Profile-guided code identification and hardening using return oriented programming

2019 ◽  
Vol 48 ◽  
pp. 102364
Author(s):  
Rajesh Kumar Shrivastava ◽  
Chittaranjan Hota
2010 ◽  
Vol 56 (10) ◽  
pp. 1554-1560 ◽  
Author(s):  
Marion L Snyder ◽  
Alexis Carter ◽  
Karen Jenkins ◽  
Corinne R Fantz

BACKGROUND Bar code technology has decreased transcription errors in many healthcare applications. However, we have found that linear bar code identification methods are not failsafe. In this study, we sought to identify the sources of bar code decoding errors that generated incorrect patient identifiers when bar codes were scanned for point-of-care glucose testing and to develop solutions to prevent their occurrence. METHODS We identified misread wristband bar codes, removed them from service, and rescanned them by using 5 different scanner models. Bar codes were reprinted in pristine condition for use as controls. We determined error rates for each bar code–scanner pair and manually calculated internal bar code data integrity checks. RESULTS As many as 3 incorrect patient identifiers were generated from a single bar code. Minor bar code imperfections, failure to control for bar code scanner resolution requirements, and less than optimal printed bar code orientation were confirmed as sources of these errors. Of the scanner models tested, the Roche ACCU-CHEK® glucometer had the highest error rate. The internal data integrity check system did not detect these errors. CONCLUSIONS Bar code–related patient misidentifications can occur. In the worst case, misidentified patient results could have been transmitted to the incorrect patient medical record. This report has profound implications not only for point-of-care testing but also for bar coded medication administration, transfusion recipient certification systems, and other areas where patient misidentifications can be life-threatening. Careful control of bar code scanning and printing equipment specifications will minimize this threat to patient safety. Ultimately, healthcare device manufacturers should adopt more robust and higher fidelity alternatives to linear bar code symbologies.


2019 ◽  
Vol 3 (3) ◽  
pp. 40
Author(s):  
Kristen W. Carlson

Artificial general intelligence (AGI) progression metrics indicate AGI will occur within decades. No proof exists that AGI will benefit humans and not harm or eliminate humans. A set of logically distinct conceptual components is proposed that are necessary and sufficient to (1) ensure various AGI scenarios will not harm humanity, and (2) robustly align AGI and human values and goals. By systematically addressing pathways to malevolent AI we can induce the methods/axioms required to redress them. Distributed ledger technology (DLT, “blockchain”) is integral to this proposal, e.g., “smart contracts” are necessary to address the evolution of AI that will be too fast for human monitoring and intervention. The proposed axioms: (1) Access to technology by market license. (2) Transparent ethics embodied in DLT. (3) Morality encrypted via DLT. (4) Behavior control structure with values at roots. (5) Individual bar-code identification of critical components. (6) Configuration Item (from business continuity/disaster recovery planning). (7) Identity verification secured via DLT. (8) “Smart” automated contracts based on DLT. (9) Decentralized applications—AI software modules encrypted via DLT. (10) Audit trail of component usage stored via DLT. (11) Social ostracism (denial of resources) augmented by DLT petitions. (12) Game theory and mechanism design.


Author(s):  
Nathanael R. Weidler ◽  
Dane Brown ◽  
Samuel A. Mitchel ◽  
Joel Anderson ◽  
Jonathan R. Williams ◽  
...  

2014 ◽  
Vol 41 (12) ◽  
pp. 1018-1025
Author(s):  
Jeehong Kim ◽  
Inhyeok Kim ◽  
Changwoo Min ◽  
Young Ik Eom

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