Kapitán Tomáš · Cvut DSpace (Czech Technical University) · 2018
Tato diplomová práce v první části popisuje metodiky pro řízení implementačních projektů ERP systémů. Práce popisuje metodiky významných globálních výrobců ERP systémů, kterými jsou Accelerated SAP společnosti SAP, Oracle Unified Method od společnosti Oracle a Microsoft Dynamics Sure Step. U jednotlivých metodik je provedeno porovnání jejich vhodnosti pro projekt implementace ERP systému. Druhá část je zaměřena na vytvoření projektové dokumentace ve formátu Cílového konceptu informačního systému v rámci projektu implementace ERP řešení Microsoft Dynamics NAV 2017 ve vybrané společnosti.
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Kangning Wang, Lu Lin · Communication in Statistics- Theory and Methods · 2014
We propose a penalized quantile regression for partially linear varying coefficient (VC) model with longitudinal data to select relevant non parametric and parametric components simultaneously. Selection consistency and oracle property are established. Furthermore, if linear part and VC part are unknown, we propose a new unified method, which can do three types of selections: separation of varying and constant effects, selection of relevant variables, and it can be carried out conveniently in one step. Consistency in the three types of selections and oracle property in estimation are established as well. Simulation studies and real data analysis also confirm our method.
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Junheng Gao, Xun Liang · IEEE Access · 2020
Oracle bone inscriptions are the earliest mature writing system discovered in China. Oracle is usually published in the form of glyphs and pictures, and the related oracle font database environment is closed and limited. Due to the lack of standardization of ancient oracle bone inscriptions, the structure and glyph of the same characters are not unified, and a large number of different shapes and complicated characters coexist. Even ancient character devotees need to complete the basic recognition of oracle with the help of professional reference books and experts. In addition, in the field of traditional literature, distinguishing variant characters of oracle-bone inscriptions needs strong expert knowledge and lacks efficiency. The recognition process consists of two stages. According to the characteristics of oracle-bone inscriptions, such as left-right symmetry, up-down symmetry and positive and negative coexistence, the corresponding data enhancement methods are used to get sufficient training samples in the first stage. In the second stage, the recognition candidate set under each threshold condition in the first stage are used to select the recognition candidate set which is empty under the specific threshold condition, and introduce the prior knowledge to distinguish the oracle variants. In the first stage, computer related methods are used to identify oracle variants. In the second stage, on the basis of the first stage, a set of recognition results that cannot be determined roughly is selected, and a priori knowledge is introduced to integrate multi-domain methods to identify oracle variants. The method proposed in this paper has achieved great results in the recognition of oracle variant characters.
Cited by 22Open access
Sohee Yang, Jonghyeon Kim, Joel Jang et al. · arXiv (Cornell University) · 2023
Previous works in prompt engineering for large language models have introduced different gradient-free probability-based prompt selection methods that aim to choose the optimal prompt among the candidates for a given task but have failed to provide a comprehensive and fair comparison between each other. In this paper, we propose a unified framework to interpret and evaluate the existing probability-based prompt selection methods by performing extensive experiments on 13 common and diverse NLP tasks. We find that each of the existing methods can be interpreted as some variant of the method that maximizes mutual information between the input and the predicted output (MI). Utilizing this finding, we develop several other combinatorial variants of MI and increase the effectiveness of the oracle prompt selection method from 87.79% to 94.98%, measured as the ratio of the performance of the selected prompt to that of the optimal oracle prompt. Furthermore, considering that all the methods rely on the output probability distribution of the model that might be biased, we propose a novel calibration method called Calibration by Marginalization (CBM) that is orthogonal to the existing methods and helps increase the prompt selection effectiveness of the best method to 96.85%, achieving 99.44% of the oracle prompt F1 without calibration.
