Semantic hierarchies for extracting, modeling, and connecting compliance requirements in information security control standards

Companies and government organizations are increasingly compelled, if not required by law, to ensure that their information systems will comply with various federal and industry regulatory standards, such as the NIST Special Publication on Security Controls for Federal Information Systems (NIST SP-800-53), or the Common Criteria (ISO 15408-2). Such organizations operate business or mission critical systems where a lack of or lapse in security protections translates to serious confidentiality, integrity, and availability risks that, if exploited, could result in information disclosure, loss of money, or, at worst, loss of life. To mitigate these risks and ensure that their information systems meet regulatory standards, organizations must be able to (a) contextualize regulatory documents in a way that extracts the relevant technical implications for their systems, (b) formally represent their systems and demonstrate that they meet the extracted requirements following an accreditation process, and (c) ensure that all third-party systems, which may exist outside of the information system enclave as web or cloud services also implement appropriate security measures consistent with organizational expectations. This paper introduces a step-wise process, based on semantic hierarchies, that systematically extracts relevant security requirements from control standards to build a certification baseline for organizations to use in conjunction with formal methods and service agreements for accreditation. The approach is demonstrated following a case study of all audit-related controls in the SP-800-53, ISO 15408-2, and related documents. Accuracy, applicability, consistency, and efficacy of the approach were evaluated using controlled qualitative and quantitative methods in two separate studies

Evolving robot software and hardware

This paper summarizes the keynote I gave on the SEAMS 2020 conference. Noting the power of natural evolution that makes living systems extremely adaptive, I describe how artificial evolution can be employed to solve design and optimization problems in software. Thereafter, I discuss the Evolution of Things, that is, the possibility of evolving physical artefacts and zoom in on a (r)evolutionary way of creating 'bodies' and 'brains' of robots for engineering and fundamental research

Physiological mechanisms through which heat stress compromises reproduction in pigs

Seasonal variations in environmental temperatures impose added stress on domestic species bred for economically important production traits. These heat‐mediated stressors vary on a seasonal, daily, or spatial scale, and negatively impact behavior and reduce feed intake and growth rate, which inevitably lead to reduced herd productivity. The seasonal infertility observed in domestic swine is primarily characterized by depressed reproductive performance, which manifests as delayed puberty onset, reduced farrowing rates, and extended weaning‐to‐estrus intervals. Understanding the effects of heat stress at the organismal, cellular, and molecular level is a prerequisite to identifying mitigation strategies that should reduce the economic burden of compromised reproduction. In this review, we discuss the effect of heat stress on an animal\u27s ability to maintain homeostasis in multiple systems via several hypothalamic‐pituitary‐end organ axes. Additionally, we discuss our understanding of epigenetic programming and how hyperthermia experienced in utero influences industry‐relevant postnatal phenotypes. Further, we highlight the recent recognized mechanisms by which distant tissues and organs may molecularly communicate via extracellular vesicles, a potentially novel mechanism contributing to the heat‐stress response

Paediatric extracranial germ-cell tumours.

Management of paediatric extracranial germ-cell tumours carries a unique set of challenges. Germ-cell tumours are a heterogeneous group of neoplasms that present across a wide age range and vary in site, histology, and clinical behaviour. Patients with germ-cell tumours are managed by a diverse array of specialists. Thus, staging, risk stratification, and treatment approaches for germ-cell tumours have evolved disparately along several trajectories. Paediatric germ-cell tumours differ from the adolescent and adult disease in many ways, leading to complexities in applying age-appropriate, evidence-based care. Suboptimal outcomes remain for several groups of patients, including adolescents, and patients with extragonadal tumours, high tumour markers at diagnosis, or platinum-resistant disease. Survivors have significant long-term toxicities. The challenge moving forward will be to translate new insights from molecular studies and collaborative clinical data into improved patient outcomes. Future trials will be characterised by improved risk-stratification systems, biomarkers for response and toxic effects, rational reduction of therapy for low-risk patients and novel approaches for poor-risk patients, and improved international collaboration across paediatric and adult cooperative research groups.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/S1470-2045(15)00545-

Comparison of carboplatin versus cisplatin in the treatment of paediatric extracranial malignant germ cell tumours: A report of the Malignant Germ Cell International Consortium.

