Search

Testing plays an essential role in ensuring the safety and quality of cyberphysical systems (CPSs). One of the main challenges in automated and software-in-the-loop simulation testing of CPSs is defining effective oracles that can check that a given system conforms to expectations of desired behavior. Manually specifying such oracles can be tedious, complex, and error-prone, and so techniques for automatically learning oracles are attractive. Characteristics of CPSs, such as limited or no access to source code, behavior that is non-deterministic and sensitive to noise, and that the system may respond differently to input based on its context introduce considerable challenges for automated oracle learning. We present Mithra, a novel, unsupervised oracle learning technique for CPSs that operates on existing telemetry data. It uses a three-step multivariate time series clustering to discover the set of unique, correct behaviors for a CPS, which it uses to construct robust oracles. We instantiate our proposed technique for ArduPilot, a popular, open-source autopilot software. On a set of 24 bugs, we show that Mithra effectively identifies buggy executions with few false positives and outperforms AR-SI, a state-of-the-art CPS oracle learning technique. We demonstrate Mithra's wider applicability by applying it to an autonomous racer built for the Robot Operating System.

This paper details RDF/OWL storage and management in two popular Relational Database Management Systems (RDBMSs): Oracle and Virtuoso. Popularity, sustainability, and conformance with the SPARQL language are the main reasons for choosing these systems. This work combines empirical and analytical studies guided by a comparative framework developed and motivated in the paper. Several dimensions are considered, including RDF data type preservation, SPARQL query and update processing, reasoning capabilities, custom inferences, blank node management, and other functional and non-functional features. Furthermore, a review of the performance assessments reported in the literature has been conducted. This study’s results identify the advantages and shortcomings of these RDBMSs for storing and managing RDF/OWL. They can help improve these systems or serve as a guide in choosing an appropriate system to use in a project context. Opportunities for further research efforts are also suggested.

Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved. We consider learning to play multiplayer imperfect-information games with simultaneous moves and large state-action spaces. Previous attempts to tackle such challenging games have largely focused on model-free learning methods, often requiring hundreds of years of experience to produce competitive agents. Our approach is based on model-based planning. We tackle the problem of partial observability by first building an (oracle) planner that has access to the full state of the environment and then distilling the knowledge of the oracle to a (follower) agent which is trained to play the imperfect-information game by imitating the oracle's choices. We experimentally show that planning with naive Monte Carlo tree search performs poorly in large combinatorial action spaces. We therefore propose planning with a fixed-depth tree search and decoupled Thompson sampling for action selection. We show that the planner is able to discover efficient playing strategies in the games of Clash Royale and Pommerman and the follower policy successfully learns to implement them by training on few hundred battles.

Despite the high stakes involved in smart contracts, they are often developed in an undisciplined manner, leaving the security and reliability of blockchain transactions at risk. In this paper, we introduce ContraMaster an oracle-supported dynamic exploit generation framework for smart contracts. Existing approaches mutate only single transactions; ContraMaster exceeds these by mutating the transaction sequences. ContraMaster uses data-flow, control-flow, and the dynamic contract state to guide its mutations. It then monitors the executions of target contract programs, and validates the results against a general-purpose semantic test oracle to discover vulnerabilities. Being a dynamic technique, it guarantees that each discovered vulnerability is a violation of the test oracle and is able to generate the attack script to exploit this vulnerability. In contrast to rule-based approaches, ContraMaster has not shown any false positives, and it easily generalizes to unknown types of vulnerabilities (e.g., logic errors). We evaluate ContraMaster on 218 vulnerable smart contracts. The experimental results confirm its practical applicability and advantages over the state-of-the-art techniques, and also reveal three new types of attacks.

Smart contracts are protocols that can automatically execute a transaction including an electronic contract when a condition is satisfied without a trusted third party. In a representative use-case, a smart contract is executed when multiple parties fairly trade on a blockchain asset. On blockchain systems, a smart contract can be regarded as a system participant, responding to the information received, receiving and storing values, and sending information and values outwards. Also, a smart contract can temporarily keep assets, and always perform operations in accordance with prior rules. Many cryptocurrencies have implemented smart contracts. At POST2018, Atzei et al. give formulations of seven fair exchange protocols using smart contract on Bitcoin: oracle, escrow, intermediated payment, timed commitment, micropayment channels, fair lotteries, and contingent payment. However, they only give an informal discussion on security.

