Black-box Implementation Research Articles

Implementasi Augmented Reality sebagai Media Informasi Jadwal Penggunaan Ruang Laboratorium di Universitas Teknologi Yogyakarta

Universitas Teknologi Yogyakarta (UTY) operates several computer laboratory rooms used daily by students according to their scheduled times. Sometimes, these rooms experience sudden schedule changes, causing conflicts with other room bookings. This research aims to employ an Augmented Reality (AR)-based application for monitoring the dynamic schedule of lab room usage. The method utilizes a marker-based AR application, tested through black box implementation. The application successfully reads markers at distances of 10–90 cm, tilt angles of 10–80 degrees, and light intensities ranging from 40 to 250 lux. Under these conditions, the markers are readable, and the application responds by displaying virtual objects. Users of the lab rooms can utilize this AR application to identify changes in the scheduled usage plans at UTY as they occur.

Toward Accurate Quantum Chemical Methods for Molecules of Increasing Dimension: The New Family of Pisa Composite Schemes.

The new versions of the Pisa composite scheme introduced in the present paper are based on the careful selection of different quantum chemical models for energies, geometries, and vibrational frequencies, with the aim of maximizing the accuracy of the overall description while retaining a reasonable cost for all the steps. In particular, the computation of accurate electronic energies has been further improved introducing more reliable complete basis set extrapolations and estimation of core-valence correlation, together with improved basis sets for third-row atoms. Furthermore, the reduced-cost frozen natural orbital (FNO) model has been introduced and validated for large molecules. Accurate molecular structures can be obtained avoiding complete basis set extrapolation and evaluating core-valence correlation at the MP2 level. Unfortunately, analytical gradients are not available for the FNO version of the model. Therefore, for large molecules, an accurate reduced-cost alternative is offered by evaluation of valence contributions with a double-hybrid functional in conjunction with the same MP2 contribution for core-valence correlation or by means of a one-parameter approximation. The same double-hybrid functional and basis set are employed to evaluate zero-point energies and partition functions. After the validation of the new models for small systems, a panel of molecular bricks of life has been used to analyze their performances for problems of current fundamental or technological interest. The fully black-box implementation of the computational workflow paves the way toward the accurate yet not prohibitively expensive study of medium- to large-sized molecules also by experimentally oriented researchers.

Multi-Client Boolean File Retrieval With Adaptable Authorization Switching for Secure Cloud Search Services

Secure cloud search services provide a cost-effective way for resource-constrained clients to search encrypted files in the cloud, where data owners can customize search authorization. Despite providing fine-grained authorization, traditional attribute-based keyword search (ABKS) solutions generally support single keyword search. Towards expressive queries over encrypted data, multi-client searchable symmetric encryption (MC-SSE) was introduced. However, current search authorizations of existing MC-SSEs:(i) cannot support dynamic updating; (ii) are (semi-)black-box implementations of attribute-based encryption; (iii) incur significant cost during system initialization and file encryption. To address these limitations, we present AasBirch, an MC-SSE system with fast fine-grained authorization that supports adaptable authorization switching from one policy to any other one. AasBirch achieves constant-size storage and lightweight time cost for system initialization, file encryption and file searching. We conduct extensive experiments based on Enron dataset in real cloud environment. Compared to state-of-the-art MC-SSE with fine-grained authorization, AasBirch achieves 30∼200× smaller public parameter and secret key size, with the assumed least frequent keyword in a query ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> -term) as 21. Moreover, it runs 10∼20× faster for file encryption and >20× faster for file searching. In addition, AasBirch outperforms 80,000× (resp. 7,850×) faster with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> -term = 1 (resp. = 21), as compared to classic dynamic ABKS system.

Main current data science and artificial intelligence advancements and challenges

Data Science and Artificial Intelligence play a major role in the nowadays fourth industrial revolution of Robotics, Artificial Intelligence and big Data. The purpose of this editorial is to identify the progress, the trends, the open problems and challenges of these research fields in a comprehensive way, so that an overview is provided that would help researchers and practitioners in their research and development orientations. A lot of research and development efforts since many years have brought Data Science and Artificial Intelligence in a good shape and level of maturity. These technologies have proved to be realizable in a wide range of real world applications with considerable success. Although the analytical theories they are based on are not all in mature level, however, their black box implementations have been very promising so far with real improvements in real world systems performance and usability. At this stage of development such an emergent revolution in all aspects of life demands a careful thinking to discriminate hype from real achievements. This is the major goal of this editorial, to start such a discussion since hype has been a serious obstacle, many times at the past, for real technological advancements. Filtering hype and revealing reality in these fields requires much effort and extensive studies in all application fields and it is not possible within this editorial. We plan more publications in depth regarding this issue. This editorial should be considered only as a first small step towards this goal. Starting from current major trends of Data Science and aspects of Artificial Intelligence

QForte: An Efficient State-Vector Emulator and Quantum Algorithms Library for Molecular Electronic Structure.