PURPOSE: To compare the outcomes of paediatric and adolescent extracranial malignant germ cell tumour (GCT) patients treated with either carboplatin or cisplatin on clinical trials conducted by the Children's Oncology Group (COG) and the Children's Cancer and Leukaemia Group (CCLG). METHODS: The Malignant Germ Cell International Consortium (MaGIC) has created a database of the GCT clinical trials conducted since 1983 by COG (United States, Canada and Australia), which used cisplatin-based regimens, and by CCLG (United Kingdom), which used carboplatin-based regimens. Using the parametric cure model, this study compared the overall 4-year event-free survival (EFS), stratified by age, stage, site and the a-priori defined MaGIC 'risk' groups: standard risk ((SR) 1 (EFS >80%; age 80%, age ≥ 11y) and poor risk (PR) (EFS ≤ 70%, age ≥ 11y). RESULTS: Cisplatin-based therapy was used in 620 patients; carboplatin was used in 163 patients. In the overall multivariate cure model, the two regimens did not differ significantly (cisplatin: 4-year EFS 86%; 95% confidence interval (CI) 83-89% versus carboplatin 4-year EFS 86%; 95% CI 79-90%; p = 0.87). No significant differences were noted in stratified analyses by site, stage, age and MaGIC risk groups: SR1 (p = 0.20), SR2 (p = 0.55) or PR (p = 0.72) patients. CONCLUSIONS: In these trials conducted contemporaneously, there is no significant difference in outcome observed overall, or any subset of patients, who were treated with regimens containing cisplatin versus carboplatin These results suggested sufficient equipoise to justify a randomised trial to evaluate the effectiveness of carboplatin versus cisplatin in the treatment of children, adolescents and young adults with standard risk GCT, which is currently underway

Practical hardware for evolvable robots

The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space

Morpho-evolution with learning using a controller archive as an inheritance mechanism

Most work in evolutionary robotics centres on evolving a controller for a fixed body-plan. However, previous studiessuggest that simultaneously evolving both controller and body-plan could open up many interesting possibilities. However, thejoint optimisation of body-plan and control via evolutionaryprocesses can be challenging in rich morphological spaces. Thisis because offspring can have body-plans that are very differentfrom either of their parents, leading to a potential mismatchbetween the structure of an inherited neural controller and thenew body. To address this, we propose a framework that combinesan evolutionary algorithm to generate body-plans and a learning algorithm to optimise the parameters of a neural controller. The topology of this controller is created once the body-plan of each offspring has been generated. The key novelty of the approach is to add an external archive for storing learned controllers that map to explicit ‘types’ of robots (where this is defined with respect to the features of the body-plan). By initiating learning froma controller with an appropriate structure inherited from thearchive, rather than from a randomly initialised one, we show that both the speed and magnitude of learning increases over time when compared to an approach that starts from scratch, using two tasks and three environments. The framework also provides new insights into the complex interactions between evolution and learnin

Bootstrapping artificial evolution to design robots for autonomous fabrication

A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolutionary Robotics has been widely used due to its capability of creating unique robot designs in simulation. Recent work has shown that it is possible to autonomously construct evolved designs in the physical domain, however this brings new challenges: the autonomous manufacture and assembly process introduces new constraints that are not apparent in simulation. To tackle this, we introduce a new method for producing a repertoire of diverse but manufacturable robots. This repertoire is used to seed an evolutionary loop that subsequently evolves robot designs and controllers capable of solving a maze-navigation task. We show that compared to random initialisation, seeding with a diverse and manufacturable population speeds up convergence and on some tasks, increases performance, while maintaining manufacturability