In this paper, we address the challenges of detecting instances from emerging classes over a non-stationary data stream during classification. In particular, instances from an entirely unknown class may appear in a data stream over time. Existing classification techniques utilize unsupervised clustering to identify the emergence of such data instances. Unfortunately, they make strong assumptions which are typically invalid in practice; (i) Most instances associated with a class are closer to each other in feature space than instances associated with different classes, (ii) Covariates of data are normalized through an oracle to overcome the effect of a few data instances having large feature values, and (iii) Labels of instances from emerging classes are readily available soon after detection. To address the challenges that occur in practice when the above assumptions are weak, we propose a practical semi-supervised emerging class detection framework. Particularly, we aim to identify similar data instances within local regions in feature space by incorporating a mutual graph clustering mechanism. Our empirical evaluation of this framework on real-world datasets demonstrates its superiority of classification performance compared to existing methods while using significantly fewer labeled instances.

Diversity is one of the most important properties in image captioning, as it reflects various expressions of important concepts presented in an image. However, the most popular metrics cannot well evaluate the diversity of multiple captions. In this paper, we first propose a metric to measure the diversity of a set of captions, which is derived from latent semantic analysis (LSA), and then kernelize LSA using CIDEr (R. Vedantam et al., 2015) similarity. Compared with mBLEU (R. Shetty et al., 2017), our proposed diversity metrics show a relatively strong correlation to human evaluation. We conduct extensive experiments, finding there is a large gap between the performance of the current state-of-the-art models and human annotations considering both diversity and accuracy; the models that aim to generate captions with higher CIDEr scores normally obtain lower diversity scores, which generally learn to describe images using common words. To bridge this "diversity" gap, we consider several methods for training caption models to generate diverse captions. First, we show that balancing the cross-entropy loss and CIDEr reward in reinforcement learning during training can effectively control the tradeoff between diversity and accuracy of the generated captions. Second, we develop approaches that directly optimize our diversity metric and CIDEr score using reinforcement learning. These proposed approaches using reinforcement learning (RL) can be unified into a self-critical (S. J. Rennie et al., 2017) framework with new RL baselines. Third, we combine accuracy and diversity into a single measure using an ensemble matrix, and then maximize the determinant of the ensemble matrix via reinforcement learning to boost diversity and accuracy, which outperforms its counterparts on the oracle test. Finally, inspired by determinantal point processes (DPP), we develop a DPP selection algorithm to select a subset of captions from a large number of candidate captions. The experimental results show that maximizing the determinant of the ensemble matrix outperforms other methods considerably improving diversity and accuracy.

Testing a program to confirm whether it consistently implements its requirements specification is a necessary but time-consuming activity in software development. Automatic testing based on specifications can significantly alleviate the workload and cost, but faces a challenge of how to ensure that both the user's concerns in the specification and possible execution paths in the program are all covered. In this paper, we describe a new method, called "Vibration-Method" or simply "V-Method", for automatic generation of test cases and test oracle from model-based formal specifications, aiming to address this challenge. The proposed method is suitable for testing information systems in which rich data types are used. Supporting the principle of "divide and conquer", the method provides a specific technique for generating test cases based on functional scenarios defined in the specification, test case generation criteria, automatic test case generation algorithms, and a well-defined mechanism for deriving test oracle. We elaborate on the method by discussing how initial test cases can be automatically generated, how additional necessary test cases are produced using the "vibration" technique, and how a test oracle can be automatically derived for a group of test cases. We also describe a controlled experiment to evaluate the effectiveness of the method and discuss the important issues in relation to the performance and applicability of the method.

There is ample research on false discovery rate (FDR) control for testing hypotheses classified according to one criterion. However, scenarios of hypotheses partitioned via two different criteria are often encountered in practice. Such two-way classification encodes more structural information in the associated multiple testing of the hypotheses than its one-way or un-classified counterparts. Unfortunately, there seems to be very little research tailored for multiple testing under that classification setting. This article proposes weighted versions of the Benjamini–Hochberg (BH) method, both in their oracle and data-adaptive forms, efficiently capturing one- or two-way classified structure of hypotheses through appropriately chosen weights. The proposed methods control FDR non-asymptotically in their oracle forms under positive regression dependence on subset (PRDS) of null p-values and in their data-adaptive forms for independent p-values. The one-way data-adaptive methods are asymptotically conservative under weak dependence. Simulations demonstrate these methods’ superior power performances over some contemporary procedures and provide evidence of their non-asymptotic conservativeness under certain dependence scenarios. The proposed two-way adaptive procedure is effectively applied to a data set from microbial abundance study.

In many real-world settings, such as healthcare, machine learning models are trained and validated on one labeled domain and tested or deployed on another where feature distributions differ, i.e., there is covariate shift. When annotations are costly or prohibitive, an unsupervised domain adaptation (UDA) regime can be leveraged requiring only unlabeled samples in the target domain. Existing UDA methods are unable to factor in a model's predictive loss based on predictions in the target domain and therefore suboptimally leverage density ratios of only the input covariates in each domain. In this work we propose a model selection method for leveraging model predictions on a target domain without labels by exploiting the domain invariance of causal structure. We assume or learn a causal graph from the source domain, and select models that produce predicted distributions in the target domain that have the highest likelihood of fitting our causal graph. We thoroughly analyze our method under oracle knowledge using synthetic data. We then show on several real-world datasets, including several COVID-19 examples, that our method is able to improve on the state-of-the-art UDA algorithms for model selection.

