Can we do better than co-citations? Bringing Citation Proximity Analysis from idea to practice in research articles recommendation

In this paper, we build on the idea of Citation Proximity Analysis (CPA), originally introduced in [1], by developing a step by step scalable approach for building CPA-based recommender systems. As part of this approach, we introduce three new proximity functions, extending the basic assumption of co-citation analysis (stating that the more often two articles are co-cited in a document, the more likely they are related) to take the distance between the co-cited documents into account. Ask- ing the question of whether CPA can outperform co-citation analysis in recommender systems, we have built a CPA based recommender system from a corpus of 368,385 full-texts articles and conducted a user survey to perform an initial evaluation. Two of our three proximity functions used within CPA outperform co-citations on our evaluation dataset

Extending the DSE: LOD support and TEI/IIIF integration in EVT

Current digital scholarly editions (DSEs) have the opportunity of evolving to dynamic objects interacting with other Internet-based resources thanks to open frameworks such as IIIF and LOD. This paper showcases and discusses two new functionalities of EVT (Edition Visualization Technology), version 2: one improving the management of named entities (f.i. personal names) through the use of LOD resources such as FOAF and DBpedia; the other, providing integration of the published text with digital images of the textual primary sources accessed from online repositories (e.g. e-codices or digital libraries such as the Vaticana or the Ambrosiana) via the IIIF protocol

Inverted pendulum virtual control laboratory

This paper describes a tool for interactive learning that can be used to improve control systems design. The developed system is ready to use and allows testing different control methods. It can be used by students for problem solving and individual learning. The virtual control laboratory was implemented as a teaching aid during lectures on control systems. As there is no need to do any special programming or debugging, the students can focus on the control items. Classical control methods such as PID and State-Space approaches are available and gains can be tuned. A friendly appearance based on openGL 3D shows a simulation of the real word: A cart with an inverted pendulum is "bumped" with a force. The dynamic equations of motion for the control system are linearized assuming that the pendulum does not move more than a few degrees away from the vertical allowing to apply linear control methods. Although, the simulated system is realistic and based on a Dynamics Engine

Deep learning for video game playing

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

The good, the bad and their kins: identifying questions with negative scores in StackOverflow

A rapid increase in the number of questions posted on community question answering (CQA) forums is creating a need for automated methods of question quality moderation to improve the effectiveness of such forums in terms of response time and quality. Such automated approaches should aim to classify questions as good or bad for a particular forum as soon as they are posted based on the guidelines and quality standards defined/listed by the forum. Thus, if a question meets the standard of the forum then it is classified as good else we classify it as bad. In this paper, we propose a method to address this problem of question classification by retrieving similar questions previously asked in the same forum, and then using the text from these previously asked similar questions to predict the quality of the current question. We empirically validate our proposed approach on the set of StackOverflow data, a massive CQA forum for programmers, comprising of about 8M questions. With the use of these additional text retrieved from similar questions, we are able to improve the question quality prediction accuracy by about 2.8% and improve the recall of negatively scored questions by about 4.2%. This improvement of 4.2% in recall would be helpful in automatically flagging questions as bad (unsuitable) for the forum and will speed up the moderation process thus saving time and human effort