Approaches In Terms Of Flexibility Research Articles

Characterizing UNAM’s Open Education System Using the OOFAT Model

Mexico’s national university (UNAM) is a public mega university with a 46-year history in open education. This article presents an analysis based on the open, online, flexible provision of technology-enhanced higher education (OOFAT) model, developed by Orr and his colleagues (2018). The aim of this analysis was to characterize UNAM’s open and distance education system in terms of openness, flexibility, and its business model, in three distinct time periods. According to this analysis, the system has evolved in all areas, and at present has a content-focused approach in terms of flexibility and openness, which differs from the OOFAT at the center approach that is desired. The study also characterized the UNAM system’s business model as a prospector-like approach, which highlights the possibilities for instilling innovation through the schools that comprise this system. The analysis allowed for mapping the current situation and thus sheds light on defining the steps necessary for creating an integrally open system.

Fraud Resilient Device Offline Micro-Payments using Bit-Exchange Algorithms

Credit and debit card data theft is one of the earliest forms of cybercrime. It is one of the most common problems now days. Attackers often aim at stealing such customer data by targeting the Point of Sale system, i.e. the point at which a retailer first acquires customer data. Modern Point of Sale systems are powerful computers equipped with a card reader and running specialized software. Increasingly often, user devices are leveraged as input to the Point of Sale. In these scenarios, malware that can steal card data as soon as they are read by the device has flourished. As such, in cases where customer and vendor are persistently or intermittently disconnected from the network, no secure on-line payment is possible. A secure online micro-payment solution that is resilient to Point of Sale data breaches. Our solution improves over up to date approaches in terms of flexibility and security. To the best of our knowledge, FRODO is the first solution that can provide secure fully on line payments while being resilient to all currently known POS breaches. In particular we detail FRODO architecture, components, and protocols. Further, a thorough analysis of FRODO functional and security properties is provided, showing its effectiveness and strong.

Declarative Dependency Specification for Inter-connected Large-scale Cyber-physical Systems

Cyber-Physical Systems (CPS) are inter-connected software-hardware artefacts ranging from small-scale bio-electrical implants, medium-scale smart cars and intelligent production lines, to large-scale critical infrastructures with power grids, telecommunication networks and railway systems. Large-scale CPS plays a central role in modern society by delivering essential services for human daily life. Depending on the delivered services, any disruption or mal-function of these systems with a duration of hours or days will have severe societal and economic impacts and consequences, in particularly when the inter-dependencies between these systems are considered: the total consequences resulted by cascading effects is usually much larger than the one caused by the original failure. Federated simulation provides an effective means to analyse the overall dynamics of inter-connected large-scale CPS. In a typical federated simulation environment, dedicated domain-specific simulators, both commercial and open-source, are applied and inter-connected together to provide realistic system behaviours of the complete CPS networks. Specifying the dependencies among different simulator models is however a challenging task, especially for networks involving multiple large-scale CPS. In this paper, a declarative approach for dependency specification based on data stream processing is proposed. Preliminary results show the benefits comparing to the traditionally imperative approaches in terms of flexibility, scalability and usability.

Convolutional Sparse Coding for Capturing High‐Speed Video Content

AbstractVideo capture is limited by the trade‐off between spatial and temporal resolution: when capturing videos of high temporal resolution, the spatial resolution decreases due to bandwidth limitations in the capture system. Achieving both high spatial and temporal resolution is only possible with highly specialized and very expensive hardware, and even then the same basic trade‐off remains. The recent introduction of compressive sensing and sparse reconstruction techniques allows for the capture of single‐shot high‐speed video, by coding the temporal information in a single frame, and then reconstructing the full video sequence from this single‐coded image and a trained dictionary of image patches. In this paper, we first analyse this approach, and find insights that help improve the quality of the reconstructed videos. We then introduce a novel technique, based on convolutional sparse coding (CSC), and show how it outperforms the state‐of‐the‐art, patch‐based approach in terms of flexibility and efficiency, due to the convolutional nature of its filter banks. The key idea for CSC high‐speed video acquisition is extending the basic formulation by imposing an additional constraint in the temporal dimension, which enforces sparsity of the first‐order derivatives over time.