Testing is more than simply demonstrating a program is correct. We must constantly challenge ourselves to make ourselves better. Likewise, we must also challenge our programs and our ideas. Testing is how we bring science into our craft and replace guesses and myths with real knowledge about how the world works. Building good tests can take a lot of time and effort, and Oracle databases present unique challenges for building reproducible test cases. Luckily, Oracle provides many tools to help us test, such as SQL, PL/SQL, the data dictionary, dynamic performance views, and much more.

Oracle SQL programming has an epistemic problem. Epistemology is the study of knowledge, or simply asking ourselves what do we know and how do we know it. We don’t need to take a philosophy class before we start programming, but we do need to carefully consider our sources. The Oracle programming culture places too much weight on unreliable epistemologies, such as tradition and scripture, and not enough weight on science and reason. If we listen to the wrong sources, we will learn the wrong lessons.

The most important thing about programming styles is to have one. This chapter shows how traditional SQL styles can prevent us from realizing Oracle SQL’s true potential. This subjective and opinionated chapter finishes the discussion about how to write beautiful SQL statements.

It’s time to discuss the most advanced Oracle features. These features can solve extremely challenging problems; the kind of problems that many developers think are impossible to solve in a database.

Now that we learned how to write advanced SQL statements to retrieve data, it is time to learn how to write advanced SQL statements to change data. Oracle Data Manipulation Language (DML) lets us insert, update, and delete data.

Solving Oracle SQL performance issues is the pinnacle of SQL development. Performance tuning requires a combination of all the skills previously discussed in this book. We need to understand the development process (to know why problems happened and weren’t caught sooner), advanced features (to find alternative ways to implement code), and programming styles (in order to understand the code and rewrite it into something better).

B-cells that induce antigen-specific immune responses in vivo produce large numbers of antigen-specific antibodies by recognizing subregions (epitopes) of antigenic proteins, in which they can inhibit the function of antigen protein. Epitope region prediction facilitates the design and development of vaccines that induce the production of antigen-specific antibodies. There are many diseases which are difficult to treat without vaccines. And the COVID-19 has destroyed many people’s lives. Therefore, making vaccines to COVID-19 is very important. Making vaccines needs a large number of experiments to get labeled targets. However, obtaining tremendous labeled data from experiments is a challenge for humans. Big data analysis has proposed some solutions to deal with this challenge. Big data technology has developed very fast and has been applied in many areas. In the bioinformatics area, big data analysis solves a large number of problems, particularly in the area of active learning. Active learning is a method of building more predictive models with less labeled data. Active learning establishes models with less data by asking the oracle (human) for the most valuable samples to train models. Hence, active learning’s application in making vaccines is meaningful that the scientists do not need to do tremendous experiments. This paper proposed a more robust active learning method based on uncertainty sampling and K-nearest density and applies it to the vaccine manufacture. This paper evaluates the new algorithm with accuracy and robustness. In order to evaluate the robustness of active learners, a new robustness index is designed in this paper. And this paper compares the new algorithm with a pool-based active learning algorithm, density-weighted active learning algorithm, and traditional machine learning algorithm. Finally, the new algorithm is applied to epitope prediction of B-cell data, which is significant to making vaccines.

One vision for program synthesis, and specifically for programming by example (PBE), is an interactive programmer's assistant, integrated into the development environment. To make program synthesis practical for interactive use, prior work on Small-Step Live PBE has proposed to limit the scope of synthesis to small code snippets, and enable the users to provide local specifications for those snippets. This paradigm, however, does not work well in the presence of loops. We present LooPy, a synthesizer integrated into a live programming environment, which extends Small-Step Live PBE to work inside loops and scales it up to synthesize larger code snippets, while remaining fast enough for interactive use. To allow users to effectively provide examples at various loop iterations, even when the loop body is incomplete, LooPy makes use of live execution , a technique that leverages the programmer as an oracle to step over incomplete parts of the loop. To enable synthesis of loop bodies at interactive speeds, LooPy introduces Intermediate State Graph , a new data structure, which compactly represents a large space of code snippets composed of multiple assignment statements and conditionals. We evaluate LooPy empirically using benchmarks from competitive programming and previous synthesizers, and show that it can solve a wide variety of synthesis tasks at interactive speeds. We also perform a small qualitative user study which shows that LooPy's block-level specifications are easy for programmers to provide.