We introduce a novel open-source software package QForte, a comprehensive development tool for new quantum simulation algorithms. QForte incorporates functionality for handling molecular Hamiltonians, Fermionic encoding, ansatz construction, time evolution, and state-vector emulation, requiring only a classical electronic structure package as a dependency. QForte also contains black-box implementations of a wide variety of quantum algorithms, including variational and projective quantum eigensolvers, adaptive eigensolvers, quantum imaginary time evolution, and quantum Krylov methods. We highlight two features of QForte: (i) how the Python class structure of QForte enables the facile implementation of new algorithms, and (ii) how existing algorithms can be executed in just a few lines of code.

XAI & I: Self-explanatory AI facilitating mutual understanding between AI and human experts

Traditionally, explainable artificial intelligence seeks to provide explanation and interpretability of high-performing black-box models such as deep neural networks. Interpretation of such models remains difficult, because of their high complexity. An alternative method is to instead force a deep-neural network to use human-intelligible features as the basis for its decisions. We tested this approach using the natural category domain of rock types. We compared the performance of a black-box implementation of transfer-learning using Resnet50 to that of a network first trained to predict expert-identified features and then forced to use these features to categorise rock images. The performance of this feature-constrained network was virtually identical to that of the unconstrained network. Further, a partially constrained network forced to condense down to a small number of features that was not trained with expert features did not result in these abstracted features being intelligible; nevertheless, an affine transformation of these features could be found that aligned well with expert-intelligible features. These findings show that making an AI intrinsically intelligible need not be at the cost of performance.

Improving reproducibility in synchrotron tomography using implementation-adapted filters.

For reconstructing large tomographic datasets fast, filtered backprojection-type or Fourier-based algorithms are still the method of choice, as they have been for decades. These robust and computationally efficient algorithms have been integrated in a broad range of software packages. The continuous mathematical formulas used for image reconstruction in such algorithms are unambiguous. However, variations in discretization and interpolation result in quantitative differences between reconstructed images, and corresponding segmentations, obtained from different software. This hinders reproducibility of experimental results, making it difficult to ensure that results and conclusions from experiments can be reproduced at different facilities or using different software. In this paper, a way to reduce such differences by optimizing the filter used in analytical algorithms is proposed. These filters can be computed using a wrapper routine around a black-box implementation of a reconstruction algorithm, and lead to quantitatively similar reconstructions. Use cases for this approach are demonstrated by computing implementation-adapted filters for several open-source implementations and applying them to simulated phantoms and real-world data acquired at the synchrotron. Our contribution to a reproducible reconstruction step forms a building block towards a fully reproducible synchrotron tomography data processing pipeline.

Implementation of the Operating Room Black Box Research Program at the Ottawa Hospital Through Patient, Clinical, and Organizational Engagement: Case Study.

BackgroundA large proportion of surgical patient harm is preventable; yet, our ability to systematically learn from these incidents and improve clinical practice remains limited. The Operating Room Black Box was developed to address the need for comprehensive assessments of clinical performance in the operating room. It captures synchronized audio, video, patient, and environmental clinical data in real time, which are subsequently analyzed by a combination of expert raters and software-based algorithms. Despite its significant potential to facilitate research and practice improvement, there are many potential implementation challenges at the institutional, clinician, and patient level. This paper summarizes our approach to implementation of the Operating Room Black Box at a large academic Canadian center.ObjectiveWe aimed to contribute to the development of evidence-based best practices for implementing innovative technology in the operating room for direct observation of the clinical performance by using the case of the Operating Room Black Box. Specifically, we outline the systematic approach to the Operating Room Black Box implementation undertaken at our center.MethodsOur implementation approach included seeking support from hospital leadership; building frontline support and a team of champions among patients, nurses, anesthesiologists, and surgeons; accounting for stakeholder perceptions using theory-informed qualitative interviews; engaging patients; and documenting the implementation process, including barriers and facilitators, using the consolidated framework for implementation research.ResultsDuring the 12-month implementation period, we conducted 23 stakeholder engagement activities with over 200 participants. We recruited 10 clinician champions representing nursing, anesthesia, and surgery. We formally interviewed 15 patients and 17 perioperative clinicians and identified key themes to include in an information campaign run as part of the implementation process. Two patient partners were engaged and advised on communications as well as grant and protocol development. Many anticipated and unanticipated challenges were encountered at all levels. Implementation was ultimately successful, with the Operating Room Black Box installed in August 2018, and data collection beginning shortly thereafter.ConclusionsThis paper represents the first step toward evidence-guided implementation of technologies for direct observation of performance for research and quality improvement in surgery. With technology increasingly being used in health care settings, the health care community should aim to optimize implementation processes in the best interest of health care professionals and patients.

