A Dynamic Stroke Segmentation Technique for Sketched Symbol Recognition

In this paper, we address the problem of ink parsing, which tries to identify distinct symbols from a stream of pen strokes. An important task of this process is the segmentation of the users’ pen strokes into salient fragments based on geometric features. This process allows users to create a sketch symbol varying the number of pen strokes, obtaining a more natural drawing environment. The proposed sketch recognition technique is an extension of LR parsing techniques, and includes ink segmentation and context disambiguation. During the parsing process, the strokes are incrementally segmented by using a dynamic programming algorithm. The segmentation process is based on templates specified in the productions of the grammar specification from which the parser is automatically constructed

Towards Syntax-Aware Editors for Visual Languages

AbstractEditors for visual languages should provide a user-friendly environment supporting end users in the composition of visual sentences in an effective way. Syntax-aware editors are a class of editors that prompt users into writing syntactically correct programs by exploiting information on the visual language syntax. In particular, they do not constrain users to enter only correct syntactic states in a visual sentence. They merely inform the user when visual objects are syntactically correct. This means detecting both syntax and potential semantic errors as early as possible and providing feedback on such errors in a non-intrusive way during editing. As a consequence, error handling strategies are an essential part of such editing style of visual sentences.In this work, we develop a strategy for the construction of syntax-aware visual language editors by integrating incremental subsentence parsers into free-hand editors. The parser combines the LR-based techniques for parsing visual languages with the more general incremental Generalized LR parsing techniques developed for string languages. Such approach has been profitably exploited for introducing a noncorrecting error recovery strategy, and for prompting during the editing the continuation of what the user is drawing

Relaxed Functional Dependencies - A Survey of Approaches

Recently, there has been a renovated interest in functional dependencies due to the possibility of employing them in several advanced database operations, such as data cleaning, query relaxation, record matching, and so forth. In particular, the constraints defined for canonical functional dependencies have been relaxed to capture inconsistencies in real data, patterns of semantically related data, or semantic relationships in complex data types. In this paper, we have surveyed 35 of such functional dependencies, providing a classification criteria, motivating examples, and a systematic analysis of them

Identifying Security and Privacy Violation Rules in Trigger-Action IoT Platforms with NLP Models

Trigger-Action platforms are systems that enable users to easily define, in terms of conditional rules, custom behaviors concerning Internet-of-Things (IoT) devices and web services. Unfortunately, although these tools stimulate the cre- ativity of users in building automation, they may also introduce serious risks for the users. Indeed, trigger-action rules can lead to the possibility of users harming themselves, for example by unintentionally disclosing non-public information, or unwillingly exposing their smart environment to cyber-threats. In this pa- per, we propose to use Natural Language Processing (NLP) techniques to detect automation rules, defined within Trigger- Action IoT platforms, that potentially violate the security or privacy of the users. The proposed NLP-based models capture the semantic and contextual information of the trigger-action rules by applying classification techniques to different combinations of rule’s features. We evaluate the proposed solution with the mainstream trigger-action platform, namely IFTTT, by training the NLP models with a dataset of 76,741 rules labeled by using an ensemble of three semi-supervised learning techniques. The experimental results demonstrate that the model based on BERT (Bidirectional Encoder Representations from Transformers) ob- tains the highest performances when trained on all features, achieving average Precision and Recall values between 88% and 93%. We also compare the achieved performances with those of a baseline system implementing information flow analysis

22nd International Conference Information Visualisation, IV 2018

22nd International Conference Information Visualisation, IV 2018, Fisciano, Italy, July 10-13, 201

Multi-Domain Recognition of Hand-Drawn Diagrams Using Hierarchical Parsing

This paper presents an approach for the recognition of multi-domain hand-drawn diagrams, which exploits Sketch Grammars (SkGs) to model the symbols’ shape and the abstract syntax of diagrammatic notations. The recognition systems automatically generated from SkGs process the input sketches according to the following phases: the user’ strokes are first segmented and interpreted as primitive shapes, then by exploiting the domain context they are clustered into symbols of the domain and, finally, an interpretation of whole diagram is given. The main contribution of this paper is an efficient model of parsing suitable for both interactive and non-interactive sketch-based interfaces, configurable to different domains, and able to exploit contextual information for improving recognition accuracy and solving interpretation ambiguities. The proposed approach was evaluated in the domain of UML class diagrams obtaining good results in terms of recognition accuracy and usability

Integrating Agents, Ontologies, and Web Services to Build Flexible Sketch-based Applications

Abstract. We present an approach based on web services, for building open and dynamic agent societies aimed at hand-drawn sketch recognition. The approach exploits ontologies to enable agents to agree on message semantics and service purposes, standard web services languages to represent agent interaction protocols in a suitable way to be exchanged and handled by agents and web services to expose low-level recognition services. The communication mechanisms that characterize our approach, as well as the modular architecture allow agent societies to self-organize at run time, for gaining the capability of recognizing new domain languages, thus obtaining new flexible sketch-based applications.