Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance. 

A Hierarchical Approach for Multi-task Logistic Regression

Peer-reviewed

Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

La Multi Language Learning Awareness y la importancia de su potenciación didáctica en clase de ELE.

Resumen tomado de la publicación

Aligning business and IT from multi-level learning perspectives

Business and IT alignment is increasingly acknowledged as a key for organisational performance. However, alignment research lack to mechanisms that enable for on-going process with multi-level effects. Multi-level learning allows on-going effectiveness through development of the organisation and improved quality of business and IT strategies. In particular, exploration and exploitation enable effective process of alignment across dynamic multi-level of learning. Hence, this paper proposes a conceptual framework that links multi-level learning and business-IT strategy through the concept of exploration and exploitation, which considers short-term and long-term alignment together to address the challenges of strategic alignment faced in sustaining organisational performance.

Multi-task control strategy for grid-tied inverters based on conservative power theory

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multi-Channel-Learning (MCL)

Lerneinheiten müssen stark variierenden Anforderungen gerecht werden. Neben unterschiedlichen Lerntypen spielen vor allem auch die Umfeldbedingungen eine wesentliche Rolle, in denen Lernprozesse stattfinden. Faktoren wie z. B. die Tagesform führen letztlich dazu, dass nicht einmal für eine einzelne Person konstante Lernpräferenzen herrschen. Mit diesem Beitrag wird vorgeschlagen, zur Lösung des Problems einer Mehrkanalstrategie zu folgen. Allerdings sind spezifische Eigenschaften von Learning-Content-Systemen (LCS) notwendig, um ein sog. Multi-Channel-Learning (MCL) zu ermöglichen. Diese Eigenschaften werden im Beitrag anhand von Informationsmodellen beschrieben werden. Sie sollen als Referenzmodell dienen, das sowohl bei der Entwicklung als auch bei der Auswahl und Anpassung von LCS hilfreich sein kann. Das Referenzmodell wird deduktiv abgeleitet und anhand praktischer Anwendungen geprüft. Vorgestellt werden sowohl Anwendungs- als auch Organisationssysteme, die nach dem Modell realisiert worden sind. Auf dieser Grundlage kann schließlich eine Nutzenabschätzung des Modells für das Multi-Channel-Learnings vorgenommen werden.

An online e-Learning authoring tool to create interactive multi-device learning objects using e-Infrastructure resources

Education can take advantage of e-Infrastructures to provide teachers with new opportunities to increase students' motivation and engagement while they learn. Nevertheless, teachers need to find, integrate and customize the resources provided by e-Infrastructures in an easy way. This paper presents ViSH Editor, an innovative web-based e-Learning authoring tool that aims to allow teachers to create new learning objects using e-Infrastructure resources. These new learning objects are called Virtual Excursions and are created as reusable, granular and interoperable learning objects. This way they can be reused to build new ones and they can be integrated in websites or Learning Management Systems. Details about the design, development and the tool itself are explained in this paper as well as the concept, structure and metadata of the new learning objects. Lastly, some real examples of how to enrich learning using Virtual Excursions are exposed.

Efficient Monte Carlo methods for multi-dimensional learning with classifier chains

Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance – at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.

Facilitating the creation of interactive multi-device Learning Objects using an online authoring tool

Learning Objects facilitate reuse leading to cost and time savings as well as to the enhancement of the quality of educational resources. However, teachers find it difficult to create or to find high quality Learning Objects, and the ones they find need to be customized. Teachers can overcome this problem using suitable authoring systems that enable them to create high quality Learning Objects with little effort. This paper presents an open source online e-Learning authoring tool called ViSH Editor together with four novel interactive Learning Objects that can be created with it: Flashcards, Virtual Tours, Enriched Videos and Interactive Presentations. All these Learning Objects are created as web applications, which can be accessed via mobile devices. Besides, they can be exported to SCORM including their metadata in IEEE LOM format. All of them are described in the paper including an example of each. This approach for creating Learning Objects was validated through two evaluations: a survey among authors and a formal quality evaluation of 209 Learning Objects created with the tool. The results show that ViSH Editor facilitates educators the creation of high quality Learning Objects.

The design and implementation of a multi-task batch operating system and paging interpreter for the PDP-II /

Thesis (M. S.)--University of Illinois at Urbana-Champaign.

A Multi-View Learning Algorithm

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014