Exploiting Dissent: Towards Fuzzing-Based Differential Black-Box Testing of TLS Implementations

The Transport Layer Security (TLS) protocol is one of the most widely used security protocols on the internet. Yet do implementations of TLS keep on suffering from bugs and security vulnerabilities. In large part is this due to the protocol's complexity which makes implementing and testing TLS notoriously difficult. In this paper, we present our work on using differential testing as effective means to detect issues in black-box implementations of the TLS handshake protocol. We introduce a novel fuzzing algorithm for generating large and diverse corpuses of mostly-valid TLS handshake messages. Stimulating TLS servers when expecting a ClientHello message, we find messages generated with our algorithm to induce more response discrepancies and to achieve a higher code coverage than those generated with American Fuzzy Lop, TLS-Attacker, or NEZHA. In particular, we apply our approach to OpenSSL, BoringSSL, WolfSSL, mbedTLS, and MatrixSSL, and find several real implementation bugs; among them a serious vulnerability in MatrixSSL 3.8.4. Besides do our findings point to imprecision in the TLS specification. We see our approach as presented in this paper as the first step towards fully interactive differential testing of black-box TLS protocol implementations. Our software tools are publicly available as open source projects.

Measuring Performances of a White-Box Approach in the IoT Context

The internet of things (IoT) refers to all the smart objects that are connected to other objects, devices or servers and that are able to collect and share data, in order to “learn” and improve their functionalities. Smart objects suffer from lack of memory and computational power, since they are usually lightweight. Moreover, their security is weakened by the fact that smart objects can be placed in unprotected environments, where adversaries are able to play with the symmetric-key algorithm used and the device on which the cryptographic operations are executed. In this paper, we focus on a family of white-box symmetric ciphers substitution–permutation network (SPN)box, extending and improving our previous paper on the topic presented at WIDECOM2019. We highlight the importance of white-box cryptography in the IoT context, but also the need to have a fast black-box implementation (server-side) of the cipher. We show that, modifying an internal layer of SPNbox, we are able to increase the key length and to improve the performance of the implementation. We measure these improvements (a) on 32/64-bit architectures and (b) in the IoT context by encrypting/decrypting 10,000 payloads of lightweight messaging protocol Message Queuing Telemetry Transport (MQTT).

Exploring stakeholder perceptions around implementation of the Operating Room Black Box for patient safety research: a qualitative study using the theoretical domains framework

BackgroundSystematically observing clinical performance in the operating room (OR) to support patient safety initiatives faces numerous logistical and methodological challenges. These may be solved by new audio-video recording technologies like...

Locating Errors in Faulty Formulas

Given a drawing of a read-once formula (called the blueprint), and a blackbox implementation with the same topology as the blueprint that purports to compute the formula, can we tell if it does? Under a fault model, where the only faults in the implementation are gates that complement their outputs, we show that there is an efficient algorithm that makes a linear number of probes to the blackbox implementation and determines if the blueprint and implementation are identical. We also show a matching lower bound. We further ask whether we can diagnose where the faults are, using blackbox testing. We prove that if the implementation has a property called polynomial balance , then it is possible to do this efficiently. To complement this result, we show that even if the blueprint is polynomially balanced and there are only logarithmically many errors in the implementation, the implementation could be unbalanced and the diagnosis problem provably requires super-polynomially many tests. We point out that this problem is one instance of a general class of problems of learning deviations from a blueprint, which we call conformance learning . Conformance learning seems worthy of further investigation in a broader context.

Computing the Weighted Geometric Mean of Two Large-Scale Matrices and Its Inverse Times a Vector

We investigate different approaches for computing the action of the weighted geometric mean of two large-scale positive definite matrices on a vector. We derive and analyze several algorithms, based on numerical quadrature and on the Krylov subspace, and compare them in terms of convergence speed and execution time. By exploiting an algebraic relation between the weighted geometric mean and its inverse, we show how these methods can be used to efficiently solve large linear systems whose coefficient matrix is a weighted geometric mean. According to our experiments, some of the algorithms proposed in both families are suitable choices for black-box implementations.

