Incorporating Zero-Knowledge Succinct Non-interactive Argument of Knowledge for Blockchain-based Identity Management with off-chain computations

In today's world, secure and efficient biometric authentication is of keen importance. Traditional authentication methods are no longer considered reliable due to their susceptibility to cyber-attacks. Biometric authentication, particularly fingerprint authentication, has emerged as a promising alternative, but it raises concerns about the storage and use of biometric data, as well as centralized storage, which could make it vulnerable to cyber-attacks. In this paper, a novel blockchain-based fingerprint authentication system is proposed that integrates zk-SNARKs, which are zero-knowledge proofs that enable secure and efficient authentication without revealing sensitive biometric information. A KNN-based approach on the FVC2002, FVC2004 and FVC2006 datasets is used to generate a cancelable template for secure, faster, and robust biometric registration and authentication which is stored using the Interplanetary File System. The proposed approach provides an average accuracy of 99.01%, 98.97% and 98.52% over the FVC2002, FVC2004 and FVC2006 datasets respectively for fingerprint authentication. Incorporation of zk-SNARK facilitates smaller proof size. Overall, the proposed method has the potential to provide a secure and efficient solution for blockchain-based identity management

THE EVOLUTION OF PHENOTYPIC PLASTICITY IN BUTTERFLIES

Ph.DDOCTOR OF PHILOSOPH

Regulating Adoption of Artificial Intelligence in the Financial Sector : Identifying Legal Risks and Opportunities for Technological Innovations in Banking

The banking and financial sector has often been synonymous with established names, with some having centuries old presence. In the recent past these incumbents have been experiencing a consequential disruption by new entrants and rapidly changing consumer demands. These disruptions to the status quo have been characterised by a shift towards adoption of technology and artificial intelligence particularly in the service and products offered to the end customers. The changing business climate in the financial sector has risen many convoluted questions for the regulators. These complications cover a vast set of issues – from the concerns relating to the privacy of data of the end users to the increasing vulnerability of the financial market, to unproportionally increased compliance requirements for new entrants, all form part of the mesh of questions that have arisen in the wake of new services and operations being designed with the aid and assistance of artificial intelligence, machine learning and big data analytics. It is in this background that this Thesis seeks to explore the trajectory of the development of the legal landscape for regulating artificial intelligence – both in general and specifically in the financial and banking sector, particularly in the European Union. During the analysis, existing legal enactments, such as the General Data Protection Regulation, have been scrutinised and certain observations have been made regarding the areas that still remain unregulated or open to debate under the laws as it stands today. In the same vein, an attempt has been made to explore the emerging discussion on a dedicated legal regime for artificial intelligence in the European Union, and those observations have been viewed from the perspective of the financial sector, thereby creating thematic underpinnings that ought to form part of any legal instrument aiming to optimally regulate technology in the financial sector. To concretise the actual application of such a legal instrument, a European Union member state has been identified and the evolution of the regulatory regime in the financial sector has been discussed with the said member states’ financial supervisory authority, thus highlighting the crucial role of the law making and enactment bodies in creating and sustaining a technologically innovative financial and banking sector. The themes recognised in this Thesis could be the building blocks upon which the future legal discourse on artificial intelligence and the financial sector could be structured

Fractal analysis and machine-learned decision system for precision and smart farming

Agriculture is the main source of income for most of the people from thousands of years but still every year farmers suffer loss of crop and money, due to misinterpretation of soil or climatic conditions. In the recent years, researchers have worked to improve this state and agriculture for production of crop by analyzing soil or climate conditions. In this paper, we proposed the methods as fractal analysis and machine-learned decision system for smart and precision farming. The long-term behavior of the different parameter on which production of crop depends is analyzed using fractal analysis which is helpful for crop maintenance and also helpful in preparing the framework for the government and farmers in advance to know the total automating of the supplements, water framework, water system controls for accuracy of cultivating in the fields. Using Hurst exponent and Fractal analysis, it is observed that all the seven parameters affecting the crops follow anti-persistent behavior which shows the drawn out exchanging among high and low qualities with a definite pattern. Machine-learned system including artificial neural network, kNN Classifier, XG Boost, Random Forest classifier conclude that different decision systems show the accuracy for different crops with parameters from 95 to 99%. Random Forest classifier gave more accuracy among all classifier for testing and providing support for crop management systems. It is concluded that the proposed technique using machine-learned classifier is giving more accuracy for precise and smart farming with good crop management and helpful for the government to make the decision and formulate policies for the stack of farmers, consumers and for the development of the nation as farmers are backbone of any country

