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This article undertakes a rhetorical reading of Oliver Wendell Holmes Jr.'s "The Path of the Law" (1897), attending particularly to Holmes's use of the trope of the "oracle" in his legal philosophy to show how Holmes utilizes this figure to interrogate the Anglo-American legal tradition and to articulate his own, new understanding of the task of the law. For Holmes, lawyers reading the "sibylline leaves" of the common law will inevitably make imperfect predictions about how judges will rule a case. In contrast, Holmes suggests that lawyers should be able to make predictions with the same degree of reliability as scientific hypotheses based on the laws of physics. Similarly, Holmes argues that laws should be considered in terms of desired ends, rather than precedent and tradition, and assessed using the new quantitative social science of statistics. Thus, by turning "prophecy" into a figure of rationality, Holmes calls for a radical reinvention of the law as scientifically based and oriented towards the future. [ABSTRACT FROM AUTHOR]

This article is intended as an essay about consciousness. The logic of morality leads humans to turn to their deepest essence, which is consciousness, as the highest faculty of human reason. Consciousness gives humans their intransitive value as the only moral beings gifted with mind, intellect, and reason, heart and soul, free will, faith, and love. Conscience is our personal god, our inner person/"homo interior", demon, oracle, and judge. [ABSTRACT FROM AUTHOR]

Oracle is a data supply mechanism that provides real-world data for blockchain. It serves as a bridge between blockchain and the IoT world, playing a crucial role in solving problems such as data sharing and device management in the IoT field. The main challenge at this stage is determining how to achieve data privacy protection in distributed Oracle machines to safeguard the value hidden in data on the blockchain. In this paper, we propose an improved scheme for distributed Oracle data aggregation based on Paillier encryption algorithm, which achieves end-to-end data privacy protection from devices to users. To address the issue of dishonest distributed Oracle machines running out of funds, we have designed an algorithm called PICA (Paillier-based InChain Aggregation). Based on the aggregation on the Chainlink chain and the Paillier encryption algorithm, random numbers are introduced to avoid the problem of dishonest Oracle machines running out of funds. We use the traffic coverage method to solve the problem of exposed request paths in distributed Oracle machines. Simulation and experimental results show that in small and medium-sized IoT application scenarios with 10,000 data nodes, each additional false request in a single request will result in a delay of about 2 s in data acquisition and can achieve a request response time of 20 s. The proposed method can achieve user data privacy protection. [ABSTRACT FROM AUTHOR]

We study point-to-point distance estimation in hypergraphs, where the query is parameterized by a positive integer s, which defines the required level of overlap for two hyperedges to be considered adjacent. To answer s-distance queries, we first explore an oracle based on the line graph of the given hypergraph and discuss its limitations: The line graph is typically orders of magnitude larger than the original hypergraph. We then introduce HypED, a landmark-based oracle with a predefined size, built directly on the hypergraph, thus avoiding the materialization of the line graph. Our framework allows to approximately answer vertex-to-vertex, vertex-to-hyperedge, and hyperedge-to-hyperedge s-distance queries for any value of s. A key observation at the basis of our framework is that as s increases, the hypergraph becomes more fragmented. We show how this can be exploited to improve the placement of landmarks, by identifying the s-connected components of the hypergraph. For this latter task, we devise an efficient algorithm based on the union-find technique and a dynamic inverted index. We experimentally evaluate HypED on several real-world hypergraphs and prove its versatility in answering s-distance queries for different values of s. Our framework allows answering such queries in fractions of a millisecond while allowing fine-grained control of the trade-off between index size and approximation error at creation time. Finally, we prove the usefulness of the s-distance oracle in two applications, namely hypergraph-based recommendation and the approximation of the s-closeness centrality of vertices and hyperedges in the context of protein-protein interactions. [ABSTRACT FROM AUTHOR]

