Improving Unstructured Text Summarization Using An Ensemble Approach

Due to the explosive amounts of text data being created and organizations increased desire to leverage their data corpora, especially with the availability of Big Data platforms, there is not usually enough time to read and understand each document and make decisions based on document contents. Hence, there is a great demand for summarizing text documents to provide a precise substitute for the original documents. In this article we present an ensemble approach that combines several of the well-researched text summarization techniques to produce better document summaries than individual techniques.An experiment that uses the ensemble approach was designed and results were evaluated. For the purpose of the experiment the ensemble combined the cosine similarity, enhanced latent semantic analysis using SVD, and maximal marginal relevance measure algorithms. The ensemble was applied on two datasets and the results were found to be promising when compared to the manual summaries developed by human evaluators

PQL: Protein Query Language

This paper introduces a Protein Query Language (PQL) for querying protein structures in an expressive yet concise manner. One of the objectives of the paper is to demonstrate how such a language would be beneficial to protein researchers to obtain in-depth protein data from a relational database without extensive SQL knowledge. The language features options such as limiting query results by key protein characteristics such as methyl donated hydrogen bond interactions, minimum and maximum phi and psi angles, repulsive forces, CH/Pi calculations, and other pertinent factors. A backend data model was designed to support storage and retrieval of protein primary and secondary sequences, atomic-level data, as well as calculations on said data. A relational DBMS is used as the persistent storage backend, with every effort made to ensure transparent portability to most relational database systems. In addition, front end applications can be developed to support retrieving, transforming, and preprocessing of information from the Research Collaboratory for Structural Bioinformatics (RCSB) into the backend data repository. The new language and associated architecture allow users to load additional protein files from RCSB into the database, issue standard queries to download pertinent data in user-friendly formats including CSV files, issue non-standard queries against secondary structures via the protein query language, and run error-detection routines against data in the database. Query results may include normalized or denormalized data, model and chain data, residue data, atom detail data, and primary as well as secondary structure data. © 2012 IEEE

iPBA: Behavioral Financial Analysis

Dealing with data is arguably one of very few commonalities between researchers of different disciplines. A year ago the interdisciplinary program for big-data analytics (iPBA) was formed with a goal of creating a forum for faculty to share thoughts and research ideas related to dealing with data. An interdisciplinary team of three faculty and two graduate students from finance and computer sciences was formed, and an extensible infrastructure was developed to collect and manage real-time twitter streams and individual stock trade data. One of the early project ideas focused on studying the social media and stock markets, which was inspired by the fact that the former may have an impact on stock prices (Hachman, 2011). We investigate whether a bi-directional intraday relationship between stock volatility and tweets exists by analyzing minute-by-minute stock price and tweet data for the 30 stocks in the Dow Jones Industrial average over a random 13-day interval from June 2 to June 18, 2014. We find strong evidence of a bi-directional relationship between returns and tweets, both between lagged innovations and current conditional volatilities and between immediate and persistent volatilities. These results may help traders achieve superior returns by buying and selling individual stocks or options

From Returns to Tweets and Back: An Investigation of the Stocks in the Dow Jones Industrial Average

A sizeable percentage of investors are using social media to obtain information about companies (Cogent Research [2008]). As a consequence, social media content about firms may have an impact on stock prices (Hachman [2011]). Various studies utilize social media content to forecast stock market-related factors such as returns, volatility, or trading volume. The objective of this article is to investigate whether a bidirectional intraday relationship between stock returns and volatility and tweets exists. The study analyzed 150,180 minute-by-minute stock price and tweet data for the 30 stocks in the Dow Jones Industrial Average over a random 13-day interval from June 2 to June 18, 2014 using a BEKK-MVGARCH methodology. Findings indicate that 87% of stock returns are influenced by lagged innovations of the tweets data, but there is little evidence to support that the direction is reciprocal, with only 7% of tweets being influenced by lagged innovations of the stock returns. Results further show that the lagged innovations from 40 percent of stock returns affect the current conditional volatility of the tweets, while 73 percent of tweets affect the current conditional volatility of stock returns. Moreover, there is strong evidence to suggest that the volatility originating from the returns to the tweets persists for 33 percent of stocks; the volatility originating from the tweets to the returns persists for 73 percent of stocks. Last, 53 percent of stocks exhibit both immediate and persistent impacts from returns to tweets, while 90 percent of stocks exhibit both immediate and persistent impacts from tweets to returns. These results may help traders achieve superior returns by buying and selling individual stocks or options. Also, asset and mutual fund managers may benefit by developing a social media strategy

Survey of Security Challenges in NFC and RFID for E-Health Applications

Hospitals worldwide have implemented High Frequency (HF) Radio Frequency Identification (RFID) networks for supplies tracking in ER setting, in-patient identification, surgical instrument management, and other applications. Merging of Web, Near Filed Communication (NFC), and HF RFID technologies for their combined use in e-Health applications is a logical next step due to the wide availability of NFC-enabled smartphones. This article outlines some resulting security challenges. Tags are often compliant with multiple standards that operate in the same frequency range. For example, HF RFID tags have already been adopted for in-patient tracking, yet smartphone NFC reader apps can freely access data on those tags. While tag- or session-centered security protocols exist for some RFID standards (e.g. ISO/IEC 29167), no ISO security standard is currently available for HF RFID tags. In such systems, proper traffic characterization can lead to better understanding of operation under normal system state conditions and could potentially help to identify security breaches

Survey of security challenges in NFC and RFID for e-Health applications

Hospitals worldwide have implemented High Frequency (HF) Radio Frequency Identification (RFID) networks for supplies tracking in ER setting, in-patient identification, surgical instrument management, and other applications. Merging of Web, Near Filed Communication (NFC), and HF RFID technologies for their combined use in e-Health applications is a logical next step due to the wide availability of NFC-enabled smartphones. This article outlines some resulting security challenges. Tags are often compliant with multiple standards that operate in the same frequency range. For example, HF RFID tags have already been adopted for in-patient tracking, yet smartphone NFC reader apps can freely access data on those tags. While tag- or session-centered security protocols exist for some RFID standards (e.g. ISO/IEC 29167), no ISO security standard is currently available for HF RFID tags. In such systems, proper traffic characterization can lead to better understanding of operation under normal system state conditions and could potentially help to identify security breaches

Heart rate variability as an indicator of COVID-19 induced myocardial injury: a retrospective cohort study

Abstract Background Heart rate variability (HRV) is a valuable indicator of autonomic nervous system integrity and can be a prognostic tool of COVID-19 induced myocardial affection. This study aimed to compare HRV indices between patients who developed myocardial injury and those without myocardial injury in COVID-19 patients who were admitted to intensive care unit (ICU). Methods In this retrospective study, the data from 238 COVID-19 adult patients who were admitted to ICU from April 2020 to June 2021 were collected. The patients were assigned to myocardial injury and non-myocardial injury groups. The main collected data were R-R intervals, standard deviation of NN intervals (SDANN) and the root mean square of successive differences between normal heartbeats (RMSSD) that were measured daily during the first five days of ICU admission. Results The R-R intervals, the SDANN and the RMSSD were significantly shorter in the myocardial injury group than the non-myocardial group at the first, t second, third, fourth and the fifth days of ICU admission. There were no significant differences between the myocardial injury and the non-myocardial injury groups with regard the number of patients who needed mechanical ventilation, ICU length of stay and the number of ICU deaths. Conclusions From the results of this retrospective study, we concluded that the indices of HRV were greatly affected in COVID-19 patients who developed myocardial injury