Data cuaca berupa suhu udara, kelembaban udara, curah hujan, dan kecepatan angin memerlukan proses penyimpanan yang baik supaya data cuaca dapat direkapitulasi selama satu tahun. Untuk itu membutuhkan proses penyimpanan data melalui Database Management System (DBMS). DBMS dapat mengelola, menyimpan sekumpulan data secara baik. Pengguna DBMS dapat membuat, mencari, memelihara, serta memiliki akses terkontrol terhadap data. Penyimpan data pada pemantauan cuaca menggunakan hardisk eksternal dalam format Comma Separated Values (CSV), penyimpanan data seperti ini kurang efisien diakibatkan data direkapitulasi secara manual ke dalam computer. Kinerja sinkronisasi database dianalisis menggunakan metode Event-Driven dan Time-Driven pada DBMS Oracle dan MySQL untuk pemantauan data cuaca. Hasilnya, pada saat proses penyimpanan 100 data ( record ), MySQL memiliki kemampuan yang lebih cepat yaitu 166 detik dibandingkan dengan Oracle dalam waktu eksekusi 188 detik. Pada saat proses penyimpanan mulai 200 sampai 1000 data ( record ) membutuhkan waktu eksekusi rata-rata pada Oracle yaitu 203,56 detik sedangkan pada MySQL waktu eksekusi rata-rata 1163,89 detik. Jadi, kinerja DBMS Oracle lebih baik dibandingkan dengan DBMS MySQL dalam proses penyimpanan data cuaca, namun DBMS Oracle belum bisa menyimpan data secara langsung dari mikrokontroler karena belum adanya program yang mendukung ke dalam DBMS Oracle . Abstract. Weather data in the form of air temperature, humidity, rainfall, and wind speed require a good storage process so that weather data can be recapitulated for one year. For this reason, it requires the process of storing data through a Database Management System (DBMS). DBMS can manage, store a collection of data well. DBMS users can create, search, maintain and have controlled access to data. Data storage in weather monitoring uses an external hard drive in Comma Separated Values (CSV) format, this kind of data storage is less efficient due to the data being recapitulated manually into a computer. Therefore, the author wants to analyze the performance of database synchronization on Oracle and MySQL DBMS by using Event-Driven and Time-Driven methods for monitoring weather data. As a result, when storing 100 data (records), MySQL has a faster capability of 166 seconds compared to Oracle in 188 seconds of execution time. When the storage process starts from 200 to 1000 data (records) the average execution time in Oracle is 203.56 seconds, while in MySQL the average execution time is 1163.89 seconds. So, the performance of the Oracle DBMS is better than the MySQL DBMS in the process of storing weather data, but the Oracle DBMS has not been able to store data directly from the microcontroller because no program supports it into the Oracle DBMS.

Automatic test oracle generation is a bottleneck in realizing full automation of the entire software testing process. This study proposes a new method for automatically generating a test oracle for a new test input on the basis of several historical test cases by using a backpropagation neural network (BPNN) model. The new method is different from existing test oracle techniques. Specifically, our method has two steps. First, the values of variables are collected as training data when several historical test inputs are used to execute the program at different breakpoints. The test oracles (pass or fail) of these test cases are utilized to classify and label the training data. Second, a new test input is used to execute the program at different breakpoints, where the trained BPNN prediction model automatically generates its test oracle on the basis of the collected values of the variables involved. We conduct an experiment to validate our method. In the experiment, 113 faulty versions of seven types of programs are used as experimental objects. Results show that the average prediction accuracy rate of 74,651 test oracles is 95.8%. Although the failed test cases in the training data account for less than 5%, the overall average recall rate (prediction accuracy of test case execution failure) of all programs is 78.9%. Furthermore, the trained BPNN can reveal not only the impact of the values of variables but also the impact of the logical correspondence between variables in test oracle generation.

Due to the impressive performance of deep neural networks (DNNs) for visual perception, there is an increased demand for their use in automated systems. However, to use deep neural networks in practice, novel approaches are needed, e.g., for testing. In this work, we focus on the question of how to test deep learning-based visual perception functions for automated driving. Classical approaches for testing are not sufficient: A purely statistical approach based on a dataset split is not enough, as testing needs to address various purposes and not only average case performance. Additionally, a complete specification is elusive due to the complexity of the perception task in the open context of automated driving. In this article, we review and discuss existing work on testing DNNs for visual perception with a special focus on automated driving for test input and test oracle generation as well as test adequacy. We conclude that testing of DNNs in this domain requires several diverse test sets. We show how such tests sets can be constructed based on the presented approaches addressing different purposes based on the presented methods and identify open research questions.

While logic locking is a promising defense to protect hardware designs, many attacks have been shown to undermine its security by retrieving the secret key. All the powerful attacks rely on a working chip, i.e., an oracle, and in particular, heavily use the test access. The proposed technique DisORC turns the oracle into a dishonest one whenever a potential attack is detected. DisORC works on the premise that structural testing of chips need not be performed with the correct functionality. We implement this capability by adding circuitry around a logic-locked design that reconfigures its functionality upon detecting access to scan chains. Any attempt to access scan chains disconnects the secret key from the circuit, and clears all of its traces, isolating and securing it. We also pair this defense with a truly random logic locking (TRLL) scheme that makes random decisions in inserting key gates and retaining signal polarities without relying on any logic synthesis technique to perform bubble pushing. Any netlist analysis-based attack, known or anticipated, will then learn nothing useful to infer the key values. The combined defense DisORC + TRLL thwarts oracle-based and netlist analysis-based attacks while delivering sufficient corruption levels at the outputs. We also show that the proposed defense is cost effective and can be integrated into the design flow easily. The proposed logic locking defense provides protection against untrusted foundry, testing facility, end users, and any combination of them colluding together.

In the present era, there is a large amount of new data available readily from different sources to collect and store. One of the main problems is to label these new data for various machine learning applications correctly. The active learning approach presents a unique case of machine learning which is widely used to solve the above problem by significantly minimizing the need for labeled data. It aims to select the most appropriate samples from the unlabeled data to be correctly labeled by the oracle and is passed to train the active learner incrementally. There are several different query sampling strategies that exist using which the appropriate samples are selected. One of the main problems with the active learning approach is that it is very time-consuming. So in this research work, a new multi-core-based algorithm is proposed to speed up the active learning approach, which can utilize the complete computational resources present in the system. The experiments have been performed for the problem of named entity recognition which deals with labeling the sequences of words in an unstructured text by classifying them into pre-existing categories. The proposed algorithm is evaluated in terms of both: the performance and execution time over three named entity recognition corpus of distinct biomedical domains. The evaluation results shows considerable improvement in terms of execution time for the proposed active learning algorithm than the existing active learning approach.

We propose a human-in-the-loop approach for oracle improvement and analyse whether the proposed oracle improvement process is helping developers to create better oracles. For this, we conducted two human studies with 68 participants overall: an oracle assessment study and an oracle improvement study. Our results show that developers exhibit poor performance (29% accuracy) when manually assessing whether an assertion oracle contains a false positive, a false negative or none of the two. This shows that automated detection of these oracle deficiencies is beneficial for the users. Our tool OASIs (Oracle ASsessment and Improvement) helps developers produce assertions with higher quality. Participants who used OASIs in the improvement study were able to achieve 33% of full and 67% of partial correctness as opposed to participants without the tool who achieved only 21% of full and 43% of partial correctness.&nbsp

This work is aimed at developing an intersubjective paradigm and an active role of the subject in reduction conceptualization. For this purpose, the general oracular scheme of conceptualization is concretized by the complementarity of composition and decomposition as explications of synthesis and analysis of entities. Pragmatically conditioned enrichment of this complementarity is carried out with the involvement of compositional programming and nominal models of data, functions, and compositions. The oracular scheme of reduction is considered, the meaning of which is that it, based on existing compositions, naturally implements the paradigm of “divide and conquer” in understanding the active role of the subject in conceptualization, supporting the real complementarity of decomposition and compositional methods of conceptualization. Representative examples of reduction conceptualization are shown, which substantiate the technology of solving programming problems.

Active learning (AL) on attributed graphs has received increasing attention with the prevalence of graph-structured data. Although AL has been widely studied for alleviating label sparsity issues with the conventional nonrelational data, how to make it effective over attributed graphs remains an open research question. Existing AL algorithms on node classification attempt to reuse the classic AL query strategies designed for nonrelational data. However, they suffer from two major limitations. First, different AL query strategies calculated in distinct scoring spaces are often naively combined to determine which nodes to be labeled. Second, the AL query engine and the learning of the classifier are treated as two separating processes, resulting in unsatisfactory performance. In this article, we propose a SEmisupervised Adversarial active Learning (SEAL) framework on attributed graphs, which fully leverages the representation power of deep neural networks and devises a novel AL query strategy for node classification in an adversarial way. Our framework learns two adversarial components; a graph embedding network that encodes both the unlabeled and labeled nodes into a common latent space, expecting to trick the discriminator to regard all nodes as already labeled, and a semisupervised discriminator network that distinguishes the unlabeled from the existing labeled nodes. The divergence score, generated by the discriminator in a unified latent space, serves as the informativeness measure to actively select the most informative node to be labeled by an oracle. The two adversarial components form a closed loop to mutually and simultaneously reinforce each other toward enhancing the AL performance. Extensive experiments on real-world networks validate the effectiveness of the SEAL framework with superior performance improvements to state-of-the-art baselines on node classification tasks.

This chapter provides an overview of the Oracle DBMS. Actually, you have been exposed to Oracle throughout Division C of the text, since the implementation of SQL that we have studied is Oracle-based. The chapter proceeds under the following subheadings.