This paper is a comparison study on aggregate functions and windows function between the three major Relational Database Management Systems (RDBMSs): Oracle, SQL Server, and MySQL. These functions are essential to handle a huge data set and prepare it for effective analysis. The research is conducted to analyse the performance of these systems, their utilization of resources, while executing aggregate queries. Further, this paper examines the integration of machine-learning abilities and NoSQL database connectivity within these platforms. All these were measured under a constant benchmarking framework. It also discusses the analysis on how indexing affects query performance and the integration of machine-learning (ML) models with these databases. The results are indicative of considerable performance variation, resource efficiency, and ML integration among the three RDBMSs. Oracle is the best solution for implementing complex aggregations and ML integration, making it the best alternative to work on large datasets. Where MySQL is very efficient for most simple tasks, it lacks advanced features and does not have native ML support. It further provides optimization strategies for each RDBMS and gives insight into securing data and integrating with NoSQL databases. This research is set out to guide database administrators and developers in choosing the most appropriate RDBMS in relation to their specific needs in aggregation, ML, NoSQL integration. However, the factor of indexing is generally what brought most success to query optimization in these databases: Oracle, SQL Server, and MySQL. Among these, Oracle still was significantly outdoing both others, which further improved by indexing. In general, MySQL was less performant and lacked some functionality in window functions. Aggregation queries seem to profit more from indexing, but the less improvement was seen for window functions (STRING_AGG). All in all, indexing is a very effective technique in optimizing query efficiency.

In the advancing world of blockchain technology, the paramount need for trustworthy oracles, the entities responsible for providing external data to blockchain's smart contracts, is becoming more apparent. This paper introduces a novel protocol, the Persona Preserving Reputation Protocol (P2RP), which leverages Decentralized Identity (DID)-based reputation systems to maintain security and user privacy while ensuring the trustworthiness of blockchain oracles. Leveraging the innovative application of Ordinary Differential Equations (ODEs), we introduce a technique for forecasting and calculating the reputation scores of users, a crucial component within the P2RP protocol. The design of P2RP features four distinct account types with varying degrees of privacy and reputation characteristics. The simulation results suggest that the P2RP protocol can lead towards a secure, privacy-preserving, and reliable oracle service in the Web3 ecosystem. [ABSTRACT FROM AUTHOR]

Most user-collaborative location privacy protection mechanisms assume that collaborative group members are trustworthy and can strictly enforce collaboration rules. Such assumptions do not match reality and reduce the usability of the schemes. In this paper, we propose Invisible Man, a blockchain-enabled framework for peer-to-peer collaborative privacy games for location-based services. The framework enables users to protect their privacy in extreme environments and allows members of a collaborative group without trust to collaborate efficiently. To defend against inference attacks, a user-collaborative privacy game model is constructed, and a dual-verification mechanism with Chainlink and Witnet oracles is developed to provide security guarantees for model generation. Members can conduct cooperative games under the guidance of the model. To realize secure and efficient in-group collaboration, a blockchain-based reward and punishment mechanism for collaboration is designed, integrating token incentives and a blacklisting mechanism to ensure the verifiability and audibility of user behaviors. Security analysis and extensive simulation results demonstrate that the proposed scheme achieves high security and privacy with low costs. [ABSTRACT FROM AUTHOR]

We investigate multitask learning in the context of multivariate linear regression with high dimensional covariates and heavy‐tailed noise, while under the constraint of limited memory. To tackle the computational complexity arising from the non‐smoothness of the quantile loss, we reformulate it as an equivalent least squares loss, which yields robust solutions even in the presence of heavy‐tailed noise. We incorporate a group lasso penalty into the quantile loss to produce sparse solutions, and an accelerated proximal sub‐gradient descent algorithm to speed up the computation while ensuring explicit forms for penalized solutions at each iteration. The proposed algorithm is general and can be applied to similar optimization problems. Moreover, we introduce a communication‐efficient distributed algorithm that guarantees optimal convergence rates after finite communication rounds in cases where computing resources such as memory are insufficient. We also study the theoretical properties of the resultant estimate and relax the widely used model selection consistency assumption on the initial estimate. We demonstrate the effectiveness of our proposal through extensive numerical studies. [ABSTRACT FROM AUTHOR]

This paper aimed to optimize the node selection problem in existing oracle machine schemes in the blockchain environment in order to improve the accuracy and reliability of oracle machine node selection. This paper introduced ORLM, a deep reinforcement learning based middleware for blockchain prophecy machine node selection. This middleware considered the consumption of multiple nodes under different service demands and modeled the reputation value of the prophecy machine nodes to sick the reputation value of the oracle machine data-providing nodes, thus avoiding the selection of nodes with malicious history as much as possible. With the deep reinforcement learning DQN algorithm, the middleware is able to optimize the process of selecting nodes for better node selection with security. The experimental results show that the oracle middleware is able to better satisfy the service requests of users. And it has high scalability and availability. It proves that the introduction of deep reinforcement learning to optimize the oracle node selection is a feasible direction. [ABSTRACT FROM AUTHOR]

