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XCube R&D Inc, a leader in massively parallel data management and analytics and software infrastructure for automotive manufacturers and ADAS suppliers to accelerate the development of autonomous vehicle programs announced today the appointment of Michael Mark to its Board of Directors.  For more information about XCube, visit us at www.x3c.com Michael Mark brings extensive knowledge and experience to XCube having served on numerous boards including as Chairman of Progress Software from 2006-2013.  He earned a B.S. in Mathematics from MIT and began his career by co-founding Intercomp, which was sold to Logicon, as well as Cadmus Computer Corporation, which was sold to Apple Computer. Since then he has been involved in personally mentoring and investing in companies such as Stonyfield Farm Yogurt (sold to Groupe Danone), Netegrity (sold to Computer Associates), Kurzweil Educational Technologies (sold to Cambium Learning), Constant Contact,  and Corbus Pharmaceuticals. Mr. Mark finds XCube compelling because "Each and every autonomous vehicle on the road today generates about 7.2 terabytes of data per hour. Such huge masses of data, distributed around the globe, produce unprecedented requirements for data organization, retrieval and analysis. I became excited in XCube when I saw how intelligently and economically their technology managed this data and the arrays of machines processing it." Welcoming Michael Mark, XCube's Chairman and CEO Satish Jha said that "XCube is delighted to have the benefit of the guidance of Michael Mark who is a legend in mentoring start-ups in the Boston area." Mikael Taveniku, XCube's founder and President expresses "Michael Mark has been deeply involved in driving the computing technologies from the very beginning and his insights and knowledge will be an invaluable addition to the XCube team."


A method, system and program product comprise communicating, to a server, a users request for a report comprising a name associated with a portfolio of patent applications, a first value indicating a time frame, and a second value indicating a period of time. The server is operable for using the name for extracting patent application data from databases to a second database and filtering the extracted data using the first value. The filtered data is organized by offices of filing, patent families, classifications, and status. Costs for patent applications associated with the filtered data are determined using the second value. Filing patterns are determined using the offices of filing and the patent families. Filing activity are determined using the classifications. Times to grant for the offices of filing are determined. At least one report is generated and received over the network.


Patent
Computer Associates | Date: 2013-10-14

A system and method is provided for generating a one-time passcode (OTP) from a user device. The method includes providing a passcode application and a cardstring defined by a provider account to the user device. The passcode application is configured to generate a passcode configured as a user OTP for the provider account, using the cardstring. The cardstring is defined by at least one key camouflaged with a personal identification number (PIN). The key may be camouflaged by modifying and encrypting the modified key under the PIN. The key may be configured as a symmetric key, a secret, a seed, and a controlled datum. The cardstring may be an EMV cardstring; and the key may be a UDKA or UDKB. The cardstring may be an OTP cardstring, and the key may be a secret configurable to generate one of a HOTP, a TOTP, and a counter-based OTP.


In some embodiments, a mobile device includes an interface configured to scan information from a communication tag associated with an asset, a memory operable to store the information, and a processor communicatively coupled to the memory. The information comprises a header describing the information, business application data, and asset identification data uniquely identifying the asset. The processor is configured to extract the business application data from the information scanned from the communication tag and extract the asset identification data from the information scanned from the communication tag.


Grant
Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: SBIR | Phase: Phase I | Award Amount: 149.10K | Year: 2015

