A Comparative Analysis Of Conventional Software Development Approaches Vs. Formal Methods In Call Distribution Systems

When we think about formal method; the first thing which comes in our mind is mathematical approach. The process of formalization is an approach based on mathematics and used to elaborate the properties of systems (hardware and software). The mathematical modeling or formal methods provide us a framework for large and complex systems. Thus these systems can be specified, analyzed, designed, and verified in a systematic way rather than the approaches which are used conventionally. Formal verification and the methods are applied using theoretical computer science fundamentals to solve the complex and difficult problems in large and complex software and hardware systems to ensure the systems will not fail with run-time errors. Conventional approaches of software verification in call distribution systems rely on quality assurance to verify the system behavior and robustness. The process of software testing cannot show the absence of errors it can only show the presence of errors in software systems. [1] In contrast, the mathematically-based techniques of verification are based on formal methods to prove certain software attributes, for example proving that software does or does not contain the occurrence of errors at run-time such as overflows, divide-by-zero, and access violation, invalid memory access and stack/heap corruption. [1] In this paper later we will have comparative analysis of formal methods vs. conventional software development approaches in call distribution systems. Using this comparison we‘ll try to identify the methodologies and approaches which would be better in SDLC for call distribution systems.

Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps

Precise localization is critical for autonomous vehicles. We present a self-supervised learning method that employs Transformers for the first time for the task of outdoor localization using LiDAR data. We propose a pre-text task that reorganizes the slices of a $360^\circ$ LiDAR scan to leverage its axial properties. Our model, called Slice Transformer, employs multi-head attention while systematically processing the slices. To the best of our knowledge, this is the first instance of leveraging multi-head attention for outdoor point clouds. We additionally introduce the Perth-WA dataset, which provides a large-scale LiDAR map of Perth city in Western Australia, covering $\sim$4km$^2$ area. Localization annotations are provided for Perth-WA. The proposed localization method is thoroughly evaluated on Perth-WA and Appollo-SouthBay datasets. We also establish the efficacy of our self-supervised learning approach for the common downstream task of object classification using ModelNet40 and ScanNN datasets. The code and Perth-WA data will be publicly released.Comment: Accepted in IEEE International Conference on Robotics and Automation (ICRA), 202

Empirical examination of societal, financial and technology-related challenges amid COVID-19 in service supply chains: evidence from emerging market

Purpose: This paper reports an empirical examination of the societal, financial and technology-related challenges amid the ongoing pandemic (COVID-19) in the service supply chain network. Design/methodology/approach: A combination of qualitative approach (for items generation pertinent to the constructs involved) and quantitative approach (self-administered questionnaires from the top and middle management of the sampled companies) was used for data collection. In total, 272 complete responses were received and analyzed through structural equation modeling. Findings: The results provided empirical evidence that social and physical distancing, travel restrictions, work from home and lockdown practices have two conflicting effects: On one hand, these practices have contributed to the reduction of economic activities, including the low economic outlook, low productivity, high unemployment, poverty, fall in customer demands, dissatisfaction and mental health, that ultimately impacts rise financial and societal issues. On the other hand, the results revealed an insignificant influence of COVID-19 on creating technology-related challenges in the service sector. It shows that the organizations are doing well in combating the technology-related challenges amidst the current pandemic. Research limitations/implications: Findings of the inquiry recommend implications for the services industry to harmonize a comprehensive strategy and revisit the global norms in sustainable supply chain management activities that have been the backdrop in their operations for a long time. Practical implications: Findings of the inquiry recommend implications for the services industry to harmonize a comprehensive strategy and revisit the global norms in supply chain management activities that have been the backdrop in their operations for a long time. Originality/value: Prior studies in the context of the COVID-19 outbreak and its implications have given more attention to the exploratory and theoretical discussion than to empirical evidence. This paper contributes to filling this knowledge gap by empirically exploring the societal, financial and technology-related challenges created by COVID-19. The analysis in this paper covers three dimensions of the PEST model, namely economic, societal and technological factors. This study also helps in laying out a platform for investigating the PEST (political, economic, social and technological) model for guiding the services industry in strategic decision-making in a new era due to COVID-19

