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This book constitutes the refereed post-proceedings of the Second IFIP WG 12.7 International Workshop on Computational History and Data-Driven Humanities, held in Dublin, Ireland, in May 2016. The 7 full papers presented together with 2 invited talks and 4 lightning talks were carefully reviewed and selected from 14 submissions. The papers focus on the challenge and opportunities of data-driven humanities and cover topics at the interface between computer science, social science, humanities, and mathematics.

Computer science. --- Database management. --- Data mining. --- Artificial intelligence. --- Text processing (Computer science). --- Application software. --- Computer Science. --- Computer Science, general. --- Data Mining and Knowledge Discovery. --- Computer Appl. in Social and Behavioral Sciences. --- Database Management. --- Artificial Intelligence (incl. Robotics). --- Document Preparation and Text Processing. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Processing, Text (Computer science) --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Informatics --- Computer software --- Database management --- Electronic data processing --- Information storage and retrieval systems --- Word processing --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Database searching --- Science --- Social sciences --- Natural language processing (Computer science). --- Artificial Intelligence. --- Natural Language Processing (NLP). --- Data processing. --- NLP (Computer science) --- Artificial intelligence --- Human-computer interaction --- Semantic computing --- Computer Application in Social and Behavioral Sciences.

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The traceability in the food sector has become increasingly important. Traceability systems are used to determine the on-going location of products and to trace products back to their origin and used production methods. Such systems are indispensable to provide transparency and security to consumers. To achieve this, an efficient traceability system must therefore provide current information about the food supply chain items in real time. This is possible by building digital traceability systems.
In this paper, the use of a graph database is proposed to facilitate and improve the performance of traceability requests. This system is presented by detailing the different aspects taken into account during its design. Particular efforts have been made to establish a maintainable and easy to evolve system.
This new solution is finally compared to a naive system relying on a classical relational database. Overall, utilizing a graph database proved to simplify the food traceability analysis and offers better query response times.

Food Traceability --- Graph Database --- Ingénierie, informatique & technologie > Sciences informatiques

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Today the use of drones is widely spread for many tasks, but for some of these,
such as firefighting, it is vital that the operator’s hands are kept free to do their job
properly. Hopefully speech recognition is an exploding discipline in deep learning. This
work therefore focuses on finding a deep learning model that recognises spoken com-
mands to control a drone from a pre-defined set.
The first part of the work was to build a training dataset of commands with the
combination of complete commands generated through the use of text-to-speech APIs
and hand-crafted commands. These were created thanks to the concatenation of an
open source spoken words dataset and another set of spoken words acquired through a
web platform created in order to complete the missing words of the vocabulary com-
mand set. The testing set was acquired by asking people to record complete commands
under real conditions.
The second part of the work focuses more on the different models that could be
developed and all the techniques that can be used. These are presented as an ablation
study, in order to improve the results on a test set in real conditions. Several meth-
ods were applied in order to achieve the final goal: the first is the use of computer
vision models where the input of these models is a simple spectrogram of the different
commands. The results using these types of models were not as good as those of the
new models which take directly the raw waveform as input and combines vision, atten-
tion and self-supervised learning. The best version of this model obtains a F1-Score of
0.9973 on a real conditions dataset.

Ingénierie, informatique & technologie > Sciences informatiques

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This work describes the development of a vector drawing application for the TikZ language with shape recognition features. Shape recognition refers to granting users the ability to draw freely on the application canvas and have their drawings automatically formalised by the algorithm.

TikZ --- vector drawing application --- shape recognition --- Ingénierie, informatique & technologie > Sciences informatiques