Applying Formal Methods to Networking: Theory, Techniques and Applications

Despite its great importance, modern network infrastructure is remarkable for the lack of rigor in its engineering. The Internet which began as a research experiment was never designed to handle the users and applications it hosts today. The lack of formalization of the Internet architecture meant limited abstractions and modularity, especially for the control and management planes, thus requiring for every new need a new protocol built from scratch. This led to an unwieldy ossified Internet architecture resistant to any attempts at formal verification, and an Internet culture where expediency and pragmatism are favored over formal correctness. Fortunately, recent work in the space of clean slate Internet design---especially, the software defined networking (SDN) paradigm---offers the Internet community another chance to develop the right kind of architecture and abstractions. This has also led to a great resurgence in interest of applying formal methods to specification, verification, and synthesis of networking protocols and applications. In this paper, we present a self-contained tutorial of the formidable amount of work that has been done in formal methods, and present a survey of its applications to networking.Comment: 30 pages, submitted to IEEE Communications Surveys and Tutorial

The Effects of Advanced Analytics and Machine Learning on the Transportation of Natural Gas

This qualitative single case study describes the effects of an advanced analytic and machine learning system (AAML) has on the transportation of natural gas pipelines and the causes for failure to fully utilize the advanced analytic and machine learning system. This study\u27s guiding theory was the Unified Theory of Acceptance and Use of Technology (UTAUT) model and Transformation Leadership. The factors for failure to fully utilize AAML systems were studied, and the factors that made the AAML system successful were also examined. Data were collected through participant interviews. This study indicates that the primary factors for failure to fully utilize AAML systems are training and resource allocation. The AAML system successfully increased the participants\u27 productivity and analytical abilities by eliminating the many manual steps involved in producing reports and analyzing business conditions. The AAML system also allowed the organization to gather and analyze real-time data in a volume and manner that would have been impossible before the AAML system was installed. The leadership team brought about the AAML system\u27s success through transformation leadership by encouraging creativity, spurring innovation while providing the proper funding, time, and personnel to support the AAML system

The doctoral research abstracts. Vol:6 2014 / Institute of Graduate Studies, UiTM

Congratulations to Institute of Graduate Studies on the continuous efforts to publish the 6th issue of the Doctoral Research Abstracts which ranged from the discipline of science and technology, business and administration to social science and humanities. This issue captures the novelty of research from 52 PhD doctorates receiving their scrolls in the UiTM’s 81st Convocation. This convocation is very significant especially for UiTM since we are celebrating the success of 52 PhD graduands – the highest number ever conferred at any one time. To the 52 doctorates, I would like it to be known that you have most certainly done UiTM proud by journeying through the scholastic path with its endless challenges and impediments, and by persevering right till the very end. This convocation should not be regarded as the end of your highest scholarly achievement and contribution to the body of knowledge but rather as the beginning of embarking into more innovative research from knowledge gained during this academic journey, for the community and country. As alumni of UiTM, we hold you dear to our hearts. The relationship that was once between a student and supervisor has now matured into comrades, forging and exploring together beyond the frontier of knowledge. We wish you all the best in your endeavour and may I offer my congratulations to all the graduands. ‘UiTM sentiasa dihati ku’ Tan Sri Dato’ Sri Prof Ir Dr Sahol Hamid Abu Bakar , FASc, PEng Vice Chancellor Universiti Teknologi MAR

The redesign of blue- and white-collar work triggered by digitalization:collar matters

The implementation of digital technologies in the context of Industry 4.0 radically changes methods of production and thereby the jobs of blue-collar workers. Although the work design effects of digitalization on the operator 4.0 have been explored in the existing literature, less is known about the simultaneous effects on white-collar work and the underlying (re)design process of human work including the factors that shape this process. To address this gap, we performed an in-depth industrial case study of an organization in the process of digitalization. Our findings confirm the concurrent impact of digitalization on blue- and white-collar work and suggest that its human implications highly depend on the extent to which, and at what moment, human factors are considered during the design and implementation process. Where work design knowledge lacked, the motivation of system designers turned out to be an important individual factor to realize favorable work design outcomes. At the organizational level, results show the importance of early involvement of system users and incorporating social performance indicators in addition to operational performance indicators in the statement of project goals. Our findings provide important empirical input for the further development of human-centric models and theories that integrate the challenges and opportunities for blue- and white-collar workers that are emerging when adopting digital technologies

Sustainability transition of production systems in the digital era - a systems perspective for building resilient and sustainable production systems

