43 research outputs found
Policy Enforcement with Proactive Libraries
Software libraries implement APIs that deliver reusable functionalities. To
correctly use these functionalities, software applications must satisfy certain
correctness policies, for instance policies about the order some API methods
can be invoked and about the values that can be used for the parameters. If
these policies are violated, applications may produce misbehaviors and failures
at runtime. Although this problem is general, applications that incorrectly use
API methods are more frequent in certain contexts. For instance, Android
provides a rich and rapidly evolving set of APIs that might be used incorrectly
by app developers who often implement and publish faulty apps in the
marketplaces. To mitigate this problem, we introduce the novel notion of
proactive library, which augments classic libraries with the capability of
proactively detecting and healing misuses at run- time. Proactive libraries
blend libraries with multiple proactive modules that collect data, check the
correctness policies of the libraries, and heal executions as soon as the
violation of a correctness policy is detected. The proactive modules can be
activated or deactivated at runtime by the users and can be implemented without
requiring any change to the original library and any knowledge about the
applications that may use the library. We evaluated proactive libraries in the
context of the Android ecosystem. Results show that proactive libraries can
automati- cally overcome several problems related to bad resource usage at the
cost of a small overhead.Comment: O. Riganelli, D. Micucci and L. Mariani, "Policy Enforcement with
Proactive Libraries" 2017 IEEE/ACM 12th International Symposium on Software
Engineering for Adaptive and Self-Managing Systems (SEAMS), Buenos Aires,
Argentina, 2017, pp. 182-19
Reliability on Pervasive Well-being: will it soon become a reality? State of the art and open issues
Unsupervised Deep Learning-based clustering for Human Activity Recognition
One of the main problems in applying deep learning techniques to recognize
activities of daily living (ADLs) based on inertial sensors is the lack of
appropriately large labelled datasets to train deep learning-based models. A
large amount of data would be available due to the wide spread of mobile
devices equipped with inertial sensors that can collect data to recognize human
activities. Unfortunately, this data is not labelled. The paper proposes DISC
(Deep Inertial Sensory Clustering), a DL-based clustering architecture that
automatically labels multi-dimensional inertial signals. In particular, the
architecture combines a recurrent AutoEncoder and a clustering criterion to
predict unlabelled human activities-related signals. The proposed architecture
is evaluated on three publicly available HAR datasets and compared with four
well-known end-to-end deep clustering approaches. The experiments demonstrate
the effectiveness of DISC on both clustering accuracy and normalized mutual
information metrics.Comment: 2022 IEEE 12th International Conference on Consumer Electronics
(ICCE-Berlin