Automated Security Assessments of Amazon Web Services Environments

Informative path planning is an important and challenging problem in robotics that remains to be solved in a manner that allows for wide-spread implementation and real-world practical adoption. Among various reasons for this, one is the lack of approaches that allow for informative path planning in high-dimensional spaces and non-trivial sensor constraints. In this work we present a sampling-based approach that allows us to tackle the challenges of large and high-dimensional search spaces. This is done by performing informed sampling in the high-dimensional continuous space and incorporating potential information gain along edges in the reward estimation. This method rapidly generates a global path that maximizes information gain for the given path budget constraints. We discuss the details of our implementation for an example use case of searching for multiple objects of interest in a large search space using a fixed-wing UAV with a forward-facing camera. We compare our approach to a sampling-based planner baseline and demonstrate how our contributions allow our approach to consistently out-perform the baseline by 18.0%. With this we thus present a practical and generalizable informative path planning framework that can be used for very large environments, limited budgets, and high dimensional search spaces, such as robots with motion constraints or high-dimensional configuration spaces. [Code] <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> Codebase: https://github.com/castacks/tigris [Video] <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> Video: https://youtu.be/bMw5nUGL5GQ

Historically high energy physics computing has been performed on large purpose-built computing systems. In the beginning there were single site computing facilities, which evolved into the Worldwide LHC Computing Grid (WLCG) used today. The vast majority of the WLCG resources are used for LHC computing and the resources are scheduled to be continuously used throughout the year. In the last several years there has been an explosion in capacity and capability of commercial and academic computing clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest amongst the cloud providers to demonstrate the capability to perform large scale scientific computing. In this presentation we will discuss results from the CMS experiment using the Fermilab HEPCloud Facility, which utilized both local Fermilab resources and Amazon Web Services (AWS). The goal was to work with AWS through a matching grant to demonstrate a sustained scale approximately equal to half of the worldwide processing resources available to CMS. We will discuss the planning and technical challenges involved in organizing the most IO intensive CMS workflows on a large-scale set of virtualized resource provisioned by the Fermilab HEPCloud. We will describe the data handling and data management challenges. Also, we will discuss the economic issues and cost and operational efficiency comparison to our dedicated resources. At the end we will consider the changes in the working model of HEP computing in a domain with the availability of large scale resources scheduled at peak times.

By 2021, cloud adoption was quite widespread with many organizations migrating from traditional data-center models to the infrastructure-as-code concept provided by Cloud Service Providers. As part of the Shared Security Responsibility model, customers are expected to monitor their own environments. To this end Amazon Web Services and Microsoft Azure provide a number of capabilities and a plethora of guidance on how to use their platforms and additional security enhancements from their Marketplaces respectively. However, the author asserts that lessons from the Capital One data breach of 2019 needs to be heeded, on the basis that the initial attack vector was a compromised Amazon Web Service instance hosting a web application firewall, thereby enabling thousands upon thousands of personal data records to be extracted from Simple Storage Service assets used by the financial institution. This security breach still occurred even though protective monitoring technologies are made available to customers. But the stark warning here is that, in addition to the aforementioned active security monitoring, there is a need for forensic readiness pertaining to host-based incident response and forensics requirements. This chapter argues that host-based digital forensics principles need to be considered as part of any customer’s portion of the security responsibility. Preparedness to this level is vital for cyber incident response activities, because organizations do not know when the next security incident might occur, or where potentially compromised instances need to be analyzed. This chapter raises awareness so this is implicitly recognized in relation to our use of these public cloud platforms.KeywordsAWSAzureProtective MonitoringDigital ForensicsHost Forensics

