Our Approach. The Parity Check Code − − Due to the fact that the computation cost of limb adaptation grows linearly with the distance of the code, we choose for the simple parity check code [n, n 1, 2]2. This code has a single parity limb that is the sum of all n 1 native limbs. Adopting such a code simplifies limb adaptation and transpositions as follows: • Xxxx adaptation modifies a limb and the parity limb. Its computation cost is twice as large as if it was computed on the native state alone. • None of the n! possible limb transpositions requires a correcting adaptation, as all limbs are in the (single) parity equation. Paradoxically, as a non-native limb transposition on the parity check code has no computation cost, it is cheaper to compute it than do the equivalent embedded mapping that requires n − 2 bitwise limb additions. The primary goal of our approach is the guaranteed detection of any single- limb fault in the computation. The secondary goal is that it should be hard to enforce two or more compensating faults in the computation or in the registers. The easiest attack on limb adaptation would be to inject two compensating faults in the two φ computations. In this respect it is a good idea in software implemen- tations to use different computation sequences and/or different registers so that the attacker has to induce two different faults for them to be compensating. For the same reason, in dedicated hardware implementations one shall not use the same combinatorial circuit for both φ. Instead of attacking the computation, an attacker could attack the registers and inject compensating faults on two limbs. To be successful, such attacks would require knowledge of the implementation details and the ability to inject faults very precisely. The parity check code offers fault detection capabilities that are close to duplication. It detects any single-limb fault instead of any single fault, but not multiple faults. On the other hand, it can be implemented much more efficiently thanks to the cheap limb transpositions and uses less memory, since the state size increases only by 1/(n − 1) instead of 2.
Our Approach. We introduce a probabilistic approach to find paths belonging to different homotopy classes for socially- aware robot motion planning. Our approach is com- plete, it can find all the possible paths, namely all the homotopy classes implicitly encoded in the navigation graph built from a Voronoi diagram that describes the scenario. Having a set of possible paths, we choose the best one according to a cost that considers social interactions between humans.
Our Approach. There is a strong history of partnership working in pan-Lancashire. Local CSPs take a pragmatic and flexible approach to joint working on shared priorities on a thematic and geographical footprint. Our approach will include: Closer collaboration with other partnerships (including the Safeguarding Boards, Children and Young People's Trust and the Health and Wellbeing Board) to addressing shared priorities, particularly the contributory factors and determinants that influence offending and vulnerability. Continually developing and improving links and activities with all local authorities to support local residents and better understand the geographic and demographic diversity of Lancashire. Working with the Office of the Police and Crime Commissioner (OPCC) to deliver community safety activity that supports the aims and priorities of the Police and Crime Plan. A commitment to taking an 'early help' approach to stop the development of issues that can often become more significant challenges. Effective commissioning of services is central to delivering key activity. Feedback from those services to CSPs will be used to further inform local action planning.
Our Approach. In response to the economic challenges facing the State, the New Jersey Economic Development Authority (NJEDA) is seeking support to assess the economic and fiscal impacts of the pandemic on the State’s economy and to develop strategies to support the State’s immediate and long-term economic recovery. Specifically, NJEDA is seeking support across 12 core tasks (Exhibit 1). In the following section, we describe how we will successfully launch each of these tasks with clear deliverables. Per NJEDA’s request, we have included four timing options for delivering Task 3-7 activities and deliverables based on various timing windows outlined in the Request for Cost Proposal. Our approach includes three core workstreams to complete the 12 tasks outlined in NJEDA’s scope, with several options to address the Task 3-7 timing options: • Workstream 1: Economic and fiscal impact assessment • Workstream 2: Economic recovery planning, advice, and coordination • Workstream 3: Economic recovery strategy Exhibit 1: Proposed workstreams, activities, and timing options. Base Period (8 weeks total for Tasks 1-12, includes 2 weeks of Tasks 3-7 support) Option 1 (Base Period plus additional 6 weeks of Tasks 3-7 support (completing Tasks 3-7 in ~2 months) Option 2 (Base Period plus additional 14 weeks of Tasks 3-7 support (completing Tasks 3-7 in ~4 months) Option 3 (Base Period plus additional 22 weeks of Tasks 3-7 support (completing Tasks 3-7 in ~6 months) 2020 Core activity May Jun Jul Aug Sep Oct Nov Dec Economic and fiscal impact assessment
Our Approach. As illustrated in Figure 3, we carry out our research activities in WP4 based on a bottom-up approach. First, we go through a large set of smart city use cases and analyse their requirements based on the data processing flows they need in order to produce their required results. After that, we try to abstract their concrete requirements into some common and generic patterns, which form the basis of our edge programming model. To realize such a programming model, we design and implement the XXxxX.xx edge computing framework, which provides specific interfaces for service developers to design and operator their IoT services. In parallel we can carry out some studies to optimize the performance of the core algorithms in such a framework, such as task allocation algorithm or task migration algorithm. Once the initial framework is finished, we start to validate its programming model and interfaces with some implemented use cases, such as smart parking use case, lost child finding use case.
Our Approach. We leverage the ordering capabilities of the Blockchain by presenting a protocol that both sends and receives information from the Blockchain to accomplish the goal of publishing a value on the Blockchain that is representative of the values from all the data sources. Towards computing a representative value, we redefine the notion of agreement. We say that two nodes agree with each other if the values that they obtained from data sources are within a pre-defined parameter called agreement distance. We say that a set of values form a coherent
Our Approach. As shown in section 2.2, to modify a program that is wo- ven may cause a problem. It is useful to verify the correct- ness of the weaving. As in the example of the Line.moveBy method in the previous section, the behavior of a program is changed by weaving. Consequently, we can verify the correctness of the weaving by verifying the correctness of the behavior change. Such behavior change during weaving can be rep- resented as the program behavior before and after weaving. Therefore, it is effective to specify this program behavior before and after weaving and to verify whether the behav- ior specification is satisfied. Program behavior can be rep- resented as a property of the control and data flow graph of the program. For instance, a control flow graph of the Line.moveBy method after weaving is shown in Figure
Our Approach. 4.1.1 We have an internationally benchmarked and widely admired curriculum which, by combining intellectual rigour in the student’s chosen discipline with a breadth of multidisciplinary experience, ensures that our graduates leave university as critical thinkers and effective communicators, well able to face the next stage of their careers.
