The Development of a Temporal Information Dictionary for Social Media Analytics

Dictionaries have been used to analyze text even before the emergence of social media and the use of dictionaries for sentiment analysis there. While dictionaries have been used to understand the tonality of text, so far it has not been possible to automatically detect if the tonality refers to the present, past, or future. In this research, we develop a dictionary containing time-indicating words in a wordlist (T-wordlist). To test how the dictionary performs, we apply our T-wordlist on different disaster related social media datasets. Subsequently we will validate the wordlist and results by a manual content analysis. So far, in this research-in-progress, we were able to develop a first dictionary and will also provide some initial insight into the performance of our wordlist

Failure to Recover from Proactive Semantic Interference Differentiates Amnestic Mild Cognitive Impairment and PreMCI from Normal Aging after Adjusting for Initial Learning Ability

Background: There is increasing evidence that the failure to recover from proactive semantic interference (frPSI) may be an early cognitive marker of preclinical Alzheimer’s disease (AD). However, it is unclear whether frPSI effects reflect deficiencies in an individual’s initial learning capacity versus the actual inability to learn new semantically related targets. Objective: The current study was designed to adjust for learning capacity and then to examine the extent to which frPSI, proactive semantic interference (PSI) and retroactive semantic interference (RSI) effects could differentiate between older adults who were cognitively normal (CN), and those diagnosed with either Pre-Mild Cognitive Impairment (PreMCI) or amnestic MCI (aMCI). Methods: We employed the LASSI-L cognitive stress test to examine frPSI, PSI and RSI effects while simultaneously controlling for the participant’s initial learning capacity among 50 CN, 35 aMCI, and 16 PreMCI participants who received an extensive diagnostic work-up. Results: aMCI and PreMCI participants showed greater frPSI deficits (50% and 43.8% respectively) compared to only 14% of CNparticipants. PSI effects were observed for aMCI but not PreMCI participants relative to their CN counterparts. RSI failed to differentiate between any of the study groups. Conclusion: By using participants as their own controls and adjusting for overall learning and memory, it is clear that frPSI deficits occur with much greater frequency in individuals at higher risk for Alzheimer’s disease (AD), and likely reflect a failure of brain compensatory mechanisms.Fil: Curiel, Rosie E.. University of Miami; Estados UnidosFil: Crocco, Elizabeth A.. University of Miami; Estados UnidosFil: Raffo, Arlene. University of Miami; Estados UnidosFil: Guinjoan, Salvador Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Nemeroff, Charles B.. University of Miami; Estados UnidosFil: Penate, Ailyn. Mount Sinai Medical Center; Estados Unidos. University of Miami; Estados UnidosFil: Piña, Daema. University of Miami; Estados UnidosFil: Loewenstein, David A.. Mount Sinai Medical Center; Estados Unidos. University of Miami; Estados Unido

Finding evidence of wordlists being deployed against SSH Honeypots - implications and impacts

This paper is an investigation focusing on activities detected by three SSH honeypots that utilise Kippo honeypot software. The honeypots were located on the same /24 IPv4 network and configured as identically as possible. The honeypots used the same base software and hardware configurations. The data from the honeypots were collected during the period 17th July 2012 and 26th November 2013, a total of 497 active day periods. The analysis in this paper focuses on the techniques used to attempt to gain access to these systems by attacking entities. Although all three honeypots are have the same configuration settings and are located on the same IPv4 /24 subnet work space, there is a variation between the numbers of activities recorded on each honeypots. Automated password guessing using wordlists is one technique employed by cyber criminals in attempts to gain access to devices on the Internet. The research suggests there is wide use of automated password tools and wordlists in attempts to gain access to the SSH honeypots, there are also a wide range of account types being probed

GPERF : a perfect hash function generator

gperf is a widely available perfect hash function generator written in C++. It automates a common system software operation: keyword recognition. gperf translates an n element user-specified keyword list keyfile into source code containing a k element lookup table and a pair of functions, phash and in_word_set. phash uniquely maps keywords in keyfile onto the range 0 .. k - 1, where k >/= n. If k = n, then phash is considered a minimal perfect hash function. in_word_set uses phash to determine whether a particular string of characters str occurs in the keyfile, using at most one string comparison.This paper describes the user-interface, options, features, algorithm design and implementation strategies incorporated in gperf. It also presents the results from an empirical comparison between gperf-generated recognizers and other popular techniques for reserved word lookup