paper, a news ontology incorporating some metadata in OpenCyc,EventsML-G2, NewsML-G2, and News Industry Text Format(NITF) is designed at first. What is more important is that relations in news domain are also described in the ontology. A model for news analysis is then proposed. The process of news analysis is analyzed from three main aspects: instancing, reasoning, and finding similar events. The results indicate that the main benefit of the news ontology is its ability to describe news, reason about news, and deal with the whole process of news analysis.
A New Method Against High-Order Differential Power Analysis

Smart cards are vulnerable to both invasive and noninvasive attacks. Specifically, non-invasive attacks using power measurements to extract the cryptographic key have drawn a lot of negative publicity for smart card usage. Side-channel attack methods for encryption circuits are being discussed. Point out unique masking method (MM) can't protect the first two rounds and the last two rounds from high-order differential power analysis. A new improved Unique Masking Method was first proposed. It use sense amplifier based logic (SABL) for a DES circuit. The circuit can ensure a highly security with reasonable area and power consumption. The theory of UMM and experiments shows its efficiency.
Central Modulatory Neurons Control Fuel Selection in Flight Muscle of Migratory Locust

Insect flight is one of the most intense and energy-demanding physiological activities. High carbohydrate oxidation rates are necessary for take-off, but, to spare the limited carbohydrate reserves, long-distance flyers, such as locusts, soon switch to lipid as the main fuel. We demonstrate that before a flight, locust muscles are metabolically poised for take-off by the release of octopamine from central modulatory dorsal unpaired median (DUM) neurons, which increases the levels of the potent glycolytic activator fructose 2,6-bisphosphate in flight muscle. Because DUM neurons innervating the flight muscles are active during rest but selectively inhibited during flight, they stimulate carbohydrate catabolism during take-off but tend to decrease muscle glycolysis during prolonged flight. cAMP-dependent protein kinase A is necessary but not sufficient for signal transduction, suggesting parallel control via a calcium-dependent pathway. Locust flight is the first reported instance of a direct and specific involvement of neuronal activity in the control of muscle glycolysis in working muscle during exercise.
Power Control Game Algorithm Based on Combination Pricing Function

Based on the non-cooperative power control game (NPG) algorithm, the utility and pricing function are analyzed, then the combination pricing function is proposed and power control game algorithm suitable for cognitive radio (CR) networks is realized, the existence and uniqueness of the Nash equilibrium are proved for proposed algorithm. Simulation results show that the proposed algorithm can balance the interests of the CR users themselves, reduce the whole system of interference and improve the performance of cognitive radio system.
PRISMA-combined α-blockers and antimuscarinics for ureteral stent-related symptoms

Background: As a monotherpay, a-blockers and anti-muscarinics are both efficacy for ureteral stent-related symptoms (SRS). The aim of the study was to systematically evaluate their efficacy of a combination therapy for SRS. Methods: Relevant studies investigating &agr;-blockers and/or anti-muscarinics for SRS were identified though searching online databases including PubMed, EMBASE, Cochrane Library, and other sources up to March 2016. The RevMan software was used for data analysis, and senesitivity analysis and inverted funnel plot were also adopted. Results: Seven randomized controlled trials (RCTs) and 1 prospective controlled trial including 545 patients were selected. Compared with &agr;-blockers, the combination group achieved significant improvements in total International Prostate Symptom Score (IPSS) [–3.93 (2.89, 4.96), P < 0.00001], obstructive subscore [–1.29 (0.68, 1.89), P < 0.0001], irritative subscore [–2.93 (2.18, 3.68), P < 0.00001], and quality of life score [–0.99 (0.42, 1.55), P < 0.001]. Compared with antimuscarinics, there were also significant differences in total IPSS [–3.49 (2.43, 4.55), P < 0.00001], obstructive subscore [–1.40 (0.78, 2.01), P < 0.00001], irritative subscore [–2.10 (1.30, 2.90), P < 0.00001], and quality of life score [–1.18 (0.58, 1.80), P < 0.001] in favor of combination group. No significant difference was found in the visual analog pain score and the urinary symptoms score in Ureteral Stent Symptom Questionnaire (USSQ). No significant difference in complications was found. Conclusions: Current analysis shows significant advantages of combination therapy compared with monotherapy of &agr;-blockers or antimuscarinics alone mainly based on IPSS. More RCTs adopting validated USSQ as outcome measures are warranted to support the finding.
Music Genre Classification Using Convolutional Neural Network

