A new object-oriented interface to MDSplus

Recent industrial and academic research has focused on data-driven analytics with smartphones by collecting user interaction, context, and device systems data through Application Programming Interfaces (APIs) and sensors. The Android operating system provides various APIs to collect such mobile usage and sensor data for third-party developers. Usage Statistics API (US API) and Accessibility Service API (AS API) are representative Android APIs for collecting app usage data and are used for various research purposes, as they can collect fine-grained interaction data (e.g., app usage history and user interaction type). Furthermore, other sensor APIs help to collect a user’s context and device state data, along with AS/US APIs. This review investigates mobile usage and sensor data-driven research using AS/US APIs by categorizing the research purposes and the data types. In this article, the surveyed studies are classified as follows: five themes and 21 subthemes and a four-layer hierarchical data classification structure. This allows us to identify a data usage trend and derive insight into data collection according to research purposes. Several limitations and future research directions of mobile usage and sensor data-driven analytics research are discussed, including the impact of changes in the Android API versions on research, the privacy and data quality issues, and the mitigation of reproducibility risks with standardized data typology.

Impact of Developer Choices on Energy Consumption of Software on Servers

is a Python® library for mice behavioural analysis. The library can be used for loading and analysing of obtained from IntelliCage™ system in an intuitive way in Python programming language. The library provides user with an object oriented application programming interface (API) and a abstraction layer. It also comes with auxiliary tools supporting development of analysis workflows, like validators and a tool for workflow configuration. We ask that that reference to our paper as well as to the library itself is provided in any published research making use of PyMICE. The recommended in-text citation format is: PyMICE (Dzik, Puścian, et al. 2017) v. 1.2.1 (Dzik, Łeski, & Puścian 2017) and the recommended bibliography entry format: Dzik J. M., Łeski S., Puścian A. (September 5, 2017) PyMICE computer software (v. 1.2.1; RRID:nlx_158570) doi: 10.5281/zenodo.884419 Dzik J. M., Puścian A., Mijakowska Z., Radwanska K., Łeski S. (June 22, 2017) A Python library for analysis of IntelliCage data Behavior Research Methods doi: 10.3758/s13428-017-0907-5 If the journal does not allow for inclusion of the resource identifier ( RRID:nlx_158570 ) in the bibliography, we ask to provide it in-text: PyMICE (RRID:nlx_158570) [1] v. 1.2.1 [2] Dzik JM, Puścian A, Mijakowska Z, Radwanska K, Łeski S. PyMICE: A Python library for analysis of IntelliCage data. Behav Res Methods. 2017. DOI: 10.3758/s13428-017-0907-5 Dzik JM, Łeski S, Puścian A. [computer software]. Version 1.2.0. Warsaw: Nencki Institute - PAS; 2017. DOI: 10.5281/zenodo.884419 We have provided a solution to facilitate referencing to the library. Please run: >>> help(pm.Citation) for more information (given that the library is imported as pm ). For more details please see the project website: https://neuroinflab.wordpress.com/research/pymice/ . Auhors of the library Jakub Dzik Szymon Łeski Author of the tutorial Alicja Puścian Changes Fixed a bug breaking installation of the library from sources available in the PyPI. Fixed a bug breaking reading from an uncompressed directory in Python 3. Simplified the welcome message on import of the library.

The Feature Characterization project (FCDMF) has the goal of building tools that can extract and analyze coherent features in a terabyte dataset. We desire to extend our feature characterization library (FClib) to support a wider variety of complex ASCI data, and to support parallel algorithms. An attractive alternative to extending the library's internal data structures is to replace them with an externally provided data object. This report is the summary of a quick exploration of two candidate data objects in use at Sandia National Laboratories: the Data Object Library (DOL) and the Parallel Mesh Object (PMO). It is our hope that this report will provide information for potential users of the data objects, as well as feedback for the objects' developers. The data objects were evaluated as to whether they (1) supported the same capabilities as the current version of FClib, (2) provided additional required capabilities, and (3) were relatively easy to use. Both data objects met the requirements of having the same capabilities as FClib and support for parallel algorithms. However, the DOL has a richer set of data structures that more closely align with the current data structures of FClib and our planned extensions. Specifically, the DOL can support time changing geometry, which is needed to represent features as datasets. Unfortunately, the DOL did not meet our ease of use requirement. The PMO was easier to learn and use, but did not support time-changing geometry. Given the above results, we will extend the FClib API (Application Programming Interface) to handle time-changing geometry. Then we will replace the internal data structures with the DOL, but we will provide the FClib API in addition to the DOL API to support simplified usage.

