Architecture for Efficient String Dictionaries in E-Learning

E-Learning is a response to the new educational needs of society and an important development in Information and Communication Technologies. However, this trend presents many challenges, such as the lack of an architecture that allows a unified management of heterogeneous string dictionaries required by all the users of e-learning environments, which we face in this paper. We mean the string dictionaries needed in information retrieval, content development, “key performance indicators” generation and course management applications. As an example, our approach can deal with different indexation dictionaries required by the course contents and the different online forums that generate a huge number of messages with an unordered structure and a great variety of topics. Our architecture will generate an only dictionary that is shared by all the stakeholders involved in the e-learning process.This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) under Project SEQUOIA-UA (TIN2015-63502-C3-3-R), Project RESCATA (TIN2015-65100-R) and Project PROMETEO/2018/089; and in part by the Spanish Research Agency (AEI) and the European Regional Development Fund (FEDER) under Project CloudDriver4Industry (TIN2017-89266-R)

ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems

Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. When the teacher is restricted to labeling examples selected by the machine, this problem is an instance of active learning. When the teacher can provide additional information to the machine (e.g., suggestions on what examples or predictive features should be used) as the learning task progresses, then the problem becomes one of interactive learning. To accommodate the two-way communication channel needed for efficient interactive learning, the teacher and the machine need an environment that supports an interaction language. The machine can access, process, and summarize more examples than the teacher can see in a lifetime. Based on the machine's output, the teacher can revise the definition of the task or make it more precise. Both the teacher and the machine continuously learn and benefit from the interaction. We have built a platform to (1) produce valuable and deployable models and (2) support research on both the machine learning and user interface challenges of the interactive learning problem. The platform relies on a dedicated, low-latency, distributed, in-memory architecture that allows us to construct web-scale learning machines with quick interaction speed. The purpose of this paper is to describe this architecture and demonstrate how it supports our research efforts. Preliminary results are presented as illustrations of the architecture but are not the primary focus of the paper

Interpretable Probabilistic Password Strength Meters via Deep Learning

Probabilistic password strength meters have been proved to be the most accurate tools to measure password strength. Unfortunately, by construction, they are limited to solely produce an opaque security estimation that fails to fully support the user during the password composition. In the present work, we move the first steps towards cracking the intelligibility barrier of this compelling class of meters. We show that probabilistic password meters inherently own the capability of describing the latent relation occurring between password strength and password structure. In our approach, the security contribution of each character composing a password is disentangled and used to provide explicit fine-grained feedback for the user. Furthermore, unlike existing heuristic constructions, our method is free from any human bias, and, more importantly, its feedback has a clear probabilistic interpretation. In our contribution: (1) we formulate the theoretical foundations of interpretable probabilistic password strength meters; (2) we describe how they can be implemented via an efficient and lightweight deep learning framework suitable for client-side operability.Comment: An abridged version of this paper appears in the proceedings of the 25th European Symposium on Research in Computer Security (ESORICS) 202