Facts, scope, style: a guide to writing papers for IEEE transactions on consumer electronics

In a previous article, I wrote a brief piece on how to enhance papers that have been published at one of the IEEE Consumer Electronics (CE) Society conferences to create papers that can be considered for publishing in IEEE Transactions on Consumer Electronics (T-CE) [1]. Basically, it included some hints and tips to enhance a conference paper into what is required for a full archival journal paper and not fall foul of self-plagiarism. This article focuses on writing original papers specifically for T-CE. After three years as the journal’s editor-in-chief (EiC), a previous eight years on the editorial board, and having reviewed some 4,000 T-CE papers, I decided to write this article to archive and detail for prospective authors what I have learned over this time. Of course, there are numerous articles on writing good papers—some are really useful [2], but they do not address the specific issues of writing for a journal whose topic (scope) is not widely understood or, indeed, is often misunderstood.

Análise do mercado de capitais brasileiro: enfoque para emissões de renda fixa de curto prazo: commercial papers

Trata do mercado de capitais no Brasil abordando as características das emissões locais de "Commercial Papers", tendo como objetivos a descrição do processo atual de originação, estruturação e distribuição destes valores mobiliários, como também, a identificação dos motivos pelos quais este mercado não se desenvolve no Brasil.

Combining collaborative and content-based filtering to recommend research papers

The number of research papers available today is growing at a staggering rate, generating a huge amount of information that people cannot keep up with. According to a tendency indicated by the United States’ National Science Foundation, more than 10 million new papers will be published in the next 20 years. Because most of these papers will be available on the Web, this research focus on exploring issues on recommending research papers to users, in order to directly lead users to papers of their interest. Recommender systems are used to recommend items to users among a huge stream of available items, according to users’ interests. This research focuses on the two most prevalent techniques to date, namely Content-Based Filtering and Collaborative Filtering. The first explores the text of the paper itself, recommending items similar in content to the ones the user has rated in the past. The second explores the citation web existing among papers. As these two techniques have complementary advantages, we explored hybrid approaches to recommending research papers. We created standalone and hybrid versions of algorithms and evaluated them through both offline experiments on a database of 102,295 papers, and an online experiment with 110 users. Our results show that the two techniques can be successfully combined to recommend papers. The coverage is also increased at the level of 100% in the hybrid algorithms. In addition, we found that different algorithms are more suitable for recommending different kinds of papers. Finally, we verified that users’ research experience influences the way users perceive recommendations. In parallel, we found that there are no significant differences in recommending papers for users from different countries. However, our results showed that users’ interacting with a research paper Recommender Systems are much happier when the interface is presented in the user’s native language, regardless the language that the papers are written. Therefore, an interface should be tailored to the user’s mother language.