The main SQL definition statements in an Oracle 10G environment are shown in figure 11-1. These statements relate to the six basic types of database objects in Oracle — tables, indexes, views, constraints, synonyms, and sequences. Other more advanced types of database objects include databases, table-spaces, data-files, users, user profiles, functions, and procedures.

In Press / En Prensa The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complexrelationship between computing and radio dynamics make vRAN resource control particularly daunting. We present vrAIn, a resourceorchestrator for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data(traffic and channel quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithmbased on an actor-critic neural network structure and a classifier to map contexts into resource control decisions.We have evaluated vrAIn experimentally, using an open-source LTE stack over different platforms, and via simulations over aproduction RAN. Our results show that: (i) vrAIn provides savings in computing capacity of up to 30% over CPU-agnostic methods;(ii) it improves the probability of meeting QoS targets by 25% over static policies; (iii) upon computing capacity under-provisioning,vrAIn improves throughput by 25% over state-of-the-art schemes; and (iv) it performs close to an optimal offline oracle. To ourknowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-freesolution that does not need to assume any particular platform or context. This work was partially supported by the European Commission through Grant No. 856709 (5Growth) and Grant No. 856950 (5G-TOURS); by Science Foundation Ireland (SFI) through Grant No. 17/CDA/4760; and AEI/FEDER through project AIM under Grant No. TEC2016-76465-C2-1-R. Furthermore, the work is closely related to the EU project DAEMON (Grant No. 101017109).

Classifying test executions automatically as pass or fail remains a key challenge in software testing and is referred to as the test oracle problem. It is being attempted to solve this problem with supervised learning over test execution traces. A programme is instrumented to gather execution traces as sequences of method invocations. A small fraction of the programme's execution traces is labelled with pass or fail verdicts. Execution traces are then embedded as fixed length vectors and a neural network (NN) component that uses the line-by-line information to classify traces as pass or fail is designed. The classification accuracy of this approach is evaluated using subject programs from different application domains—1. Module from Ethereum Blockchain, 2. Module from PyTorch deep learning framework, 3. Microsoft SEAL encryption library components, 4. Sed stream editor, 5. Nine network protocols from Linux packet identifier, L7-Filter and 6. Utilities library, commons-lang for Java. For all subject programs, it was found that test execution classification had high precision, recall and specificity, averaging to 93%, 94% and 96%, respectively, while only training with an average 14% of the total traces. Experiments show that the proposed NN-based approach is promising in classifying test executions from different application domains.

Stream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier’s performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label’s utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label’s arrival date a priori and the classifier’s training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency.

SUMMARY This article summarizes “Auditing the Blockchain Oracle Problem” (Sheldon 2021), which introduces auditors to the risks of having an irreversible business agreement codified on a blockchain using a short software program called a smart contract that relies on an oracle to provide information from outside the blockchain in order to execute correctly. The article begins with an explanation of the role that oracles play in the blockchain ecosystem and proposes a working definition of oracles. Next, the article highlights how the auditing standards from both the AICPA and PCAOB can be interpreted to classify an oracle as part of smart contract users' information systems, and that the oracle provides a robust set of services that qualify it as a service organization. Finally, the article describes the process used by an oracle to collect, store, transform, and transmit data, and highlights relevant risks and illustrative control objectives related to this process.

An increasing amount of data is stored and managed in today's information systems. For this purpose, relational databases are used very often. Fast access to data is becoming increasingly important and great emphasis is placed on its improvement. The data access time can be significantly improved by creating partitions and index structures, or their combinations. From the point of view of efficiency, it is not appropriate or necessary to access all data. They can be divided into smaller parts, which will facilitate the execution of certain operations and bring efficiency, whether in terms of time or performance. As far as indexing is concerned, there is also an opportunity to access only the data sought, thus avoiding the Table Access Full method. This paper deals with the effect of partitioning and indexing on data access time, which is compared in seven different scenarios of different combinations of partitions created over tables and indexes.

Modern-day demands for services often require an availability on a 24/7 basis as well as online accessibility around the globe. For this sake, personalized virtual assistants, called chatbots, are implemented. Such systems offer services, goods or information in natural language. These natural language processing (NLP) programs respond to the user in real time and offer an intuitive and simple interface to interact with. Advantages like these make them increasingly popular. Therefore, ensuring correct functionality of chatbots is of increasing importance. However, since different implementations and user behaviour result in unpredictable results, the chatbot’s input and output data are difficult to predict and classify as well. Under such circumstances, test cases can be inferred from the domain of possible inputs of a system under test (SUT). Ontologies are concepts used in AI to provide formal representations of knowledge for a specific domain. Such ontological models contain structured information that is used for test generation. On the other hand, testing of chatbots represents a challenge because of the absence of a test oracle. In this paper, both challenges are addressed by conceptualizing ontologies for input generation and output processing in form of a metamorphic testing approach. In this scenario, both concepts are applied for automated testing of chatbots. The approach is demonstrated on a real system from the tourism domain, thereby discussing the obtained results.

