An Efficient Mobile-Based Middleware Architecture for Building Robust, High-Performance Apps

Romero, Oscar J.; Akoju, Sushma A.

doi:10.1109/icsa-c.2018.00034

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…The user may then describe his problems and converse with the crowd to figure out which rules to create (the workers converse by text, and the user, may either use text or voice). Once the rule is created, it is sent back to the user's phone, where a Decision Rule Engine component (Tomazini et al, 2017;Romero and Akoju, 2018) will store, validate and process that rule. Currently, the system is implemented and tested on the Android OS 6.0.1 and the server is implemented in Java.…”

Section: Conversational Agent For the End-usermentioning

confidence: 99%

InstructableCrowd: Creating IF-THEN Rules for Smartphones via Conversations with the Crowd

Huang¹,

Azaria²,

Romero³

et al. 2019

HCj

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Natural language interfaces have become a common part of modern digital life. Chatbots utilize text-based conversations to communicate with users; personal assistants on smartphones such as Google Assistant take direct speech commands from their users; and speech-controlled devices such as Amazon Echo use voice as their only input mode. In this paper, we introduce InstructableCrowd, a crowd-powered system that allows users to program their devices via conversation. The user verbally expresses a problem to the system, in which a group of crowd workers collectively respond and program relevant multi-part IF-THEN rules to help the user. The IF-THEN rules generated by InstructableCrowd connect relevant sensor combinations (e.g., location, weather, device acceleration, etc.) to useful effectors (e.g., text messages, device alarms, etc.). Our study showed that non-programmers can use the conversational interface of InstructableCrowd to create IF-THEN rules that have similar quality compared with the rules created manually. InstructableCrowd generally illustrates how users may converse with their devices, not only to trigger simple voice commands, but also to personalize their increasingly powerful and complicated devices.arXiv:1909.05725v1 [cs.HC]

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Section: Conversational Agent For the End-usermentioning

confidence: 99%

InstructableCrowd: Creating IF-THEN Rules for Smartphones via Conversations with the Crowd

Huang¹,

Azaria²,

Romero³

et al. 2019

HCj

Self Cite

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“…NLSC was architected to be deployed on distributed environments and to support different kind of client applications (e.g., standalone, web, mobile, etc.). This requirement, along with dynamicity, low-latency, high-performance, modularity, and support to Android Runtime Environment (ART), were the main architectural significant requirements we took into consideration over its implementation [29]. Extending our initial definition of "service", we consider that an Androidbased service can be a device sensor, a local service (installed on the Android device), or an app, hence, we avoid making the assumption that only a web service model should be applicable to NLSC.…”

Section: Service Development Processmentioning

confidence: 99%

NLSC: Unrestricted Natural Language-Based Service Composition through Sentence Embeddings

Romero

Dangi

Akoju

2019

2019 IEEE International Conference on Services Computing (SCC)

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Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we claim that the effort to developing service descriptions, request translations, and service matching could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural languagebased service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 36% while keeping a high precision/recall when matching highlevel user requests with low-level service method invocations.

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“…Given the previous example we focus on five challenges, so service composition should: (1) consider preferences from multiple users; (2) coordinate the interaction between services hosted by different service providers; (3) consider resource scarcity in smart devices [21]; (4) perform dynamic adaptation to unpredictable changes occurring in the environment; (5) deal with both short-term and long-term user's goals. Performing service composition while taking into account a myriad of variable factors as described above (e.g., users, services, service providers, QoS values, context, etc.…”

Section: Introductionmentioning

confidence: 99%

Cognitively-Inspired Agent-Based Service Composition for Mobile and Pervasive Computing

Romero

2019

Lecture Notes in Computer Science

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Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment. Common approaches consider service composition as a decision problem whose solution is usually addressed from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host's mobility, and time constraints to tailor composition plans. Thus, our main contribution is the development of a cognitivelyinspired agent-based service composition model focused on bounded rationality rather than optimality, which allows the system to compensate for limited resources by selectively filtering out continuous streams of data. Our approach exhibits features such as distributedness, modularity, emergent global functionality, and robustness, which endow it with capabilities to perform decentralized service composition by orchestrating manifold service providers and conflicting goals from multiple users.The evaluation of our approach shows promising results when compared against state-of-the-art service composition models.

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An Efficient Mobile-Based Middleware Architecture for Building Robust, High-Performance Apps

Cited by 5 publications

References 5 publications

InstructableCrowd: Creating IF-THEN Rules for Smartphones via Conversations with the Crowd

InstructableCrowd: Creating IF-THEN Rules for Smartphones via Conversations with the Crowd

NLSC: Unrestricted Natural Language-Based Service Composition through Sentence Embeddings

Cognitively-Inspired Agent-Based Service Composition for Mobile and Pervasive Computing

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