DESCRIPTION provided by applicant Our research is focused on the use of advanced ontological models as a foundation for computerized Clinical Decision Support CDS systems that link hospitalized patient data routinely captured in electronic medical records EMRs with medical knowledge to effectively influence timely awareness and treatment choices by clinicians Our phase goal is to demonstrate how our advanced CDS technology has the potential to improve preventable mortality outcomes associated with sepsis in ICU patients We distinguish two types of CDS algorithms available today for detecting and or predicting sepsis using EMR data evidence based andquot knowledge drivenandquot detection algorithms and data driven andquot predictiveandquot algorithms based on machine learning ML techniques Recent studies indicate currently available CDS tools do not reduce risk of death in hospitalized patients We believe this may be because diseases such as sepsis are time sensitive complex syndromes and also due to the challenges of computerized reuse of unstructured EMR data Our sepsis ontology models this complexity to a provide enhanced knowledge driven sepsis risk stratified cohort classifications that help guide interventions b support accurate natural language processing NLP of free text clinical notes to enhance real time sepsis risk detection and c improve the accuracy of data driven prediction models when used in conjunction with ML training algorithms Our CDS is based on proprietary cluster computing technology we call andquot VFusionandquot designed to efficiently deal with the generation and practical use of large application domain specific ontologies Our sepsis ontology employs a family of upper level ontologies combined with granular evidence based domain ontologies configurable rule sets e g first order logic based and required components of reference terminologies Our research will use openly available ICU patient data to establish statistical detection prediction performance metrics using this ontology in modes of use as a knowledge based screening tool to detect subtle signs of sepsis in individualized hospitalized patients used in conjunction with ML to improve data driven predictive performance We will measure specificity sensitivity and positive negative predictive power of our hybrid ontology based technology to demonstrate dramatically improved performance over existing CDS algorithms In Phase II we plan a retrospective demonstration with a much larger sample of patients to include non ICU patients in collaboration with a major healthcare system Our product vision is an early inpatient sepsis detection algorithm with high accuracy embodied as a andquot plug in applicationandquot compatible with any modern EMR platform in use at a client hospital effective in both ICU and non ICU care settings PUBLIC HEALTH RELEVANCE Even with the advent of powerful new computer medical record technologies used in hospitals the risk of death has not been reduced in hospitalized patients The goal of this research effort is to combine medical record systems with advanced cognitive technologies that are commonly used today in products such as the iPhone andquot Siriandquot to improve clinician awareness and decisions that will dramatically reduce preventable mortality Our initial use case will be the management of sepsis the leading cause of death in non coronary intensive care units in hospitals


Patent
Computer Associates | Date: 2013-02-08

A method and system for identifying a machine used for an online session with an online provider includes executing a lightweight fingerprint code from a provider interface during an online session to collect and transmit machine and session information; generating and storing a machine signature or identity including a machine effective speed calibration (MESC) which may be used to identify the machine when the machine is used in a subsequent online session by a method of matching the machine signature and MESC to a database of machine identities, analyzing a history of the machines online sessions to identify one or more response indicators, such as fraud indicators, and executing one or more responses to the response indicators, such as disabling a password or denying an online transaction, where the response and response indicator may be provider-designated.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: Secure &Trustworthy Cyberspace | Award Amount: 30.29K | Year: 2015

This project supports 10-15 students for their travel to attend the 2015 Annual Computer Security Applications Conference (ACSAC). ACSAC, which just celebrated its 30th year, is a top-tier academic security conference that attracts averages about 250 highly skilled security professionals and student attendees per year. ACSAC has carved a unique role as a security conference equally attended by academia, industry, and government. In addition to the technical papers, the conference program will include keynotes by distinguished speakers, tutorials, case studies, panels, a poster session, and works-in-progress talks. This project will support attendance by students, with particular attention given to those have a paper, poster, or works-in-progress submission accepted at the conference. In so doing, it will help to provide cybersecurity students with a broad perspective of the discipline.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: Secure &Trustworthy Cyberspace | Award Amount: 50.00K | Year: 2014

The Learning from Authoritative Security Experiment Results (LASER) workshop series focuses on learning from and improving cyber security experiment results. The workshop explores both positive and negative results, the latter of which are not often published. The workshop strives to provide a highly interactive, collegial environment for discussing and learning from experiment design, issues, and outcomes. Ultimately, it seeks to foster a dramatic change in the paradigm of cyber security research and experimentation, improving the overall quality and reporting of practiced science.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: Secure &Trustworthy Cyberspace | Award Amount: 10.00K | Year: 2014

This award provides travel funding and conference registration fees for US-based undergraduate and graduate students to attend the 2014 Annual Computer Security Applications Conference (ACSAC) to be held in New Orleans, LA, on December 8-12, 2014. ACSAC is a top-tier academic security conference that regularly brings together the foremost minds in academic, governmental and commercial computer security to both address the most pressing problems in applied computer security, as well discuss seminal works in information security. The broader impact is to facilitate interaction, and to groom the next generation of security professionals.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: Secure &Trustworthy Cyberspace | Award Amount: 29.94K | Year: 2016

This project provides travel support for 10-15 students attending the 2016 Annual Computer Security Applications Conference (ACSAC). ACSAC, which is celebrating its 32nd year, is a top-tier academic security conference that attracts averages about 250-300 highly skilled security professionals and student attendees per year. ACSAC has carved a unique role as a security conference equally attended by academia, industry, and government. In addition to the technical papers, the conference program will include keynotes by distinguished speakers, tutorials, case studies, panels, a poster session, and works-in-progress talks. This project will help to groom the next generation of cybersecurity professionals by facilitating interaction of students with experts in the field.

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