International Journal of Software Engineering and Knowledge Engineering

A computation model is proposed for senior citizen health self-care through adaptive multi-level computation cycles according to the principles of slow intelligence. An experimental system, called the TDR system, is being implemented on a smart phone to serve as the test bed for the proposed approach. In this paper, we describe the computation model, the basic concepts and the prototype experimental system. The main theoretical concept is centered on adaptive multi-level computation cycles, events and event icons. Further experimental as well as theoretical investigations of the proposed computation model are discussed.

Users engage in various activities in different regions of the city, how to infer their roles through the area of the user’s activity, and how to infer the possible geographical structure of the user’s role through the user’s role, it is the case of the challenging issues in the user’s mobility field. The necessary context elements are extracted from the user’s microblogging, such as user ID, check-ins and GPS coordinates, the mutual inference model of the user’s role and the urban functional zones is established for the user’s movement law. The experimental results show that the model has achieved good performance in terms of using the user role to infer the urban geographical structure, or in the use of the urban functional zones to infer the user roles.

In Internet of Things (IoT) environment, a number of sensors and devices exist and they have various and heterogeneous characteristics. Applications which provide a variety of services based on the sensors also have different service requirements. Therefore, a middleware that is located between sensors and application systems is needed for integrating two layers. This paper proposes a general purpose middleware for providing intelligent services based on heterogeneous sensors existing in IoT environment. The proposed middleware acquires and manages sensing data in real time. The middleware stores and manages heterogeneous sensors, node, and network metadata. Especially, the middleware in this paper includes a component for providing intelligent services based on inferencing the situation based on ontologies and rules.

Cleanliness of city streets has an important impact on city environment and public health. Conventional street cleaning methods involve street sweepers going to many spots and manually confirming if the street needs to be clean. However, this method takes a substantial amount of manual operations for detection and assessment of street’s cleanliness which leads to a high cost for cities. Using pervasive mobile devices and AI technology, it is now possible to develop smart edge-based service system for monitoring and detecting the cleanliness of streets at scale. This paper explores an important aspect of cities — how to automatically analyze street imagery to understand the level of street litter. A vehicle (i.e. trash truck) equipped with smart edge station and cameras is used to collect and process street images in real time. A deep learning model is developed to detect, classify and analyze the diverse types of street litters such as tree branches, leaves, bottles and so on. In addition, two case studies are reported to show its strong potential and effectiveness in smart city systems.

The Alternating-time Temporal Logic (ATL) is a temporal logic for formal verification of open systems, which supports selective quantification over paths. The open system can be represented as a game where the system and the environment pick their moves in turn or simultaneously. The ATL model checker Mocha has been developed by using Binary Decision Diagrams (BDDs), and successfully employed for open system verification. However, since Mocha symbolically manipulates a set of states by a BDD, it is hard to generate a winning strategy as a witness or a counter-example. In this paper, we propose a novel algorithm to efficiently construct a winning strategy tree and report that our technique has been successfully implemented on Mocha.

A substantial effort, in general, is required for understanding APIs of application frameworks. High-quality API documentation may alleviate the effort, but the production of such documentation still poses a major challenge for modern frameworks. To facilitate the production of framework instantiation documentation, we hypothesize that the framework code itself and the code of existing instantiations provide useful information. However, given the size and complexity of existent code, automated approaches are required to assist the documentation production. Our goal is to assess an automated approach for constructing relevant documentation for framework instantiation based on source code analysis of the framework itself and of existing instantiations. The criterion for defining whether documentation is relevant would be to compare the documentation with an traditional framework documentation, considering the time spent and correctness during instantiation activities, information usefulness, complexity of the activity, navigation, satisfaction, information localization and clarity. We propose an automated approach for constructing relevant documentation for framework instantiation based on source code analysis of the framework itself and of existing instantiations. The proposed approach generates documentation in a cookbook style, where the recipes are programming activities using the necessary API elements driven by the framework features. We performed an empirical study, consisting of three experiments with 44 human subjects executing real framework instantiations aimed at comparing the use of the proposed cookbooks to traditional manual framework documentation (baseline). Our empirical assessment shows that the generated cookbooks performed better or, at least, with non-significant difference when compared to the traditional documentation, evidencing the effectiveness of the approach.