A model for opinion agreement and confidence in multi-expert multi-criteria decision making

In multi-expert multi-criteria decision making problems, we often have to deal with different opinions, different importance of criteria and experts, missing data, unexpressed opinions and experts who are not fully confident with their judgment. All these factors make the problem more dificult to solve, and run the risk of making the model logic less transparent. In this paper, we present a model based on simple assumptions described by logical rules, in order to maintain the model transparency and verifiability. In particular the model explicitly considers the level of agreement of experts, such as their importance and confidence

A bayesian approach for on-line max and min auditing

In this paper we consider the on-line max and min query auditing problem: given a private association between fields in a data set, a sequence of max and min queries that have already been posed about the data, their corresponding answers and a new query, deny the answer if a private information is inferred or give the true answer otherwise. We give a probabilistic definition of privacy and demonstrate that max and min queries, without “no duplicates”assumption, can be audited by means of a Bayesian network. Moreover, we show how our auditing approach is able to manage user prior-knowledge

A Taxonomy of Information Retrieval Models and Tools

Information retrieval is attracting significant attention due to the exponential growth of the amount of information available in digital format. The proliferation of information retrieval objects, including algorithms, methods, technologies, and tools, makes it difficult to assess their capabilities and features and to understand the relationships that exist among them. In addition, the terminology is often confusing and misleading, as different terms are used to denote the same, or similar, tasks. This paper proposes a taxonomy of information retrieval models and tools and provides precise definitions for the key terms. The taxonomy consists of superimposing two views: a vertical taxonomy, that classifies IR models with respect to a set of basic features, and a horizontal taxonomy, which classifies IR systems and services with respect to the tasks they support. The aim is to provide a framework for classifying existing information retrieval models and tools and a solid point to assess future developments in the field

Editorial : A five year retrospective

Non peer reviewedProo

beacon based context aware architecture for crowd sensing public transportation scheduling and user habits

Abstract: Crowd sourcing and sensing are relatively recent paradigms that, enabled by the pervasiveness of mobile devices, allow users to transparently contribute in complex problem solving. Their effectiveness depends on people voluntarism, and this could limit their adoption. Recent technologies for automating context-awareness could give a significant impulse to spread crowdsourcing paradigms. In this paper, we propose a distributed software system that exploits mobile devices to improve public transportation efficiency. It takes advantage of the large number of deployed personal mobile devices and uses them as both mule sensors, in cooperation with beacon technology for geofecing, and clients for getting information about bus positions and estimated arrival times. The paper discusses the prototype architecture, its basic application for getting dynamic bus information, and the long-term scope in supporting transportation companies and municipalities, reducing costs, improving bus lines, urban mobility and planning

s2ipt: A Lightweight Network Intrusion Detection/Prevention System based on IPtables

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Effectiveness of Opcode ngrams for Detection of Multi Family Android Malware

With the wide diffusion of smartphones and their usage in a plethora of processes and activities, these devices have been handling an increasing variety of sensitive resources. Attackers are hence producing a large number of malware applications for Android (the most spread mobile platform), often by slightly modifying existing applications, which results in malware being organized in families. Some works in the literature showed that opcodes are informative for detecting malware, not only in the Android platform. In this paper, we investigate if frequencies of ngrams of opcodes are effective in detecting Android malware and if there is some significant malware family for which they are more or less effective. To this end, we designed a method based on state-of-the-art classifiers applied to frequencies of opcodes ngrams. Then, we experimentally evaluated it on a recent dataset composed of 11120 applications, 5560 of which are malware belonging to several different families. Results show that an accuracy of 97% can be obtained on the average, whereas perfect detection rate is achieved for more than one malware family

“Won’t we fix this issue?” : qualitative characterization and automated identification of wontfix issues on GitHub

Context: Addressing user requests in the form of bug reports and Github issues represents a crucial task of any successful software project. However, user-submitted issue reports tend to widely differ in their quality, and developers spend a considerable amount of time handling them. Objective: By collecting a dataset of around 6,000 issues of 279 GitHub projects, we observe that developers take significant time (i.e., about five months, on average) before labeling an issue as a wontfix. For this reason, in this paper, we empirically investigate the nature of wontfix issues and methods to facilitate issue management process. Method: We first manually analyze a sample of 667 wontfix issues, extracted from heterogeneous projects, investigating the common reasons behind a “wontfix decision”, the main characteristics of wontfix issues and the potential factors that could be connected with the time to close them. Furthermore, we experiment with approaches enabling the prediction of wontfix issues by analyzing the titles and descriptions of reported issues when submitted. Results and conclusion: Our investigation sheds some light on the wontfix issues’ characteristics, as well as the potential factors that may affect the time required to make a “wontfix decision”. Our results also demonstrate that it is possible to perform prediction of wontfix issues with high average values of precision, recall, and F-measure (90%-93%)

An Extensible Ubiquitous Architecture for Networked Devices in Smart Living Environments

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