Cloud service ranking as a multi objective optimization problem

Cloud computing is a kind of computing model on subscription basis. In cloud computing environment, there are a lot of cloud providers that present variety kind of services with different quality of services. Users have various kinds of applications that should be carried out on suitable cloud services. Consequently the users might encounter problems in choosing the best service. Hence selection of a method to compare services and to choose best service has been regarded as a challenge. In this paper we presented NSGA_SR approach that utilizes both objective and subjective assessments and models ranking problem as a multi objective optimization and then solves it with use of non-dominated sorting genetic algorithm. Numerical experiments, confirmed that the proposed approach outperforms available approaches in terms of flexibility and scalability with increasing number of users and services. Also it converges to optimization of goals and has good stability during the different generations. Also it includes no limitation regarding any additive new quality attribute, service or supplementary function.

FRoDO: Fraud Resilient Device for Off-Line Micro-Payments

Credit and debit card data theft is one of the earliest forms of cybercrime. Still, it is one of the most common nowadays. Attackers often aim at stealing such customer data by targeting the Point of Sale (for short, PoS) system, i.e. the point at which a retailer first acquires customer data. Modern PoS systems are powerful computers equipped with a card reader and running specialized software. Increasingly often, user devices are leveraged as input to the PoS. In these scenarios, malware that can steal card data as soon as they are read by the device has flourished. As such, in cases where customer and vendor are persistently or intermittently disconnected from the network, no secure on-line payment is possible. This paper describes FRoDO, a secure off-line micro-payment solution that is resilient to PoS data breaches. Our solution improves over up to date approaches in terms of flexibility and security. To the best of our knowledge, FRoDO is the first solution that can provide secure fully off-line payments while being resilient to all currently known PoS breaches. In particular, we detail FRoDO architecture, components, and protocols. Further, a thorough analysis of FRoDO functional and security properties is provided, showing its effectiveness and viability.

Using fuzzy policies to improve context interpretation in adaptive systems

Adaptation is an increasingly important requirement for software systems executing in large-scale, heterogeneous, and dynamic environments. A central aspect of the adaptation methodology is management of contextual information needed to support the adaptation process. A major design challenge of managing contextual data lies in the fact that the information is partial, uncertain, and inherently suitable for diverging interpretations. While existing adaptation solutions focus on techniques, methods, and tools, the challenge of managing and interpreting ambiguous contextual information remains largely unresolved. In this paper, we present a new approach to knowledge management in adaptation feedback control loops that aims to overcome these issues by applying fuzzy set theory and approximate reasoning. Our new knowledge management scheme interprets imprecise information and effectively integrates this information into the adaptation feedback control loop. To test and evaluate our solution, we implemented it in an adaptation engine to perform rate control for media streaming applications. The evaluation results show the benefits of our approach in terms of flexibility and performance when compared to more traditional methods, such as TCP-friendly rate control.

Optimal depth-based regional frequency analysis

Abstract. Classical methods of regional frequency analysis (RFA) of hydrological variables face two drawbacks: (1) the restriction to a particular region which can lead to a loss of some information and (2) the definition of a region that generates a border effect. To reduce the impact of these drawbacks on regional modeling performance, an iterative method was proposed recently, based on the statistical notion of the depth function and a weight function φ. This depth-based RFA (DBRFA) approach was shown to be superior to traditional approaches in terms of flexibility, generality and performance. The main difficulty of the DBRFA approach is the optimal choice of the weight function ϕ (e.g., φ minimizing estimation errors). In order to avoid a subjective choice and naïve selection procedures of φ, the aim of the present paper is to propose an algorithm-based procedure to optimize the DBRFA and automate the choice of ϕ according to objective performance criteria. This procedure is applied to estimate flood quantiles in three different regions in North America. One of the findings from the application is that the optimal weight function depends on the considered region and can also quantify the region's homogeneity. By comparing the DBRFA to the canonical correlation analysis (CCA) method, results show that the DBRFA approach leads to better performances both in terms of relative bias and mean square error.

Flexible and Scalable Route to HDAc Inhibitors Containing an Unusual Trisubstituted Pyridine Core

A scalable route to histone deacetylase inhibitors containing an unusual 2-aryl-3-cyano-5-aminomethylpyridine core has been developed which has the flexibility to deliver a range of compounds on at least a multigram scale. The key step involves a novel Mannich reaction using 3-dimethylaminoacrolein, formaldehyde, and a secondary amine to yield a 2-(alkylaminomethyl)-3-dimethylaminoacrolein. Tuning of this reaction in process development was fundamental to the success of the approach in terms of flexibility and operability on scale-up. This new methodology will also enable access an underutilised family of 3,5-disubstituted pyrid-2-ones and 2,3,5-trisubstituted pyridines.

