Gracoli

This article investigates the potential of artificial intelligence (AI) in optimizing interface design to enhance task completion time across various types of interfaces. By examining the characteristics, strengths, and weaknesses of each interface type and the potential integration of AI, we aim to provide insights into how AI can be leveraged to improve user experience and task efficiency. We will discuss the influence of AI on Graphical User Interfaces (GUIs), Command Line Interfaces (CLIs), Voice User Interfaces (VUIs), Gesture-based Interfaces, Haptic Interfaces, and Mixed Reality Interfaces (Augmented Reality and Virtual Reality). Through a review of existing research and case studies, we explore the current state of AI integration in interface design and future opportunities for enhancing task completion time and overall system usability.

The evolution of input device technologies led to identification of the natural user interface (NUI) as the clear evolution of the human-machine interaction, following the shift from command-line interfaces (CLI) to graphical user interfaces (GUI). The design of user interfaces requires a careful mapping of complex user “actions” in order to make the human-computer interaction (HCI) more intuitive, usable, and receptive to the user’s needs: in other words, more user-friendly and, why not, fun. NUIs constitute a direct expression of mental concepts and the naturalness and variety of gestures, compared with traditional interaction paradigms, can offer unique opportunities also for new and attracting forms of human-machine interaction. In this paper, a kinect-based NUI is presented; in particular, the proposed NUI is used to control the Ar.Drone quadrotor.

BackgroundEven though R is one of the most commonly used statistical computing environments, it lacks a graphical user interface (GUI) that appeals to students, researchers, lecturers, and practitioners in medicine and pharmacy for conducting standard data analytics. Current GUIs built on top of R, such as EZR and R-Commander, aim to facilitate R coding and visualization, but most of the functionalities are still accessed through a command-line interface (CLI). To assist practitioners of medicine and pharmacy and researchers to run most routines in fundamental statistical analysis, we developed an interactive GUI; i.e., MEPHAS, to support various web-based systems that are accessible from laptops, workstations, or tablets, under Windows, macOS (and IOS), or Linux. In addition to fundamental statistical analysis, advanced statistics such as the extended Cox regression and dimensional analyses including partial least squares regression (PLS-R) and sparse partial least squares regression (SPLS-R), are also available in MEPHAS.ResultsMEPHAS is a web-based GUI (https://alain003.phs.osaka-u.ac.jp/mephas/) that is based on a shiny framework. We also created the corresponding R package mephas (https://mephas.github.io/). Thus far, MEPHAS has supported four categories of statistics, including probability, hypothesis testing, regression models, and dimensional analyses. Instructions and help menus were accessible during the entire analytical process via the web-based GUI, particularly advanced dimensional data analysis that required much explanation. The GUI was designed to be intuitive for non-technical users to perform various statistical functions, e.g., managing data, customizing plots, setting parameters, and monitoring real-time results, without any R coding from users. All generated graphs can be saved to local machines, and tables can be downloaded as CSV files.ConclusionMEPHAS is a free and open-source web-interactive GUI that was designed to support statistical data analyses and prediction for medical and pharmaceutical practitioners and researchers. It enables various medical and pharmaceutical statistical analyses through interactive parameter settings and dynamic visualization of the results.

We present an open source software implementation of a popular mathematical method developed by M.R. Yeadon for calculating the body and segment inertia parameters of a human body. The software is written in a high level open source language and provides three interfaces for manipulating the data and the model: a Python API, a command-line user interface, and a graphical user interface. Thus the software can fit into various data processing pipelines and requires only simple geometrical measures as input.

An operating system (OS) is a piece of software that, after being first loaded by a boot programme, governs all other application programs in a computer.The application programs request services from the operating system using a specific application programme interface (API).A user interface, such as a graphical user interface (UI) or command-line interface (CLI), allows users to interact with the operating system directly as well (GUI).An operating system is extremely useful for computer applications and software development.Without an operating system, each application would have to develop its own user interface (UI) and all the required code to control the computer's low-level tasks, such as managing disc storage, network connections, and other tasks.This would greatly increase the size of every application and make software development challenging, especially in light of the wide range of underlying hardware options.

