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Institution

University of Colorado Colorado Springs

EducationColorado Springs, Colorado, United States
About: University of Colorado Colorado Springs is a education organization based out in Colorado Springs, Colorado, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 6664 authors who have published 10872 publications receiving 323416 citations. The organization is also known as: UCCS & University of Colorado at Colorado Springs.


Papers
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Reference EntryDOI
15 Jul 2008

118 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a formalized adaptive open world framework for stealth malware recognition and relate it mathematically to research from other machine learning domains and suggest that several flawed assumptions inherent to most recognition algorithms prevent a direct mapping between the stealth malware detection problem and a machine learning solution.
Abstract: As our professional, social, and financial existences become increasingly digitized and as our government, healthcare, and military infrastructures rely more on computer technologies, they present larger and more lucrative targets for malware. Stealth malware in particular poses an increased threat because it is specifically designed to evade detection mechanisms, spreading dormant, in the wild for extended periods of time, gathering sensitive information or positioning itself for a high-impact zero-day attack. Policing the growing attack surface requires the development of efficient anti-malware solutions with improved generalization to detect novel types of malware and resolve these occurrences with as little burden on human experts as possible. In this paper, we survey malicious stealth technologies as well as existing solutions for detecting and categorizing these countermeasures autonomously. While machine learning offers promising potential for increasingly autonomous solutions with improved generalization to new malware types, both at the network level and at the host level, our findings suggest that several flawed assumptions inherent to most recognition algorithms prevent a direct mapping between the stealth malware recognition problem and a machine learning solution. The most notable of these flawed assumptions is the closed world assumption: that no sample belonging to a class outside of a static training set will appear at query time. We present a formalized adaptive open world framework for stealth malware recognition and relate it mathematically to research from other machine learning domains.

118 citations

Journal ArticleDOI
TL;DR: Flaxseed intake decreased glucose and insulin and improved insulin sensitivity as part of a habitual diet in overweight or obese individuals with pre-diabetes.

118 citations

Journal ArticleDOI
TL;DR: This paper found that shifting individuals' base of selfesteem to more stable, intrinsic self-attributes would reduce psychological defensiveness in the form of self-handicapping attributions and conformity.
Abstract: Two studies were conducted to assess the hypothesis that shifting individuals’ base of self-esteem to more stable, intrinsic self-attributes would reduce psychological defensiveness in the form of self-handicapping attributions and conformity. In Study 1, participants visualized an individual who liked them contingently or noncontingently, or who was neutral toward them, and then made attributions for an impending test performance. Participants who visualized the noncontingently accepting other made fewer self-handicapping attributions. In Study 2, partici pants wrote about an intrinsic self-attribute, an achievement, or a neutral event and then evaluated several abstract art paintings while knowing how other participants purportedly rated the paintings. Participants for whom the intrinsic self was primed conformed less to others’ judgments relative to achievement self-primed and control participants. Discussion focuses on the implications of these findings for understanding the connection between self-es...

118 citations

Journal ArticleDOI
TL;DR: Trauma laparoscopy is a safe method for the evaluation of selected patients with abdominal trauma and can reduce the number of negative and nontherapeutic trauma laparotomies performed.
Abstract: Purpose : To assess the therapeutic potential of emergent laparoscopy in the trauma setting, a retrospective review was performed in a busy urban trauma center. Patients and methods : Between December 1991 and October 1993, 133 hemodynamically stable patients with suspected abdominal injury were evaluated laparoscopically. All laparoscopic procedures were performed in the operating room under general anesthesia. Mechanism of injury was stab wound (58), gunshot wound (57), and blunt trauma (18). No significant injuries were found in 72 patients (54%), and these patients received no further treatment. On the basis of laparoscopic findings, 52 patients underwent formal exploratory laparotomy. Surgical exploration confirmed the presence of significant injuries in 44 of the 52 patients (85%). Therapeutic laparoscopy was performed in 6 patients (5%) for diaphragm repair (4), gastrotomy repair (1), and splenorrhaphy (1). Additionally, 10 patients underwent laparoscopy-guided blood salvage for autotransfusion during laparoscopic evaluation of blunt trauma. Three small-bowel enterotomies were repaired during minilaparotomy. Results : No significant injuries were missed as a result of our use of laparoscopy in trauma assessment. Complications—trocar enterotomy, trocar laceration of the interior epigastric artery, and transient hypotension—occurred in 3 patients secondary to the use of laparoscopy. Conclusions : Trauma laparoscopy is a safe method for the evaluation of selected patients with abdominal trauma and can reduce the number of negative and nontherapeutic trauma laparotomies performed. Limited therapeutic intervention is possible in a small number of patients.

118 citations


Authors

Showing all 6706 results

NameH-indexPapersCitations
Jeff Greenberg10554243600
James F. Scott9971458515
Martin Wikelski8942025821
Neil W. Kowall8927934943
Ananth Dodabalapur8539427246
Tom Pyszczynski8224630590
Patrick S. Kamath7846631281
Connie M. Weaver7747330985
Alejandro Lucia7568023967
Michael J. McKenna7035616227
Timothy J. Craig6945818340
Sheldon Solomon6715023916
Michael H. Stone6537016355
Christopher J. Gostout6533413593
Edward T. Ryan6030311822
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202325
202246
2021568
2020543
2019479
2018454