Towards FAIR Data Access

Web services have become widely used in bioinformatics analysis, but there exist incompatibilities in interfaces and data types, which prevent users from making full use of a combination of these services. Therefore, we have developed the TogoWS service to provide an integrated interface with advanced features. In the TogoWS REST (REpresentative State Transfer) API (application programming interface), we introduce a unified access method for major database resources through intuitive URIs that can be used to search, retrieve, parse and convert the database entries. The TogoWS SOAP API resolves compatibility issues found on the server and client-side SOAP implementations. The TogoWS service is freely available at: http://togows.dbcls.jp/.

The last two decades brought an explosion of computational tools and processes in many scientific domains (e.g., life-, social- and geo-science). Scientific workflows, i.e., computational pipelines, accompanied by workflow management systems, were soon adopted as a de-facto standard among non-computer scientists for orchestrating such computational processes. The goal of this dissertation is to provide a framework that would automate the orchestration of such computational pipelines in practice. We refer to such problems as scientific workflow synthesis problems. This dissertation introduces the temporal logic SLTLx, and presents a novel SLTLx-based synthesis approach that overcomes limitations in handling data object dependencies present in existing synthesis approaches. The new approach uses transducers and temporal goals, which keep track of the data objects in the synthesised workflow. The proposed SLTLx-based synthesis includes a bounded and a dynamic variant, which are shown in Chapter 3 to be NP-complete and PSPACE-complete, respectively. Chapter 4 introduces a transformation algorithm that translates the bounded SLTLx-based synthesis problem into propositional logic. The transformation is implemented as part of the APE (Automated Pipeline Explorer) framework, presented in Chapter 5. It relies on highly efficient SAT solving techniques, using an off-the-shelf SAT solver to synthesise a solution for the given propositional encoding. The framework provides an API (application programming interface), a CLI (command line interface), and a web-based GUI (graphical user interface). The development of APE was accompanied by four concrete application scenarios as case studies for automated workflow composition. The studies were conducted in collaboration with domain experts and presented in Chapter 6. Each of the case studies is used to assess and illustrate specific features of the SLTLx-based synthesis approach. (1) A case study on cartographic map generation demonstrates the ability to distinguish data objects as a key feature of the framework. It illustrates the process of annotating a new domain, and presents the iterative workflow synthesis approach, where the user tries to narrow down the desired specification of the problem in a few intuitive steps. (2) A case study on geo-analytical question answering as part of the QuAnGIS project shows the benefits of using data flow dependencies to describe a synthesis problem. (3) A proteomics case study demonstrates the usability of APE as an “off-the-shelf” synthesiser, providing direct integration with existing semantic domain annotations. In addition, a manual evaluation of the synthesised results shows promising results even on large real-life domains, such as the EDAM ontology and the complete bio.tools registry. (4) A geo-event question-answering study demonstrates the usability of APE within a larger question-answering system. This dissertation answers the goals it sets to solve. It provides a formal framework, accompanied by a lightweight library, which can solve real-life scientific workflow synthesis problems. Finally, the development of the library motivated an upcoming collaborative project in the life sciences domain. The aim of the project is to develop a platform which would automatically compose (using APE) and benchmark workflows in computational proteomics.

