Robustness. The probability that the following experiment outputs “Eve wins” is at most δ: sample (w, wj, e) from (W, Wj, E); let ca, cb be the communication upon execution of (A, B) with Xxx(e) actively controlling the channel, and let A(w, ca, ra) = kA, B(wj, cb, rb) = kB. Output “Eve wins” if (kA =ƒkB ∧ kA ƒ=⊥ ∧kB ƒ=⊥).
Robustness. We developed GAM with the goal of being able to handle graphs with “in- correct” edges (i.e. those that connect nodes with differing labels). We consider such edges “incorrect" under the label propagation assump- tion, despite the fact that they may refer to real- world connections between these nodes (e.g., citations between research articles on different topics). In Xxxx, Citeseer, and Pubmed, 19%, 26%, and 20% of the edges, respectively, are in- correct. To demonstrate the ability of GAM to MLP128 MLP128 + NGM MLP128 + GAM Accuracy (%) 70 60 50 40 30 20 30 40 50 60 70 74 handle these incorrect edges and perhaps even higher levels of noise, we performed a robust- ness analysis by introducing spurious edges to the graph, and testing whether our agreement model learns to ignore them. We added spuri- Percent correct edges Figure 4: Robustness to noisy graphs. The x axis represents the percentage of correct edges remain- ing after adding wrong edges to the Citeseer dataset. ous edges by randomly sampling pairs of nodes with different true labels until the percentage of incorrect edges met a desired target. We tested the performance of GAM on a set of graphs created in this manner. MLPs are good base model candidates for testing this because they can only be affected by the graph quality through the GAM regularization terms (unlike GCN or GAT, where the graph is implicitly used in the model). The results are shown in Figure 4 on the Citeseer dataset (the hardest of the three datasets), for graphs containing between 5% and 74% correct edges. A plain MLP with 128 hidden units obtains 52.2% accuracy independent of the level of noise in the graph. Adding GAM to this MLP increases its accuracy by about 19%. This improvement persists even as the fraction of correct edges decreases. For example, the accuracy remains 70% even in the case where only 5% of the graph edges are correct. In contrast, the performance of NGM steadily decreases as the fraction of incorrect edges increases, to the point where it starts performing worse than the plain MLP (when the percent of correct edges ≤ 60%), and it is thus preferable not to use it.
Robustness. In view of the uncertainties, are the model results robust enough for policy advice or are there alternative ways conceivable for attaining more robust conclusions?
Robustness. The interface between Secure Media Recording Client Software and DVD Players, and the interface between Secure Media Recording Client Software and Integrated Products, shall meet the CSS Procedural Specifications robustness requirements for software and hardware, in accordance with Sections 6.2.4 and 6.2.5 herein.
Robustness. Robustness has two distinct dimensions, strong and fragile tenacity. The resilience of the social choice mechanism addresses regime’s capacity to adapt to changes or disturbances that occur in the wider social environment without radical transformation (Young, 1992: 179). Japan and Indonesia implement a strong institutional system. It lies in provision 10 of the MoU which points out clearly that both parties facilitate each other through financial, technology and capacity building supports necessary for JCM implementation. Article 2 of the UNFCCC as the objective of JCM program implementation becomes a milestone of cooperation relation between the two countries that is not easily vulnerable. Not to mention, two parties conduct close policy consultation at various levels as mentioned in provision 2. Transformation Rules Rules in the MoU agreed by Japan and Indonesia are dynamic, meaning it is very possible if there is an amendment or change of rules in accordance with the terms and agreement of both parties. Moreover, this is supported by form of soft legalization both parties apply with the result that it allows for changes in the rules if required in the future in a line with provisions 13 & 14 in the MoU.
Robustness. Informally, a scheme is robust if no adversary can prevent sufficiently many honest parties from generating an accepting signature on a message. We define robustness as a game between a challenger and an adversary . The game is formally defined in Figure 2 and comprises of three phases. In the setup and corruption phase, the challenger generates the public parameters pp and a pair of signature keys for every party. Given pp and all verification keys vk1, . . . , vkn, the adversary can adaptively corrupt a subset of t parties and learn their secret keys. In the case of a bulletin-board PKI (but not of trusted PKI), the adversary can replace the verification key of the corrupted party by another key of its choice. Unless specified otherwise, we consider the bulletin-board PKI to be the default setup model. mode,Π,A Experiment Exptrobust (κ, n, t) A The experiment Exptrobust is a game between a challenger and the adversary . The game is parametrized by an SRDS scheme Π and proceeds as follows: A
Robustness. Optional redundancy must be possible for every element of the system. The adopted technologies must fulfil this requirement implicitly. The chosen database software and the server that will receive all the queries and commands must support redundancy. Budget of real use case adopters will decide whether to use redundancy or not, but the system must be designed to have the chance to do it. The system functionality must not be affected by corrupted data input (invalid video or audio files, for example). This kind of input must be rejected if there isn’t any profitable data in them, but the system availability cannot be compromised.
