Experience and Prediction: A Metric of Hardness for a Novel Litmus Test

In the last decade, the Winograd Schema Challenge (WSC) has become a central aspect of the research community as a novel litmus test. Consequently, the WSC has spurred research interest because it can be seen as the means to understand human behavior. In this regard, the development of new techniques has made possible the usage of Winograd schemas in various fields, such as the design of novel forms of CAPTCHAs. Work from the literature that established a baseline for human adult performance on the WSC has shown that not all schemas are the same, meaning that they could potentially be categorized according to their perceived hardness for humans. In this regard, this \textit{hardness-metric} could be used in future challenges or in the WSC CAPTCHA service to differentiate between Winograd schemas. Recent work of ours has shown that this could be achieved via the design of an automated system that is able to output the hardness-indexes of Winograd schemas, albeit with limitations regarding the number of schemas it could be applied on. This paper adds to previous research by presenting a new system that is based on Machine Learning (ML), able to output the hardness of any Winograd schema faster and more accurately than any other previously used method. Our developed system, which works within two different approaches, namely the random forest and deep learning (LSTM-based), is ready to be used as an extension of any other system that aims to differentiate between Winograd schemas, according to their perceived hardness for humans. At the same time, along with our developed system we extend previous work by presenting the results of a large-scale experiment that shows how human performance varies across Winograd schemas.Comment: 33 pages, 10 figures

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum

ICSEA 2021: the sixteenth international conference on software engineering advances

The Sixteenth International Conference on Software Engineering Advances (ICSEA 2021), held on October 3 - 7, 2021 in Barcelona, Spain, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. The tracks treated the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learnt. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. The conference had the following tracks: Advances in fundamentals for software development Advanced mechanisms for software development Advanced design tools for developing software Software engineering for service computing (SOA and Cloud) Advanced facilities for accessing software Software performance Software security, privacy, safeness Advances in software testing Specialized software advanced applications Web Accessibility Open source software Agile and Lean approaches in software engineering Software deployment and maintenance Software engineering techniques, metrics, and formalisms Software economics, adoption, and education Business technology Improving productivity in research on software engineering Trends and achievements Similar to the previous edition, this event continued to be very competitive in its selection process and very well perceived by the international software engineering community. As such, it is attracting excellent contributions and active participation from all over the world. We were very pleased to receive a large amount of top quality contributions. We take here the opportunity to warmly thank all the members of the ICSEA 2021 technical program committee as well as the numerous reviewers. The creation of such a broad and high quality conference program would not have been possible without their involvement. We also kindly thank all the authors that dedicated much of their time and efforts to contribute to the ICSEA 2021. We truly believe that thanks to all these efforts, the final conference program consists of top quality contributions. This event could also not have been a reality without the support of many individuals, organizations and sponsors. We also gratefully thank the members of the ICSEA 2021 organizing committee for their help in handling the logistics and for their work that is making this professional meeting a success. We hope the ICSEA 2021 was a successful international forum for the exchange of ideas and results between academia and industry and to promote further progress in software engineering research

The Integrated Realization of Materials, Products and Associated Manufacturing Processes

Problem: A materials design revolution is underway in the recent past where the focus is to design (not select) the material microstructure and processing paths to achieve multiple property or performance requirements that are often in conflict. The advancements in computer simulations have resulted in the speeding up of the process of discovering new materials and has paved way for rapid assessment of process-structure-property-performance relationships of materials, products, and processes. This has led to the simulation-based design of material microstructure (microstructure-mediated design) to satisfy multiple property or performance goals of the product/process/system thereby replacing the classical material design and selection approaches. The foundational premise for this dissertation is that systems-based materials design techniques offer the potential for tailoring materials, their processing paths and the end products that employ these materials in an integrated fashion for challenging applications to satisfy conflicting product and process level property and performance requirements. The primary goal in this dissertation is to establish some of the scientific foundations and tools that are needed for the integrated realization of materials, products and manufacturing processes using simulation models that are typically incomplete, inaccurate and not of equal fidelity by managing the uncertainty associated. Accordingly, the interest in this dissertation lies in establishing a systems-based design architecture that includes system-level synthesis methods and tools that are required for the integrated design of complex materials, products and associated manufacturing processes starting from the end requirements. Hence the primary research question: What are the theoretical, mathematical and computational foundations needed for establishing a comprehensive systems-based design architecture to realize the integrated design of the product, its environment, manufacturing processes and material as a system? Major challenges to be addressed here are: a) integration of models (material, process and product) to establish processing-structure-property-performance relationships, b) goal-oriented inverse design of material microstructures and processing paths to meet multiple conflicting performance/property requirements, c) robust concept exploration by managing uncertainty across process chains and d) systematic, domain-independent, modular, reconfigurable, reusable, computer interpretable, archivable, and multi-objective decision support in the early stages of design to different users. Approach: In order to address these challenges, the primary hypothesis in this dissertation is to establish the theoretical, mathematical and computational foundations for: 1) forward material, product and process workflows through systematic identification and integration of models to define the processing-structure-property-performance relationships; 2) a concept exploration framework supporting systematic formulation of design problems facilitating robust design exploration by bringing together robust design principles and multi-objective decision making protocols; 3) a generic, goal-oriented, inverse decision-based design method that uses 1) and 2) to facilitate the systems-based inverse design of material microstructures and processing paths to meet multiple product level performance/property requirements, thereby generating the problem-specific inverse decision workflow; and 4) integrating the workflows with a knowledge-based platform anchored in modeling decision-related knowledge facilitating capture, execution and reuse of the knowledge associated with 1), 2) and 3). This establishes a comprehensive systems-based design architecture to realize the integrated design of the product, its environment, manufacturing processes and material as a system. Validation: The systems-based design architecture for the integrated realization of materials, products and associated manufacturing processes is validated using the validation-square approach that consists of theoretical and empirical validation. Empirical validation of the design architecture is carried out using an industry driven problem namely the ‘Integrated Design of Steel (Material), Manufacturing Processes (Rolling and Cooling) and Hot Rolled Rods (Product) for Automotive Gears’. Specific sub-problems are formulated within this problem domain to address various research questions identified in this dissertation. Contributions: The contributions from the dissertation are categorized into new knowledge in four research domains: a) systematic model integration (vertical and horizontal) for integrated material and product workflows, b) goal-oriented, inverse decision support, c) robust concept exploration of process chains with multiple conflicting goals and d) knowledge-based decision support for rapid and robust design exploration in simulation-based integrated material, product and process design. The creation of new knowledge in this dissertation is associated with the development of a systems-based design architecture involving systematic function-based approach of formulating forward material workflows, a concept exploration framework for systematic design exploration, an inverse decision-based design method, and robust design metrics, all integrated with a knowledge-based platform for decision support. The theoretical, mathematical and computational foundations for the design architecture are proposed in this dissertation to facilitate rapid and robust exploration of the design and solution spaces to identify material microstructures and processing paths that satisfy conflicting property and performance for complex materials, products and processes by managing uncertainty

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available