Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Dive Into Visual Representation and Interrater Agreement Using Data From a High-School Diving Competition

Abstract In several sporting events, the winner is chosen on the basis of a subjective score. These sports include gymnastics, ice skating, and diving. Unlike for other subjectively judged sports, diving competitions consist of multiple rounds in quick succession on the same apparatus. These multiple rounds lead to an extra layer of complexity in the data, and allow the introduction of graphical constructs and interrater-agreement methods to statistics students. The data are sufficiently easy to understand for students in introductory statistics courses, yet sufficiently complex for upper level students. In this article, I present data from a high-school diving competition that allows for investigation in graphical methods, data manipulation, and interrater agreement methods. I also provide a list of questions for exploration at the end of the document to suggest how an instructor can effectively use the data with students. These questions are not meant to be exhaustive, but rather generative of ideas for an instructor using the data in a classroom setting. Supplementary materials for this article are available online.
Using a Nonlinear Forcing Singular Vector Approach to Reduce Model Error Effects in ENSO Forecasting

Nonlinear forcing singular vector (NFSV)-based assimilation is adopted to determine the model tendency errors that represent the combined effect of different kinds of model errors; then, an NFSV-tendency error forecast model is formulated. This error forecast model is coupled with an intermediate complex model (ICM) and makes the ICM output closer to the observations; finally, an NFSV-ICM forecast model for ENSO is constructed. The competing aspect of the NFSV-ICM is to consider not only model errors but also the interaction between model errors and initial errors because of the mathematical nature of the NFSV-tendency errors. Based on the prediction experiments for tropical SSTAs during either the training period (1960–96; i.e., when the NFSV-ICM is formulated) or the cross-validation period (1997–2016), the NFSV-ICM is determined to have a much higher forecast skill in predicting ENSO that, specifically, extends the skillful predictions of ENSO from a lead time of 6 months in the original ICM to a lead time of 12 months. The higher skill of the NFSV-ICM is especially reflected in the predictions of SSTAs in the central and western Pacific. For the well-known spring predictability barrier (SPB) phenomenon that greatly limits ENSO forecasting skill, the NFSV-ICM also shows great abilities in suppressing its negative effect on ENSO predictions. Although the NFSV-ICM is presently only involved with the NFSV-related assimilation of SSTs, it has shown its usefulness in predicting ENSO. It is clear that the NFSV-based assimilation approach is effective in dealing with the effect of model errors on ENSO forecasts.
A Visualization Approach for Bug Reports in Software Systems

For widely-used and large software systems, there is huge number of daily submitted bug reports. These bug reports are mainly submitted by different developers with different roles during software development process. Therefore, understanding and monitoring the status of all submitted bug reports is not an easy task. Each bug report has various and different statuses over time. Furthermore, the relationship between bug reports and developers is complicated and hard to detect. In this paper, we propose a solution to help software project managers or bug trackers to understand the current and the previous statuses of large number of submitted bug reports. We propose a visualization approach to model the different statuses of bug reports and their relationships with developers who work on them. The visualization is based on extracting information from bug tracking systems and modeling them in comprehensive and meaningful views.
Health Monitoring of Tree Trunks Using Ground Penetrating Radar

Ground penetrating radar (GPR) is traditionally applied to smooth surfaces in which the assumption of half-space is an adequate approximation that does not deviate much from reality. Nonetheless, using GPR for internal structure characterization of tree trunks requires measurements on an irregularly shaped closed curve. A typical hyperbola fitting has no physical meaning in this new context since the reflection patterns are strongly associated with the shape of the tree trunk. Instead of a clinical hyperbola, the reflections give rise to complex-shaped patterns that are difficult to be analyzed even in the absence of clutter. In this paper, a novel processing scheme is described which can interpret complex reflection patterns assuming a circular target subject to any arbitrary shaped surface. The proposed methodology can be applied using commercial hand-held antennas in real time, avoiding computationally costly tomographic approaches that require the usage of custom-made bespoke antenna arrays. The validity of the current approach is illustrated both with numerical and real experiments.
Ionization corrections in a multiphase interstellar medium: lessons from a zabs∼ 2 sub‐DLA