In the article, we aim to understand the responses of living organisms, exemplified by mycelium, to external stimuli through the lens of a Turing machine with an oracle (oTM). To facilitate our exploration, we show that a variant of an oTM is a cellular automaton with an oracle, which aptly captures the intricate behaviours observed in organisms such as fungi, shedding light on their dynamic interactions with their environment. This interaction reveals forms of reflection that can be interpreted as a minimum volume of consciousness. Thus, in our study, we interpret consciousness as a mathematical phenomenon when an arithmetic function is arbitrarily modified. We call these modifications the hybridization of behaviour. oTMs are the mathematical language of this hybridization.

Estimating the variance matrix has an important role in statistical applications and conclusions, in high–dimensional matrices if the number of variables is greater than the number of observations P > n, the traditional statistical methods are not reliable because they give uncontrolled estimates. Shrinkage methods are used to estimate the high–dimensional variance matrix. In this research, the high–dimensional variance matrix was estimated using the robust Nonparametric method Minimum Regularized Covariance Determinant (MRCD), which is based on Mahalanobis distance, and compared with the variance matrix estimated by the Oracle method, which is based on the Frobenius criterion.

Abstract Species-habitat associations are correlative, can be quantified, and used for powerful inference. Nowadays, Species Distribution Models (SDMs) play a big role, e.g. using Machine Learning and AI algorithms, but their best-available technical opportunities remain still not used for their potential e.g. in the policy sector. Here we present Super SDMs that invoke ML, OA Big Data, and the Cloud with a workflow for the best-possible inference for the 300 + global squirrel species. Such global Big Data models are especially important for the many marginalized squirrel species and the high number of endangered and data-deficient species in the world, specifically in tropical regions. While our work shows common issues with SDMs and the maxent algorithm (‘Shallow Learning'), here we present a multi-species Big Data SDM template for subsequent ensemble models and generic progress to tackle global species hotspot and coldspot assessments for a more inclusive and holistic inference.

Introduction: It was a century ago, whether used separately or in combination with other medicines, that radiotherapy evolved into a successful cancer treatment. In traditional radiation therapy, a Dose Volume Histogram (DVH) is utilized for quantitative analysis of the treatment plan after the adoption of the treatment planning system. An isodose distribution is also used for qualitative analysis and evaluation of the treatment plan during this phase. The right treatment plan is assessed using physical and radiobiological models by inhouse software developed for radiation oncologists. Materials and Methods: The first process was the OSCAR (Object Scoring with Coloured Area of Regret) treatment planning system, developed by Theratronics International, Kanata, Canada in 1991, which was one of the numerous plan evaluation software programs that were established in the field of radiation medicine to analyse and regularize the dose distribution. Many plan evaluation programs have been created over the developing years, with the commercial software used primarily and the primary inclusion MATLAB, and very few have been created using Microsoft Visual Basic, C++, and Java. As a substitute for MATLAB, Python is utilized because it is freely available and has no commercial value. Results: This study's primary objective is to personalize the radiobiological effects to predict each patient by using the DVH data, which are now accessible, to improve the overall performance of the prior model. Several programming languages were initially investigated to check the portability and quick program execution to illustrate the novel ideas. It was discovered that Python was adequate for this research even if it has no economic value compared to other languages. The software receives the DVH data in text format as an input, and for convenience, it displays the output using a variety of Python widgets. Discussion: The major radiobiological parameter values, TD50/5, slope parameter (m), and volume parameter (n), are used to calculate the Tumour Control Probability (TCP) and NTCP values of numerous targets and oars from their respective DVH statistics using Python software. The evaluation of the physical indices of the treatment plans, the AAPM, RTOG, and QUANTEC protocols were used in a clinical analysis for the execution of treatment plans. Conclusion: The problem in the previous plan evaluation tool was fixed by the custom-made PYTHON program used for this research investigation, which also added clinical and radiobiological understanding of the treatment plan. The software produces a report using Microsoft Excel for the comprehensive radiobiological and dosimetric plan evaluation study for cancer patients. [ABSTRACT FROM AUTHOR]

