Algorithm. Next, we present an algorithm that solves Byzantine agree- ment assuming ℓ > 3t. Our agreement algorithm is generic: given any synchronous Byzantine agreement algorithm for ℓ processes with unique identifiers (such algorithms exist when ℓ = n > 3t, e.g., [13]), we transform it into an algorithm for n processes and ℓ identifiers, where n ≥ ℓ. Without loss of generality, we assume that the algorithm to be transformed uses broadcasts: a process sends the same message to all other processes. (If a process wishes to send a message only to specific recipients, it could include the recipient’s identi- fier in the broadcasted message.) In our transformation, we divide processes into groups ac- cording to their identifiers. Each group simulates a single process. If all processes within a group are correct, then they can reach agreement and cooperatively simulate a sin- gle process. If any process in the group is Byzantine, we allow the simulated process of that group to behave in a Byzantine manner. The correctness of our simulation re- lies on the fact that more than two-thirds of the simulated processes will be correct (since ℓ > 3t), which is enough to achieve agreement.
Algorithm. We now describe an algorithm that solves Byzantine agree- ment in the basic partially synchronous model when ℓ > n+3t . Our algorithm is based on the algorithm given by Xxxxx, Xxxxx and Xxxxxxxxxx [9] for the classical case where n = ℓ, with several novel features. Generalizing the algo- rithm is not straightforward. Some of the difficulty stems from the following scenario. Suppose two correct processes share an identifier and follow the traditional algorithm of [9]. They could send very different messages (for example, if they have different input values), but recipients of those messages would have no way of telling apart the messages of the two correct senders, so it could appear to the recipients as if a single Byzantine process was sending out contradictory information. Thus, the algorithm has to guard against in- consistent information coming from correct homonym pro- cesses as well as malicious messages sent by the Byzantine processes.
Algorithm. We now describe the basic step of the reconciliation mechanism, i.e., the reconcili- ation between two sites on a given object. Section 8 describes when to invoke the reconciliation mechanism, and the options that exist in its use. The basic step is as follows.
Algorithm. Prior to GEMS commencing --------- discussions with the Institutional Review Board ("IRB"), R2 shall confirm in writing to GEMS that the R2 Product algorithm can process the PMA (pre-market approval) cases which are done in feasibility format and that R2 can use them for the PMA submission. In the event that the FDA does not accept these cases for R2's FDA submission, then GEMS and R2 shall negotiate in good faith to determine how to acquire additional cases as outlined in Section 2.8.3.
Algorithm. The shared memory Under x-obstruction-freedom, up to x processes may concurrently progress without preventing termination. As a consequence, in comparison to obstruction-freedom, solving k-set agreement in this setting requires to deal with more contention scenarios. To cope with these additional interleavings of processes, we increase the number of entries in REG. More precisely, REG now contains m = (n − k + x) entries. Ordering the quadruplets In the base algorithm, the four fields of some quadruplet X are the round number X.rd, the level X.ℓvℓ, the conflict flag X.xxℓ, and the value X.val. Coping with x-concurrency requires to replace the last field, which was initially a singleton, with a set of values. Hereafter, this new field is denoted X.valset . In line with the definitions of Section 4.1, let “>” denote the lexicographical order over the set of quadruplets, where the relation ⊐ is generalized as follows to take into account the fact that the last field of a quadruplet is now a non-empty set of values: X ⊐ Y d=ef (X > Y ) ∧ [(X.rd > Y.rd) ∨ (X.xx ℓ) ∨ (X.valset ⊇ Y.valset )]. In comparison to the definition appearing in Section 4, the sole new case where the ordering X ⊐ Y holds is (X > Y ) ∧ (X.valset ⊇ Y.valset ). This case captures the fact that, as long as at most x input values are competing at some round, there is no conflict. If such a situation arises, we simply construct a quadruplet that aggregates the different input values. function sup(T ) is % T is a set of quadruplets whose last field is now a set of values % (S1) (S2) (S3) (S4) (S5) (S6) let ⟨r, ℓeveℓ, conf ℓict, valset ⟩ be max(T ); let tuples(T ) be {X | X ∈ T ∧ X.rnd = r}; let values(T ) be {v | X ∈ T ∧ v ∈ X.valset }; let conf ℓict (T ) be conflict ∨ |tuples(T )| > x ∨ |values(T )| > x; % lexicographical order % l et valset be the (at most) x greatest values in values(T ); return ⟨r, ℓeveℓ, conf ℓict (T ), valset ⟩ . Figure 4: Function sup() suited to x-obstruction-freedom Modifications to the sup() function Figure 4 describes the new definition of function sup(). Compared with the original algorithm in Figure 1, it introduces a few modifications (underlined and in blue). Those are detailed below. • Line S1. As pointed out previously, the last field of a quadruplet is now a set of values. The lexicographical ordering over such sets is as follows: sets are ordered first according to their size, and second using some arbitrary order over their elements. By abuse of notation, thi...
