Journal of Innovation in Digital Ecosystems最新文献

Filtering data generated by so-called Voting Advice Applications (VAAs) in order to remove entries that exhibit unrealistic behavior (i.e., cannot correspond to a real political view) is of primary importance. If such entries are significantly present in VAA generated datasets, they can render conclusions drawn from VAA data analysis invalid. In this work we investigate approaches that can be used for automating the process of identifying entries that appear to be suspicious in terms of a users’ answer patterns. We utilize two unsupervised data mining techniques and compare their performance against a well established psychometric approach. Our results suggest that the performance of data mining approaches is comparable to those drawing on psychometric theory with a fraction of the complexity. More specifically, our simulations show that data mining techniques as well as psychometric approaches can be used to identify truly ‘rogue’ data (i.e., completely random data injected into the dataset under investigation). However, when analysing real datasets the performance of all approaches dropped considerably. This suggests that ‘suspect’ entries are neither random nor clustered. This finding poses some limitations on the use of unsupervised techniques, suggesting that the latter can only complement rather than substitute existing methods to identifying suspicious entries.

The Internet of Things (IoT) is an ecosystem comprising interrelated wireless devices. Web services developed over Service oriented Architectures (SoA) are among the most promising solution to facilitate the communication of things in the IoT. Web services interact with each other, irrespective of features such as operating system, or programming language. One of the main challenges facing such a platform is the declaration of SLAs, and the monitoring of violations. This is because the IoT allows users to build large, distributed, and complex applications. Therefore, it critical to develop a method to facilitate the supervision and management of SLAs. The method proposed in this paper aims to automate the generation of a QoS-Aware service, providing real-time monitoring. The algorithm used to produce the service is inspired by the diagnosability theory of Discrete Event System (DES). In this approach, the SLAs defined by users are automatically mapped into a UML QoS model. The SoA composite application for each site is then mapped into a Petri net. The UML QoS model is then transferred and combined with the Petri net model. An algorithm to compute the QoS-Aware service is then applied to the Petri net model, and the service produced finally incorporated into the system.

This paper presents CogWatch - an automatic prompting system designed to guide stroke survivors during activities of daily living, such as tea-making. In order to provide guidance during such activities, CogWatch needs to plan which optimal action should be done by users at each step of the task, and detect potential errors in their behavior. This paper focuses on the CogWatch Task Manager, which contains the modules responsible for action planning and human’s error detection under uncertainty. We first give an overview of the global assistive system where the Task Manager is implemented, and explain how it can interact with a user during the tea-making task. We then analyze how novel algorithms allow the Task Manager to increase the system’s performance.

Sophisticated malware authors can sneak hidden malicious contents into portable executable files, and this contents can be hard to detect, especially if encrypted or compressed. However, when an executable file switches between contents regimes (e.g., native, encrypted, compressed, text, and padding), there are corresponding shifts in the file’s representation as an entropy signal. In this paper, we develop a method for automatically quantifying the extent to which patterned variations in a file’s entropy signal make it “suspicious”. In Experiment 1, we use wavelet transforms to define a Suspiciously Structured Entropic Change Score (SSECS), a scalar feature that quantifies the suspiciousness of a file based on its distribution of entropic energy across multiple levels of spatial resolution. Based on this single feature, it was possible to raise predictive accuracy on a malware detection task from 50.0% to 68.7%, even though the single feature was applied to a heterogeneous corpus of malware discovered “in the wild”. In Experiment 2, we describe how wavelet-based decompositions of software entropy can be applied to a parasitic malware detection task involving large numbers of samples and features. By extracting only string and entropy features (with wavelet decompositions) from software samples, we are able to obtain almost 99% detection of parasitic malware with fewer than 1% false positives on good files. Moreover, the addition of wavelet-based features uniformly improved detection performance across plausible false positive rates, both in a strings-only model (e.g., from 80.90% to 82.97%) and a strings-plus-entropy model (e.g. from 92.10% to 94.74%, and from 98.63% to 98.90%). Overall, wavelet decomposition of software entropy can be useful for machine learning models for detecting malware based on extracting millions of features from executable files.