Locked-in manufacturing industries with highly structured operations and path dependencies are major contributors to the sustainability challenges currently burdening our planet. The effects of the ongoing pandemic, large-scale environmental impacts due to climate change and constant economic and social downturns are just some examples of these sustainability challenges. Increased digitalisation, awareness, global initiatives and regulations are pressuring manufacturing industries to transition towards sustainable development. However, there exists a multitude of interpretations in implementing sustainability in manufacturing industries. This makes proposing tangible actions to translate global initiatives complicated, thus hindering the sustainability transition process.The purpose of this thesis is to support the advancement of resilient production systems which can overcome sustainability challenges in the Industry 4.0 era. Hence, the thesis aims to investigate: (i) the systemic challenges of manufacturing companies which hinder their sustainability transition process and (ii) the mechanisms by which a systems perspective may be applied to support the transition. A mixed-methods approach was used to carry out the research, using qualitative and quantitative data from three (empirical and theoretical) studies. Applying a systems perspective helped reveal the challenges which hinder the sustainability transition of production systems. Understanding the production ‘system’ as a whole (and the underlying web of intricate dependencies and challenges in production operations) required this holistic perspective. Regarding the challenges, it was observed that manufacturing industries across different domains face three main types of challenge: internal (such as organisational routines, strategies and cultural mindset), external (such as regulations and collaboration with stakeholders) and technological (such as maturity levels and data). Three different enabling mechanisms were explored which may help overcome the above sustainability challenges and support the sustainability transition of manufacturing industries: (1) Industry 4.0 technologies, (2) dynamic capabilities and (3) resilience engineering. It was observed that Industry 4.0 technologies (such as artificial intelligence/machine learning, virtual development tools and sensors) are largely implemented to enable sustainable manufacturing in the form of resource efficiency and waste reduction. The results also revealed five microfoundations of dynamic capabilities – communication, organisation, resources, collaboration and technology. Based on Industry 4.0 opportunities to promote sustainability transitions, the results revealed five industrial resilience factors – robustness, agility, resourcefulness, adaptability and flexibility.This research contributes to theory by studying the convergence of emergent research topics, such as Industry 4.0, dynamic capabilities and resilience engineering in the context of sustainability transitions. In terms of a practical contribution, the sustainability transitions model developed in this thesis may support industrial practitioners in gaining a holistic understanding of the systemic challenges to sustainability, plus corresponding mechanisms to promote the sustainability transition of industries and the building of resilient production systems

Technological roadmap on AI planning and scheduling

At the beginning of the new century, Information Technologies had become basic and indispensable constituents of the production and preparation processes for all kinds of goods and services and with that are largely influencing both the working and private life of nearly every citizen. This development will continue and even further grow with the continually increasing use of the Internet in production, business, science, education, and everyday societal and private undertaking. Recent years have shown, however, that a dramatic enhancement of software capabilities is required, when aiming to continuously provide advanced and competitive products and services in all these fast developing sectors. It includes the development of intelligent systems – systems that are more autonomous, flexible, and robust than today’s conventional software. Intelligent Planning and Scheduling is a key enabling technology for intelligent systems. It has been developed and matured over the last three decades and has successfully been employed for a variety of applications in commerce, industry, education, medicine, public transport, defense, and government. This document reviews the state-of-the-art in key application and technical areas of Intelligent Planning and Scheduling. It identifies the most important research, development, and technology transfer efforts required in the coming 3 to 10 years and shows the way forward to meet these challenges in the short-, medium- and longer-term future. The roadmap has been developed under the regime of PLANET – the European Network of Excellence in AI Planning. This network, established by the European Commission in 1998, is the co-ordinating framework for research, development, and technology transfer in the field of Intelligent Planning and Scheduling in Europe. A large number of people have contributed to this document including the members of PLANET non- European international experts, and a number of independent expert peer reviewers. All of them are acknowledged in a separate section of this document. Intelligent Planning and Scheduling is a far-reaching technology. Accepting the challenges and progressing along the directions pointed out in this roadmap will enable a new generation of intelligent application systems in a wide variety of industrial, commercial, public, and private sectors

Autonomous Systems, Robotics, and Computing Systems Capability Roadmap: NRC Dialogue

Contents include the following: Introduction. Process, Mission Drivers, Deliverables, and Interfaces. Autonomy. Crew-Centered and Remote Operations. Integrated Systems Health Management. Autonomous Vehicle Control. Autonomous Process Control. Robotics. Robotics for Solar System Exploration. Robotics for Lunar and Planetary Habitation. Robotics for In-Space Operations. Computing Systems. Conclusion