Challenges in interface and interaction design for context-aware augmented memory systems

The human long-term memory is astonishingly powerful but fallible at the same time. This makes it very easy to forget information one is sure one actually knows. We propose context-aware augmented memory systems as a solution to this problem. In this paper, we analyse the user interface and interaction design challenges that need to be overcome to build such a system. We hope for fruitful interdisciplinary discussions on how best to address these challenges

Brook Auto: High-Level Certification-Friendly Programming for GPU-powered Automotive Systems

Modern automotive systems require increased performance to implement Advanced Driving Assistance Systems (ADAS). GPU-powered platforms are promising candidates for such computational tasks, however current low-level programming models challenge the accelerator software certification process, while they limit the hardware selection to a fraction of the available platforms. In this paper we present Brook Auto, a high-level programming language for automotive GPU systems which removes these limitations. We describe the challenges and solutions we faced in its implementation, as well as a complete evaluation in terms of performance and productivity, which shows the effectiveness of our method.This work has been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P and the HiPEAC Network of Excellence.Peer ReviewedPostprint (author's final draft

Towards a New Design Metaphor: Supporting Boundary Objects as Means of Knowledge Sharing in Community Networks

Working in the knowledge sector means dealing with increasing amounts of information, technology and people. Organizations as well as individuals in communities need to constantly maintain large repositories and networks of people, including colleagues, clients, experts, acquaintances and friends. This situation leads to complexity where person’s cognitive capability is insufficient when dealing with huge repositories of information and interaction. Viewing it as an individual problem has resulted in applications that highlight the need for structure and organization. We here define these applications in different levels where the first level is the office application generation, referring to “desktops” metaphors. The next generation, groupware applications, offers structure and process support for collaboration, but is still a rather limited “forum” metaphor. Our main argument is that current application generations and design metaphors are too limited when supporting the sharing of thoughts and associations in different community networks. We believe that a large portion of this problem is not related to information itself, but rather to processes of information categorization, navigation and interaction within and between communities. In our results we advocate the need for a new application generation and a new design metaphor, i.e. brainware applications based on “neural” metaphors. The result is a review of three application generations based on different design metaphors. We discuss several implications for a new design metaphor and suggest a design draft that supports boundary objects as means of knowledge sharing within and between communities

Data Processing and the Envision

Data is being generated very rapidly due to increase in information in everyday life. Huge amount of data gets accumulated from various organizations that is difficult to analyze and exploit. Data created by an expanding number of sensors in the environment such as traffic cameras and satellites, internet activities on social networking sites, healthcare database, government database, sales data etc., are example of huge data. Processing, analyzing and communicating this data are a challenge. Online shopping websites get flooded with voluminous amount of sales data every day. Analyzing and visualizing this data for information retrieval is a difficult task. There are large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. With today’s data management systems, it is only possible to view quite small portions of the data. If the data is presented textually, the amount of data which can be displayed is in the range of some 100 data items, but this is like a drop in the ocean when dealing with data sets containing millions of data items. Data is being generated very rapidly due to increase in information in everyday life. Huge amount of data gets accumulated from various organizations that is difficult to analyze and exploit. Data created by an expanding number of sensors in the environment such as traffic cameras and satellites, internet activities on social networking sites, healthcare database, government database, sales data etc., are example of huge data. Processing, analyzing and communicating this data are a challenge. Online shopping websites get flooded with voluminous amount of sales data every day. Analyzing and visualizing this data for information retrieval is a difficult task. Therefore, a system is required which will effectively analyze and visualize data. This paper focuses on a system which will visualize sales data which will help users in applying intelligence in business, revenue generation, and decision making, managing business operation and tracking progress of tasks. Effective and efficient data visualization is the key part of the discovery process. It is the intermediate between the human intuition and quantitative context of the data, thus an essential component of the scientific path from data into knowledge and understanding. Therefore, a system is required which will effectively analyze and visualize data. This paper focuses on a system which will visualize data which will help users in interactive data visualization applying in business, revenue generation, and decision making, managing business operation and tracking progress of tasks