We present a high resolution (FWHM= 2.7 km s −1 ), high S/N echelle spectrum for the zem = 2.26 QSO J2123−0050 and determine elemental abundances for the zabs = 2.06 subDLA in its line of sight. This high redshift sub-DLA has a complex kinematic structure and harbours detections of neutral (Si, Ci), singly (e.g. Cii, Sii) and multiply ionized (e.g. Civ, Siiv) species as well as molecular H2 and HD. The plethora of detected transitions in various ionization stages is indicative of a complex multi-phase st ructure present in this high redshift galaxy. We demonstrate that the ionization corrections in this sub-DLA are significant (up to �0.7 dex). For example, if no ionization correction is applie d, a super-solar metallicity is derived ([S/H] = +0.36), whereas a single phase ionization correction reduces this to [S/H] = −0.19. The theoretical impact of a multi-phase medium is investigated through Cloudy modelling and it is found that the abundances of Si, S and Fe are always over-estimated (by up to 0.15 dex in our experiments) if a single-phase is assumed. Therefore, although Cloudy models improve estimates of metal column densities, the simplification of a single phase medium leaves a systematic error in the result, so that even ionizat ion-corrected abundances may still be too high. Without ionization corrections the properties of this sub-DLA appear to require extreme scenarios of nucleosynthetic origins. After ioniz ation corrections are applied the ISM of this galaxy appears to be similar to some of the sightlines through the Milky Way.
Ontology-based constrained anonymization for domain-driven data mining outsourcing

Introduction. This thesis focuses on the data mining outsourcing scenario whereby a data owner publishes data to an application service provider who returns mining results. To ensure data privacy against an un-trusted party, protection techniques are required. Anonymization, a widely used method provides the benefit of preserving true attribute values as well as the capability of supporting various data mining algorithms. Although this is so, several issues emerge when anonymization is applied in a real world outsourcing scenario. Most methods have focused on the traditional data mining paradigm, therefore they do not implement domain knowledge nor optimize data for domain-driven purposes. Furthermore, existing techniques limit users’ control while assuming their natural capability of producing Domain Generalization Hierarchies (DGH). Moreover, previous utility metrics have not considered attribute correlations during generalization. Objective. The research objective is to create an ontology-based constrained anonymization framework which aims to preserve meaningful and actionable models for domain-driven data mining while protecting privacy. Framework. In contrast with existing works, this framework integrates the Unified Medical Language Systems (UMLS) as a form of domain ontology knowledge during DGH creation to preserve value meanings. Furthermore, it allows for user constraints based on attribute semantic types and relations to suit physician mining tasks. Also, attribute correlations are determined with external domain knowledge in the form of MEDLINE literatures to improve attribute selection during anonymization. Results. Experiments show that ontology-based DGHs manage to preserve semantic meaning after attribute generalization. Additionally, by setting constraints, important attributes for specific mining tasks can be preserved. Finally, utilizing a correlation-based measure can improve attribute selection during anonymization for domain-driven purposes. Conclusion. There is an urgent need for privacy preserving methods capable of anonymizing data for domain-driven usage. The proposed framework proves
A background EEG removal method combining PCA with multivariate empirical mode decomposition for event‐related potential measurements

The event‐related potential (ERP) is a neural response to an internal or external event, and can be obtained by averaging time‐locked scalp potentials. The ERP measured in a single trial often has a low signal‐to‐noise ratio (SNR) because of the relatively large background due to the rhythmic electroencephalogram (EEG) noise. This paper proposes a novel method to enhance ERPs by combining principal component analysis (PCA) with multivariate empirical mode decomposition (M‐EMD). EMD is a data‐driven time–frequency analysis of nonlinear and nonstationary signals, and M‐EMD is its multivariate extension. In the proposed method, PCA reduces the data dimensions, while M‐EMD removes the relatively large background EEGs. The performance of the method is evaluated with simulated and measured P300 ERP components obtained from a visual oddball experiment. The results demonstrate that the proposed method can substantially reduce the background EEGs and improve the SNR of P300s. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
The nanosyntax of French negation : a diachronic perspective

This paper presents a nanosyntactic account for negation in what is referred to as le bon usage French (Grevisse and Goosse [1936] 1993, Rooryck 2010) and in colloquial French. The analysis captures the obligatory presence of ne and pas for the expression of sentence negation in le bon usage French, whilst also accounting for the loss of ne in colloquial French. The difference between ne and pas in le bon usage French boils down to a structural difference: ne consists of only one feature or syntactic layer, a polarity feature, whereas pas consists of four features, focus, degree, quantity and proper negation. Only the combination of these five features can yield what is conceived of as sentential negation. With respect to colloquial French the analysis involves that the syntactic structure of pas, encoded in a post-syntactic lexicon, starts spelling out five features, thus making ne redundant for the expression of sentential negation. Given that the use of negative markers in le bon usage French and colloquial French represents two important and well-described stages in what is referred to as Jespersen’s Cycle (Dahl 1979) this account is also relevant from a diachronic perspective.
Geographic information systems and artificial intelligence for predicting the presence or absence of mountain reedbuck