Feature extraction is a crucial part of many MIR tasks. Many manual-selected features such as MFCC have been applied to music processing but they are not effective for music genre classification. In this work, we present an algorithm based on spectrogram and convolutional neural network (CNN). Compared with MFCC, the spectrogram contains more details of music components such as pitch, flux, etc. We use feature detector as filter to convolve spectrogram to get four feature maps, which can catch trends of spectrogram in both time and frequency scale. Then sub-sample layer is applied to reduce dimension and enhance resistance to translation in pitch and tempo. Finally the extracted high-level features are connected to a multi-layer perceptron (MLP) classifier. A classification accuracy of 72.4% is obtained on Tzanetakis dataset by uing the proposed features, which performs better than MFCC.
The Effectiveness of Strategies for Coping With Career Indecision

The present research investigated which strategies Israeli young adults (N = 254) use to cope with their career indecision and the perceived effectiveness of these strategies. Their perceptions of the effectiveness of coping strategies were compared to the respective judgments of career counselors (N = 36). The similarity between the young adults’ and the career counselors’ perceptions (r = .98) suggests that young adults have a fairly accurate judgment of the effectiveness of various coping strategies. However, career counselors perceived emotional help-seeking as a more effective strategy and helplessness and submission as less effective strategies than did young adults (|d| > 0.89). The results also show that Productive coping strategies, although perceived as effective by the young adults, were actually used less by them, whereas Nonproductive coping strategies, although perceived as ineffective, were used more. The counseling implications of the finding that the Nonproductive coping strategies but not the productive ones predict career decision-making difficulties are discussed.
Weighted Markov‐type inequalities, norms of Volterra operators, and zeros of Bessel functions

The first term of the asymptotics of the best constants in Markov‐type inequalities for higher derivatives of polynomials is determined in the two cases where the underlying norm is the L2 norm with Laguerre weight or the L2 norm with Gegenbauer weight. The coefficient in this term is shown to be the norm of a certain Volterra integral operator which depends on the weight and the order of the derivative. For first order derivatives, the norms of the Volterra operators are expressed in terms of the zeros of Bessel functions. The asymptotic behavior of the coefficients is studied and tight bounds for them are given (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Deconstructing iterative optimization

Iterative optimization is a popular compiler optimization approach that has been studied extensively over the past decade. In this article, we deconstruct iterative optimization by evaluating whether it works across datasets and by analyzing why it works. Up to now, most iterative optimization studies are based on a premise which was never truly evaluated: that it is possible to learn the best compiler optimizations across datasets. In this article, we evaluate this question for the first time with a very large number of datasets. We therefore compose KDataSets, a dataset suite with 1000 datasets for 32 programs, which we release to the public. We characterize the diversity of KDataSets, and subsequently use it to evaluate iterative optimization. For all 32 programs, we find that there exists at least one combination of compiler optimizations that achieves at least 83% or more of the best possible speedup across all datasets on two widely used compilers (Intel's ICC and GNU's GCC). This optimal combination is program-specific and yields speedups up to 3.75× (averaged across datasets of a program) over the highest optimization level of the compilers (-O3 for GCC and -fast for ICC). This finding suggests that optimizing programs across datasets might be much easier than previously anticipated. In addition, we evaluate the idea of introducing compiler choice as part of iterative optimization. We find that it can further improve the performance of iterative optimization because different programs favor different compilers. We also investigate why iterative optimization works by analyzing the optimal combinations. We find that only a handful optimizations yield most of the speedup. Finally, we show that optimizations interact in a complex and sometimes counterintuitive way through two case studies, which confirms that iterative optimization is an irreplaceable and important compiler strategy.
Prevalence of and Risk Factors for Age-Related and Anterior Polar Cataracts in a Korean Population

Objective To investigate the potential risk factors associated with nuclear, cortical, posterior subcapsular, and anterior polar cataracts (APC) in the Korean population. Research Design and Methods This was a population-based, cross-sectional study of 7992 adults (over 40 years of age) from the data of the fourth annual Korea National Health and Nutrition Examination Survey, performed from 2007 to 2009. The presence of lens opacity was examined by slit-lamp biomicroscopy and evaluated according to LOCS II standard photographs. The subtype of cataract present, including nuclear, cortical, posterior subcapsular, and anterior polar cataracts, was noted. Multivariable adjusted logistic regression analysis was conducted to examine the odds ratio (OR) and 95% confidence interval (CI) for association of each specific type of cataract with age, sex, health examination, and medical history. Results The prevalence of nuclear, cortical, and posterior subcapsular cataract increased gradually with increasing age. However, the prevalence of APC peaked in the 50- to 59-year-old subjects. All types of cataract except for APCs were more prevalent in women. Oral steroid use was associated with a lower risk of APC. Conclusions These findings showed the unique characteristics of APC in the Korean population.