The Pulitzer prizewinner shares his advice for pleasing readers, editors and yourself.

Publishing a paper in academia is challenging, stimulating, and a bit baffling. Challenging because the research might fail. Stimulating because research may start assuming one outcome and finish with a totally different one. Baffling because after the paper is written and ready, I have to find it a home for

Can you spot a fake conference? It seems many researchers can’t. So here are 9 ways to spot a fake.

Summary

1. The conference has an overly ambitious title
2. The technical programme is broad. Very broad!
3. The language on the conference website is… off
4. Renowned organisations are sponsoring a low-profile conference
5. The organisers’ contact details are missing, or aren’t quite right
6. Another conference with a suspiciously similar name already exists
7. The conference or its organisers have known associates
8. The organisers are charging higher-than-normal fees
9. The conference is unusually frequent

At the heart of decentralized systems today is a demoralizing irony. Vast resources---intellect, equipment, and energy---go into avoiding centralized control and creating "trustless" systems like Bitcoin. But hapless users then defeat the whole purpose of these systems by handing over their private keys to centralized entities like Coinbase.

Would it be nice if there were a truly decentralized system that could do the impossible? I.e.,

• Make key management easier for ordinary users.
• Manage secret keys for transparent objects without secret state, like smart contracts.
• Operate seamlessly when nodes come and go.

Clever tricks work around major hurdles, but it's not a route to high performance.

N-grams have been a common tool for information retrieval and machine learning applications for decades. In nearly all previous works, only a few values of $n$ are tested, with $n > 6$ being exceedingly rare. Larger values of $n$ are not tested due to computational burden or the fear of overfitting.

In this work, we present a method to find the top-$k$ most frequent $n$-grams that is 60$\times$ faster for small $n$, and can tackle large $n\geq1024$. Despite the unprecedented size of $n$ considered, we show how these features still have predictive ability for malware classification tasks. More important, large $n$-grams provide benefits in producing features that are interpretable by malware analysis, and can be used to create general purpose signatures compatible with industry standard tools like Yara. Furthermore, the counts of common $n$-grams in a file may be added as features to publicly available human-engineered features that rival efficacy of professionally-developed features when used to train gradient-boosted decision tree models on the EMBER dataset.

With the increasing number of scientific publications, the analysis of the trends and the state-of-the-art in a certain scientific field is becoming very time-consuming and tedious task. In response to urgent needs of information, for which the existing systematic review model does not well, several other review types have emerged, namely the rapid review and scoping reviews.

The paper proposes an NLP powered tool that automates most of the review process by automatic analysis of articles indexed in the IEEE Xplore, PubMed, and Springer digital libraries. We demonstrate the applicability of the toolkit by analyzing articles related to Enhanced Living Environments and Ambient Assisted Living, in accordance with the PRISMA surveying methodology. The relevant articles were processed by the NLP toolkit to identify articles that contain up to 20 properties clustered into 4 logical groups.

The analysis showed increasing attention from the scientific communities towards Enhanced and Assisted living environments over the last 10 years and showed several trends in the specific research topics that fall into this scope. The case study demonstrates that the NLP toolkit can ease and speed up the review process and show valuable insights from the surveyed articles even without manually reading of most of the articles. Moreover, it pinpoints the most relevant articles which contain more properties and therefore, significantly reduces the manual work, while also generating informative tables, charts and graphs.

Supplementary Materials for the paper Tshitoyan et al. "Unsupervised word embeddings capture latent knowledge from materials science literature", Nature (2019).

In a nutshell, it is a type of statistical model used for tagging abstract “topics” that occur in a collection of documents that best represents the information in them.

Many techniques are used to obtain topic models. This post aims to demonstrate the implementation of LDA: a widely used topic modeling technique.

The outputs from scientific research are many and varied, including: research articles reporting new knowledge, data, reagents, and software; intellectual property; and highly trained young scientists. Funding agencies, institutions that employ scientists, and scientists themselves, all have a desire, and need, to assess the quality and impact of scientific outputs. It is thus imperative that scientific output is measured accurately and evaluated wisely.

Scientists used machine learning to reveal new scientific knowledge hidden in old research papers.

Using just the language in millions of old scientific papers, a machine learning algorithm was able to make completely new scientific discoveries.

In a study published in Nature on July 3, researchers from the Lawrence Berkeley National Laboratory used an algorithm called Word2Vec sift through scientific papers for connections humans had missed. Their algorithm then spit out predictions for possible thermoelectric materials, which convert heat to energy and are used in many heating and cooling applications.

Natural language processing algorithms applied to three million materials science abstracts uncover relationships between words, material compositions and properties, and predict potential new thermoelectric materials.

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure–property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.

In research & news articles, keywords form an important component since they provide a concise representation of the article’s content. Keywords also play a crucial role in locating the article from information retrieval systems, bibliographic databases and for search engine optimization. Keywords also help to categorize the article into the relevant subject or discipline.

Conventional approaches of extracting keywords involve manual assignment of keywords based on the article content and the authors’ judgment. This involves a lot of time & effort and also may not be accurate in terms of selecting the appropriate keywords. With the emergence of Natural Language Processing (NLP), keyword extraction has evolved into being effective as well as efficient.

And in this article, we will combine the two — we’ll be applying NLP on a collection of articles (more on this below) to extract keywords.

In the midst of the deep learning hype, p-values might not be the hottest topic in data science. However, association mapping remains a fundamental tool to justify and underpin scientific conclusions. Inspired by an approach for time series classification based on predictive subsequences (i.e shapelets [1]), we developed S3M, a method that identifies short time series subsequences that are statistically associated with a class or phenotype while tackling the multiple hypothesis problem.

Daniel J. Bernstein, Bo-Yin Yang. "Fast constant-time gcd computation and modular inversion."

Lookup the rank of your conference.

"Solo" secondi con il progetto ELIO su più di 80 progetti sottoposti.

A collection of command-line and GUI tools for capturing and analyzing audio data. The most interesting tool is called keytap - it can guess pressed keyboard keys only by analyzing the audio captured from the computer's microphone.

This is the abstract of the paper published on this painting UAV.

This paper describes a system for autonomous spray painting using a UAV, suitable for industrial applications. The work is motivated by the potential for such a system to achieve accurate and fast painting results. The PaintCopter is a quadrotor that has been custom fitted with an arm plus a spray gun on a pan-tilt mechanism. To enable long deployment times for industrial painting tasks, power and paint are delivered by lines from an external unit. The ability to paint planar surfaces such as walls in single color is a basic requirement for a spray painting system. But this work addresses more sophisticated operation that subsumes the basic task, including painting on 3D structure, and painting of a desired texture appearance. System operation consists of (a) an offline component to capture a 3D model of the target surface, (b) an offline component to design the painted surface appearance, and generate the associated robotic painting commands, (c) a live system that carries out the spray painting. Experimental results demonstrate autonomous spray painting by the UAV, doing area fill and versatile line painting on a 3D surface.