Blockchainsand smart contracts are pivotal in transforming interactions between systems and individuals, offering secure, immutable, and transparent trust-building mechanisms without central oversight. However, Smart Contracts face limitations due to their reliance on blockchain-contained data, a gap addressed by 'Oracles'. These bridges to external data sources introduce the 'Oracle problem', where maintaining blockchain-like security and transparency becomes vital to prevent data integrity issues. This paper presents Veritas, a novel decentralized oracle system leveraging a layer-2 scaling solution, enhancing smart contracts' efficiency and security on Ethereum blockchains. The proposed architecture, explored through simulation and experimental analyses, significantly reduces operational costs while maintaining robust security protocols. An innovative node selection process is also introduced to minimize the risk of malicious data entry, thereby reinforcing network security. Veritas offers a solution to the Oracle problem by aligning with blockchain principles of security and transparency, and demonstrates advancements in reducing operational costs and bolstering network integrity. While the study provides a promising direction, it also highlights potential areas for further exploration in blockchain technology and oracle system optimization. [ABSTRACT FROM AUTHOR]

Species-habitat associations are correlative, can be quantified, and used for powerful inference. Nowadays, Species Distribution Models (SDMs) play a big role, e.g. using Machine Learning and AI algorithms, but their best-available technical opportunities remain still not used for their potential e.g. in the policy sector. Here we present Super SDMs that invoke ML, OA Big Data, and the Cloud with a workflow for the best-possible inference for the 300 + global squirrel species. Such global Big Data models are especially important for the many marginalized squirrel species and the high number of endangered and data-deficient species in the world, specifically in tropical regions. While our work shows common issues with SDMs and the maxent algorithm ('Shallow Learning'), here we present a multi-species Big Data SDM template for subsequent ensemble models and generic progress to tackle global species hotspot and coldspot assessments for a more inclusive and holistic inference. [ABSTRACT FROM AUTHOR]

Copyright of Studia Universitatis Babes-Bolyai, Educatio Artis Gymnasticae is the property of Babes-Bolyai University, Cluj-Napoca, Romania and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article is part of a more extensive research work on the oracular system of the snail [Mones 2022]. The article, as a part of a previous work, it is expressly dedicated to the review of the postulates of some authors on certain categories used in the analysis of the religious system of the Regla de Ocha. In order to structurally analyze the ritual system, it is necessary to examine the link of its main categories with the oracle, as well as with the concept of person managed within the ritual system. This proposal is aimed exclusively at bibliographic analysis and dialogue with these authors. Which was revealed fundamental for the development of fieldwork, reflected in the original research. [ABSTRACT FROM AUTHOR]

The database management system (DBMS) for any application is crucial for developers because each application needs high performance to run efficiently. Therefore, database Benchmarking is the process of performing several Defined tests on those databases to evaluate their performance. The electronic benchmarking System (EBS) facilitates and improves human resource management (HRM) in all aspects of real life. EBS designed by four different database backends and three different web technology. This paper presents a comparative evaluation of the performance of the top DBMS systems namely (MySQL, SQL Server, Oracle, and MS Access). The middleware is designed using three dynamic web technologies (PHP, ASP, and PYTHON). In order to evaluate the backend performance for the four mentioned databases system by using two Parameters Response Time (RT) and Throughput (TT) over different Tire Architectures namely: One-Tier Architecture (1TA), Two-Tier Architecture (2TA). This paper will show which of the database has a better Response time (RT) and Throughput (TT). [ABSTRACT FROM AUTHOR]

Oracle is a data supply mechanism that provides real-world data for blockchain. It serves as a bridge between blockchain and the IoT world, playing a crucial role in solving problems such as data sharing and device management in the IoT field. The main challenge at this stage is determining how to achieve data privacy protection in distributed Oracle machines to safeguard the value hidden in data on the blockchain. In this paper, we propose an improved scheme for distributed Oracle data aggregation based on Paillier encryption algorithm, which achieves end-to-end data privacy protection from devices to users. To address the issue of dishonest distributed Oracle machines running out of funds, we have designed an algorithm called PICA (Paillier-based InChain Aggregation). Based on the aggregation on the Chainlink chain and the Paillier encryption algorithm, random numbers are introduced to avoid the problem of dishonest Oracle machines running out of funds. We use the traffic coverage method to solve the problem of exposed request paths in distributed Oracle machines. Simulation and experimental results show that in small and medium-sized IoT application scenarios with 10,000 data nodes, each additional false request in a single request will result in a delay of about 2 s in data acquisition and can achieve a request response time of 20 s. The proposed method can achieve user data privacy protection.