Symbolic Model-based Test Selection

This paper addresses the problem of model-based off-line selection of test cases for testing the conformance of a black-box implementation with respect to a specification, in the context of reactive systems. Efficient solutions to this problem have been proposed for LTS finite-state models, based on the ioco conformance testing theory. In this paper, the approach is extended for infinite-state specifications, modelled as automata extended with variables. When considering the selection of test cases according to test purposes (abstract scenarii focused by test cases), the selection of test cases relies on approximate co-reachability analyses using abstract interpretation and syntactical transformations guided by this analysis, while test execution uses constraint solving.

A block inverse-free preconditioned Krylov subspace method for symmetric generalized eigenvalue problems

The inverse-free preconditioned Krylov subspace method of Golub and Ye [G.H. Golub, Q. Ye, An inverse free preconditioned Krylov subspace method for symmetric generalized eigenvalue problems, SIAM J. Sci. Comp. 24 (2002) 312–334] is an efficient algorithm for computing a few extreme eigenvalues of the symmetric generalized eigenvalue problem. In this paper, we first present an analysis of the preconditioning strategy based on incomplete factorizations. We then extend the method by developing a block generalization for computing multiple or severely clustered eigenvalues and develop a robust black-box implementation. Numerical examples are given to illustrate the analysis and the efficiency of the block algorithm.

Integrating formal verification and conformance testing for reactive systems

In this paper, we describe a methodology integrating verification and conformance testing. A specification of a system - an extended input-output automaton, which may be infinite-state - and a set of safety properties (nothing bad ever happens) and possibility properties (something good may happen) are assumed. The properties are first tentatively verified on the specification using automatic techniques based on approximated state-space exploration, which are sound, but, as a price to pay for automation, are not complete for the given class of properties. Because of this incompleteness and of state-space explosion, the verification may not succeed in proving or disproving the properties. However, even if verification did not succeed, the testing phase can proceed and provide useful information about the implementation. Test cases are automatically and symbolically generated from the specification and the properties and are executed on a black-box implementation of the system. The test execution may detect violations of conformance between implementation and specification; in addition, it may detect violation/satisfaction of the properties by the implementation and by the specification. In this sense, testing completes verification. The approach is illustrated on simple examples and on a bounded retransmission protocol.

Algorithm 845

eigifp is a MATLAB program for computing a few extreme eigenvalues and eigenvectors of the large symmetric generalized eigenvalue problem Ax = λ Bx . It is a black-box implementation of an inverse free preconditioned Krylov subspace projection method developed by Golub and Ye [2002]. It has important features that allow it to solve some difficult problems without any input from users. It is particularly suitable for problems where preconditioning by the standard shift-and-invert transformation is not feasible.

Simplified Fuzzy ARTMAP as Pattern Recognizer

Pattern recognition has turned out to be an important aspect of a dominant technology such as machine intelligence. Domain specific fuzzy-neuro models particularly for the black box implementation of PR applications have been recently investigated. In this paper, Kasuba’s simplified fuzzy ARTMAP has been discussed as a pattern recognizer/classifier for image processing problems. The model inherently recognizes only noise free patterns and in the case of patterns with noise or perturbations (rotation/scaling/translation) misclassifies the images. To tackle this problem, a conventional moment based rotation/scaling/translation invariant feature extractor has been employed. However, since the conventional feature extractor is not strictly invariant to most perturbations, certain mathematical modifications have been proposed that have resulted in an excellent performance by the pattern recognizer. The potential of the model has been demonstrated on two problems, namely, prediction of load from the yield patterns of elastoplastic analysis of clamped and simply supported plates and prediction of modes from mode shapes.

FASTBUS standard fiber optic data transfer link

The authors describe a unidirectional/bidirectional fiber-optic data transfer link being specified by the FASTBUS Standards Committee that can be incorporated into FASTBUS as well as other data acquisition systems. The FASTBUS or bidirectional mode allows relatively slow, long-distance communication of the full FASTBUS protocol. The high-speed transfer or unidirectional mode is optimized for pipelined data transfers with minimum handshaking overhead. This link is bus-independent and can be used to transfer data at high speeds between sets of devices each packaged in any bus standard or black-box implementation. Serial protocol flexibility permits the link to be used also as a central link. Simple messages are transmitted over this link to common data flow from front-end equipment to buffer memories or processor farms. The proposed FASTBUS serial line protocol and recommended optical hardware are detailed. An application example using the fiver-optic data link in a FASTBUS data acquisition system is shown.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>