Cited by 2Open access
Dinella, Elizabeth, Ryan, Gabriel, Shuvendu K. Lahiri et al. · Zenodo (CERN European Organization for Nuclear Research) · 2022
This repository contains the replication artifact for TOGA: A Neural Method for Test Oracle Generation to appear in ICSE 2022. Testing is widely recognized as an important stage of the softwaredevelopment lifecycle. Effective software testing can provide benefits such as documentation, bug finding, and preventing regressions. In particular, unit tests document a unit’s intended functionality. A test oracle, typically expressed as an condition, documents the intended behavior of the unit under a given test prefix. Synthesizing a functional test oracle is a challenging problem, as it has to capture the intended functionality and not the implemented functionality. In our paper, we propose TOGA (Test Oracle GenerAtion), a unified transformer-based neural approach to infer both exceptional and assertion test oracles based on the context of the focal method. Our artifact reproduces the results for all RQs in the paper's evaluation. The artifact includes source code and download links for datasets and models produced in the paper, fulfilling the requirements for reproduced, resuable, and available badges. We assume basic unix familiarity and ability to run python. Our artifact is given as a docker image for linux. <strong>Note:</strong> For convenience, we provide a self-contained docker image to reproduce all results without any setup. We recommend using this to reproduce the results in the paper. See directions for using the docker image in the README.
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Ying Liu (18461) · Journal of Quantum Information Science · 2026
Grover’s algorithm is widely celebrated as providing quadratic quantum speedup for unsorted database search, forming the theoretical foundation for numerous claimed quantum advantages in machine learning, optimization, and computational applications. We demonstrate that Grover’s algorithm and oracle-based quantum search algorithms face three fundamental barriers that severely limit their practical applicability. First, we identify the Grover Dilemma: when the computational space exceeds the valid data space, Grover’s algorithm must choose between 1) creating uniform superposition over all computational states—including invalid states that lead to incorrect results, 2) restricting superposition to only valid states containing solutions—requiring prior knowledge that reduces the problem to trivial O(1) complexity, or 3) constructing superposition over valid states through classical preprocessing—requiring O(N) cost that eliminates the claimed O( N ) quantum advantage. We extend this analysis to establish a unified framework of three independent barriers affecting oracle-based quantum search: 1) the Grover Dilemma (superposition construction for structured problems), 2) the Setup Cost Dilemma (oracle construction and data loading costs), and 3) Oracle Circularity (oracle specification requiring solution of the target problem). These barriers are logically independent—a quantum algorithm must avoid all three to achieve genuine advantage. Through systematic analysis of major problem classes and specific quantum algorithms, we found that oracle-based quantum search provides genuine computational advantage only in an exceptionally narrow class of problems. Our contributions are formalized in ten theorems: Theorems 6.1-6.5 establish the general framework (Grover Dilemma Barrier, Setup Cost Barrier, Oracle Circularity Barrier, Composite Barrier, and general conditions for no quantum advantage); Theorems 6.6-6.8 prove no quantum advantage for Deutsch’s Algorithm, Deutsch-Jozsa Algorithm, and Simon’s Algorithm in practical scenarios; Theorems 6.9-6.10 prove no quantum advantage for NP-complete problems and learning/optimization problems subject to Oracle Circularity. The three-barrier framework provides a systematic method for evaluating quantum search algorithm claims and distinguishing genuine advantages from artifacts of incomplete cost analysis.
Cited by 2Open access
Fuming Lin · ArXiv.org · 2025
In this paper, we consider high-dimensional Lp-quantile regression which only requires a low order moment of the error and is also a natural generalization of the above methods and Lp-regression as well. The loss function of Lp-quantile regression circumvents the non-differentiability of the absolute loss function and the difficulty of the squares loss function requiring the finiteness of error's variance and thus promises excellent properties of Lp-quantile regression. Specifically, we first develop a new method called composite Lp-quantile regression(CLpQR). We study the oracle model selection theory based on CLpQR (call the estimator CLpQR-oracle) and show in some cases of p CLpQR-oracle behaves better than CQR-oracle (based on composite quantile regression) when error's variance is infinite. Moreover, CLpQR has high efficiency and can be sometimes arbitrarily more efficient than both CQR and the least squares regression. Second, we propose another new regression method,i.e. near quantile regression and prove the asymptotic normality of the estimator when p converges to 1 and the sample size infinity simultaneously. As its applications, a new thought of smoothing quantile objective functions and a new estimation are provided for the asymptotic covariance matrix of quantile regression. Third, we develop a unified efficient algorithm for fitting high-dimensional Lp-quantile regression by combining the cyclic coordinate descent and an augmented proximal gradient algorithm. Remarkably, the algorithm turns out to be a favourable alternative of the commonly used liner programming and interior point algorithm when fitting quantile regression.
