International Conference Publications
We consider the problem of the automatic synthesis of road traffic rules, motivated by a future scenario in which human and machine-based drivers will coexist on the roads: in that scenario, current road rules may be either unsuitable or inefficient. We approach the problem using Grammatical Evolution (GE). To this end, we propose a road traffic model which includes concepts amenable to be regulated (e.g., lanes, intersections) and which allows drivers to temporarily evade traffic rules when there are no better alternatives. In our GE framework, each individual is a set of rules and its fitness is a weighted sum of traffic efficiency and safety, as resulting from a number of simulations where all drivers are subjected to the same rules. Experimental results show that our approach indeed generates rules leading to a safer and more efficient traffic than enforcing no rules or rules similar to those currently used.
Grammatical Evolution (GE) is an Evolutionary Algorithm which can address any problem whose solution space may be described in terms of a context-free grammar: its most salient feature is a procedure which maps genotypes to phenotypes using the grammar production rules. The search effectiveness of GE may be affected by low locality and high redundancy, which can prevent GE to comply with the basic principle that offspring should inherit some traits from their parents. Indeed, many studies previously investigated the locality and redundancy of GE as originally proposed in [Ryan et al., 1998]. In this paper, we extend those results by considering redundancy and locality during the evolution, rather than statically, hence trying to understand if and how they are influenced by the selective pressure determined by the fitness. Moreover, we consider not only the original GE formulation, but three other variants proposed later. We experimentally find that there is an interaction between locality/redundancy and other evolution-related measures, namely diversity and growth of individual size.
Preservation and restoration of ancient mosaics is a crucial activity for the perpetuation of cultural heritage of many countries. Such an activity is usually based on manual procedures which are typically lengthy and costly. Digital imaging technologies have a great potential in this important application domain, from a number of points of view including smaller costs and much broader functionalities. In this work, we propose a mosaic-oriented image segmentation algorithm aimed at identifying automatically the tiles composing a mosaic based solely on an image of the mosaic itself. Our proposal consists of a Genetic Algorithm, in which we represent each candidate segmentation with a set of quadrangles whose shapes and positions are modified during an evolutionary search based on multi-objective optimization. We evaluate our proposal in detail on a set of real mosaics which differ in age and style. The results are highly promising and in line with the current state-of-the-art.
Motivated by the aim of developing a vision-based system to assist the social interaction of blind persons, the performance of some face detectors are evaluated. The detectors are applied to manually annotated video sequences acquired by blind persons with a glass-mounted camera and a necklace-mounted one. The sequences are relevant to the specific application and demonstrate to be challenging for all the considered detectors. A further analysis is performed to reveal how the performance is affected by some features such as occlusion, rotations, size and position of the face within the frame.
Your Paper has been Accepted, Rejected, or whatever: Automatic Generation of Scientific Paper Reviews
Peer review is widely viewed as an essential step for ensuring scientific quality of a work and is a cornerstone of scholarly publishing. On the other hand, the actors involved in the publishing process are often driven by incentives which may, and increasingly do, undermine the quality of published work, especially in the presence of unethical conduits. In this work we investigate the feasibility of a tool capable of generating fake reviews for a given scientific paper automatically. While a tool of this kind cannot possibly deceive any rigorous editorial procedure, it could nevertheless find a role in several questionable scenarios and magnify the scale of scholarly frauds.
A key feature of our tool is that it is built upon a small knowledge base, which is very important in our context due to the difficulty of finding large amounts of scientific reviews. We experimentally assessed our method with tens of human subjects. We presented to these subjects a mix of genuine and machine generated reviews and we measured the ability of our proposal to actually deceive subjects judgment. The results highlight the ability of our method to produce reviews that often look credible and may subvert the decision.
Consumer reviews are an important information resource for people and a fundamental part of everyday decision-making. Product reviews have an economical relevance which may attract malicious people to commit a review fraud, by writing false reviews. In this work, we investigate the possibility of generating hundreds of false restaurant reviews automatically and very quickly.
We propose and evaluate a method for automatic generation of restaurant reviews tailored to the desired rating and restaurant category. A key feature of our work is the experimental evaluation which involves human users. We assessed the ability of our method to actually deceive users by presenting to them sets of reviews including a mix of genuine reviews and of machine-generated reviews. Users were not aware of the aim of the evaluation and the existence of machine-generated reviews. As it turns out, it is feasible to automatically generate realistic reviews which can manipulate the opinion of the user.
We consider the problem of the filtering of Twitter posts, that is, the hiding of those posts which the user prefers not to visualize on his/her timeline. We define a language for specifying filtering policies suitable for Twitter posts. The language allows each user to decide which posts to filter out based on his/her sensibility and preferences. Since average users may not have the skills necessary to translate their filtering needs into a set of rules, we also propose a method for inferring a policy automatically, based solely on examples of the desired filtering behavior. The method is based on an evolutionary approach driven by a multi-objective optimization scheme. We assess our proposal experimentally on a real Twitter dataset and the results are highly promising.
While mobile devices become more pervasive every day, the interest in them from attackers is also increasing, making effective malware detection tools of ultimate importance for malware investigation and users protection.
The most informative way of malware identification is to say when exactly and how malicious behavior is exposed. In this way, better understanding of malware can be achieved and effective tools for its detection can be written.However, due to complexity of such task, most of the current approaches classify complete application into malicious or benign, without giving further insight into which parts of it were malicious.
In this work, we propose a technique for the automatic analysis of mobile applications which allows users/analysts to identify the subsequences of execution traces where malicious activity happens, hence making easier further manual analysis and understanding of malware. Our technique is based on dynamic features concerning resources usage and system calls, which are jointly collected while the application is executed. An execution trace is then split in shorter chunks that are analyzed with machine learning techniques to detect local malicious behavior. Obtained results on the analysis of 3232 Android applications show that collected features contain enough information to identify suspicious execution traces that should be further analysed and investigated.
The rapid growth in smartphone and tablet usage over the last years has led to the inevitable rise in targeting of these devices by cyber-criminals. The exponential growth of Android devices, and the buoyant and largely unregulated Android app market, produced a sharp rise in malware targeting that platform. Furthermore, malware writers have been developing detection-evasion techniques which rapidly make anti-malware technologies ineffective. It is hence advisable that security expert are provided with tools which can aid them in the analysis of existing and new Android malware.
In this paper, we explore the use of topic modeling as a technique which can assist experts to analyse malware applications in order to discover their characteristic. We apply Latend Dirichlet Allocation (LDA) to mobile applications represented as opcode sequences, hence considering a topic as a discrete distribution of opcode. Our experiments on a dataset of 900 malware applications of different families show that the information provided by topic modeling may help in better understanding malware characteristics and similarities.
Several research efforts have shown that a similarity function synthesized from examples may capture an application-specific similarity criterion in a way that fits the application needs more effectively than a generic distance definition. In this work, we propose a similarity learning algorithm tailored to problems of syntax-based entity extraction from unstructured text streams. The algorithm takes in input pairs of strings along with an indication of whether they adhere or not adhere to the same syntactic pattern. Our approach is based on Grammatical Evolution and explores systematically a similarity definition space including all functions that may be expressed with a specialized, simple language that we have defined for this purpose. We assessed our proposal on patterns representative of practical applications. The results suggest that the proposed approach is indeed feasible and that the learned similarity function is more effective than the Levenshtein distance and the Jaccard similarity index.
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