9 research outputs found
Developing an Algorithm for Securing the Biometric Data Template in the Database
This research article published by the International Journal of Advanced Computer Science and Applications, Vol. 10, No. 10, 2019In the current technology advancement, biometric
template provides a dependable solution to the problem of user
verification in an identity control system. The template is saved
in the database during the enrollment and compared with query
information in the verification stage. Serious security and
privacy concerns can arise, if raw, unprotected data template is
saved in the database. An attacker can hack the template
information in the database to gain illicit access. A novel
approach of encryption-decryption algorithm utilizing a design
pattern of Model View Template (MVT) is developed to secure
the biometric data template. The model manages information
logically, the view shows the visualization of the data, and the
template addresses the data migration into pattern object. The
established algorithm is based on the cryptographic module of
the Fernet key instance. The Fernet keys are combined to
generate a multiFernet key to produce two encrypted files (byte
and text file). These files are incorporated with Twilio message
and securely preserved in the database. In the event where an
attacker tries to access the biometric data template in the
database, the system alerts the user and stops the attacker from
unauthorized access, and cross-verify the impersonator based on
the validation of the ownership. Thus, helps inform the users and
the authority of, how secure the individual biometric data
template is, and provided a high level of the security pertaining
the individual data privac
Intelligent Circuits and Systems
ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society. This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering
Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress
Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018
Knowledge for Governance
This open access book focuses on theoretical and empirical intersections between governance, knowledge and space from an interdisciplinary perspective. The contributions elucidate how knowledge is a prerequisite as well as a driver of governance efficacy, and conversely, how governance affects the creation and use of knowledge and innovation in geographical context. Scholars from the fields of anthropology, economics, geography, public administration, political science, sociology, and organization studies provide original theoretical discussions along these interdependencies. Moreover, a variety of empirical chapters on governance issues, ranging from regional and national to global scales and covering case studies in Australia, Europe, Latina America, North America and South Africa demonstrate that geography and space are not only important contexts for governance that affect the contingent outcomes of governance blueprints. Governance also creates spaces. It affects the geographical confines as well as the quality of opportunities and constraints that actors enjoy to establish legitimate and sustainable ways of social and environmental co-existence
Natural Language Processing: Emerging Neural Approaches and Applications
This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains
Evaluating Machine Intelligence with Question Answering
Humans ask questions to learn about the world and to test knowledge understanding. The ability to ask questions combines aspects of intelligence unique to humans: language understanding, knowledge representation, and reasoning. Thus, building systems capable of intelligent question answering (QA) is a grand goal of natural language processing (NLP). To measure progress in NLP, we create "exams" for computer systems and compare their effectiveness against a reference point---often based on humans. How precisely we measure progress depends on whether we are building computer systems that optimize human satisfaction in information-seeking tasks or that measure progress towards intelligent QA. In the first part of this dissertation, we explore each goal in turn, how they differ, and describe their relationship to QA formats. As an example of an information-seeking evaluation, we introduce a new dialog QA task paired with a new evaluation method. Afterward, we turn our attention to using QA to evaluate machine intelligence.
A good evaluation should be able to discriminate between lesser and more capable QA models. This dissertation explores three ways to improve the discriminative power of QA evaluations: (1) dynamic weighting of test questions, (2) a format that by construction tests multiple levels of knowledge, and (3) evaluation data that is created through human-computer collaboration.
By dynamically weighting test questions, we challenge a foundational assumption of the de facto standard in QA evaluation---the leaderboard. Namely, we contend that contrary to nearly all QA and NLP evaluations which implicitly assign equal weights to examples by averaging scores, that examples are not equally useful for estimating machine (or human) QA ability. As any student may tell you, not all questions on an exam are equally difficult and in the worst-case questions are unsolvable. Drawing on decades of research in educational testing, we propose adopting an alternative evaluation methodology---Item Response Theory---that is widely used to score human exams (e.g., the SAT). By dynamically weighting questions, we show that this improves the reliability of leaderboards in discriminating between models of differing QA ability while also being helpful in the construction of new evaluation datasets.
Having improved the scoring of models, we next turn to improving the format and data in QA evaluations. Our idea is simple. In most QA tasks (e.g., Jeopardy!), each question tests a single level of knowledge; in our task (the trivia game Quizbowl), we test multiple levels of knowledge with each question. Since each question tests multiple levels of knowledge, this decreases the likelihood that we learn nothing about the difference between two models (i.e., they are both correct or both wrong), which substantially increases discriminative power.
Despite the improved format, we next show that while our QA models defeat accomplished trivia players, that they are overly reliant on brittle pattern matching, which indicates a failure to intelligently answer questions. To mitigate this problem, we introduce a new framework for building evaluation data where humans and machines cooperatively craft trivia questions that are difficult to answer through clever pattern matching tricks alone---while being no harder for humans.
We conclude by sketching a broader vision for QA evaluation that combines the three components of evaluation we improve---scoring, format, and data---to create living evaluations and re-imagine the role of leaderboards