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Vassilios Digalakis, Christophe Pérignon, Sébastien Saurin et al. · ArXiv.org · 2025
We study the problem of deciding whether, and when an organization should replace a trained incumbent model with a challenger relying on newly available features. We develop a unified economic and statistical framework that links learning-curve dynamics, data-acquisition and retraining costs, and discounting of future gains. First, we characterize the optimal switching time in stylized settings and derive closed-form expressions that quantify how horizon length, learning-curve curvature, and cost differentials shape the optimal decision. Second, we propose three practical algorithms: a one-shot baseline, a greedy sequential method, and a look-ahead sequential method. Using a real-world credit-scoring dataset with gradually arriving alternative data, we show that (i) optimal switching times vary systematically with cost parameters and learning-curve behavior, and (ii) the look-ahead sequential method outperforms other methods and is able to approach in value an oracle with full foresight. Finally, we establish finite-sample guarantees, including conditions under which the sequential look-ahead method achieve sublinear regret relative to that oracle. Our results provide an operational blueprint for economically sound model transitions as new data sources become available.
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Vassilios Digalakis, Christophe Pérignon, Sébastien Saurin et al. · arXiv (Cornell University) · 2025
We study the problem of deciding whether, and when an organization should replace a trained incumbent model with a challenger relying on newly available features. We develop a unified economic and statistical framework that links learning-curve dynamics, data-acquisition and retraining costs, and discounting of future gains. First, we characterize the optimal switching time in stylized settings and derive closed-form expressions that quantify how horizon length, learning-curve curvature, and cost differentials shape the optimal decision. Second, we propose three practical algorithms: a one-shot baseline, a greedy sequential method, and a look-ahead sequential method. Using a real-world credit-scoring dataset with gradually arriving alternative data, we show that (i) optimal switching times vary systematically with cost parameters and learning-curve behavior, and (ii) the look-ahead sequential method outperforms other methods and is able to approach in value an oracle with full foresight. Finally, we establish finite-sample guarantees, including conditions under which the sequential look-ahead method achieve sublinear regret relative to that oracle. Our results provide an operational blueprint for economically sound model transitions as new data sources become available.
Cited by 0Open access
Usman Taqui Syed, Emiliano Dall’Anese, Bin Hu · arXiv (Cornell University) · 2023
In this paper, we develop a unified framework for analyzing the tracking error and dynamic regret of inexact online optimization methods under a variety of settings. Specifically, we leverage the quadratic constraint approach from control theory to formulate sequential semidefinite programs (SDPs) whose feasible points naturally correspond to tracking error bounds of various inexact online optimization methods including the inexact online gradient descent (OGD) method, the online gradient descent-ascent method, the online stochastic gradient method, and the inexact proximal online gradient method. We provide exact analytical solutions for our proposed sequential SDPs, and obtain fine-grained tracking error bounds for the online algorithms studied in this paper. We also provide a simple routine to convert the obtained tracking error bounds into dynamic regret bounds. The main novelty of our analysis is that we derive exact analytical solutions for our proposed sequential SDPs under various inexact oracle assumptions in a unified manner.
Cited by 1Open access
Liucang Wu, Zeyu Zhang, Dengke Xu · DergiPark (Istanbul University) · 2012
The lognormal distribution is widely used in applications. Variable se- lection is an important issue in all regression analysis and in this paper we investigate simultaneous variable selection in the joint mean and dispersion models of the lognormal distribution. We propose a unified penalized likelihood method which can simultaneously select significant variables in the mean and dispersion models. Furthermore, the pro- posed variable selection method can simultaneously perform parameter estimation and variable selection in the mean and dispersion models. With appropriate selection of the tuning parameters, we establish the consistency and the oracle property of the regularized estimators. Some simulation studies and a real example are used to illustrate the proposed method.
