Discovering Communication

What kind of motivation drives child language development? This article presents a computational model and a robotic experiment to articulate the hypothesis that children discover communication as a result of exploring and playing with their environment. The considered robotic agent is intrinsically motivated towards situations in which it optimally progresses in learning. To experience optimal learning progress, it must avoid situations already familiar but also situations where nothing can be learnt. The robot is placed in an environment in which both communicating and non-communicating objects are present. As a consequence of its intrinsic motivation, the robot explores this environment in an organized manner focusing first on non-communicative activities and then discovering the learning potential of certain types of interactive behaviour. In this experiment, the agent ends up being interested by communication through vocal interactions without having a specific drive for communication

Discovering Communication Routes in Translation

Various areas of knowledge provide the opportunity to discover and explore communication routes in all fields of human activity involving intuition, creative power, even speculative insight. The aim of this study is to point out such communication routes in translation, a tool of paramount importance in the arsenal of the comparatist as a translator of legal culture. A recurrent theme in debates about the nature and aims of comparative law, the concept of legal culture has aroused the interest of comparative law scholars, especially after the 1990s. Considering the strong relationship between language, culture and law, one can rightly note that a comparatist, while inevitably assuming the role of translator, should constantly undergo a process of becoming an intercultural person, one mediating between (at least) two legal cultures. He should identify similarities and interpret differences that exist between legal systems for the purpose of establishing communication in a cultural framework, thus contributing to the dislocation of functionalist, instrumentalist understandings of law and focusing on meaning as determined by context

Neural-Augmented Static Analysis of Android Communication

We address the problem of discovering communication links between applications in the popular Android mobile operating system, an important problem for security and privacy in Android. Any scalable static analysis in this complex setting is bound to produce an excessive amount of false-positives, rendering it impractical. To improve precision, we propose to augment static analysis with a trained neural-network model that estimates the probability that a communication link truly exists. We describe a neural-network architecture that encodes abstractions of communicating objects in two applications and estimates the probability with which a link indeed exists. At the heart of our architecture are type-directed encoders (TDE), a general framework for elegantly constructing encoders of a compound data type by recursively composing encoders for its constituent types. We evaluate our approach on a large corpus of Android applications, and demonstrate that it achieves very high accuracy. Further, we conduct thorough interpretability studies to understand the internals of the learned neural networks.Comment: Appears in Proceedings of the 2018 ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE

LEAN LIBRARY COMMUNICATION : MIND THE CUSTOMER

Academic libraries have been discovering communication as an important organizational process. Communication seems to be a relevant and important factor to connect with library customers, and - as a result - to involve and to captivate library customers. However, academic libraries tend to fail in organizing communication processes in a customer oriented way. This is most visible in the use of a wide range of (technology enhanced) communication channels like – for example - social media. This behavior is risky because of probably wrong choices, the waste of valuable resources and forget what library customers want. In this research is investigated how academic libraries should organize communication processes in a customer oriented way. The research has been carried out by a literature study about communication processes, communications channels and effective communication. In the empirical part a group of University students were asked to respond to a survey about their communication preferences with an academic library. Results show that students prefer to communicate face-toface, e-mail, or simply check the library’s website

Deductive Verification of Parallel Programs Using Why3

The Message Passing Interface specification (MPI) defines a portable message-passing API used to program parallel computers. MPI programs manifest a number of challenges on what concerns correctness: sent and expected values in communications may not match, resulting in incorrect computations possibly leading to crashes; and programs may deadlock resulting in wasted resources. Existing tools are not completely satisfactory: model-checking does not scale with the number of processes; testing techniques wastes resources and are highly dependent on the quality of the test set. As an alternative, we present a prototype for a type-based approach to programming and verifying MPI like programs against protocols. Protocols are written in a dependent type language designed so as to capture the most common primitives in MPI, incorporating, in addition, a form of primitive recursion and collective choice. Protocols are then translated into Why3, a deductive software verification tool. Source code, in turn, is written in WhyML, the language of the Why3 platform, and checked against the protocol. Programs that pass verification are guaranteed to be communication safe and free from deadlocks. We verified several parallel programs from textbooks using our approach, and report on the outcome.Comment: In Proceedings ICE 2015, arXiv:1508.0459

Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg