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Automatic Derivation of Requirements for Components Used in Human-Intensive Systems
Human-intensive systems (HISs), where humans must coordinate with each other along with software and/or hardware components to achieve system missions, are increasingly prevalent in safety-critical domains (e.g., healthcare). Such systems are often complex, involving aspects such as concurrency and exceptional situations. For these systems, it is often difficult but important to determine requirements for the individual components that are necessary to ensure the system requirements are satisfied. In this thesis, we investigated an approach that employs interface synthesis methods developed for software systems to automatically derive such requirements for components used in HISs.
In previous work, we investigated a requirement deriver that employs a regular language learning algorithm to iteratively refine the derived requirement based on counterexamples generated by model checking techniques. Since this learning-based requirement deriver often did not scale well, we investigated several learning and model checking optimizations. These optimizations significantly improved performance but affected the counterexample generation heuristics, often widely varying the permissiveness of the derived requirements. For comparison purposes, we investigated a direct requirement deriver that was purported to have poor performance but guarantees the derived requirements are adequately permissive, conceptually meaning the requirements are permissive as possible without violating the system requirements. For our evaluation, we applied these requirement derivers to case studies in two important domains, healthcare and election administration.
Based on this evaluation, the direct requirement deriver with all optimizations applied had reasonable performance and ensures the derived requirements are adequately permissive. For the learning-based requirement deriver, many of the optimizations and heuristics have been presented previously, but we recommend how to selectively combine them to obtain reasonable performance while usually producing the adequately permissive derived requirements.
Since such derived requirements often reflect the system complexity, these requirements can be easily misunderstood. Thus, we also investigated building views of the requirements that abstract away or highlight certain aspects to try to improve their understandability. Each single view appears to improve understandability and the multiple views seem to complement each other further improving understandability. Such derived requirements and their views can be used to safely develop and deploy the components used in HISs
The Neural Representation Benchmark and its Evaluation on Brain and Machine
A key requirement for the development of effective learning representations
is their evaluation and comparison to representations we know to be effective.
In natural sensory domains, the community has viewed the brain as a source of
inspiration and as an implicit benchmark for success. However, it has not been
possible to directly test representational learning algorithms directly against
the representations contained in neural systems. Here, we propose a new
benchmark for visual representations on which we have directly tested the
neural representation in multiple visual cortical areas in macaque (utilizing
data from [Majaj et al., 2012]), and on which any computer vision algorithm
that produces a feature space can be tested. The benchmark measures the
effectiveness of the neural or machine representation by computing the
classification loss on the ordered eigendecomposition of a kernel matrix
[Montavon et al., 2011]. In our analysis we find that the neural representation
in visual area IT is superior to visual area V4. In our analysis of
representational learning algorithms, we find that three-layer models approach
the representational performance of V4 and the algorithm in [Le et al., 2012]
surpasses the performance of V4. Impressively, we find that a recent supervised
algorithm [Krizhevsky et al., 2012] achieves performance comparable to that of
IT for an intermediate level of image variation difficulty, and surpasses IT at
a higher difficulty level. We believe this result represents a major milestone:
it is the first learning algorithm we have found that exceeds our current
estimate of IT representation performance. We hope that this benchmark will
assist the community in matching the representational performance of visual
cortex and will serve as an initial rallying point for further correspondence
between representations derived in brains and machines.Comment: The v1 version contained incorrectly computed kernel analysis curves
and KA-AUC values for V4, IT, and the HT-L3 models. They have been corrected
in this versio
CitySpec with Shield: A Secure Intelligent Assistant for Requirement Formalization
An increasing number of monitoring systems have been developed in smart
cities to ensure that the real-time operations of a city satisfy safety and
performance requirements. However, many existing city requirements are written
in English with missing, inaccurate, or ambiguous information. There is a high
demand for assisting city policymakers in converting human-specified
requirements to machine-understandable formal specifications for monitoring
systems. To tackle this limitation, we build CitySpec, the first intelligent
assistant system for requirement specification in smart cities. To create
CitySpec, we first collect over 1,500 real-world city requirements across
different domains (e.g., transportation and energy) from over 100 cities and
extract city-specific knowledge to generate a dataset of city vocabulary with
3,061 words. We also build a translation model and enhance it through
requirement synthesis and develop a novel online learning framework with
shielded validation. The evaluation results on real-world city requirements
show that CitySpec increases the sentence-level accuracy of requirement
specification from 59.02% to 86.64%, and has strong adaptability to a new city
and a new domain (e.g., the F1 score for requirements in Seattle increases from
77.6% to 93.75% with online learning). After the enhancement from the shield
function, CitySpec is now immune to most known textual adversarial inputs
(e.g., the attack success rate of DeepWordBug after the shield function is
reduced to 0% from 82.73%). We test the CitySpec with 18 participants from
different domains. CitySpec shows its strong usability and adaptability to
different domains, and also its robustness to malicious inputs.Comment: arXiv admin note: substantial text overlap with arXiv:2206.0313
Assisting School Management Teams to construct their school improvement plans: an action learning approach
This article reports on a first cycle of a larger action research study conducted to determine how Circuit Teams could support School Management Teams of underperforming high schools towards whole-school development. Although it is a mandated requirement by the Department of Education, none of the four schools involved in the study had developed a school improvement plan, a necessary first step towards whole-school development. In this article we focus on the collaborative intervention we designed to meet the identified needs of the participants regarding the construction of a school improvement plan. A qualitative baseline study revealed the School Management Teams’ general disregard towards the school improvement plan as well as limited insight into what skills they needed to develop it, and their imperfect understanding of whole-school development. We explain the action research process we took to facilitate a clearer understanding of the school improvement plan and how to develop it. The data analysis revealed that the collaborative learning experience ignited feelings of empowerment, increased motivation to collaborate with the Circuit Teams towards whole-school development, and generally assisted the School Management Teams’ resolve to improve the management of their respective schools. These findings present evidence that suggests the value of an action learning approach to the professional development of School Management Teams, but the process could be equally useful to encourage sustainable change in varied contexts of continued professional development.Keywords: Action learning, action research, Circuit Team, school improvement plan, School Management Team(s), school self-evaluation, systems theory approach, whole-school development, whole-school evaluation
Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review
Context: Processing Software Requirement Specifications (SRS) manually takes
a much longer time for requirement analysts in software engineering.