Rule-based models were developed to predict the presence or absence of mountain reedbuck Redunca fulvorufula on a nature reserve in Karoo arid dwarf shrubland in South Africa. A geographic information system (GIS) was used to store, generate and retrieve model variables. Variables used as input to the models were mean, minimum and maximum slope, mean, minimum and maximum elevation, aspect, vegetation community, distance to drinking water, and terrain ruggedness as the number of contour lines crossing the grid. The models were constructed by inductive inference, using an artificial intelligence technique known as iterative dichotomizing. The technique used a complex sequence of dichotomous rules, similar to decision trees, to predict the presence or absence of mountain reedbuck at a site. The rules were converted to expert systems. The expert systems were validated by testing their ability to classify correctly independent data from the same study area. The classification success of the models ranged between 63 and 66%. In both instances this was significantly greater than 50%. The advantages and disadvantages of models created by inductive inference from GIS-based data are discussed. Iterative dichotomizing is a promising technique to develop models from existing data which are stored in information systems, provided that the appropriate data are available. Such data will vary according to the animal being studied and the scale of study. Modelle wat op reels gebaseer is, is ontwikkel om die teenwoordigheid of afwesigheid van rooiribbokke Redunca fulvorufula in 'n natuurreservaat in dorre dwergstruikveld in die Karoo te bepaal. 'n Geografiese Inligtingstelsel (GIS) is gebruik om data te stoor, te skep en te onttrek. Veranderlikes wat as inset tot die model gebruik is, was gemiddelde, minimum en maksimum helling, gemiddelde, minimum en maksimum elevasie, aspek, plantgemeenskap, afstand vanaf water en terreingebrokenheid. Die modelle is deur induktiewe afleiding saamgestel en het gebruik gemaak van 'n kunsmatige intelligensietegniek bekend as 'opeenvolgende tweesplitsing'. Die modelle het 'n ingewikkelde stel reels, soortgelyk aan besluitnemingsbome, gebruik om die teenwoordigheid of afwesigheid van rooiribbokke by 'n lokalitiet te voorspel. Die reels is omskep in kundigheidsisteme. Die kundigheidsisteme is getoets volgens hul vermoens om onafhanklike data van dieselfde studiegebied te klassifiseer. Die klassifikasiesukses van die modelie het gewissel van 63 tot 66%. In beide gevalle was dit betekenisvol groter as 50%. Die voordele van GIS was dat data toeganklik was en dat dit as 'n sentrale stelsel vir die stoor van data gedien het. Die resultate van opeenvolgende tweesplitsing was maklik om te interpreteer en toe te pas. Dit is 'n belowende tegniek om modelie te ontwikkel van bestaande data wat in inligtingstelselsgestoor word, mits die toepaslike data beskikbaar is.
Detection and classification of obfuscated malware

vi List of Figures . xi List of Tables . xii Chapter 1: Introduction . 1 1.1 Problem Statement . 2 1.1.1 Attack Against Static Analysis . 2 1.1.2 Attack Against Dynamic Analysis . 4 1.2 Current Research . 6 1.2. 1 Obfuscation Detection . 6 1.2. 2 Zero-day Malware Detection . 7 1.3 Thesis Contribution . 8 1.4 Thesis Organization . 9 Chapter 2: Instructions-based Detection of Sophisticated Obfuscation and Packing . 11 2.
ANALISIS HAZARD IDENTIFICATION DAN RISK ASSESSMENT DI LINGKUNGAN KERJA KEGIATAN PENGECORAN LOGAM TRADISIONAL CEPER KLATEN

Abstrak Keberadaan tenaga kerja yang bekerja pada kegiatan pengecoran dapur kupola yang melakukan kegiatan dari mulai persiapan pengadaan bahan baku sampai pada proses kegiatan pengecoran berlangsung pada dapur kupola merupakan kegiatan yang paling berisiko untuk terpapar partikel yang mengandung zat kimia. Untuk mengatasi masalah kesehatan akibat kerja maka peran Standar Operasional Prosedur (SOP) sangat penting. Melalui penelitian ini perencanaan SOP kegiatan persiapan bahan baku, pengisian dan pengoperasian dapur kupola berdasarkan hazard dalam lingkungan tempat kerja, dapat mengetahui bahaya kerja dan resiko kerja yang dilakukan, sehingga mampu memberikan solusi dalam mengatasi masalah kesehatan kerja yang berkaitan dengan pencegahan timbulnya kecelakaan kerja dan penyakit akibat kerja. Untuk itu perlu adanya usaha untuk menurunkan kadar pencemaran udara melalui konsep pembangunan industri yang berwawasan lingkungan.
A Study of Efficiency, Accuracy, and Robustness in Intensity-Based Rigid Image Registration

