No Arabic abstract
Product Data Management (PDM) desktop and web based systems maintain the organizational technical and managerial data to increase the quality of products by improving the processes of development, business process flows, change management, product structure management, project tracking and resource planning. Though PDM is heavily benefiting industry but PDM community is facing a very serious unresolved issue in PDM system development with flexible and user friendly graphical user interface for efficient human machine communication. PDM systems offer different services and functionalities at a time but the graphical user interfaces of most of the PDM systems are not designed in a way that a user (especially a new user) can easily learn and use them. Targeting this issue, a thorough research was conducted in field of Human Computer Interaction; resultant data provides the information about graphical user interface development using rich internet applications. The accomplished goal of this research was to support the field of PDM with a proposition of a conceptual model for the implementation of a flexible web based graphical user interface. The proposed conceptual model was successfully designed into implementation model and a resultant prototype putting values to the field is now available. Describing the proposition in detail the main concept, implementation designs and developed prototype is also discussed in this paper. Moreover in the end, prototype is compared with respective functions of existing PDM systems .i.e., Windchill and CIM to evaluate its effectiveness against targeted challenge
It is necessary to improve the concepts of the present web based graphical user interface for the development of more flexible and intelligent interface to provide ease and increase the level of comfort at user end like most of the desktop based applications. This research is conducted targeting the goal of implementing flexible GUI consisting of a visual component manager with different components by functionality, design and purpose. In this research paper we present a Rich Internet Application (RIA) based graphical user interface for web based product development, and going into the details we present a comparison between existing RIA Technologies, adopted methodology in the GUI development and developed prototype.
Neurofeedback games are an effective and playful approach to enhance certain social and attentional capabilities in children with autism, which are promising to become widely accessible along with the commercialization of mobile EEG modules. However, little industry-based experiences are shared, regarding how to better design neurofeedback games to fine-tune their playability and user experiences for autistic children. In this paper, we review the experiences we gained from industry practice, in which a series of mobile EEG neurofeedback games have been developed for preschool autistic children. We briefly describe our design and development in a one-year collaboration with a special education center involving a group of stakeholders: children with autism and their caregivers and parents. We then summarize four concrete implications we learnt concerning the design of game characters, game narratives, as well as gameplay elements, which aim to support future work in creating better neurofeedback games for preschool children with autism.
Extensible 3D (X3D) modeling language is one of the leading Web3D technologies. Despite the rich functionality, the language does not currently provide tools for rapid development of conventional graphical user interfaces (GUIs). Every X3D author is responsible for building from primitives a purpose specific set of required interface components, often for a single use. We address the challenge of creating consistent, efficient, interactive, and visually appealing GUIs by proposing the X3D User Interface (X3DUI) library. This library includes a wide range of cross-compatible X3D widgets, equipped with configurable appearance and behavior. With this library, we attempt to standardize the GUI construction across various X3D-driven projects, and improve the usability, compatibility, adaptability, readability, and flexibility of many existing applications.
Graphical User Interface (GUI) is ubiquitous in almost all modern desktop software, mobile applications, and online websites. A good GUI design is crucial to the success of the software in the market, but designing a good GUI which requires much innovation and creativity is difficult even to well-trained designers. Besides, the requirement of the rapid development of GUI design also aggravates designers working load. So, the availability of various automated generated GUIs can help enhance the design personalization and specialization as they can cater to the taste of different designers. To assist designers, we develop a model GUIGAN to automatically generate GUI designs. Different from conventional image generation models based on image pixels, our GUIGAN is to reuse GUI components collected from existing mobile app GUIs for composing a new design that is similar to natural-language generation. Our GUIGAN is based on SeqGAN by modeling the GUI component style compatibility and GUI structure. The evaluation demonstrates that our model significantly outperforms the best of the baseline methods by 30.77% in Frechet Inception distance (FID) and 12.35% in 1-Nearest Neighbor Accuracy (1-NNA). Through a pilot user study, we provide initial evidence of the usefulness of our approach for generating acceptable brand new GUI designs.
The data ingestion pipeline, responsible for storing and preprocessing training data, is an important component of any machine learning training job. At Facebook, we use recommendation models extensively across our services. The data ingestion requirements to train these models are substantial. In this paper, we present an extensive characterization of the data ingestion challenges for industry-scale recommendation model training. First, dataset storage requirements are massive and variable; exceeding local storage capacities. Secondly, reading and preprocessing data is computationally expensive, requiring substantially more compute, memory, and network resources than are available on trainers themselves. These demands result in drastically reduced training throughput, and thus wasted GPU resources, when current on-trainer preprocessing solutions are used. To address these challenges, we present a disaggregated data ingestion pipeline. It includes a central data warehouse built on distributed storage nodes. We introduce Data PreProcessing Service (DPP), a fully disaggregated preprocessing service that scales to hundreds of nodes, eliminating data stalls that can reduce training throughput by 56%. We implement important optimizations across storage and DPP, increasing storage and preprocessing throughput by 1.9x and 2.3x, respectively, addressing the substantial power requirements of data ingestion. We close with lessons learned and cover the important remaining challenges and opportunities surrounding data ingestion at scale.