Socio-economic and technological new normal in supply chain management : lessons from COVID-19 pandemic

Purpose This paper explores the new normal activities and strategic responses of the service industry towards the challenges created by the coronavirus disease 2019 (COVID-19) outbreak and other constructs and validates the measurement scale for socio-economic and technological new normal activities following lockdown and social distancing practices. Design/methodology/approach First, structured interviews with 28 participants helped us generate items and develop survey instruments for cross-sectional data collection in the second phase. So, the authors received 256 complete responses from the top and middle management of the services industry. Exploratory factor analysis helped us explore the factors and reliability of the items. Confirmatory factor analysis aided us in generating and confirming the factorial structure of the constructs. Findings Results indicated that amid COVID-19's pandemic, new normal activities are emerging in which organizations are deploying crisis strategies to safeguard their business and stakeholders. Organizations are re-opening swiftly, focusing on digital transformation, developing digital platforms for ease in working and improved consumer services, to name a few operational changes. Practical implications Discussion on empirical analysis revolves around the guidelines to service industry's managers and top management to improve shortcomings in combating the challenges they face in their operations. Originality/value Prior studies have provided substantial insights on the COVID-19 pandemic, but relatively little research exists on new normal activities in the supply chain network of the service industry. Among other reasons for such less empirical evidence on new normal activities is the unavailability of a comprehensive tool for measuring the socio-economic and technological new normal activities. This paper is a contribution to bridging this knowledge gap.©2022 Emerald Publishing Limited. This manuscript version is made available under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY–NC 4.0) license, https://creativecommons.org/licenses/by-nc/4.0/fi=vertaisarvioitu|en=peerReviewed

Socio-economic and technological new normal in supply chain management: lessons from COVID-19 pandemic

Purpose: This paper explores the new normal activities and strategic responses of the service industry towards the challenges created by the coronavirus disease 2019 (COVID-19) outbreak and other constructs and validates the measurement scale for socio-economic and technological new normal activities following lockdown and social distancing practices. Design/methodology/approach: First, structured interviews with 28 participants helped us generate items and develop survey instruments for cross-sectional data collection in the second phase. So, the authors received 256 complete responses from the top and middle management of the services industry. Exploratory factor analysis helped us explore the factors and reliability of the items. Confirmatory factor analysis aided us in generating and confirming the factorial structure of the constructs. Findings: Results indicated that amid COVID-19\u27s pandemic, new normal activities are emerging in which organizations are deploying crisis strategies to safeguard their business and stakeholders. Organizations are re-opening swiftly, focusing on digital transformation, developing digital platforms for ease in working and improved consumer services, to name a few operational changes. Practical implications: Discussion on empirical analysis revolves around the guidelines to service industry\u27s managers and top management to improve shortcomings in combating the challenges they face in their operations. Originality/value: Prior studies have provided substantial insights on the COVID-19 pandemic, but relatively little research exists on new normal activities in the supply chain network of the service industry. Among other reasons for such less empirical evidence on new normal activities is the unavailability of a comprehensive tool for measuring the socio-economic and technological new normal activities. This paper is a contribution to bridging this knowledge gap

A Comprehensive Overview of Large Language Models

Large Language Models (LLMs) have shown excellent generalization capabilities that have led to the development of numerous models. These models propose various new architectures, tweaking existing architectures with refined training strategies, increasing context length, using high-quality training data, and increasing training time to outperform baselines. Analyzing new developments is crucial for identifying changes that enhance training stability and improve generalization in LLMs. This survey paper comprehensively analyses the LLMs architectures and their categorization, training strategies, training datasets, and performance evaluations and discusses future research directions. Moreover, the paper also discusses the basic building blocks and concepts behind LLMs, followed by a complete overview of LLMs, including their important features and functions. Finally, the paper summarizes significant findings from LLM research and consolidates essential architectural and training strategies for developing advanced LLMs. Given the continuous advancements in LLMs, we intend to regularly update this paper by incorporating new sections and featuring the latest LLM models