In this paper, we describe brkgaAPI, an efficient and easy-to-use object-oriented application programming interface for the algorithmic framework of biased random-key genetic algorithms. Our cross-platform library aut...
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In this paper, we describe brkgaAPI, an efficient and easy-to-use object-oriented application programming interface for the algorithmic framework of biased random-key genetic algorithms. Our cross-platform library automatically handles the large portion of problem-independent modules that are part of the framework, including population management and evolutionary dynamics, leaving to the user the task of implementing a problem-dependent procedure to convert a vector of random keys into a solution to the underlying optimization problem. Our implementation is written in the C++programming language and may benefit from shared-memory parallelism when available.
In this paper, we describe brkgaAPI, an efficient and easy-to-use object-oriented application programming interface for the algorithmic framework of biased random-key genetic algorithms. Our cross-platform library aut...
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In this paper, we describe brkgaAPI, an efficient and easy-to-use object-oriented application programming interface for the algorithmic framework of biased random-key genetic algorithms. Our cross-platform library automatically handles the large portion of problem-independent modules that are part of the framework, including population management and evolutionary dynamics, leaving to the user the task of implementing a problem-dependent procedure to convert a vector of random keys into a solution to the underlying optimization problem. Our implementation is written in the C++programming language and may benefit from shared-memory parallelism when available.
Objectives This study sought to capture current digital health company experiences integrating with electronic health records (EHRs), given new federally regulated standards-based application programming interface (AP...
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Objectives This study sought to capture current digital health company experiences integrating with electronic health records (EHRs), given new federally regulated standards-based application programming interface (API) *** and methods We developed and fielded a survey among companies that develop solutions enabling human interaction with an EHR API. The survey was developed by the University of California San Francisco in collaboration with the Office of the National Coordinator for Health Information Technology, the California Health Care Foundation, and ScaleHealth. The instrument contained questions pertaining to experiences with API integrations, barriers faced during API integrations, and API-relevant policy *** About 73% of companies reported current or previous use of a standards-based EHR API in production. About 57% of respondents indicated using both standards-based and proprietary APIs to integrate with an EHR, and 24% worked about equally with both APIs. Most companies reported use of the Fast Healthcare Interoperability Resources standard. Companies reported that standards-based APIs required on average less burden than proprietary APIs to establish and maintain. However, companies face barriers to adopting standards-based APIs, including high fees, lack of realistic clinical testing data, and lack of data elements of interest or *** The industry is moving toward the use of standardized APIs to streamline data exchange, with a majority of digital health companies using standards-based APIs to integrate with EHRs. However, barriers *** A large portion of digital health companies use standards-based APIs to interoperate with EHRs. Continuing to improve the resources for digital health companies to find, test, connect, and use these APIs "without special effort" will be crucial to ensure future technology robustness and durability.
The evolution of Graphics Processing Units (GPUs) has allowed the industry to overcome long-lasting problems and challenges. Many belong to the stream processing domain, whose central aspect is continuously receiving ...
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The evolution of Graphics Processing Units (GPUs) has allowed the industry to overcome long-lasting problems and challenges. Many belong to the stream processing domain, whose central aspect is continuously receiving and processing data from streaming data producers such as cameras and sensors. Nonetheless, programming GPUs is challenging because it requires deep knowledge of many-core programming, mechanisms and optimizations for GPUs. Current GPU programming standards do not target stream processing and present programmability and code portability limitations. Among our main scientific contributions resides GSParLib, a C++ multi-level programminginterface unifying CUDA and OpenCL for GPU processing on stream and data parallelism with negligible performance losses compared to manual implementations;GSParLib is organized in two layers: one for general-purpose computing and another for high-level structured programming based on parallel patterns;a methodology to provide unified and driver agnostic interfaces minimizing performance losses;a set of parallelism strategies and optimizations for GPU processing targeting stream and data parallelism;and new experiments covering GPU performance on applications exposing stream and data parallelism.
Objective Interest in application programming interfaces (APIs) is increasing as key stakeholders look for technical solutions to interoperability challenges. We explored three thematic areas to assess the current sta...