A heuristic approach to optimization of complex physicochemical processes in the form of a genetic algorithm for solving problems is presented. In comparison with other evolutionary methods, the genetic algorithm allows working with large search spaces and complex evaluation functions, which is especially important in the study of multifactor physicochemical systems. Due to the relatively high need for computing resources when working with large and complex search spaces, optimization of existing calculation organization schemes has a positive effect on the accuracy of the calculated results. The paper presents a modified genetic algorithm that minimizes the number of iterations to achieve a given accuracy when solving the problem of finding the optimal composition of the initial reaction mixture. For a complex physicochemical process, an optimization problem is formulated which consists in finding the composition of the initial reaction mixture that promotes maximization (or minimization) of a given target parameter. The optimality criterion is determined by the type of the problem being solved and, when organizing calculations, is focused on the maximum yield of the target product. The main steps of implementing the genetic algorithm include creating an initial set of solutions and subsequent iterative evaluation of their quality for subsequent combination and modification until optimal values are achieved using mechanisms similar to biological evolution. To improve the efficiency of the method and reduce the number of iterations, a modification of the genetic algorithm is proposed which boils down to a dynamic estimate of the “mutation” parameter, depending on the diversity of individuals in the formed population of solutions. In a series of computational experiments, an analysis was made of the influence of the genetic algorithm parameters on the accuracy and efficiency of solving the problem using the example of studying the kinetics of the Michaelis-Menten enzymatic reaction. The results of calculations to determine the optimal composition of the reaction mixture showed that the dynamic determination of the “mutation” parameter contributes to an increase in the accuracy of the problem solution and a multiple decrease in the relative error value reaching 0.77 % when performing 200 iterations and 0.21 % when performing 400 iterations. The presented modified approach to solving the optimization problem is not limited by the type and content of the studied physicochemical process. The calculations performed showed a high degree of influence of the “mutation” parameter on the accuracy and efficiency of the problem solution, and dynamic control of the value of this parameter allowed increasing the speed of the genetic algorithm and reduce the number of iterations to achieve an optimal solution of a given accuracy. This is especially relevant in the study of multifactorial systems when the influence of parameters is non-trivial.

Compared to General Purpose Modeling Languages (GPMLs), Domain Specific Modeling Languages (DSMLs) have brought increasing expressivity and conciseness through the use of concepts and notations, which are customized by the characteristics of a specific problem domain. However, the creation of completely new languages can significantly increase software development costs. An alternative approach to creating new DSMLs refers to a customization of existing modeling languages. The DSML creation process through a customization of existing languages is a special case of Language Embedding. The embedding provides customization of an inherited infrastructure to a given domain. This paper elaborates a Domain-Specific Modeling environment for embedding an arbitrary DSML into an arbitrary GPML. The environment enables uniform development of DSMLs in the form of a lightweight extension of existing modeling languages.

Chromosome refinement for optimising multiple supply chains

Objective:Nutrition practice in India has generally been confined to pen and paper with little use of algorithms and technology. Nutritionists in practice find it difficult to go beyond macronutrient analysis for dietary intake or diet plans. Since nutrition practice requires lot of data processing, we decided to develop software to aid in these calculations and to empower nutritionist with technology enabled scientific practice using software.Method: Ntuitive software was developed by a team of nutrition experts and technologists over a period of 18 months. Data for nutrient values was sources from IFCT 2017 [6], RDA’s were taken from ICMR guidelines and growth charts from IAP publications [7]. Design and coding of the software was done inhouse. Ntuitive database has over 8500 recipes and packed foods and has most of the national and international cuisine items. The software module enables client / patient management, detailed profiling, recording anthropometric data and plotting on WHO and IAP growth charts, recording food allergies and medical condition. The dietary recall be it one day, three day or seven day can be easily recorded and analysis of nutrients can be obtained for the same. The software is hosted on Amazon Web Server and applied as SAAS platform for practice.Result:800 children and adults underwent analysis and consultation using Ntuitive software. All 189 micronutrients could be calculated using the software and deficiency or excess quantified when compared with RDA.Conclusion: Nutrition practice can be made easy and technology enabled keeping in mind scientific standards. This helps in data collection not just in macro nutrients, but also in micronutrients.