This paper explores applying deep learning techniques for automatically recognizing and transcribing oracle bone and gold texts. We significantly enhance model recognition efficiency by leveraging the power of Generative Adversarial Networks (GANs) for image data enhancement and the Pix2Pix model for text repair. Our approach integrates the ResNet50 model for robust feature extraction with unsupervised domain adaptation, utilizing multiple pseudo labels to achieve efficient text recognition and transcription. We improve the model’s repair capabilities by generating hard-to-distinguish sample data through GANs and employing a U-Net-based text repair model enhanced with dense connectivity and spectral normalization. Further, combining ResNet50 for feature extraction and advanced domain adaptation techniques strengthens the model’s generalization. Our results on the Oracle dataset show an increase in recognition accuracy from 82% to 94.5%, highlighting the effectiveness of our image enhancement strategies. The ResNet50 extractor outperforms others across various Intersection over Union (IoU) metrics, establishing its feature extraction superiority. In real-world scenarios, testing with a combined Oracle and Jinwen dataset yields a recognition accuracy above 80%, demonstrating our model’s ability to effectively fulfill the recognition task. This research underscores the potential of deep learning algorithms in automating the recognition and transcription of ancient texts, offering a novel solution that significantly boosts recognition accuracy through a synergistic blend of image enhancement, feature extraction, and domain adaptation techniques.

In the aviation industry, the issuance of airside passes often encounters significant delays, posing logistical challenges and hindering crucial operations. This study delves into the potential of implementing blockchain technology, particularly smart contracts, to streamline and expedite airport security processes. Our analysis of data from leading UK airports reveals notable inefficiencies in the current airside pass issuance procedures, necessitating a transformative solution. We advocate for the integration of blockchain smart contracts as a pioneering approach to substantially reduce processing times. By automating execution based on predefined conditions, smart contracts have the potential to revolutionize airport security operations. This research signifies a groundbreaking advancement in the use of smart contracts within the airline industry, underscoring the substantial efficiency improvements that can be achieved. As we conclude this study, we foresee further research and practical implementations to unlock the full transformative impact of blockchain technology on aviation security. [ABSTRACT FROM AUTHOR]

Methods of data transfer from the Oracle environment to the PostgreSQL environment with stage-by-stage organization of actions and determination of the sequence of migration are considered. The main directions for the transformation of the structure of databases, data formats and executed objects are identified. Methods for converting stored procedures and triggers written in procedural languages (PL/SQL and PL/pgSQL) are specified. Software change on nodes directly connected (associated) with the database server and minimization of downtimes during migration are considered, which allows the transfer to be performed for geographically distributed and round-the-clock functioning objects. Examples of changing the speed of query execution before and after migration are given for systems of various types. [ABSTRACT FROM AUTHOR]

Land administration systems are being modernised to streamline the cadastral data lodgement. However, in many jurisdictions, cadastral data are still stored as a flat file. This method of data storage has significant limitations in terms of effective access, management, query, and analysis of cadastral data. Therefore, this study elicited the requirements and proposed an approach to automate the cadastral data storage. The proposed approach was successfully implemented within the land registry organisation in Victoria, Australia and the database management system was rigorously tested. The outcomes can potentially contribute to the implementation of a similar data storage infrastructure in other jurisdictions. [ABSTRACT FROM AUTHOR]

Due to the inequality regarding the susceptibility level of people’s souls as well as their temperamental balance, differences occur in how they accept news containing messages arriving from the celestial realm. People who have a more competent mind, stronger intuition (hadth), and higher imitation (muhākāt) ability compared to others more strongly accept what is revealed from the celestial realm. This article provides information about people who accept what is conveyed from metaphysical substances and who’ve been informed about unknown events in three stages: (i) The place of Plato’s distinction among poets, oracles, and lovers in Aristotelian and Plutarchian philosophy; (ii) the position of poets and oracles in the pre-Islamic period, polytheists’ attribution of both poetry and divination to Prophet Muhammad, and the description of Muhammad as a nabī in the Qur’an; and (iii) the meanings al-Fārābī and Avicenna attributed to oracles, poets, lovers, and nabīs (prophet). In this context, the article attempts to find answers to the questions of what the philosophical basis of both al-Fārābī’s and Avicenna’s ideas is regarding poets and oracles and what the religious basis of their views is regarding the concept of nabī. While trying to find answers to these questions, the study also aims to determine how a nabī differs from poets, oracles, and lovers by considering the superiority nubuwwa (prophecy) has over poetry and divination.

