Hewlett-Packard

About: Hewlett-Packard is a company organization based out in Palo Alto, California, United States. It is known for research contribution in the topics: Signal & Layer (electronics). The organization has 34663 authors who have published 59808 publications receiving 1467218 citations. The organization is also known as: Hewlett Packard & Hewlett-Packard Company.

Pyrolysis of Cellulose under Ammonia Leads to Nitrogen-Doped Nanoporous Carbon Generated through Methane Formation

Abstract: Here, we present a simple one-step fabrication methodology for nitrogen-doped (N-doped) nanoporous carbon membranes via annealing cellulose filter paper under NH3. We found that nitrogen doping (up to 10.3 at %) occurs during cellulose pyrolysis under NH3 at as low as 550 °C. At 700 °C or above, N-doped carbon further reacts with NH3, resulting in a large surface area (up to 1973.3 m2/g). We discovered that the doped nitrogen, in fact, plays an important role in the reaction, leading to carbon gasification. CH4 was experimentally detected by mass spectrometry as a product in the reaction between N-doped carbon and NH3. When compared to conventional activated carbon (1533.6 m2/g), the N-doped nanoporous carbon (1326.5 m2/g) exhibits more than double the unit area capacitance (90 vs 41 mF/m2).

Assessing sentence scoring techniques for extractive text summarization

Abstract: Text summarization is the process of automatically creating a shorter version of one or more text documents. It is an important way of finding relevant information in large text libraries or in the Internet. Essentially, text summarization techniques are classified as Extractive and Abstractive. Extractive techniques perform text summarization by selecting sentences of documents according to some criteria. Abstractive summaries attempt to improve the coherence among sentences by eliminating redundancies and clarifying the contest of sentences. In terms of extractive summarization, sentence scoring is the technique most used for extractive text summarization. This paper describes and performs a quantitative and qualitative assessment of 15 algorithms for sentence scoring available in the literature. Three different datasets (News, Blogs and Article contexts) were evaluated. In addition, directions to improve the sentence extraction results obtained are suggested.

JouleSort: a balanced energy-efficiency benchmark

Abstract: The energy efficiency of computer systems is an important concern in a variety of contexts. In data centers, reducing energy use improves operating cost, scalability, reliability, and other factors. For mobile devices, energy consumption directly affects functionality and usability. We propose and motivate JouleSort, an external sort benchmark, for evaluating the energy efficiency of a wide range of computer systems from clusters to handhelds. We list the criteria, challenges, and pitfalls from our experience in creating a fair energy-efficiency benchmark. Using a commercial sort, we demonstrate a JouleSort system that is over 3.5x as energy-efficient as last year's estimated winner. This system is quite different from those currently used in data centers. It consists of a commodity mobile CPU and 13 laptop drives connected by server-style I/O interfaces.

Automatic construction of a context-aware sentiment lexicon: an optimization approach

Abstract: The explosion of Web opinion data has made essential the need for automatic tools to analyze and understand people's sentiments toward different topics. In most sentiment analysis applications, the sentiment lexicon plays a central role. However, it is well known that there is no universally optimal sentiment lexicon since the polarity of words is sensitive to the topic domain. Even worse, in the same domain the same word may indicate different polarities with respect to different aspects. For example, in a laptop review, "large" is negative for the battery aspect while being positive for the screen aspect. In this paper, we focus on the problem of learning a sentiment lexicon that is not only domain specific but also dependent on the aspect in context given an unlabeled opinionated text collection. We propose a novel optimization framework that provides a unified and principled way to combine different sources of information for learning such a context-dependent sentiment lexicon. Experiments on two data sets (hotel reviews and customer feedback surveys on printers) show that our approach can not only identify new sentiment words specific to the given domain but also determine the different polarities of a word depending on the aspect in context. In further quantitative evaluation, our method is proved to be effective in constructing a high quality lexicon by comparing with a human annotated gold standard. In addition, using the learned context-dependent sentiment lexicon improved the accuracy in an aspect-level sentiment classification task.