An infrastructure for managing context information in pervasive computing environments
Bibliographic record
Abstract
The increasing popularity of mobile devices, such as mobile phones and personal digital assistants, and advances in wireless networking technologies, are enabling new classes of applications that raise challenging problems to application developers. These applications have to be aware of the variations in the execution context such as location, time, user activities, and device capabilities in order to tune and adapt their intended functionalities. We argue that developing and managing applications that are context-aware would be extremely hard, tedious, and error-prone if not supported by a computing infrastructure. This is because application developers would have to deal with issues such as context interpretation, context reasoning, and context adaptability, and consequently would be distracted from the actual requirements of the applications they are developing. We investigate the principles of ontologies, negotiation, and approximate reasoning, and their usage to support context-awareness and dynamic adaptation for applications in pervasive environments. These principles are integrated in our proposed infrastructure, which offers application developers a set of APIs and computing components to facilitate the process of developing and managing context-aware applications. The APIs that the infrastructure provides allow users to manipulate the information encoded in their semantic profiles and to negotiate context information causing the infrastructure to tailor its behaviour to applications' need. Accordingly, the infrastructure reasons and makes automated decisions that are based on this negotiated contextual information to achieve adaptability and to cope with the frequent changes in the environment. To manifest the effectiveness of these principles in developing and managing context-aware applications, we discuss the architecture and implementation of an agent-based context-aware infrastructure that implements these principles, and report on performance and usability results obtained from a thorough evaluation of the infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".