Intelligent Autonomous Vehicles (IAVs) constitute one of the component systems of Intelligent Transportation System (ITS) that can operate in confined private spaces, as well as in open and public spaces. The seaports or container terminals are one of the important confined spaces that have attracted extensive research interests over the last decade in the use of information communication technology to improve the operation of ITS. The main goal of research works undertaken so far in this area was to improving the efficiency and cost-effectiveness of the indoor traffic, by transporting optimally and sustainably freight from ship to the logistics and unloading areas. The use of a team of IAVs with wireless communication capabilities by rearranging efficiently all operations of handling, routing,…is a strategic objective for seaport authorities and their customers. In this paper, we consider inter-Vehicles communication system in which IAVs can communicate and cooperate to avoid collision problem in the predetermined intersection areas in the yard. We investigate the performance of our solution through simulations using Omnet++/Veins Simulation framework. We show that the implemented cooperation mechanism can significantly reduce the unloading time in the seaport.

This paper will review the evolution of validation techniques and their current status in Requirements Engineering (RE). We start by answering the following questions: What validate? Why the benefits of having the requirements validation activities during the RE process? Who are the stakeholders involved in the requirements validation process? Where applied the validation in the RE process? and How the techniques and the approaches of requirements validation?

The vacuum cleaner robot should have a mechanism such as the artificial intelligence to solve the problem of cleaning the entire environment areas taking into account some factors such as the number of turns and the length of the trajectory. This robot’s mechanism or task is known as the path planning of coverage region (PPCR). In this paper, to resolve the problem of PPCR in a room environment, we propose an evolutionary approach. The latter is based on Genetic Algorithms (GA) which, consist of several steps to get the solutions. Each gene represents the robot position and some of chromosomes represent also the mini-path. In addition, this algorithm helps the robot to pass through every part of the environment by avoiding obstacles using different sensors. The results of simulation and comparison studies demonstrate the effectiveness and efficiency of the proposed approach.

A DF-ALC (Decomposing fuzzy ALC) is proposed in this paper to satisfy the need for representing and reasoning with fuzzy ontologies in the context of semantic Web. A DF-ALC is also proposed to satisfy the need for seeing the necessity of decomposing ontology into several sub-ontologies in order to optimize the fuzzy reasoning process.

The main contribution of this work is to decompose the axioms of the ontology into sub-axioms according to a degree of certainty which is assigned to the fuzzy concepts and roles. It is also to define the syntax and semantics and to propose a local reasoning algorithm and a way of using gateways to infer between local TBox.

In this paper, a distributed solution is proposed for detecting boundaries and holes in the WSN using only the nodes connectivity information. The run of our protocol is divided imto three main steps. In the first step, each node collects connectivity information of its one-hop neighbors and constructs its one-hop neighbors’ graph. In the second step, independent sets are constructed. In the last step, the independent sets are connected in order to find the closed path. Therefore, the node can make its own decision to be an internal or a boundary node. Simulation results show that our algorithm can detect fine-grained boundaries with high accuracy, low energy consumption and less communication overhead compared to some former works. In addition, this algorithm performs better than some exiting approaches (BCP, THD, and SDBR).

A key aspect of the design of any software system is its architecture. One issue for perpetually designing good and robust architectures is the new security concepts. Many new applications are running on powerful platforms that have ample rich architecture models to support multiples security techniques and to explicit several security constraints. The design of an architecture meta-model that considers security connectors is required in order to ensure a realistic secure assembly and to address the problems of vulnerability of exchanging data flow. Our research proposes a generic meta-modelling approach called SMSA (Security Meta-model for Software Architecture) for describing a software system as a collection of components that interact through security connectors. SMSA metamodel is modeled as a UML SMSA profile. We exploit UML powerful capacities (meta-models and models) to define security concepts of SMSA (e.g. security connectors, composite and domain). A major benefit of UML profile is to the faithful representation of connectors to support the definition of security connector types explicitly and to support them with the ability to associate semantic properties. We also provide a set of model transformations to fit security requirements of a system. These transformations are detailed and validated with phosphate support system (SAGE) for the company FERPHOS: a case study described in SMSA. The model is tested and validated with the semantic constraints defined by the profile using Eclipse 3.1 plug-in in this case study.