Semantically annotating the desktop : towards a personal ontology

The advent of the World-Wide Web brought with it a proliferation of information from e-mail, forums, chat, sites that rapidly led to information overload and a subsequent storage problem and maintenance on users’ personal computers. The desktop has become a repository of data that hosts various types of files. The recent massive increase in data has resulted in a continuous attempt to enhance our data organisation techniques and hence to the development of personal information management software. In this paper we present an overview of data organisation techniques related to personal data management that have been an active research area for decades. We will look at how personal information managers handle different types of files, and abstract these file types into a single user inter- face. Despite their advanced user interfaces, we argue that traditional personal information managers tend to be very domain specific and lack in user adaptability. To address these limitations we propose a semantic desktop application that exploits the flexibility of semantic web technologies, and introduces the concept of a Personal Ontology to aid in data organisation and can be used by other desktop applications such as information retrieval and intelligent software agents.peer-reviewe

Are episodic context features helpful for refinding tasks? Lessons learnt from a case study with lifelogs

Both psychological theories and findings in information science suggest that people may remember the episodic context of previously encountered information. This implies that a user’s episodic memory might be utilized to improve the efficiency or effectiveness of refinding tasks. In this paper, we report a case study which aims to explore the feasibility of integrating episodic context into the design of information refinding systems. The subjects in this study collected 20 months of rich contextual data along including the full text of all documents, emails, web pages and so on, which they accessed during the collection period. We developed a “memory-friendly” system based on psychological theories to test the hypothesis through user studies requiring the subjects to find their personal data using this system. From examination of the user activity log and a post-task questionnaire, we found that although our designed features, which support or utilize episodic context or autobiographical memory, were not used as frequently as we expected, they did improve the effectiveness of the refinding tasks

Pervasive Personal Information Spaces

Each user’s electronic information-interaction uniquely matches their information behaviour, activities and work context. In the ubiquitous computing environment, this information-interaction and the underlying personal information is distributed across multiple personal devices. This thesis investigates the idea of Pervasive Personal Information Spaces for improving ubiquitous personal information-interaction. Pervasive Personal Information Spaces integrate information distributed across multiple personal devices to support anytime-anywhere access to an individual’s information. This information is then visualised through context-based, flexible views that are personalised through user activities, diverse annotations and spontaneous information associations. The Spaces model embodies the characteristics of Pervasive Personal Information Spaces, which emphasise integration of the user’s information space, automation and communication, and flexible views. The model forms the basis for InfoMesh, an example implementation developed for desktops, laptops and PDAs. The design of the system was supported by a tool developed during the research called activity snaps that captures realistic user activity information for aiding the design and evaluation of interactive systems. User evaluation of InfoMesh elicited a positive response from participants for the ideas underlying Pervasive Personal Information Spaces, especially for carrying out work naturally and visualising, interpreting and retrieving information according to personalised contexts, associations and annotations. The user studies supported the research hypothesis, revealing that context-based flexible views may indeed provide better contextual, ubiquitous access and visualisation of information than current-day systems

Whitelisting System State In Windows Forensic Memory Visualizations

Examiners in the field of digital forensics regularly encounter enormous amounts of data and must identify the few artifacts of evidentiary value. The most pressing challenge these examiners face is manual reconstruction of complex datasets with both hierarchical and associative relationships. The complexity of this data requires significant knowledge, training, and experience to correctly and efficiently examine. Current methods provide primarily text-based representations or low-level visualizations, but levee the task of maintaining global context of system state on the examiner. This research presents a visualization tool that improves analysis methods through simultaneous representation of the hierarchical and associative relationships and local detailed data within a single page application. A novel whitelisting feature further improves analysis by eliminating items of little interest from view, allowing examiners to identify artifacts more quickly and accurately. Results from two pilot studies demonstrates that the visualization tool can assist examiners to more accurately and quickly identify artifacts of interest