Cited by 4Open access
Huang, Wenxin, Su, Liangjun, Wang, Yiru · Figshare · 2025
This paper considers unified estimation and inference in panel autoregressive (PAR) models. The PAR coefficients are assumed to contain a latent group structure that allows the degree of persistence for each time series to be heterogeneous and unknown. We propose a novel penalized weighted least squares approach to simultaneously identify the unknown group membership and consistently estimate the PAR coefficients, regardless of whether the underlying PAR process is stationary, unit-root, near-integrated, or even explosive. Theoretically, we establish the classification consistency, oracle properties, and unified asymptotic normal distributions for the proposed Lasso-type estimators. Empirically, we apply our data-driven method to uncover the existence of firm-level hidden bubbles in the U.S. stock market that have not been accounted for in previous studies.
Cited by 0Open access
Huang, Wenxin, Su, Liangjun, Wang, Yiru · Figshare · 2025
This paper considers unified estimation and inference in panel autoregressive (PAR) models. The PAR coefficients are assumed to contain a latent group structure that allows the degree of persistence for each time series to be heterogeneous and unknown. We propose a novel penalized weighted least squares approach to simultaneously identify the unknown group membership and consistently estimate the PAR coefficients, regardless of whether the underlying PAR process is stationary, unit-root, near-integrated, or even explosive. Theoretically, we establish the classification consistency, oracle properties, and unified asymptotic normal distributions for the proposed Lasso-type estimators. Empirically, we apply our data-driven method to uncover the existence of firm-level hidden bubbles in the U.S. stock market that have not been accounted for in previous studies.
Cited by 0Open access
Qian Huang, Jinhong You, Liwen Zhang · Scandinavian Journal of Statistics · 2021
Abstract In the analysis of longitudinal or functional data, a time‐varying additive model (tvAM) has been introduced that is effective at avoiding the curse of dimensionality and capturing dynamic features. The present article focuses on the unified two‐step estimators of a tvAM with sparse or dense longitudinal or functional data. It is proved that the two‐step estimators have the same asymptotic distribution as that of oracle estimators. Furthermore, a unified convergence theory is established, based on which a unified inference is proposed without deciding whether the data are sparse or dense. Also, a testing statistic that can adapt to the sparse and dense cases in a unified framework is proposed to check whether the bivariate nonparametric functions are time varying, and the asymptotic distribution of the proposed test statistic is derived. Simulation studies are conducted to assess the finite‐sample performance of the proposed model and methods, and two different types of data are considered to illustrate the proposed method.
Cited by 3
Jie Li, Kai Hu, Ji Wan et al. · 2023
Blockchain applications, especially DeFi, frequently require off-chain data through oracles. Distributed oracles have higher security, but the system is also more complex. This paper designs a unified formal description method for the communication process, security protocol, data aggregation and other related procedures and protocols of the oracle. This method has no ambiguity and has a high logical description ability. Therefore, it has good convertibility with Event-B and some other formal verification tools, as well as logic methods such as first-order logic, set theory, and CTL, which can provide a basis for technical personnel to establish formal models, protocol development, and implementation. The paper defines and analyzes this protocol description method and presents examples of some core protocols.