This chapter focuses on inserting data into the DataSet and the database. Insert data into the DataSet or exactly insert data into the data tables in the DataSet is totally different with inserting data into the database or inserting data into the data tables in the database. The chapter discusses inserting data into the database using the Visual Studio.NET design tools and wizards. It also discusses inserting data into the Faculty table in the Oracle database. The chapter describes how to insert data into the database using the stored procedures with run‐time object method. It provides a very detailed introduction about the Oracle packages and the Oracle stored procedures and illustrated how to use these methods to perform the data query for the Student Form and STUDENT table in sample database. Language‐Integrated Query can be considered as a pattern or model that is supported by a collection of so‐called Standard Query Operator methods.

This chapter provides a detailed description and discussion in how to use Visual Studio.NET 2019 tools and wizards to build simple but efficient database applications without touching complicated coding. It presents a deeper digging in how to develop advanced database applications using runtime objects and focuses only on the data query applications. A data query will be executed via a dynamic Oracle statement that is built during the configuration of each TableAdapter in the Data Source wizard. When building a Windows Forms App (.NET Framework) application that needs to interface to database, a powerful and simple way is to use the design tools and wizards provided by Visual Studio.NET. The chapter illustrates how to utilize design tools and wizards to build a data‐driven application using our project SelectWizard Project. The Language Integrated Query LINQ To DataSet method is an updated method provided by .NET Framework.

Compression technique is basically used to compress the size of table or reduce the storage area. Oracle already gives this feature for the table compression as well as for the index compression. when index is created on particular column of a table then it contain some space, which require some storage or disk space by this technique we can save our disk space because in industry the company have to purchase the disk space according to the size of the their data and pay according to their disk space. To utilize this disk space for useful records data rather than wasting it. In this paper used the data pump utility for the compression of Bitmap index and table. Data pump utility performed for the logical backups in database.in this paper implemented data pump for compression, to release the space and change the index pointing location. It will not release the space even after deletion of records. This is of special interest for the case to compress the bitmap index and table space along with the’S (Data Manipulation Language).

The notion of Local Differential Privacy (LDP) enables users to answer sensitive questions while preserving their privacy. The basic LDP frequency oracle protocol enables the aggregator to estimate the frequency of any value. But when the domain of input values is large, finding the most frequent values, also known as the heavy hitters, by estimating the frequencies of all possible values, is computationally infeasible. In this paper, we propose an LDP protocol for identifying heavy hitters. In our proposed protocol, which we call Prefix Extending Method (PEM), users are divided into groups, with each group reporting a prefix of her value. We analyze how to choose optimal parameters for the protocol and identify two design principles for designing LDP protocols with high utility. Experiments show that under the same privacy guarantee and computational cost, PEM has better utility on both synthetic and real-world datasets than existing solutions.

This article describes a novel data retirement tool (DRT) that removes data without taking the system offline and suffering increased load. Compared to Structured Query Language Delete, the standard data deletion method, DRT is superior in overall deletion time and reduced system load in Oracle.

The validation of a cloud system can be complicated by the size of the system, the number of users that can concurrently request services, and the virtualization used to give the illusion of using dedicated machines. Unfortunately, it is not feasible to use conventional testing methods with cloud systems. This article proposes a framework, called TEA- Cloud , that integrates simulation with testing methods for validating cloud system designs. Testing is applied on both functional and nonfunctional aspects of the cloud, like performance and cost. The aim of the framework is to provide a complete methodology to help users to model both software and hardware parts of cloud systems and automatically test the validity of these clouds using a cost-effective approach. Metamorphic testing is used to overcome the lack of an oracle that checks whether the behavior observed in testing is allowed. Metamorphic testing is based on metamorphic relations (MRs). We define three families of MRs, which target issues such as performance, resource provisioning, and cost. TEA- Cloud was evaluated through an empirical study that used fault seeding (mutation) and ten MRs for testing different cloud configurations. The results were promising, with TEA- Cloud finding all seeded faults.

In this article, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of agents—synthetic and humanlike—and two distinct approaches to create them. Our agents are derived from Sarsa and Monte Carlo tree search (MCTS) but focus on finding defects, while traditional game-playing agents focus on maximizing game scores. The synthetic agent uses test goals generated from game scenarios, and these goals are further modified to examine the effects of unintended game transitions. The humanlike agent uses test goals extracted by our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL) algorithm from tester trajectories. MGP-IRL captures multiple policies executed by human testers. We use our agents to produce test sequences, and run the game with these sequences. At each run, we use an automated test oracle to check for bugs. We analyze the proposed method in two parts—we compare the success of humanlike and synthetic agents in bug finding, and we evaluate the similarity between humanlike agents and human testers. We collected 427 trajectories from human testers using the General Video Game Artificial Intelligence (GVG-AI) framework and created three games with 12 levels that contain 45 bugs. Our experiments reveal that humanlike and synthetic agents compete with human testers’ bug finding performances. Moreover, we show that MGP-IRL increases the humanlikeness of agents while improving the bug finding performance.

Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building low-resource syntactic analyzers such as part-of-speech (POS) taggers. Existing AL heuristics are generally designed on the principle of selecting uncertain yet representative training instances, where annotating these instances may reduce a large number of errors. However, in an empirical study across six typologically diverse languages (German, Swedish, Galician, North Sami, Persian, and Ukrainian), we found the surprising result that even in an oracle scenario where we know the true uncertainty of predictions, these current heuristics are far from optimal. Based on this analysis, we pose the problem of AL as selecting instances which maximally reduce the confusion between particular pairs of output tags. Extensive experimentation on the aforementioned languages shows that our proposed AL strategy outperforms other AL strategies by a significant margin. We also present auxiliary results demonstrating the importance of proper calibration of models, which we ensure through cross-view training, and analysis demonstrating how our proposed strategy selects examples that more closely follow the oracle data distribution., To appear in TACL 2020. This is a pre-MIT Press publication version

We revisit the problem of \textit{online linear optimization} in case the set of feasible actions is accessible through an approximated linear optimization oracle with a factor $\alpha$ multiplicative approximation guarantee. This setting is in particular interesting since it captures natural online extensions of well-studied \textit{offline} linear optimization problems which are NP-hard, yet admit efficient approximation algorithms. The goal here is to minimize the $\alpha$\textit{-regret} which is the natural extension of the standard \textit{regret} in \textit{online learning} to this setting. We present new algorithms with significantly improved oracle complexity for both the full information and bandit variants of the problem. Mainly, for both variants, we present $\alpha$-regret bounds of $O(T^{-1/3})$, were $T$ is the number of prediction rounds, using only $O(\log{T})$ calls to the approximation oracle per iteration, on average. These are the first results to obtain both average oracle complexity of $O(\log{T})$ (or even poly-logarithmic in $T$) and $\alpha$-regret bound $O(T^{-c})$ for a constant $c>0$, for both variants., Comment: Accepted to Conference on Neural Information Processing System (NIPS) 2017

Vehicular crowdsensing is attracting more and more attention because of its wide sensing coverage and diverse usage in smart cities. However, privacy issues that stem from traditional vehicular crowdsensing scenarios, violate the participant's privacy. Although some privacy-preserving schemes have been designed that aim to protect the sensitive information of sensed data, the reliability cannot be guaranteed because of the system's centralized structure. The introduction of blockchain in crowdsensing applications provides reliable data storage, however, the reliability of data sources remains an open challenge. Under these circumstances, the crowdsourcing service requester may not be able to obtain quality data. To solve these problems, we propose a novel P rivacy-preserving and R eliable V ehicular crowdsensing via B lockchain oracle, called PRVB. More specifically, a privacy-preserving vehicular data aggregation scheme is presented to protect the data privacy and unlinkability between participant vehicles and sensed data. Besides, two protocols are designed to protect data privacy and to achieve fair rewards for data providers. Thorough theoretical analysis and experimental evaluations have proved that the proposed PRVB achieves privacy protection, reliability, and fairness with significant computation & communication efficiency.

Transport information systems store and manage an increasing amount of data. For this purpose, database systems are used, which can significantly increase the execution time of data selection queries by creating index structures. However, in cases of frequent additions, changes, or deletions of data, the performance slows down due to the editing of the created corresponding indexes. This paper deals with the effect of the number of indexes on the performance of insert, update, and delete operations that have been observed in our practical experiments in Oracle.

In recent years, Artificial Intelligence based disease diagnosis has drawn considerable attention both in academia and industry. In medical scenarios, a well-trained classifier can effectively detect a disease with sufficient features associating with medical tests. However, such features are not always readily available due to the high cost of time and money associating with medical tests. To address this, this study identifies the diagnostic strategy learning problem and proposes a novel framework consisting of three components to learn a diagnostic strategy with limited features. First, as we often encounter incomplete medical records of the patients, a sequence encoder is designed to encode any set of information in various sizes into fixed-length vectors. Second, taking the output of the encoder as the input, a feature selector based on reinforcement learning techniques is proposed to learn the best feature sequence for diagnosis. Finally, with the best feature sequence, an oracle classifier is used to give the final diagnosis. To evaluate the performance of the proposed method, experiments are conducted on nine real medical datasets. The results suggest that the proposed method is effective for providing personalized diagnostic strategies and makes better diagnoses with fewer features compared with existing methods.