Geographic analysis has long supported transport plans that are appropriate to local contexts. Many incumbent ‘tools of the trade’ are proprietary and were developed to support growth in motor traffic, limiting their utility for transport planners who have been tasked with twenty-first century objectives such as enabling citizen participation, reducing pollution, and increasing levels of physical activity by getting more people walking and cycling. Geographic techniques—such as route analysis, network editing, localised impact assessment and interactive map visualisation—have great potential to support modern transport planning priorities. The aim of this paper is to explore emerging open source tools for geographic analysis in transport planning, with reference to the literature and a review of open source tools that are already being used. A key finding is that a growing number of options exist, challenging the current landscape of proprietary tools. These can be classified as command-line interface, graphical user interface or web-based user interface tools and by the framework in which they were implemented, with numerous tools released as R, Python and JavaScript packages, and QGIS plugins. The review found a diverse and rapidly evolving ‘ecosystem’ tools, with 25 tools that were designed for geographic analysis to support transport planning outlined in terms of their popularity and functionality based on online documentation. They ranged in size from single-purpose tools such as the QGIS plugin AwaP to sophisticated stand-alone multi-modal traffic simulation software such as MATSim, SUMO and Veins. Building on their ability to re-use the most effective components from other open source projects, developers of open source transport planning tools can avoid ‘reinventing the wheel’ and focus on innovation, the ‘gamified’ A/B Street https://github.com/dabreegster/abstreet/#abstreet simulation software, based on OpenStreetMap, a case in point. The paper, the source code of which can be found at https://github.com/robinlovelace/open-gat, concludes that, although many of the tools reviewed are still evolving and further research is needed to understand their relative strengths and barriers to uptake, open source tools for geographic analysis in transport planning already hold great potential to help generate the strategic visions of change and evidence that is needed by transport planners in the twenty-first century.

Implementation of a Chatbot System using AI and NLP

Purpose: The rapid advancement of digital technologies has necessitated robust security measures to protect information systems against escalating cyber threats. The objective is to study the effectiveness of the command line interface (CLI) in IT system security management. Design/methodology/approach: This paper explores the efficacy of the command line interface (CLI) in managing IT system security and examines the potential of integrating biometric data into cryptographic mechanisms. We delve into the CLI's precision and flexibility, which enable the execution of complex security tasks and its seamless integration with advanced security tools. Furthermore, we investigate the incorporation of biometrics, such as fingerprints and facial recognition, into encryption processes, offering enhanced security by binding access to individual biometric identifiers. Findings: Our findings suggest that while CLI remains a vital tool for security specialists, the convergence of CLI with biometric authentication can significantly fortify the security of information systems. Practical implications: The paper addresses the challenges and opportunities presented by this integration, including privacy concerns and the need for secure handling of biometric data. We also discuss the implications of such technologies in the context of the European Union's legal framework on cybersecurity. Originality/value: The article is aimed at those involved in cyber security management. The article presents the possibility of using biometric attestations to support the security of IT systems. Keywords: cybersecurity, European Union cybersecurity legislation, cryptographic mechanisms, data encryption, multi-factor authentication. Category of the paper: research paper.

As Internet technology becomes more complex, the policy information for managing the Internet grows beyond the capability of a simple protocol like SNMP. IETF suggested COPS (common open policy service) as an alternative, but it has not been widely accepted. For that reason, many administrators have developed network management systems which control network devices using CLI (command line interface), but systems based on CLI have a maintenance problem: when the syntax of CLI changes, the implementation of the system must be modified. We suggest X-CLI (XML wrapper API for CLI) as a solution for this problem, and describe its design principles.