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Ali Reza Ibrahimzada, Yigit Varli, Dilara Tekinoglu et al. · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · 2022
Automation of test oracles is one of the most challenging facets of software testing, but remains comparatively less addressed compared to automated test input generation. Test oracles rely on a ground-truth that can distinguish between the correct and buggy behavior to determine whether a test fails (detects a bug) or passes. What makes the oracle problem challenging and undecidable is the assumption that the ground-truth should know the exact expected, correct, or buggy behavior. However, we argue that one can still build an accurate oracle without knowing the exact correct or buggy behavior, but how these two might differ. This paper presents , a learning-based approach that in the absence of test assertions or other types of oracle, can determine whether a unit test passes or fails on a given method under test (MUT). To build the ground-truth, jointly embeds unit tests and the implementation of MUTs into a unified vector space, in such a way that the neural representation of tests are similar to that of MUTs they pass on them, but dissimilar to MUTs they fail on them. The classifier built on top of this vector representation serves as the oracle to generate “fail” labels, when test inputs detect a bug in MUT or “pass” labels, otherwise. Our extensive experiments on applying to more than 5K unit tests from a diverse set of open-source Java projects show that the produced oracle is (1) effective in predicting the fail or pass labels, achieving an overall accuracy, precision, recall, and F1 measure of 93%, 86%, 94%, and 90%, (2) generalizable, predicting the labels for the unit test of projects that were not in training or validation set with negligible performance drop, and (3) efficient, detecting the existence of bugs in only 6.5 milliseconds on average. Moreover, by interpreting the neural model and looking at it beyond a closed-box solution, we confirm that the oracle is valid, i.e., it predicts the labels through learning relevant features.
Cited by 19Open access
Hansheng Wang, Chenlei Leng · Journal of the American Statistical Association · 2007
We propose a method of least squares approximation (LSA) for unified yet simple LASSO estimation. Our general theoretical framework includes ordinary least squares, generalized linear models, quantile regression, and many others as special cases. Specifically, LSA can transfer many different types of LASSO objective functions into their asymptotically equivalent least squares problems. Thereafter, the standard asymptotic theory can be established and the LARS algorithm can be applied. In particular, if the adaptive LASSO penalty and a Bayes information criterion–type tuning parameter selector are used, the resulting LSA estimator can be as efficient as the oracle. Extensive numerical studies confirm our theory.
Cited by 351
Hansheng Wang, Chenlei Leng · 2007
We propose a method of least squares approximation (LSA) for unified yet simple LASSO estimation. Our general theoretical framework includes ordinary least squares, generalized linear models, quantile regression, and many others as special cases. Specifically, LSA can transfer many different types of LASSO objective functions into their asymptotically equivalent least-squares problems. Thereafter, the standard asymptotic theory can be established and the LARS algorithm can be applied. In particular, if the adaptive LASSO penalty and a BIC-type tuning parameter selector are used, the resulting LSA estimator can be as efficient as oracle. Extensive numerical studies confirm our theory.
Cited by 0
Eidukynaitė, Vilma · Laba (Lietuvos akademinių bibliotekų direktorių asociacija) · 2006
The user interface is the direct mediator between the user and the system. It is one of main factors which influences how fluently and with what time resources system could be integrated into business process and how fast systems deployment could be performed. User interface is one of the most important points in software design, because it determines quality and rate of project implementation. Software design methodologies, based on Unified Modeling Language (UML), Oracle CASE, introduced by C. Finkelstein, D. J. Anderson, V. Balasubramanian, A. Granlund, D. Lafreniere, D. Carr are analyzed in this paper. The user interface modeling method based on data flow specification is presented in this work; the software prototype of modeling user interface based on this method is implemented.
Cited by 0
Wenyan Yang, Tomáš Janovec, Samantha Bavautdin · arXiv (Cornell University) · 2026
We propose \geogap{}, a geometric method for detecting missing requirement types in software specifications. The method represents each requirement as a unit vector via a pretrained sentence encoder, then measures coverage deficits through $k$-nearest-neighbour distances z-scored against per-project baselines. Three complementary scoring components -- per-point geometric coverage, type-restricted distributional coverage, and annotation-free population counting -- fuse into a unified gap score controlled by two hyperparameters. On the PROMISE NFR benchmark, \geogap{} achieves 0.935 AUROC for detecting completely absent requirement types in projects with $N \geq 50$ requirements, matching a ground-truth count oracle that requires human annotation. Six baselines confirm that each pipeline component -- per-project normalisation, neural embeddings, and geometric scoring -- contributes measurable value.
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