The Impact of AI Tool on Engineering at ANZ Bank An Empirical Study on GitHub Copilot within Corporate Environment

The increasing popularity of AI, particularly Large Language Models (LLMs), has significantly impacted various domains, including Software Engineering. This study explores the integration of AI tools in software engineering practices within a large organization. We focus on ANZ Bank, which employs over 5000 engineers covering all aspects of the software development life cycle. This paper details an experiment conducted using GitHub Copilot, a notable AI tool, within a controlled environment to evaluate its effectiveness in real-world engineering tasks. Additionally, this paper shares initial findings on the productivity improvements observed after GitHub Copilot was adopted on a large scale, with about 1000 engineers using it. ANZ Bank's six-week experiment with GitHub Copilot included two weeks of preparation and four weeks of active testing. The study evaluated participant sentiment and the tool's impact on productivity, code quality, and security. Initially, participants used GitHub Copilot for proposed use-cases, with their feedback gathered through regular surveys. In the second phase, they were divided into Control and Copilot groups, each tackling the same Python challenges, and their experiences were again surveyed. Results showed a notable boost in productivity and code quality with GitHub Copilot, though its impact on code security remained inconclusive. Participant responses were overall positive, confirming GitHub Copilot's effectiveness in large-scale software engineering environments. Early data from 1000 engineers also indicated a significant increase in productivity and job satisfaction.Comment: 16 pages, 4 figures. in proceeding for 10th International Conference on Software Engineering (SEC 2024

Swarm UAVs Communication

The advancement in cyber-physical systems has opened a new way in disaster management and rescue operations. The usage of UAVs is very promising in this context. UAVs, mainly quadcopters, are small in size and their payload capacity is limited. A single UAV can not traverse the whole area. Hence multiple UAVs or swarms of UAVs come into the picture managing the entire payload in a modular and equiproportional manner. In this work we have explored a vast topic related to UAVs. Among the UAVs quadcopter is the main focus. We explored the types of quadcopters, their flying strategy,their communication protocols, architecture and controlling techniques, followed by the swarm behaviour in nature and UAVs. Swarm behaviour and a few swarm optimization algorithms has been explored here. Swarm architecture and communication in between swarm UAV networks also got a special attention in our work. In disaster management the UAV swarm network must have to search a large area. And for this proper path planning algorithm is required. We have discussed the existing path planning algorithm, their advantages and disadvantages in great detail. Formation maintenance of the swarm network is an important issue which has been explored through leader-follower technique. The wireless path loss model has been modelled using friis and ground ray reflection model. Using this path loss models we have managed to create the link budget and simulate the variation of communication link performance with the variation of distance.Comment: 50 pages, 17 figure

A Comprehensive Review of Website Content Filtering Algorithms: Techniques, Challenges, and Future Directions

The exponential growth in web content fueled an increased demand for smart, scalable and responsible filters for website content. This talk explores the origins, classification, and specifications comparison (traditional rule based and keyword based system to modern machine learning based algorithm and hybrid attempts) of the content filtering algorithm. Particular attention is paid to their working methods, the evaluation processes, deployment complexities as well as their action in practical deployments. The article outlines how static filters are on the verge of becoming entirely inadequate for combating dynamic and encrypted threats and it describes how more recent innovations - visual phishing detection, context aware systems and federated learning - are changing the landscape of filtering paradigm. This chapter draws on the latest 30 academic works on literature on the theoretical concepts and direct practices of content filtering systems and compare them along the axis of accuracy, precision, recall, and scalability. Besides, it identifies major challenges including privacy compromise, false positives, and the need for explainable AI. The review ends with the directions for future research, base on personalization, transparency, and balloon of the deep learning architectures. Through this comprehensive study the rearing provides useful inferences to researchers, cybersecurity professionals; and platform developers to develop safer and more diligent web spaces

Topological Superconductivity by Engineering Noncollinear Magnetism in Magnet/ Superconductor Heterostructures: A Realistic Prescription for 2D Kitaev Model