MotivationFrom image stacks to computational models, processing digital representations of neuronal morphologies is essential to neuroscientific research. Workflows involve various techniques and tools, leading in certain cases to convoluted and fragmented pipelines. The existence of an integrated, extensible and free framework for processing, analysis and visualization of those morphologies is a challenge that is still largely unfulfilled.ResultsWe present NeuroMorphoVis, an interactive, extensible and cross-platform framework for building, visualizing and analyzing digital reconstructions of neuronal morphology skeletons extracted from microscopy stacks. Our framework is capable of detecting and repairing tracing artifacts, allowing the generation of high fidelity surface meshes and high resolution volumetric models for simulation and in silico imaging studies. The applicability of NeuroMorphoVis is demonstrated with two case studies. The first simulates the construction of three-dimensional profiles of neuronal somata and the other highlights how the framework is leveraged to create volumetric models of neuronal circuits for simulating different types of in vitro imaging experiments.Availability and implementationThe source code and documentation are freely available on https://github.com/BlueBrain/NeuroMorphoVis under the GNU public license. The morphological analysis, visualization and surface meshing are implemented as an extensible Python API (Application Programming Interface) based on Blender, and the volume reconstruction and analysis code is written in C++ and parallelized using OpenMP. The framework features are accessible from a user-friendly GUI (Graphical User Interface) and a rich CLI (Command Line Interface).Supplementary informationSupplementary data are available at Bioinformatics online.

Statistical modeling (regression analyses) have been growing rapidly into various directions to accommodate various data conditions. For longitudinal or repeated measures data, one of the suitable models is GEE (Generalized Estimating Equation). In practice, to do complex modeling such as GEE, the use of statistical software is necessary and it is available on free open source software R. However, GEE modeling on R can only be access through command line interface (CLI), and most practical researchers very much rely on Graphical User Interface (GUI) based statistics software. To make access to GEE (both order 1 and 2) much easier, we developed, using Shiny toolkit, two types of web-based GUI, standard pull down menu type and e-module type (with narrative theories) that can be utilized for learning and fitting GEE. This paper discusses the features of the interfaces and illustrates the use of them.
 Keywords: longitudinal data, Generalized Estimating Equation (GEE), exponential families, statistical modeling, correlated response, nonparametric, natural splines, shiny toolkit

The standardisation and correction of taxonomic names in large biodiversity databases remain persistent challenges for researchers, as errors in species names can compromise ecological analyses, land-use planning and conservation efforts, particularly when inaccurate data are shared on global biodiversity portals. We present pytaxon, a Python software designed to resolve and correct taxonomic names in biodiversity data by leveraging the Global Names Verifier (GNV) API and employing fuzzy matching techniques to suggest corrections for discrepancies and nomenclatural inconsistencies. The pytaxon offers both a Command Line Interface (CLI) and a Graphical User Interface (GUI), ensuring accessibility to users with different levels of computing expertise. Tests on spreadsheets derived from datasets published in the Global Biodiversity Information Facility (GBIF) demonstrated its effectiveness in identifying and resolving taxonomic errors. By mitigating the propagation of inaccuracies from researchers' datasets to global biodiversity databases, pytaxon supports more reliable conservation decisions and robust scientific investigations. Its contributions enhance data integrity and promote informed biodiversity management in a rapidly evolving global environment.

GUIDEMOL is a Python computer program based on the RDKit software to process molecular structures and calculate molecular descriptors with a graphical user interface using the tkinter package. It can calculate descriptors already implemented in RDKit as well as grid representations of 3D molecular structures using the electrostatic potential or voxels. The GUIDEMOL app provides easy access to RDKit tools for chemoinformatics users with no programming skills and can be adapted to calculate other descriptors or to trigger other procedures. A command line interface (CLI) is also provided for the calculation of grid representations. The source code is available at https://github.com/jairesdesousa/guidemol.

ADMET (absorption, distribution, metabolism, excretion, and toxicity)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of PD-PK-T properties using in silico tools has become very important in pharmaceutical research to reduce cost and enhance efficiency. PaDEL-DDPredictor is an in silico tool for rapid prediction of PD-PK-T properties of compounds from their chemical structures. It is free and open-source software that, has both graphical user interface and command line interface, can work on all major platforms (Windows, Linux, and MacOS) and supports more than 90 different molecular file formats. The software can be downloaded from http://padel.nus.edu.sg/software/padelddpredictor.

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.