Blockchainsand smart contracts are pivotal in transforming interactions between systems and individuals, offering secure, immutable, and transparent trust-building mechanisms without central oversight. However, Smart Contracts face limitations due to their reliance on blockchain-contained data, a gap addressed by ’Oracles’. These bridges to external data sources introduce the ’Oracle problem’, where maintaining blockchain-like security and transparency becomes vital to prevent data integrity issues. This paper presents Veritas, a novel decentralized oracle system leveraging a layer-2 scaling solution, enhancing smart contracts’ efficiency and security on Ethereum blockchains. The proposed architecture, explored through simulation and experimental analyses, significantly reduces operational costs while maintaining robust security protocols. An innovative node selection process is also introduced to minimize the risk of malicious data entry, thereby reinforcing network security. Veritas offers a solution to the Oracle problem by aligning with blockchain principles of security and transparency, and demonstrates advancements in reducing operational costs and bolstering network integrity. While the study provides a promising direction, it also highlights potential areas for further exploration in blockchain technology and oracle system optimization.

Since the introduction of genomic selection in plant breeding, high genetic gains have been realized in different plant breeding programs. Various methods based on genomic estimated breeding values (GEBVs) for selecting parental lines that maximize the genetic gain as well as methods for improving the predictive performance of genomic selection have been proposed. Unfortunately, it remains difficult to measure to what extent these methods really maximize long-term genetic values. In this study, we propose oracle selection, a hypothetical frame of mind that uses the ground truth to optimally select parents or optimize the training population in order to maximize the genetic gain in each breeding cycle. Clearly, oracle selection cannot be applied in a true breeding program, but allows for the assessment of existing parental selection and training population update methods and the evaluation of how far these methods are from the optimal utopian solution. [ABSTRACT FROM AUTHOR]

The Te'omim Cave is a large karst cave located in the Jerusalem Hills. Since 2009, the cave has been explored by our team as a joint project of the Martin (Szusz) Department of Land of Israel Studies and Archaeology at Bar-Ilan University and the Cave Research Center at the Hebrew University of Jerusalem. Over 120 intact oil lamps were collected in the 2010–2016 survey seasons from all sections of the cave; most of them were dated to the second to fourth centuries CE. All of these lamps had been deliberately inserted in narrow, deep crevices in the main chamber walls or beneath the rubble. Some crevices contained groups of oil lamps mixed with weapons and pottery vessels from earlier periods or placed with human skulls. This article discusses the possibility that the oil lamps, weapons, human skulls, and other artifacts were used as part of necromancy ceremonies that took place in the cave during the Late Roman period, and that the cave may have served as a local oracle (nekyomanteion). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dynamic Ensemble Selection (DES) algorithms have obtained better performance in many tasks compared to monolithic classifiers and static ensembles. However, it is reasonable to assume that no DES algorithm is the optimal solution in different scenarios since diversity plays an important role. Thus, this paper addresses this research gap by proposing a novel approach called Hardness-aware Oracle with Dynamic Ensemble Selection (HaO-DES) that operates as a post-selection strategy, evaluating and selecting the best DES techniques per instance. Each DES technique ensemble is evaluated using a new measure called Hardness-aware Oracle (HaO). HaO extends the traditional Oracle concept by assessing a DES technique based on how the classifiers in the selected ensemble work together, contrasting with the individual classifier evaluation in the traditional assessment. We performed experiments over 30 databases, using three base classifiers (Perceptron, Logistic Regression, and Naive Bayes) in homogeneous and heterogenous pools’ configurations, to assess HaO-DES with four DES approaches (KNORA-U, KNOP, DES-P, and META-DES). We use three performance metrics to evaluate the experiments: accuracy, F-score, and Matthews Correlation Coefficient (MCC). The results show that our approach outperforms or obtains similar results against the four individual DES approaches, mainly when considering heterogeneous pool settings. We also demonstrated the HaO-DES efficiency in choosing suitable DES techniques in different situations.