Some user needs can only be met by leveraging the capabilities of others to undertake particular tasks. In this paper, we develop a framework, named CROWDSERVICE, which supplies crowd intelligence and labor as publicly accessible crowd services via mobile crowdsourcing. It employs a genetic algorithm to dynamically synthesize and update near-optimal cost and time constraints for each crowd service involved in a composite service, and selects a near-optimal set of workers for each crowd service to be executed. We implement the proposed framework on Android platforms, and evaluate its effectiveness, scalability and usability in both experimental and user studies.
Guest Editorial: The Provenance of Online Data
Transactions are records that contain a set of items about individuals, and are increasingly collected from many activities on the Internet. Such data are often shared and analyzed, but can contain private information that must be protected. In this paper, we study how well set-based generalization can protect transactions. We propose methods to attack set-generalized transactions by exploiting contextual information available within the released data. Our results show that set-based generalization may not provide adequate protection, and up to 70% of the items added into transactions during generalization to obfuscate original data can be detected with a precision over 80%.
We model the abuse data generation process, using phishing sites across 45,358 hosting providers. We find 84% of the variation in abuse is explained with structural factors alone. We enrich a subset of 105 homogeneous ``statistical twins'' with additional explanatory variables and found abuse is positively associated with the website popularity and with the prevalence of CMSes and negatively associated with price. These factors explain 77% of the remaining variation, questioning premature inferences from raw abuse indicators on security efforts of provider, and suggesting the adoption of similar analysis in all domains where network measurement aims at informing technology policy.
While the number of cloud solutions is continuously increasing, the development and operation of large and distributed cloud-based applications is still challenging. A major challenge is the lack of interoperability between the existing cloud solutions, which increases the complexity of maintaining and evolving complex applications potentially deployed across multiple cloud infrastructures and platforms. In this paper, we show how CloudMF leverages upon MDE and support the DevOps ideas to tame this complexity by providing: (i) a domain-specific language for specifying the provisioning and deployment of multi-cloud applications, and (ii) a models@run-time environment for their continuous provisioning, deployment, and adaptation.
Finding the responsible of an unpleasant situation is often difficult, especially in artificial agents societies. SCIFF is a successful formalization of agent societies, including a language to describe rules and protocols, and an abductive proof-procedure for compliance checking. However, identifying the responsible for a violation is not always clear. In this work, a definition of accountability for artificial societies is formalized in SCIFF. Two tools are provided for the designer of interaction protocols: a guideline, in terms of syntactic features that ensure accountability of the protocol, and a software to identify, for a given protocol, if non-accountability issues could arise.
When card data is exposed in a data breach but has not yet been used to attempt fraud, the overall social costs of that breach depend on whether the financial institutions that issued those cards immediately cancel them and issue new cards or instead wait until fraud is at-tempted. We use a parameterized model and Monte Carlo simulation to compare the cost of reissuing cards to the total expected cost of fraud if cards are not reissued. We find that automatically reissuing cards may have lower social costs than the costs of waiting until fraud is attempted.
As a critical part of DevOps, testing drives seamless mobile Application cycle. However, traditional testing is hard to cover protean user scenarios. Hence, many companies crowdsource testing tasks to workers from open platforms. In crowdsourced testing, test reports are highly redundant and their qualities vary sharply. Hence, it becomes a tedious challenge to manually inspect these test reports. To reduce the inspection cost, we issue the new problem of CLUstering TEst Reports and propose a new framework named TERFUR by aggregating test reports into clusters. Experimental results validate the effectiveness of the proposed framework against comparative methods.
Message routing in mobile opportunistic networks is challenging due to the lack of contemporaneous end-to-end paths. In this paper, we present FGAR, a routing protocol designed by leveraging fine-grained contact characterisation and adaptive message replication. In FGAR, contact history is characterised in a fine-grained manner with timing information using a sliding window mechanism, and future contacts are predicted based on the fine-grained contact information. We design an efficient message replication scheme, in which replication is controlled in a fully decentralised manner. We evaluate our scheme through trace-driven simulations, and results show FGAR outperforms existing schemes.
Modern applications are typically implemented as distributed systems comprising several components. Deciding where to deploy which component is a difficult task that is usually assisted by logical topology recommendations. Choosing inefficient topologies leads to unnecessary operation costs, or results in poor performance. This work introduces a deployment topology optimization approach for distributed applications. We use a performance model generator that extracts models from running applications. The extracted model is used to optimize performance and runtime costs of distributed enterprise applications. We demonstrate the accuracy using the SPECjEnterpriseNEXT industry benchmark as distributed application in an on-premise and in a cloud environment.
Smart Communities are composed of organisations and individuals who share information and make use of that shared information for better decision making. The shared information can be either sensor-generated or user-generated. Social media has become an important source of near-instantaneous user-generated information. One domain where social media data has value is transport and this paper looks at the exploitation of user-generated data: Twitter data, in traffic management domain. This paper proposes an instant traffic alert and warning system based on a novel LDA-based approach (tweet-LDA) for classification of traffic-related tweets and geo-coded incident detection.
The cloud is a distributed Internet-based architecture providing platform and application software resources as services. Through service-orientation and virtualisation, the deployment and provisioning of applications can be managed dynamically, resulting in cloud platforms and applications as interdependent adaptive systems. We discuss principles and patterns for a software architectural style for cloud-based software systems based on a control-theoretic, model-based architectural - taking service-orientation, uncertainty, composition and controller-based adaptation into account for this multi-tiered, distributed environment. A discussion of different use cases with development, implementation and management aspects evaluates the usefulness of the proposed style in an empirical style.
Mobile cloud computing is emerging as a promising approach to enrich user experiences. The computation offloading decision making and tasks scheduling among heterogeneous shared resources in mobile clouds are becoming challenging problems. We address these two problems together as an optimization problem and propose a context-aware mixed integer programming model to provide offline optimal solutions for making offloading decisions and scheduling offloaded tasks among shared computing resources in heterogeneous mobile clouds. The objective is to minimize the global task completion time. we further propose an online algorithm OCOS algorithm based on the rent/buy problem and prove the algorithm is 2-competitive.
Conventional private data publication schemes are targeted at publication of sensitive datsets. Typically these schemes are designed with the objective of retaining as much utility as possible. Such an approach is inapplicable when users have different levels of access to the same data. In this paper, we present an anonymization framework for publishing large datasets with the goals of providing different levels of utility based on access privilege levels. Our experiments on large association graphs show that the proposed techniques are effective, scalable and yield the required level of privacy and utility for each user privacy and access privilege levels.
In the proposed system, various sensors (can be wearable devices) are attached to the human body that measure the data and transmit it to Primary Mobile Device (PMD). The amount of collected data is then forwarded to the Intelligent Building using the internet to process and perform necessary actions. Intelligent Building is composed of big data collection unit (used for filtration and load balancing), Hadoop Processing Unit (HPU) (comprises HDFS and MapReduce), and Analysis and decision unit. The HPU and Analysis and decision unit are equipped with the medical expert system, which reads the sensor data and performs actions.