Evolutionary design of optimum distillation column sequence

Synthesis of the optimum distillation column sequence (DCS), which incorporates a huge search space composed of both conventional and complex arrangements, is a highly complicated combinatorial problem in the field of chemical process design and optimisation. In this study, a novel procedure for the synthesis of optimum DCS proposed by Boozarjomehry et al. [Boozarjomehry et al., Can. J. Chem. Eng. 87, 477–492 (2009)] is expanded to include the complex distillation arrangements. The method is based on evolutionary algorithms, and the total annual cost (TAC) is the main criterion used to screen alternatives. Efficient procedure has been proposed for encoding mechanism to include and classify various complex arrangements together with conventional distillation columns. All columns existing in each DCS alternative are designed using the most recommended short-cut methods to estimate the TAC of the DCS. Four standard benchmark case studies are carried out to clearly demonstrate the excellent performance of the proposed method. The produced results for these problems indicate that the proposed method outperforms the other existing approaches in terms of flexibility, accuracy and comprehensiveness. © 2011 Canadian Society for Chemical Engineering

Genetic algorithm (GA)-enhanced almost difference set (ADS)-based approach for array thinning

This study proposes a genetic algorithm (GA)-enhanced almost difference set (ADS)-based methodology to design thinned linear arrays with low-peak sidelobe levels (PSLs). The method allows one to overcome the limitations of the standard ADS approach in terms of flexibility and performances. The numerical validation, carried out in the far-field and for narrow-band signals, points out that with affordable computational efforts it is possible to design array arrangements that outperform standard ADS-based designs as well as standard GA approaches.

Sensor-steered fire simulation

A sensor-linked modelling tool for live prediction of uncontrolled compartment fires, K-CRISP, has been developed in order to facilitate emergency response via novel systems such as FireGrid. The modelling strategy is an extension of the Monte-Carlo fire model, CRISP, linking simulations to sensor inputs which controls evolution of the parametric space in which new scenarios are generated, thereby representing real-time “learning” about the fire. CRISP itself is based on a zone model representation of the fire, with linked capabilities for egress modelling and failure prediction for structural members, thus providing a major advantage over more detailed approaches in terms of flexibility and practicality, though with the conventional limitations of zone models. Large numbers of scenarios are required, but computational demands are mitigated to some extent by various procedures to limit the parameters which need to be varied. HPC (high performance computing) resources are exploited in “urgent computing” mode. The approach adopted for steering is shown to be effective in directing the evolution of the fire parameters, thereby driving the fire predictions towards the measurements. Moreover, the availability of probabilistic information in the output assists in providing potential end users with an indication of the likelihood of various hazard scenarios. The best forecasts are those for the immediate future, or for relatively simple fires, with progressively less confidence at longer lead times and in more complex scenarios. Given the uncertainties in real fire development the benefits of more detailed model representations may be marginal and the system developed thus far is considered to be an appropriate engineering approach to the problem, providing information of potential benefit in emergency response.

Development of a re-configurable ambisonic decoder for irregular loudspeaker configuration

This study reports a heuristic genetic algorithm to determine the decoding parameters in a first-order ambisonic system for reconstructing a three-dimensional sound field with an arbitrary quad speaker configuration. On this basis, a hardware prototype has been developed using a field programmable gate array (FPGA) to decode ambisonic signals that are encoded in the standard B-format. To allow direct coupling with digital audio sources, the input and output channels of the decoder are implemented with the I2S interface. Evaluations reveal that the decoding parameters derived by this method are superior to existing approaches in terms of flexibility in loudspeaker configuration and optimisation of some of the essential factors in surround sound reconstruction.

Reduction of total powerflow in a fluid filled pipe using an active/passive approach

This presentation describes the implementation of active/passive approaches for the reduction of total acoustic power flow in fluid filled pipes. Active/passive systems are essentially passive devices whose properties can be altered using a control system. For example, a Helmholtz resonator has been fitted with a variable neck aperture allowing the resonant frequency of the device to be altered. Other passive devices such as a delay line and a quarter wavelength tube have been used as active/passive devices. These active/passive devices have been designed to gain maximum performance (trade-off between flexibility of application and control authority). Various sensing approaches using PVDF (polyvinylidene fluoride) cables/films and an array of microphones have been tested and are compared. The various active/passive approaches developed have been compared to totally active approaches in terms of flexibility and control authority. [Work supported by University of Karlskrona/Ronneby.]