Tracing the Ghosts in Our Ethical Shells

2020 ◽  
Author(s):  
Tam ngoc Nguyen
Keyword(s):  
Language Processing ◽  
Behavioral Science ◽  
Model Development ◽  
Continuous Model ◽  
Research Approach ◽  
Information Warfare ◽  
Scientific Model ◽  
Defense Strategies ◽  
Ethical Behaviors ◽  
Attack And Defense

We proposes a new scientific model that enables the ability to collect evidence, and explain the motivations behind people's cyber malicious/ethical behaviors. Existing models mainly focus on detecting already-committed actions and associated response strategies, which is not proactive. That is the reason why little has been done in order to prevent malicious behaviors early, despite the fact that issues like insider threats cost corporations billions of dollars annually, and its time to detection often lasts for more than a year.We address those problems by our main contributions of:+ A better model for ethical/malicious behavioral analysis with a strong focus on understanding people's motivations. + Research results regarding ethical behaviors of more than 200 participants, during the historic Covid-19 pandemic. + Novel attack and defense strategies based on validated model and survey results. + Strategies for continuous model development and integration, utilizing latest technologies such as natural language processing, and machine learning. We employed mixed-mode research approach of: integrating and combining proven behavioral science models, case studying of hackers, survey research, quantitative analysis, and qualitative analysis. For practical deployments, corporations may utilize our model in: improving HR processes and research, prioritizing plans based on the model's informed human behavioral metrics, better analysis in existing or potential cyber insider threat cases, generating more defense tactics in information warfare and so on.

Anatomy of SIP registration removal attack and defense strategies

2014 ◽  
Author(s):  
Ismail Melih Tas ◽  
Onur Ozbirecikli ◽  
Ugur Cagai ◽  
Erhan Taskin ◽  
Huseyin Tas
Keyword(s):  
Defense Strategies ◽  
Attack And Defense

Technology-Mediated Collaboration, Shared Mental Model and Task Performance

2012 ◽  
Vol 24 (1) ◽  
pp. 64-81 ◽  
Author(s):  
Hayward P. Andres
Keyword(s):  
Social Influence ◽  
Direct Observation ◽  
Mental Model ◽  
Social Impact ◽  
Model Development ◽  
Research Approach ◽  
Team Process ◽  
Shared Mental Model ◽  
Team Productivity ◽  
The Impact

This study takes a direct observation research approach to examine how the impact of collaboration mode on team productivity and process satisfaction is mediated by shared mental model. Team cognition and social impact theories are integrated to provide a framework for explaining how technology-mediated collaboration constrains or enhances team shared mental model development and its subsequent impact on task outcomes. Partial least squares analysis revealed that technology-mediated collaboration impacts shared mental model development. The results also demonstrate that timely and accurate development of shared mental model facilitates increases in both productivity and team process satisfaction. Direct observation of team process behaviors suggests that collaboration modes differ not only in their impact on communication facilitation but efficacy-based, motivational, and social influence factors (e.g., self-efficacy and team-efficacy, perceived salience and credibility of contributions, social influence on action, etc.) as well. Shared mental model development requires quality communication among team members that are motivated to participate by a positive team climate that promotes idea convergence.

Evaluation of lexicon- and syntax-based negation detection algorithms using clinical text data

2017 ◽  
Vol 13 (4) ◽  
Author(s):  
J. Manimaran ◽  
T. Velmurugan
Keyword(s):  
Language Processing ◽  
Patient Discharge ◽  
Computational Time ◽  
Practical Implementation ◽  
Automatic Identification ◽  
Kappa Statistics ◽  
Research Approach ◽  
Clinical Text ◽  
Medical Reports ◽  
Negation Detection