Despite of the observed Cloud Services' (CS) heterogeneity, e.g., different APIs (Application Programming Interface), different accounting and charging models, and different security and privacy levels, one common aspect is that CSs provide a large amount of storage. CSs provide generic storage when it accepts to store data in any data type (e.g., Dropbox, Amazon S3). CSs provides data-specific storage when it accepts to store data only represented in specific data types (e.g., Google Picasa, SoundCloud). Therefore, PiCsMu (Platform-independent Cloud Storage System for Multiple-Usage) was developed to (1) aggregate multiple CSs' storage despite of the accepted data type, (2) provide enhanced privacy, and (3) enable a Peer-to-Peer (P2P) file sharing network relying on CSs' storage instead of peers' storage. The demonstrator use case shows how a PiCsMu user can upload, download, and share files with other PiCsMu users, especially highlighting: (a) the hybrid approach used by the PiCsMu system, showing that data is stored in CSs and the P2P network acts as a management overlay; (b) the PiCsMu social capabilities, showing that PiCsMu users are able to find and share content to existing Online Social Network friends; and (c) the fragmentation and data encoding, showing that both processes enable higher privacy levels since data is stored in multiple different CSs and is imperceptible to CSs' data validation.

General Matrix to Matrix multiplication (GEMM) is the cornerstone for a wide gamut of applications in high performance computing (HPC), scientific computing (SC) and more recently, deep learning. In this work, we present a customizable matrix multiplication framework for the Intel HARPv2 CPU+FPGA platform that includes support for both traditional single precision floating point and reduced precision workloads. Our framework supports arbitrary size GEMMs and consists of two parts: (1) a simple application programming interface (API) for easy configuration and integration into existing software and (2) a highly customizable hardware template. The API provides both compile and runtime options for controlling key aspects of the hardware template including dynamic precision switching; interleaving and block size control; and fused deep learning specific operations. The framework currently supports single precision floating point (FP32), 16, 8, 4 and 2 bit Integer and Fixed Point (INT16, INT8, INT4, INT2) and more exotic data types for deep learning workloads: INT16xTernary, INT8xTernary, BinaryxBinary.

Fusion 2+ of air-to-air engagement involves pressing, real-time constraints and very large amounts of imperfect data. Real-time data acquired during an air-to-air engagement will have different types of imperfection; two representative classes of imperfection are vagueness and ambiguity in the data. However, the current approaches of managing Fusion 2+ are limited to utilize either vague data or ambiguous data. The most popular fusion technique for vague data is Fuzzy Logic, and for ambiguous data, the Bayesian Network. The challenge addressed in this proposal is to explore the framework of a hybrid processing Fusion 2+ model that can formally process both vague (fuzzy) and ambiguous (probabilistic) data types. There are two major issues for building this Fusion 2+ model. The first issue is to mathematically integrate the heterogeneous models, which have different domains, probability and possibility. The second issue is to programmatically integrate two different S/Ws. For solving the first issue, this research explores and adopts two novel transformation methods between probability and possibility and compares the sensitivity between methods. Also this research provides an Object Oriented Tool for building a hybrid model by adopting an Application Programming Interface, so that we can model the complex (multi-to-multi) Fusion 2+ model of an air-to-air engagement.

Numerical simulations based on particle methods have been widely used in various fields including astrophysics. To date, various versions of simulation software have been developed by individual researchers or research groups in each field, through a huge amount of time and effort, even though the numerical algorithms used are very similar. To improve the situation, we have developed a framework, called FDPS (Framework for Developing Particle Simulators), which enables researchers to develop massively parallel particle simulation codes for arbitrary particle methods easily. Until version 3.0, FDPS provided an API (application programming interface) for the C++ programming language only. This limitation comes from the fact that FDPS is developed using the template feature in C++, which is essential to support arbitrary data types of particle. However, there are many researchers who use Fortran to develop their codes. Thus, the previous versions of FDPS require such people to invest much time to learn C++. This is inefficient. To cope with this problem, we developed a Fortran interface layer in FDPS, which provides API for Fortran. In order to support arbitrary data types of particle in Fortran, we design the Fortran interface layer as follows. Based on a given derived data type in Fortran representing particle, a Python script provided by us automatically generates a library that manipulates the C++ core part of FDPS. This library is seen as a Fortran module providing an API of FDPS from the Fortran side and uses C programs internally to interoperate Fortran with C++. In this way, we have overcome several technical issues when emulating a ‘template’ in Fortran. Using the Fortran interface, users can develop all parts of their codes in Fortran. We show that the overhead of the Fortran interface part is sufficiently small and a code written in Fortran shows a performance practically identical to the one written in C++.