On the stability of composite plate shear walls under fire loading

Composite plate shear walls (C-PSWs) are being considered for commercial building construction due to their benefits of modularization and schedule contraction. Fire hazard is an important consideration for building design, and the applicability evaluation of the C-PSW system for buildings would be incomplete without consideration of fire loads. The existence of steel plates (analogous to reinforcing bars in traditional reinforced concrete construction) on the surface of the shear walls means that the steel plates will be directly exposed to fire temperatures. Fire loading will result in elevated steel and concrete temperatures and non-linear thermal gradients through the cross-section of the walls. Elevated temperatures result in the degradation of the mechanical properties of steel and concrete. This can result in local buckling of steel plates or global instability of the walls, leading to the collapse of the walls at gravity load magnitudes significantly lower than the ambient compression strength of the walls. The authors have initiated a research project focusing on stability of C-PSWs under fire loading. The existing standard fire tests conducted on scaled C-PSW specimens in S. Korea and China are summarized in the paper. This paper focuses on the development of detailed finite element models to evaluate the stability response of C-PSWs under fire loading. The existing experimental database has been used to benchmark finite element models for the thermal and structural response of the system. The numerical models are conservative in comparison to the experimental results. The surface temperature at failure is a better indicator of the fire resistance of the C-PSWs (in comparison to time to failure). The time to failure can be determined from the surface temperature at failure. The benchmarked analyses will be employed to develop full-scale models of C-PSWs. Parametric studies will be conducted to study the effect of variation in section thickness, steel reinforcement ratio, steel plate slenderness on the local and global stability behavior of C-PSWs subjected to standard fire curves. The authors will also be conducting a series of experimental studies where C-PSW specimens will be subjected to standard fire curves. Results of experimental and numerical studies will be employed to determine the fire resistance of C-PSWs, obtain fire ratings for walls, and provide detailing or design recommendation for performance-based fire design of C-PSWs. These recommendations will enable the engineers to consider fire loading in the design of C-PSWs for building structures

Origin of the mechanism of phenotypic plasticity in satyrid butterfly eyespots

Plasticity is often regarded as a derived adaptation to help organisms survive in variable but predictable environments, however, we currently lack a rigorous, mechanistic examination of how plasticity evolves in a large comparative framework. Here, we show that phenotypic plasticity in eyespot size in response to environmental temperature observed in Bicyclus anynana satyrid butterflies is a complex derived adaptation of this lineage. By reconstructing the evolution of known physiological and molecular components of eyespot size plasticity in a comparative framework, we showed that 20E titer plasticity in response to temperature is a pre-adaptation shared by all butterfly species examined, whereas expression of EcR in eyespot centers, and eyespot sensitivity to 20E, are both derived traits found only in a subset of species with eyespots.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Sex Differences in 20-Hydroxyecdysone Hormone Levels Control Sexual Dimorphism in Bicyclus anynana Wing Patterns

10.1093/molbev/msx301MOLECULAR BIOLOGY AND EVOLUTION352465-47

Genetic Characterization of Endangered Indian Mithun (<i>Bos frontalis</i>), Indian Bison/Wild Gaur (<i>Bos gaurus</i>) and Tho-tho Cattle (<i>Bos indicus</i>) Populations Using SSR Markers Reveals Their Diversity and Unique Phylogenetic Status

Mithun (Bos frontalis) or gayal and Indian Bison or wild gaur (Bos gaurus) are listed among the rare and endangered bovine species of India. The remote location of mithun in four North Eastern Hill states (Arunachal Pradesh, Nagaland, Manipur, and Mizoram), scattered population size, and non-availability of genetic diversity status are major limitations towards devising a suitable breeding and conservation policy of these species. Since several studies have demonstrated the successful applicability of microsatellite/SSR markers across related genera/families in both crop plants and animal species, 30 FAO recommended cattle microsatellites were utilized for the assessment of the genetic diversity of Indian mithun, bison, and local Tho-tho cattle. Mitochondrial transmembrane protein coding cytochrome B (CYTB) complete sequence data of 71 bovine samples from India were also used to reinforce the study. Population structuring clustered the all bovines into three subgroups as per geographical location and species. Bottleneck analysis indicated a mode shift in the allelic frequency distribution of gaur, indicating minor genetic bottleneck events in the past, while no bottleneck was found in mithun and Tho-tho cattle. To our knowledge, this study represents the first report of molecular genetic characterization showing the population structure and status of genetic diversity in rare Indian bovines, namely, Mithun, Gaur, and Tho-tho cattle