This viewpoint paper addresses the ongoing challenges and opportunities within the data-for-health ecosystem, drawing insights from a multistakeholder workshop. Despite notable progress in the digitization of health c...
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This viewpoint paper addresses the ongoing challenges and opportunities within the data-for-health ecosystem, drawing insights from a multistakeholder workshop. Despite notable progress in the digitization of health care systems, data sharing and interoperability remain limited, so the full potential of health care data is not realized. There is a critical need for data ecosystems that can enable the timely, safe, efficient, and sustainable collection and sharing of health care data. However, efforts to meet this need face risks related to privacy, data protection, security, democratic governance, and exclusion. Key challenges include poor interoperability, inconsistent approaches to data governance, and concerns about the commodification of data. While emerging platforms such as social media play a growing role in gathering and sharing health information, their integration into formal data systems remains limited. A robust and secure data-for-health ecosystem requires stronger frameworks for data governance, interoperability, and citizen engagement to build public trust. This paper argues that reframing health care data as a common good, improving the transparency of data acquisition and processing, and promoting the use of application programming interfaces (APIs) for real-time data access are essential to overcoming these challenges. In addition, it highlights the need for international norms and standards guided by multisector leadership, given the multinational nature of data sharing. Ultimately, this paper emphasizes the need to balance risks and opportunities to create a socially acceptable, secure, and effective data-sharing ecosystem in health care.
Building Expert Systems is an attempt to capture the experience of people who are experts in a subject and incorporate it into computer programs. This task is based on finding out what they know and how they use their...
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ISBN:
(纸本)9783031341465;9783031341472
Building Expert Systems is an attempt to capture the experience of people who are experts in a subject and incorporate it into computer programs. This task is based on finding out what they know and how they use their knowledge to resolve problems. Law and legal reasoning is one of the new targets for Artificial Intelligence systems. This work is a continuation of previous work, where a prototype of Expert Systems called Experticia was designed and implemented by a public University of the Argentine Republic, aims to improve the resolution of judicial files, optimizing time and minimizing data loading errors. Experticia, in its first version, interacts with the Integral System of the Judicial Branch of the Province of Buenos Aires, in an asynchronous way. This article presents part of the work carried out within the framework of a research that aims to optimize the exchange of information between both systems. For this purpose, the use of application programming interfaces is proposed to synchronously access the information of the judicial files. First, the technologies used are described, then their specification and design, and finally, the implementation details and the tests performed are explained. The results indicate the feasibility of incorporating this technology in the new version of the Experticia.
Introduction: This study aimed to investigate whether the facial soft tissue changes of individuals who had undergone surgically assisted rapid maxillary expansion (SARME) would be detected by three different well-kno...
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This paper discusses the role of technology under the framework of Critical Technical Practice specifically in the form of constructing artefacts and deconstructing tools in order to produce what Philip Agre would des...
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This paper discusses the role of technology under the framework of Critical Technical Practice specifically in the form of constructing artefacts and deconstructing tools in order to produce what Philip Agre would describe as 'reflexive work of critique' (Agre, 1997:155). By presenting the activities and methods used in the teaching and shaping of undergraduate courses, this paper aims to show how technical objects, such as data, datasets, application programming interfaces and machine learning models, can be considered as discursive subjects, demonstrating pedagogical understanding across fields. The courses operate in the humanities tradition and take critical technical practice as a didactic approach, insofar as software and data are understood and manipulated on an instrumental level, while encouraging critical engagement and embodied reflection that bridge the technical and social/cultural domains. Within this pedagogical approach, critical is not only understood as a paradigm of rationality or quantitative, data-driven argumentation, but as adopting a critical position - that is, to research and reflect on the social structures and cultural phenomena entangled with digital objects, bodies, tools, methods and software production. By embracing work-in-progress and reflexive exploration, we aim to extend the notion of critical technical practice by unfolding how (de)constructing machines can be achieved beyond thinking of technology as neutral instrumentalisation. The challenge is how to find a balance, not only as researchers but as educators, unfolding aspects of both formality and functionality as well as questioning and understanding technology at a discursive and critical level. We argue that learning technical practice in an educational setting is not an end, but rather a means to question existing technological structures and create further changes in socio-technical systems.
ANARI is a new 3-D rendering API, an emerging Khronos standard that enables visualization applications to leverage the state-of-the-art rendering techniques across diverse hardware platforms and rendering engines. Vis...