We report on a realistic and rather general scheme where noncollinear magnetic textures proximitized with the most common $s$-wave superconductor can appear as the alternative to $p$-wave superconductor{--}the prime proposal to realize two-dimensional (2D) Kitaev model for topological superconductors (TSCs) hosting Majorana flat edge mode (MFEM). A general minimal Hamiltonian suitable for magnet/superconductor heterostructures reveals robust MFEM within the gap of Shiba bands due to the emergence of an effective ``$p_x+p_y$"-type $p$-wave pairing, spatially localized at the edges of a 2D magnetic domain of spin-spiral. We finally verify this concept by considering Mn (Cr) monolayer grown on a $s$-wave superconducting substrate, Nb(110) under strain (Nb(001)). In both 2D cases, the antiferromagnetic spin-spiral solutions exhibit robust MFEM at certain domain edges that is beyond the scope of the trivial extension of 1D spin-chain model in 2D. This approach, particularly when the MFEM appears in the TSC phase for such heterostructure materials, offers a perspective to extend the realm of the TSC in 2D.Comment: This is the published versio

Stress mitigation strategies of plant growth-promoting rhizobacteria: Plant growth-promoting rhizobacteria mechanisms

One of the major challenges that the world is facing currently is the inadequate amount of food production with high nutrient content in accordance with the increase in population size. Moreover, availability of cultivable area with fertile soil is reducing day by day owing to ever increasing population. Further, water scarcity and expensive agricultural equipment have led to the use of agrochemicals and untreated water. Excessive use of chemical fertilizers to increase crop yield have resulted in deleterious effects on the environment, health and economy, which can be overcome to a great extent by employing biological fertilizers. There are various microbes that grows in the rhizospheric region of plants known as plant growth-promoting rhizobacteria (PGPR). PGPR act by direct and indirect modes to stimulate plant growth and improve stress reduction in plants. PGPRs are used for potential agriculture practices having a wide range of benefits like increase in nutrients content, healthy growth of crops, production of phytohormones, prevention from heavy metal stress conditions and increase in crop yield. This review reports recent studies in crop improvement strategies using PGPR and describes the mechanisms involved. The potential mechanisms of PGPR and its allies pave the way for sustainable development towards agriculture and commercialization of potential bacteria

The Stock Market Evaluation of IPO-Firm Takeovers

We conduct an event study to assess the stock market evaluation of public takeover announcements. Unlike the majority of previous research, we specifically focus on acquisitions targeted at newly public IPO-firms and show that the stock market positively evaluates these M&As as R&D. However, bidders' abnormal announcement returns are significantly lower for takeovers directed at targets with critical intangible assets and innovative capabilities inalienably bound to their initial owners than for those that have internally accumulated respective resources and capabilities. We explain these findings with the acquirer's post-acquisition dependence on continued access to the IPO-firm founders' target-specific human capital. Our results contribute to literature in that they show that the stock market perceives these potential impediments to successful exploitation of acquired strategic resources and thus identify a potential cause for heretofore mostly inconsistent evidence on bidder abnormal returns in corporate takeovers found in previous research

Observation of γγ → ττ in proton-proton collisions and limits on the anomalous electromagnetic moments of the τ lepton

The production of a pair of τ leptons via photon–photon fusion, γγ → ττ, is observed for the f irst time in proton–proton collisions, with a significance of 5.3 standard deviations. This observation is based on a data set recorded with the CMS detector at the LHC at a center-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 138 fb−1. Events with a pair of τ leptons produced via photon–photon fusion are selected by requiring them to be back-to-back in the azimuthal direction and to have a minimum number of charged hadrons associated with their production vertex. The τ leptons are reconstructed in their leptonic and hadronic decay modes. The measured fiducial cross section of γγ → ττ is σfid obs = 12.4+3.8 −3.1 fb. Constraints are set on the contributions to the anomalous magnetic moment (aτ) and electric dipole moments (dτ) of the τ lepton originating from potential effects of new physics on the γττ vertex: aτ = 0.0009+0.0032 −0.0031 and |dτ| < 2.9×10−17ecm (95% confidence level), consistent with the standard model