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Objective Interest in application programming interfaces (APIs) is increasing as key stakeholders look for technical solutions to interoperability challenges. We explored three thematic areas to assess the current state of API use for data access and exchange in health care: (1) API use cases and standards;(2) challenges and facilitators for read and write capabilities;and (3) outlook for development of write capabilities. Methods We employed four methods: (1) literature review;(2) expert interviews with 13 API stakeholders;(3) review of electronic health record (EHR) app galleries;and (4) a technical expert panel. We used an eight-dimension sociotechnical model to organize our findings. Results The API ecosystem is complicated and cuts across five of the eight sociotechnical model dimensions: (1) app marketplaces support a range of use cases, the majority of which target providers' needs, with far fewer supporting patient access to data;(2) current focus on read APIs with limited use of write APIs;(3) where standards are used, they are largely Fast Healthcare Interoperability Resources (FHIR);(4) FHIR-based APIs support exchange of electronic health information within the common clinical data set;and (5) validating external data and data sources for clinical decision making creates challenges to provider workflows. Conclusion While the use of APIs in health care is increasing rapidly, it is still in the pilot stages. We identified five key issues with implications for the continued advancement of API use: (1) a robust normative FHIR standard;(2) expansion of the common clinical data set to other data elements;(3) enhanced support for write implementation;(4) data provenance rules;and (5) data governance rules. Thus, while APIs are being touted as a solution to interoperability challenges, they remain an emerging technology that is only one piece of a multipronged approach to data access and use.
The paper presents the application programming interfaces (API) for remote multiple user control of the oscilloscope and arbitrary waveform generator. The developed APIs were tested on Rigol DS1054z oscilloscope and R...
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ISBN:
(纸本)9781665444347
The paper presents the application programming interfaces (API) for remote multiple user control of the oscilloscope and arbitrary waveform generator. The developed APIs were tested on Rigol DS1054z oscilloscope and Rigol DG822 arbitrary waveform generator and can be used by students to learn the basics of measurements automation and by laboratory instructors to design online laboratory works.
Entering its 25th year, IT Professional faces various new challenges posed by the very technology upon which it reports. On one hand, generative artificial intelligence (GenAI) is poised to significantly assist author...
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Entering its 25th year, IT Professional faces various new challenges posed by the very technology upon which it reports. On one hand, generative artificial intelligence (GenAI) is poised to significantly assist authors, reviewers, and editors in easing their publishing tasks. On the other hand, existing limitations and the ubiquitous nature of GenAI threaten to weaken scientific publication by replacing originality and innovation with shrewd prompting. This may blur the line that distinguishes true professionalism from mere hucksterism. With an emphasis on the positive, this article probes this ticklish question, whose answer is only slowly evolving. To conclude, we offer a use case that involves editing a scientific magazine.
Third-party applications have become an essential part of today's online ecosystem, enhancing the functionality of popular platforms. However, the intensive data exchange underlying their proliferation has raised ...
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Third-party applications have become an essential part of today's online ecosystem, enhancing the functionality of popular platforms. However, the intensive data exchange underlying their proliferation has raised concerns about interdependent privacy (IDP). This paper investigates the IDP issues of third-party apps that were previously not studied comprehensively. Specifically, first, we analyze the permission structure of multiple app platforms, identifying permissions that have the potential to cause interdependent privacy issues by enabling a user to share someone else's personal data with an app. Second, we collect datasets and characterize the extent to which existing apps request these permissions, revealing the relationship between characteristics such as the respective app platform, the app's type, and the number of interdependent privacy-related permissions it requests. Third, we analyze why IDP is neglected by both data protection regulations and app platforms and then devise the principles that should be followed when designing a mitigation solution. Finally, based on these principles and satisfying clearly defined objectives, we propose IDPFilter, a platform-agnostic API that enables application providers to minimize collateral information collection by filtering out data collected from their users, but implicating others as data subjects. We implement a proof-of-concept prototype, IDPTextFilter, that implements the filtering logic on textual data, and provide its initial performance evaluation concerning privacy, accuracy, and efficiency.
Classification with costly features (CwCF) is a classification problem that includes the cost of features in the optimization criteria. Individually for each sample, its features are sequentially acquired to maximize ...
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Classification with costly features (CwCF) is a classification problem that includes the cost of features in the optimization criteria. Individually for each sample, its features are sequentially acquired to maximize accuracy while minimizing the acquired features' cost. However, existing approaches can only process data that can be expressed as vectors of fixed length. In real life, the data often possesses rich and complex structure, which can be more precisely described with formats such as XML or JSON. The data is hierarchical and often contains nested lists of objects. In this work, we extend an existing deep reinforcement learning-based algorithm with hierarchical deep sets and hierarchical softmax, so that it can directly process this data. The extended method has greater control over which features it can acquire and, in experiments with seven datasets, we show that this leads to superior performance. To showcase the real usage of the new method, we apply it to a real-life problem of classifying malicious web domains, using an online service.
Some maintain the metaverse is everywhere, and we are already deeply immersed, as it continues to excite the senses via the growing integration of avatars, holograms, shared virtual experience, and extended reality. A...
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Some maintain the metaverse is everywhere, and we are already deeply immersed, as it continues to excite the senses via the growing integration of avatars, holograms, shared virtual experience, and extended reality. At the same time, the field of low code/no code is blossoming. This article examines how these two fields, both fueled by generative artificial intelligence, are coevolving.
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