Conceptualising the FinDD API plug-in: A study of BIM-FM integration

BackgroundOpen Source cheminformatics toolkits such as OpenBabel, the CDK and the RDKit share the same core functionality but support different sets of file formats and forcefields, and calculate different fingerprints and descriptors. Despite their complementary features, using these toolkits in the same program is difficult as they are implemented in different languages (C++ versus Java), have different underlying chemical models and have different application programming interfaces (APIs).ResultsWe describe Cinfony, a Python module that presents a common interface to all three of these toolkits, allowing the user to easily combine methods and results from any of the toolkits. In general, the run time of the Cinfony modules is almost as fast as accessing the underlying toolkits directly from C++ or Java, but Cinfony makes it much easier to carry out common tasks in cheminformatics such as reading file formats and calculating descriptors.ConclusionBy providing a simplified interface and improving interoperability, Cinfony makes it easy to combine complementary features of OpenBabel, the CDK and the RDKit.

In the article “Converting Zip Code Data into Distances: A Case Study for Teaching Business Analytics” [Huggins E (2019) INFORMS Trans. Ed. 19(2):105–107], the author demonstrates how to convert a column of zip code data into approximate distances from a default zip code using standard Microsoft (MS) Excel functions and a zip code database. In this case article, we update that case in three specific areas. First, we explore using the relatively new MS Excel Geography Data Type that, in theory at least, eliminates the need to use the zip code database by accessing required information about each zip code directly from the web. Second, we show how to apply the MS Excel Maps feature to this data. Third, although the original article uses approximate straight-line distances, we show how to extract driving distances between two zip codes using public maps and an application programming interface with MS Excel’s =WEBSERVICE() function. Two of these updates are not as straightforward as one might hope: The Geography Data Type returns a significant number of errors and, although driving distances can be determined, there is no “easy” way to do so yet. We discuss these issues in the case teaching note. Supplemental Material: The Teaching Note and data files are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

Effectively querying unstructured text information in large databases is a highly demanding task. Conventional approaches, such as an exact match or fuzzy search, return valid and thorough results only when the user query adequately matches the wording within the text or the query is included in keyword-tag lists. The GEOSS portal relies on conventional search tools for data and services exploration and retrieval, limiting its capacity. This challenge, recent advances in Artificial Intelligence (AI)-based Natural Language Processing (NLP) try to surpass with enhanced information retrieval and cognitive search. Rather than relying on exact or fuzzy text matching, it detects documents that semantically and conceptually are close enough to the search query. The EIFFEL EU-funded project aims to reveal the role of GEOSS as the default Digital Portal for building Climate Change (CC) adaption and mitigation applications and offer the Earth Observation community the ground-breaking capacity of exploiting existing GEOSS datasets. To this end, as a lead technological partner of the EIFFEL consortium, LIBRA AI Technologies, designs and develops an end-to-end advanced cognitive search system dedicated to the GEOSS Portal and exceeds current challenges.The proposed system comprises an AI language model optimized for CC-related text and queries, a framework for collecting a sizeable CC-specific corpus used for the language model specialization, a back-end that adopts modern database technologies with advanced capabilities for embedding-based cognitive search matching, and an open Application Programming Interface (API). The cognitive search component is the backbone of the EIFFEL visualisation engine, which will allow any GEOSS user, as well as the EIFFEL Climate Change application developing teams, to detect GEOSS data objects and services that are of interest for their research and application but could not effectively get accessed with the available GEOSS Portal search engine.The work described in this abstract is part of the EIFFEL European project. The EIFFEL project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101003518. We thank all partners for their valuable contributions.