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ANARI is a new 3-D rendering API, an emerging Khronos standard that enables visualization applications to leverage the state-of-the-art rendering techniques across diverse hardware platforms and rendering engines. Visualization applications have historically embedded custom-written renderers to enable them to provide the necessary combination of features, performance, and visual fidelity required by their users. As computing power, rendering algorithms, dedicated rendering hardware acceleration operations, and associated low-level APIs have advanced, the effort and costs associated with maintaining renderers within visualization applications have risen dramatically. The rising cost and complexity associated with renderer development creates an undesirable barrier for visualization applications to be able to fully benefit from the latest rendering methods and hardware. ANARI directly addresses these challenges by providing a high-level, visualization-oriented API that abstracts low-level rendering algorithms and hardware acceleration details while providing easy and efficient access to diverse ANARI implementations, thereby enabling visualization applications to support the state of the art rendering capabilities.
Microservices architecture is a promising approach for developing reusable scientific workflow capabilities for integrating diverse resources, such as experimental and observational instruments and advanced computatio...
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application programming interfaces (APIs) are playing a vital role in every online business. The objective of this study is to analyse the incoming requests to a target API and flag any malicious activity. This paper ...
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application programming interfaces (APIs) are playing a vital role in every online business. The objective of this study is to analyse the incoming requests to a target API and flag any malicious activity. This paper proposes a solution based on sequence models and transformers for the identification of whether an API request has SQL injections, code injections, XSS attacks, operating system (OS) command injections, and other types of malicious injections or not. In this paper, we observe that transformers outperform B-RNNs in detecting malicious activity which is present in API requests. We also propose a novel heuristic procedure that minimises the number of false positives. We observe that the RoBERTa transformer outperforms and gives an accuracy of 100% on our dataset. We observe that the heuristic procedure works well in reducing the number of false positives when a large number of false positives exist in the predictions of the models.
As Internet of Things (IoT) technology becomes more widespread and commonplace in homes, the efficiency of these devices using available bandwidth is becoming more of a concern, as the number of connected devices in a...
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ISBN:
(纸本)9781665406529
As Internet of Things (IoT) technology becomes more widespread and commonplace in homes, the efficiency of these devices using available bandwidth is becoming more of a concern, as the number of connected devices in a home increase drastically. If each device is controlled using a separate applicationprogramming Interface (API), the strain on a network will be much worse than it would if all these devices are controlled from a single point. This single point could handle all commands to and from the devices, thereby decreasing the network load. The framework of a testbed presented in this paper will allow developers to build an API around the devices included in the testbed. Then test their algorithms and other research methods from a remote location.
Simon Fraser University (SFU) aims to make a significant contribution to the study of electric vehicle (EV) utilization and power grid management by providing a comprehensive dataset [Simon Fraser University electric ...
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Simon Fraser University (SFU) aims to make a significant contribution to the study of electric vehicle (EV) utilization and power grid management by providing a comprehensive dataset [Simon Fraser University electric vehicle parking dataset (SFU-EVP)] of EV charging sessions since 2019. This dataset will be continually updated in the future. This extensive dataset presents valuable information on EV charging patterns, providing critical input for power grid planning, policy development, rate design, and infrastructure placement. It also offers opportunities to improve load forecasting, ensure grid stability, and improve the integration of renewable energy. Furthermore, data can facilitate research toward optimizing various vehicle-to-grid (V2G) services, including harnessing EVs as distributed energy storage systems. All data are stored in the commonly used and easily accessible comma-separated value (CSV) file format. By making this dataset publicly available, SFU has created a vital dataset that can drive further innovation and efficiency in EV technology and grid management, fostering a more sustainable and environmentally friendly future. IEEE SOCIETY/COUNCIL Power and Energy Society (PES) DATA TYPE/LOCATION Time-Series; SFU Campuses, Metro Vancouver, Canada DATA DOI/PID 10.21227/ya1w-m583
application programming interfaces (APIs) continually evolve to meet ever-changing user needs, and documentation provides an authoritative reference for their usage. However, API documentation is commonly outdated bec...
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application programming interfaces (APIs) continually evolve to meet ever-changing user needs, and documentation provides an authoritative reference for their usage. However, API documentation is commonly outdated because nearly all of the associated updates are performed manually. Such outdated documentation, especially with regard to API names, causes major software development issues. In this paper, we propose a method for automatically updating outdated API names in API documentation. Our insight is that API updates in documentation can be derived from API implementation changes between code revisions. To evaluate the proposed method, we applied it to four open source projects. Our evaluation results show that our method, FreshDoc, detects outdated API names in API documentation with 48 percent higher accuracy than the existing state-of-the-art methods do. Moreover, when we checked the updates suggested by FreshDoc against the developers' manual updates in the revised documentation, FreshDoc detected 82 percent of the outdated names. When we reported 40 outdated API names found by FreshDoc via issue tracking systems, developers accepted 75 percent of the suggestions. These evaluation results indicate that FreshDoc can be used as a practical method for the detection and updating of API names in the associated documentation.
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