General-purpose modeling languages are inadequate to model and visualize business processes precisely. An enterprise has its own vocabulary for modeling processes and its specific tasks may have attached data that define the tasks precisely. We propose using Domain Specific Modeling (DSM) languages to model business processes, such that an enterprise can define its own DSM language(s) capturing its vocabulary and data requirement. We suggest using UML profiles and UML activity diagrams as the semantic base for these DSM languages and present tools that are able to create a DSM language and tool support for a given domain. One tool, called ADSpecializer, can generate a UML profile and its tool support of a given application domain. The other tool, ADModeler, is used to create UML activity diagrams within such a domain-specific UML profile. The two tools enable an enterprise to efficiently define and utilize their own DSM language.

Educational games are serious games that specifically used for education yet it is fun to play. There are numbers of domain specific modeling languages for educational games focus in various aspect and theories of learning. By having domain specific modeling language which applies a visual diagramming language, it helps in represent design ideas and logic. This paper analyzes existing modelling languages to assess the coverage of strategy genre and transfer learning concepts. The results show that existing modelling languages covers important strategy genre concepts but cover very little of the transfer learning concepts. The authors conclude that the existing modelling languages can be used to model small strategy games but will have trouble to scale to large strategy games, and will not be able to model games that guarantee students learning.

Cloud service provider, infrastructure vendor and clients/Cloud user’s are main actors in any cloud enterprise like Amazon web service’s cloud or Google’s cloud. Now these enterprises take care in infrastructure deployment and cloud services management (IaaS/PaaS/SaaS). Cloud user ‘s need to provide correct amount of services needed and characteristic of workload in order to avoid over – provisioning of resources and it’s the important pricing factor. Cloud service provider need to manage the resources and as well as optimize the resources to maximize the profit. To manage the profit we consider the M/M/m queuing model which manages the queue of job and provide average execution time. Resource Scheduling is one of the main concerns in profit maximization for which we take HYBRID PSO-MOBA as it resolves the global convergence problem, faster convergence, less parameter to tune, easier searching in very large problem spaces and locating the right resource. In HYBRID PSO-MOBA we are combining the features of PSO and MOBA to achieve the benefits of both PSO and MOBA and have greater compatibility.

In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison.

Impetigo is a common, superficial bacterial skin infection, which is most frequently encountered in children. There is no generally agreed standard therapy, and guidelines for treatment differ widely. Treatment options include many different oral and topical antibiotics as well as disinfectants. This is an updated version of the original review published in 2003. To assess the effects of treatments for impetigo, including non-pharmacological interventions and 'waiting for natural resolution'. We updated our searches of the following databases to July 2010: the Cochrane Skin Group Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (from 2005), EMBASE (from 2007), and LILACS (from 1982). We also searched online trials registries for ongoing trials, and we handsearched the reference lists of new studies found in the updated search. Randomised controlled trials of treatments for non-bullous, bullous, primary, and secondary impetigo. Two independent authors undertook all steps in data collection. We performed quality assessments and data collection in two separate stages. We included 57 trials in the first version of this review. For this update 1 of those trials was excluded and 12 new trials were added. The total number of included trials was, thus, 68, with 5578 participants, reporting on 50 different treatments, including placebo. Most trials were in primary impetigo or did not specify this.For many of the items that were assessed for risk of bias, most studies did not provide enough information. Fifteen studies reported blinding of participants and outcome assessors.Topical antibiotic treatment showed better cure rates than placebo (pooled risk ratio (RR) 2. 24, 95% confidence interval (CI) 1.61 to 3.13) in 6 studies with 575 participants. In 4 studies with 440 participants, there was no clear evidence that either of the most commonly studied topical antibiotics (mupirocin and fusidic acid) was more effective than the other (RR 1.03, 95% CI 0.95 to 1.11).In 10 studies with 581 participants, topical mupirocin was shown to be slightly superior to oral erythromycin (pooled RR 1.07, 95% CI 1.01 to 1.13). There were no significant differences in cure rates from treatment with topical versus other oral antibiotics. There were, however, differences in the outcome from treatment with different oral antibiotics: penicillin was inferior to erythromycin, in 2 studies with 79 participants (pooled RR 1.29, 95% CI 1.07 to 1.56), and cloxacillin, in 2 studies with 166 participants (pooled RR 1.59, 95% CI 1.21 to 2.08).There was a lack of evidence for the benefit of using disinfectant solutions. When 2 studies with 292 participants were pooled, topical antibiotics were significantly better than disinfecting treatments (RR 1.15, 95% CI 1.01 to 1.32).The reported number of side-effects was low, and most of these were mild. Side-effects were more common for oral antibiotic treatment compared to topical treatment. Gastrointestinal effects accounted for most of the difference.Worldwide, bacteria causing impetigo show growing resistance rates for commonly used antibiotics. For a newly developed topical treatment, retapamulin, no resistance has yet been reported. There is good evidence that topical mupirocin and topical fusidic acid are equally, or more, effective than oral treatment. Due to the lack of studies in people with extensive impetigo, it is unclear if oral antibiotics are superior to topical antibiotics in this group. Fusidic acid and mupirocin are of similar efficacy. Penicillin was not as effective as most other antibiotics. There is a lack of evidence to support disinfection measures to manage impetigo.