Algorithm. 5.1 Algorithm 1: Add Transaction to Blockchain
5.2 Algorithm 2: Add Block to Blockchain
Algorithm. A recursive mathematical formulation or model applied to transform input data streams from a form not suitable to users to output data streams in a form suitable to users by combining, reformatting, calculating, mathematically transforming, adjusting and/or some combination of these manipulations.
Algorithm. Based on the requirements, a design for an algorithm is proposed. Figure 13 shows a UML activity diagram of the steps an algorithm should take to conform to the requirements. The activity diagram was created using Draw.io2. The algorithm should start as soon as the FDT arrives at a data station, then it should check if the target asset has access conditions in place. If this is not the case, the algorithm should already end, since the FDT should be able to access the asset without needing an access request. However, if access conditions are in place, the algorithm has to check if the FDT has an access request included in its metadata. If this is not the case, the algorithm should end displaying a message that an access request should be included within the FDT, since there are access conditions present. It could even display the access conditions, to save time in the future when trying to match a request. Then the algorithm checks if the request mentions the same target asset as the conditions. This check could be complex if the scenario is similar to the scenario of Section 4.9, since in that case, the algorithm should be able to compare SHACL nodes with the actual target asset. Because of this, the algorithm should be able to process SHACL in the access request. If there is a match between the request and conditions, the algorithm continues with the next step. On the other hand when there is no match, a message is displayed informing that the FDT should adjust the target asset or the characteristics of the asset. Following those checks, the algorithm will need to check what types of permissions and prohibitions are present in the access request and access conditions. It needs to check the type of duties and constraints of these permissions and prohibitions. If these differ, then there should be no match automatically. The algorithm should then return a message specifying that the request should add/remove duties or constraints based on the types present in the access conditions. If there is a match between the types of duties/constraints, then the algorithm continues. The next step is that the algorithm compares the values of these duties/constraints to see if they are matching. In case these are not matching like in the scenario of Section 4.6, a message should be returned stating the values of the access conditions and that the request should be adjusted to be granted access to the target. 2 xxxxx://xxx.xxxxxxxx.xxx/
Algorithm. Consider a cluster-based infrastructure-less network with cluster-head ‘CH’ and several cluster members. Consider two cluster members CMA and CHB want to authenticate each other. The public information about the cluster is, {PN, IDCH, IDCMA, IDCMB, TKprh (PN)} , Hash function, symmetric encipherment. Step1: Cluster member CMA selects the Private key Kprcma and calculate the value of TKpr𝔀ma (PN) and KCH−CMA = TKpr𝔀ma TKprh (PN) with the help of public information. Then CMA constructs the message mCMA as follows mCMA = {IDCMA, IDCMB, IDCH, TKpr𝔀ma (PN), CTCMA} Where, CTCMA = E (KCH−CMA, {IDCMA||IDCMB||IDCH||HCMA}) HCMA = {IDCMA||IDCMB||IDCH||TKpr𝔀ma (PN)} Cluster member CMA sends the mCMA to ‘CH, and this message indicates that it wants to authenticate with Cluster member CMB
Algorithm. Diagnoeasy algorithm for the listing is an automated system that is able to list profiles of the Diagnostic Center s and information regarding the practices in the website and app. These listings do not translate to any fixed objectives, rankings, or endorsements by Diagnoeasy. Diagnoeasy will not be liable for any change in the search results, which may take place from time to time. The listing of the Diagnostic Center s will be based on the automated computation of various factors and criteria including inputs from users including other user’s comments and feedback. Such factors may change from time to time. Diagnoeasy in no event will be held responsible for the accuracy or relevancy or repeatability of the listing or listing orders of the Diagnostic Center s on the website or app.