AbstractBackground:Clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing (NLP) system. In recent development modules of cTAKES, a negation detection (ND) algorithm is used to improve annotation capabilities and simplify automatic identification of negative context in large clinical documents. In this research, the two types of ND algorithms used are lexicon and syntax, which are analyzed using a database made openly available by the National Center for Biomedical Computing. The aim of this analysis is to find the pros and cons of these algorithms.Methods:Patient medical reports were collected from three institutions included the 2010 i2b2/VA Clinical NLP Challenge, which is the input data for this analysis. This database includes patient discharge summaries and progress notes. The patient data is fed into five ND algorithms: NegEx, ConText, pyConTextNLP, DEEPEN and Negation Resolution (NR). NegEx, ConText and pyConTextNLP are lexicon-based, whereas DEEPEN and NR are syntax-based. The results from these five ND algorithms are post-processed and compared with the annotated data. Finally, the performance of these ND algorithms is evaluated by computing standard measures including F-measure, kappa statistics and ROC, among others, as well as the execution time of each algorithm.Results:This research is tested through practical implementation based on the accuracy of each algorithm’s results and computational time to evaluate its performance in order to find a robust and reliable ND algorithm.Conclusions:The performance of the chosen ND algorithms is analyzed based on the results produced by this research approach. The time and accuracy of each algorithm are calculated and compared to suggest the best method.

scholarly journals P311 Detection and characterisation of extra-intestinal manifestations of IBD in clinical office notes using natural language processing

2020 ◽  
Vol 14 (Supplement_1) ◽  
pp. S309-S310
Author(s):  
R Stidham ◽  
D Yu ◽  
S Lahiri ◽  
V Vydiswaran
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Past History ◽  
Model Development ◽  
Population Based ◽  
Equal Weight ◽  
Disease Experience ◽  
Status Classification ◽  
Therapeutic Decision Making

Abstract Background Extra-Intestinal Manifestations (EIM) occur in nearly 40% of patients with IBD and impact both disease experience and therapeutic decision-making, but are not well captured by administrative codes. We aimed to pilot computational natural language processing (NLP) methods to characterise EIMs using consultant notes. Methods Subjects with a diagnosis of IBD were identified in a single-centre retrospective review of electronic health records (EHR) between 2014–2017. Gastroenterology (GI) notes were annotated by two reviewers for the presence and activity of EIMs. EIM concepts were identified using NLP methods leveraging UMLS libraries and hand-crafted features. EIM characterisation occurred within a ±25-word window around identified EIMs with classifications including inactive concepts (negated, historical, resolved) and active concepts (improved, worsened, active but unchanged). Decisions on EIM status when repeatedly referenced in a document used section-based weighting for status inference, with greatest to least weight ranking for assessment/plan, subjective, past history, exam, and other, respectively. EIM status was classified as ambiguous when multiple conflicting references were present within the same document of approximately equal weight. Model development and testing used an 80/20 dataset split. Results In 4108 unique IBD patients, 1640 (39.9%) had at least 1 EIM identified. The mean age was 41.9 years, 47.2% were male, and 27.0% had biologic exposure. A total of 1240 manually annotated documents (first GI notes) were comprised of 51.1% arthritis, 16.5% ocular, 16.2% psoriasis, with erythema nodosum (EN), pyoderma gangrenosum (PG), and hidradenitis suppurativa (HS) together comprising 16.2% of the cohort. NLP models performed well for correctly classifying both EIM presence and status in a testing set, with overall accuracy, sensitivity, and specificity of 91.2%, 92.9% and 81.8% across all EIMs in notes automatically classified as non-ambiguous (Table 1). NLP methods identified EIM status classification as ambiguous in 38.9% of cases. Conclusion NLP methods can detect and classify EIMs with reasonable performance and efficiency compared with traditional manual chart review. Though source document variation and ambiguity present challenges, NLP offers exciting possibilities for population-based research and decision support.

scholarly journals Towards a scientific data framework to support scientific model development

Data Science ◽  
2019 ◽  
Vol 2 (1-2) ◽  
pp. 245-273
Author(s):  
Gabriele Scalia ◽  
Matteo Pelucchi ◽  
Alessandro Stagni ◽  
Alberto Cuoci ◽  
Tiziano Faravelli ◽  
...  
Keyword(s):  
Model Development ◽  
Scientific Data ◽  
Scientific Model ◽  
Data Framework
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