Image registration is widely used in different areas nowadays. Usually, the efficiency, accuracy, and robustness in the registration process are concerned in applications. This thesis studies these issues by presenting an efficient intensity-based mono-modality rigid 2D-3D image registration method and constructing a novel mathematical model for intensity-based multi-modality rigid image registration. For mono-modality image registration, an algorithm is developed using RapidMind Multi-core Development Platform (RapidMind) to exploit the highly parallel multi-core architecture of graphics processing units (GPUs). A parallel ray casting algorithm is used to generate the digitally reconstructed radiographs (DRRs) to efficiently reduce the complexity of DRR construction. The optimization problem in the registration process is solved by the Gauss-Newton method. To fully exploit the multi-core parallelism, almost the entire registration process is implemented in parallel by RapidMind on GPUs. The implementation of the major computation steps is discussed. Numerical results are presented to demonstrate the efficiency of the new method. For multi-modality image registration, a new model for computing mutual information functions is devised in order to remove the artifacts in the functions and in turn smooth the functions so that optimization methods can converge to the optimal solutions accurately and efficiently. With the motivation originating from the objective to harmonize the discrepancy between the image presentation and the mutual information definition in previous models, the new model computes the mutual information function using both the continuous image function representation and the mutual information definition for continuous random variables. Its implementation and complexity are discussed and compared with other models. The mutual information computed using the new model appears quite smooth compared with the functions computed by others. Numerical experiments demonstrate the accuracy and efficiency of optimization methods in the case that the new model is used. Furthermore, the robustness of the new model is also verified.
Extending Auditing Models to Correspond with Clients’ Needs in Cloud Environments

The user control over the life cycle of data is of an extreme importance in clouds in order to determine whether the service provider adheres to the client’s pre-specified needs in the contract between them or not, significant clients concerns raise on some aspects like social, location and the laws to which the data are subject to. The problem is even magnified more with the lack of transparency by Cloud Service Providers (CSPs). Auditing and compliance enforcement introduce different set of challenges in cloud computing that are not yet resolved. In this paper, a conducted questionnaire showed that the data owners have real concerns about not just the secrecy and integrity of their data in cloud environment, but also for spatial, temporal, and legal issues related to their data especially for sensitive or personal data. The questionnaire results show the importance for the data owners to address mainly three major issues: Their ability to continue the work, the secrecy and integrity of their data, and the spatial, legal, temporal constraints related to their data. Although a good volume of work was dedicated for auditing in the literature, only little work was dedicated to the fulfillment of the contractual obligations of the CSPs. The paper contributes to knowledge by proposing an extension to the auditing models to include the fulfillment of contractual obligations aspects beside the important aspects of secrecy and integrity of client’s data.
Spark Level Sparsity and the $\ell_1$ Tail Minimization

Solving compressed sensing problems relies on the properties of sparse signals. It is commonly assumed that the sparsity s needs to be less than one half of the spark of the sensing matrix A, and then the unique sparsest solution exists, and recoverable by $\ell_1$-minimization or related procedures. We discover, however, a measure theoretical uniqueness exists for nearly spark-level sparsity from compressed measurements Ax = b. Specifically, suppose A is of full spark with m rows, and suppose $\frac{m}{2}$ $\frac{m}{2}$ in thousands and thousands of random tests. We further show instead that the mere $\ell_1$-minimization would actually fail if s > $\frac{m}{2}$ even from the same measure theoretical point of view.
Automatic Parallelization of Loops with Data Dependent Control Flow and Array Access Patterns

With the era of increasing clock speeds coming to an end, parallel computing architectures have now become main-stream. Due to the wide range of architectures available today that can be used to exploit parallelism, ranging from multicore CPUs, to GPGPUs, to distributed memory machines; adapting applications for efficient execution on all these architectures poses a significant challenge. Scientific computing applications exhibit significant coarse-grained parallelism. Domain experts have exploited this to target distributed memory machines through the use of Message Passing Interface (MPI) libraries. Many such applications have been shown to scale to hundreds of thousands of processors. While powerful, programming in MPI is tedious and error prone, with significant portion of the parallelized application dedicated to managing communication and synchronization between the processes. Developing compiler techniques that can automatically generate parallel distributed memory versions of such codes is challenging due to the presence of datadependent control flow and data access patterns. In this thesis we develop compiler algorithms for automatic parallelization of a class of computations common to many scientific applications. Under the inspector/executor paradigm, the generated code partitions and executes the computation in load-balanced manner, while reducing the communication costs. This approach is further enhanced by developing a framework capable of expressing both affine and non-affine parts of the code. This enables the
High-Pass Filtering for Accuracy Enhancement in Dark-Pulse Brillouin Optical Time Domain Analysis

In this letter, we demonstrate a simple technique aimed to enhance the accuracy in dark-pulse Brillouin optical time-domain analysis (BOTDA) sensors. Modeling the stimulated Brillouin scattering in the frequency domain, we show that the degrading features related to the pump background can be removed by applying a high-pass filter to the raw data, at the cost of some degradation of the signal-to-noise ratio. A numerical analysis, as well as experimental tests carried out at 20-cm spatial resolution, validate the proposed technique.