Robustness. Regarding (1), an attack which is unavoidable in the classical setting is a splitting attack, where the (corrupted) server splits the users into two or more groups, and then only relays messages within those groups, forcing parties in different groups into different and inconsistent states. With such an attack one can, for example, enforce that only a particular subset of users sees some set of messages. If the protocol messages are on a blockchain, all parties will agree on the same view, and thus this attack is prevented. With regards to (2), another attack that is unavoidable in the single server setting is the censoring of a particular party. An untrusted server can ignore messages from a party, this way e.g. preventing them from ever updating. This is severe as, should this party be corrupted, the corrupted key can be indefinitely pre- vented from healing. In the blockchain setting, the “liveness property” of the blockchain, in combination with the fact that our protocol allows for concurrent updates (so there are no denial-of-service-type attacks where some parties prevent another one from updating by flooding the mempool) prevents this attack: if a user wants to update, their request will be added with high probability within a few blocks. Finally, in the single server setting, the group can be shut down by taking out a single server. One can achieve better robustness with several servers, but then needs to solve the machine state replication problem. This is what our protocol does if using a permissioned blockchain. With a permissionless blockchain, robustness guarantees are even stronger. Let us mention that in order to avoid all three issues mentioned above we need to record all the protocol messages on chain, which is probably no problem in the permissioned setting, but could be expensive in a permissionless blockchain. If we are only interested in (1) and (2), but not (3), one can just post a single hash of all the messages which each block contains on chain, while the actual messages are stored off chain. This loses property (3) unless we solve the data availability problem separately2 As a further observation, note that any CGKA in the classical setting can be “compiled” to the blockchain setting: in the blockchain setting, the block producer simply emulates the server to compile the message that would be broadcast in the classical setting, and adds this message (or hash) to the block. One further advantage over the classical server setting i...
Robustness. An important property CGKA protocols aim for is robustness: ensuring that parties have consistent views of the tree. TreeKEM achieves what [5] refers to as weak robustness: all honest parties accepting some message M (potentially generated adversarially) will transition to compatible states.7 To capture the CoCoA protocol, the definition of weak robustness needs to be slightly adapted: parties now receive different personalized packages as opposed to a unique one that gets broadcast to everyone, and do not have access to the complete ratchet tree. Accordingly, we require that if two parties receive and accept messages Mi and Mj satisfying a certain relation, they will transition into consistent states (where malformed messages not satisfying this relationship will immediately force users into inconsistent states). TreeKEM achieves weak robustness through a value called confirmation tag [5]. This consists of a MAC of the entire CGKA transcript (encoded in a running hash, called the transcript hash) up to and including that epoch, which is sent together with every Commit message. The MAC key, a.k.a. the confirmation key, is derived from the new epoch key schedule, which ensures correct processing of the commit message and also that the sender had knowledge of the previous epoch’s key schedule. To ensure consistency, users compute the transcript hash locally and verify the MAC. There are two issues when attempting to apply this to our scheme: 1) a user issuing an operation at a given round n will not have knowledge of the operations taking place concurrently, and thus will not be able to pre-compute the resulting transcript hash at the moment of crafting their message. And 2), since users only have a partial view of the ratchet tree, they are not able to compute the transcript hash. Note that users need to ensure they received consistent sets of operations, as e.g. in Figure 2, if C is not sent A’s partial update, they will disagree on the key for node Int(A, B) after processing. We solve 1) by effectively only authenticating the transcript up to the last round, i.e. not including the current operations. This ensures that if ID accepts a packet, it comes from a user whom they agreed with up until the beginning of that round. We solve 2) by what we call a round hash: a hash value computed over the public part of the new state of the ratchet tree (and any add and remove operations applied concurrently in that round). Clearly, none of the users can compute this ...
Robustness. Our results could be sensitive to the pre-disaster periods. We conduct a sensitivity analysis by extending the pre-disaster period to 12 quarters. Tables 7a to 9b show the sensitivity analysis for Thailand’s disasters, while Tables 10a and 10b show those results for the Philippine typhoons. As can be seen, the results are similar to those of the baseline. In the case of Thailand, we generally find a decline in total consumption. This decline stems from a reduction in expenditures on the service sector including transportation, hotels, and restaurants. In contrast, we generally observe increased household spending on food and non- alcoholic drinks, alcoholic beverages and tobacco products, clothing, and utilities. As seen from Table 7a, the total immediate expenditures declined by approximately 26 billion Thai baht after the Indian Ocean tsunami. Similar to our baseline results, we find housing- related expenses, including utilities and furniture, increased during this disaster. However, the estimates of the immediate expenditure declines in recreation, restaurants, and hotels are imprecise (Table 7b); yet we find the expenditure on transportation immediately dropped. Table 8a shows total consumption expenditure immediately dropped by approximately 70 billion Thai baht due to the 2011 Thailand floods. The results presented in Tables 8a and 8b resemble those of the baseline. Specifically, we find households immediately increased spending on both durable and non-durable goods. On the other hand, we find consumers immediately reduced their spending on transportation, restaurants, and hotels. Tables 9a and 9b show the results pertaining to the 2016-17 Thailand floods. We again find similar results to the baseline estimates. The total immediate consumption dropped approximately 31 billion Thai baht. Similar to aforementioned disasters in Thailand, households immediately increased spending on non-durable goods including food, beverages, tobacco, and clothing. Households also increased immediate spending on utilities. However, they reduced their spending on transportation, restaurants, and hotels. For the Philippines, the effects of the typhoons on consumption are usually small. Among the three typhoons, we still find that Typhoon Haiyan had the largest immediate effects on consumption expenditures; the total household spending immediately declined by approximately 40 billion pesos after Typhoon Haiyan. Although the magnitude of estimate shown in Table 10a is simi...