The voices of people living with dementia are rarely included in primary data collection due to cognitive challenges and the concerns of researchers and others about limitations associated with informed consent. This article presents a successfully implemented, step-by-step process enabling effective participation of aged care residents with dementia using a case study approach. Three methodological and critical steps in data collection were identified that led to the successful participation of residents with dementia in research. The process corresponds with, yet is uniquely different from the common elements in the qualitative research process. These are active participation during data collection, researcher familiarization with participants, and their set interval and time-lapse considerations (timeline). The process of involvement of people with dementia in research should proceed at a pace that is guided by the participants. It is important to consider participant interview pace, pattern, and the conversation time points when interruptions occur, to restart the whole interview process. Researchers need to facilitate active engagement by building and maintaining authentic relationships with the participants.

Domain-specific modelling langugages, which are tailored to the requirements of their users, can significantly increase the acceptance of formal (or at least semi-formal) modelling in scenarios where informal diagrams and natural language descriptions are predominant today. We show in this article how the Resource Description Framework (RDF), which is a standard for the fundamental data structures of the Semantic Web, and algebraic graph transformations on these data structures can be used to realise and modify the abstract syntax of models in such domain-specific languages. We examine a small domain-specific modelling language for IT infrastructures--inspired by real-world requirements from a banking environment--as an application scenario. From this scenario, we derive four key requirements for a domain-specific modelling framework: (1) distributed modelling, (2) evolution of language definitions, (3) migration of legacy models and (4) integration of modelling languages. RDF and transformation rules are then used to provide a solution which meets these requirements simultaneously, where all kinds of modifications--from simple editing steps via model migration to language integration--are realised in an integrated manner by the single, declarative formalism of algebraic graph transformation.

ADELFE is a French acronym that means “Toolkit for Designing Software with Emergent Functionalities” (“Atelier de DEveloppement de Logiciels à Fonctionnalité Emergente” in French). ADELFE methodology is dedicated to applications characterized by openness and the need of the system adaptation to an environment. Its main goal is to help and guide any designer during the development of an Adaptive Multi-agent System (AMAS). An AMAS is characterized by the following points: it is plunged into an environment and composed of interdependent agents, each agent carries out a partial function and the agents, organization during runtime makes the system realize an emergent function. Actually, an agent is locally cooperative, i.e. it is able to recognize cooperation failures called Non-cooperative Situations (NCS, which could be related to exceptions in classical programs) and treat them.ADELFE includes five Work Definitions that were initially inspired from the Rational Unified Process (RUP) and gathers 21 activities, producing or refining 12 work products. These products are aggregating modelling diagrams or structured or free text. ADELFE, which is a Model-Driven (model-centred) development method, is not hardly dependent on Domain Specific Modelling Languages (DSML) but currently the recommendation is to use UML2 for general activities and to use AMASML (AMAS Modelling Language) and SpeADL (Species-based Modelling Language) for specific activities appearing in Analysis, Design or Implementation phases.KeywordsAdaptive Multi-agent Systems (AMAS)Domain-specific Modeling Languages (DSML)AMAS ApproachAMAS TheoryManagement Studies ConferenceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.