Researchers have been working on making an automatic approach to ease this
task. Most of the existing approaches require some intervention from an analyst
or are challenging to use. Some automatic and semi-automatic approaches were
developed based on heuristic rules or machine learning algorithms. However,
there are various constraints to the existing approaches of UML generation,
such as restriction on ambiguity, length or structure, anaphora,
incompleteness, atomicity of input text, requirements of domain ontology, etc.
Objective: This study aims to better understand the effectiveness of existing
systems and provide a conceptual framework with further improvement guidelines.
Method: We performed a systematic literature review (SLR). We conducted our
study selection into two phases and selected 70 papers. We conducted
quantitative and qualitative analyses by manually extracting information,
cross-checking, and validating our findings. Result: We described the existing
approaches and revealed the issues observed in these works. We identified and
clustered both the limitations and benefits of selected articles. Conclusion:
This research upholds the necessity of a common dataset and evaluation
framework to extend the research consistently. It also describes the
significance of natural language processing obstacles researchers face. In
addition, it creates a path forward for future research
Analysis of Illegal Parking Behavior in Lisbon: Predicting and Analyzing Illegal Parking Incidents in Lisbon´s Top 10 Critical Streets
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIllegal parking represents a costly and pervasive problem for most cities, as it not only leads to an
increase in traffic congestion and the emission of air pollutants but also compromises pedestrian,
biking, and driving safety. Moreover, it obstructs the flow of emergency vehicles, delivery services, and
other essential functions, posing a significant risk to public safety and impeding the efficient operation
of urban services. These detrimental effects ultimately diminish the cleanliness, security, and overall
attractiveness of cities, impacting the well-being of both residents and visitors alike.
Traditionally, decision-support systems utilized for addressing illegal parking have heavily relied on
costly camera systems and complex video-processing algorithms to detect and monitor infractions in
real time. However, the implementation of such systems is often challenging and expensive,
particularly considering the diverse and dynamic road environment conditions. Alternatively, research
studies focusing on spatiotemporal features for predicting parking infractions present a more efficient
and cost-effective approach.
This project focuses on the development of a machine learning model to accurately predict illegal
parking incidents in the ten highly critical streets of Lisbon Municipality, taking into account the hour
period and whether it is a weekend or holiday. A comprehensive evaluation of various machine
learning algorithms was conducted, and the k-nearest neighbors (KNN) algorithm emerged as the top performing model. The KNN model exhibited robust predictive capabilities, effectively estimating the
occurrence of illegal parking in the most critical streets, and together with the creation of an interactive
and user-friendly dashboard, this project contributes valuable insights for urban planners,
policymakers, and law enforcement agencies, empowering them to enhance public safety and security
through informed decision-making
Concepts and practices in agricultural extension in developing countries: a source book
The first chapter outlines the emerging challenges faced by agricultural R&D sectors and how paradigms are evolving in response to these changes and challenges. The second chapter traces the evolution of agricultural extension thinking and practice. It highlights some generic problems faced at various stages of evolution and approaches to address them. It highlights the factors identified in literature as contributing to successful knowledge dissemination processes and creating higher access to clients to the services. While reflecting on the challenges and opportunities, the chapter also explores the possible future of extension services in developing countries. The third chapter gives an account of the various extension models, approaches and methods that have been tried out in developing countries and the experiences. The chapter concludes with the transition being made to agricultural innovation systems from Research & Extension systems and highlights the role of extension services in this context. Chapter four highlights the importance of farmer groups in providing effective extension services and promoting innovation. It explains in detail the processes, approaches and methods involved in group formation and development, management, performance assessment and, monitoring and evaluation. Chapter five lists and describes in detail the various tools and methods used in participatory research and development processes. Chapter six focuses on the very important issues of Monitoring and Evaluation as systems for learning and for facilitating reflective action cycles. The importance of participatory approaches in M&E, process monitoring and outcome mapping are highlighted. This book can be used by students and practitioners of extension, researchers and decision-makers. This is a collation of knowledge regarding the practice of extension and is not intended to be used as a recipe or blue print. Based on the context and the requirement, the approaches and tools should be selected, adapted and used. There is a built-in flexibility that would allow the user to employ his/her experience, creativity and imagination in adapting and using the approaches and tools described in this source book
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