Related Works Sample Clauses

Related Works. The number of nodes in a fully secure network can be increased by using multiple key spaces. In [14], ω key spaces are generated and each node is given a sub-set of τ randomly chosen keys from ω. After deployment, nodes discover their common keys and use the Xxxx’x scheme to form pairwise keys. The scheme uses a similar idea to the probabilistic scheme of Eschenaeur-Gligor [3] where nodes are given a random set of keys from a global key space. In these schemes the aim is to achieve full connectivity, but not necessarily complete connectivity like a full mesh. Another approach also uses Xxxx’x scheme with multiple key spaces to improve resistance to the Xxxxx attack [15]. In [16], the scheme for a clustered topology is proposed. Here, the cluster-heads implement the Xxxx’x scheme to derive pairwise keys among themselves. Non cluster-head nodes do not implement the Xxxx’x scheme. Instead, they store a pre-computed secret key Ki for use with a clus- ter head. Prior to deployment, the base station computes the pairwise keys of this node with a certain number of associated cluster-heads. These are then combined into a secret key Ki and stored in the node, together with the identities (IDs) of the associated cluster-heads. When a node needs to establish a secure link with a physical cluster-head, it sends its own ID and the IDs of its associated cluster- heads. The physical cluster-head forwards the node’s ID to the associated cluster-heads to compute the pairwise keys using Xxxx’x scheme and thereby derives the secret key Ki. In this way, non-cluster head nodes store minimum keying material and do not need to perform any key computation computation. Instead, these are delegated to the cluster heads which carry the additional load of communicating with other cluster heads to derive the key with a non cluster-head node. The network size would still be limited to the (m 1) nodes for a fully secure network. Since cluster-heads establish pairwise keys among themselves using the basic Xxxx’x scheme, the key size and memory requirements, and network size would still be limited to the original scheme.
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Related Works. 11.1 Should the Subcontractor’s performance depend in any way on the proper performance of another person, for example, a consultant or another contractor, the Subcontractor must take all reasonable steps to enquire into and discover any defects in such performance and the Subcontractor must promptly provide a written report to Savcor ART relating to any defects it discovers. 11.2 The Subcontractor must co-operate fully with other subcontractors and consultants and with Savcor ART’s employees, contractors and agents. 11.3 The Subcontractor shall incorporate any reasonable changes in scheduling and performance of the Works to accommodate the needs of other subcontractors or consultants and the Subcontractor shall comply with the directions given by Savcor ART’s project manager. 11.4 Any consequent delay or disruption claims must be dealt with in accordance with Part 6.
Related Works. Arbor will provide to Comshare all Related Works owned and developed by Arbor. If a Related Work is developed by a third party and Arbor retains for itself or obtains from the third party, as the case may be, the right to distribute the Related Work to Arbor's customers and distributors, Arbor will use reasonable commercial efforts to obtain or retain the right to provide the Related Work to Comshare for distribution by Comshare under this * Indicates that information has been omitted and confidential treatment has been requested therefore. All such omitted information has been filed separately with the Securities and Exchange Commission pursuant to Rule 24b-2. License Agreement. In such event, Arbor agrees that it will not withhold or encourage any third party to withhold from Arbor the right to relicense the Related Work to Comshare; provided, however, that Arbor does not guarantee that it will be able to retain or obtain such right for Comshare. Notwithstanding the foregoing and by way of limitation, the Parties agree that the rights granted by Arbor to Comshare hereunder shall in no event be greater than the rights granted to or retained by Arbor with respect to the Related Works. In addition, and notwithstanding anything to the contrary herein, (i) with respect to any Related Work owned by a third party, Arbor makes no different representations or warranties to Comshare than those provided by the third party to Arbor and (ii) with respect to any Related Work owned or developed by a third party, Arbor undertakes no obligation of support or maintenance greater than that provided by Arbor to any of its other distributors. In the event that Arbor does retain or obtain rights for Comshare with respect to any Related Work of a third party, Comshare shall pay Arbor an additional royalty as described in Section I(E) of Exhibit D to the License Agreement.
Related Works. For each unit of a Related Work (as defined in Section 2(b) of the Second Amendment) distributed to a customer by Comshare, Comshare shall pay Arbor a royalty in the amount of (i) the applicable royalty as provided in Subsection I(A)(1), I(B)(1) or I(C)(1) above plus (ii) * Arbor shall bear any one time payment or lump sum fee payable by Arbor to a third party for distribution rights to a Related Work where the rights obtained include distribution rights for any party in addition to Comshare.
Related Works. The Semantic Web was introduced by Xxx Xxxxxxx-Xxx for Dec/31, 2000 June 30, 2008 2000-2008 Africa 4,514,400 51,065,630 1,031.2 % Asia 114,304,000 578,538,257 406.1 % Europe 105,096,093 384,633,765 266.0 % Middle East North America 3,284,800 108,096,800 41,939,200 248,241,969 1,176.8 % 129.6 % America/Caribbean Oceania / Australia 18,068,919 7,620,480 139,009,209 20,204,331 669.3 % 165.1 % WORLD TOTAL 360,985,492 1,463,632,361 305.5 % the first time in one of his speeches in 1998 as an extension to the current web [3]. He described the different versions of the Semantic Web architecture in 2000 [4], 2003 [5], 2005[6], 2006 [7]. Fensel is one of the main contributors in the Semantic Web field discussed the Semantic Web and the languages associated with its architecture in 2000 [8], while in 2002, he describeed OIL and its relation to OWL and the future capabilities of OWL [9]. Xxxxxx was not the only scientist who made great efforts in this area, but there are Xxx Xxxxxxxx [10], Xxxxx-Xxxxxxxxx [11] and Xxxxxx [12] also participated in this domain. There is still a long way for the full vision for the Semantic Web and the full implementation of it [13] [14].
Related Works. Author Manuscript We follow the well-known PRISMA approach to search and select related works on inter- annotator reliability assessment in cervical cancer images and chest X-Rays (CXRs). We used different keywords to search in PubMed and Google Scholar. Examples of these keywords include “heatmap”, “kappa”, “multi-rater”, “multiple annotations”, “inter-rater”, “inter-annotator”, “cervical cancer images”, “CXR”, etc. Then, we read the abstract to confirm whether the papers or methods fit well. These criteria resulted in a total of 18 papers that are discussed below. A. HEATMAP B. KAPPA STATISTICS
Related Works. LICENSEE hereby grants LICENSOR a non-exclusive, world-wide, royalty free, irrevocable license in and to any changes, transformations, modifications, adaptations, or derivations made using the Licensed Software, as permitted by this Agreement, to use, modify, reproduce, release, perform, display or disclose such works for Unlimited Rights as defined in DFARS Section 252.227-7013(a)(16) and 252.227-7014(a)(16). 1.2.1 LICENSEE shall, in good faith and in a reasonable manner, notify LICENSOR of all changes, transformations, modifications, adaptations, or derivations made using the Licensed Software. 1.2.2 As part of Related Works, LICENSEE is invited to submit any Genesis-CFD or Capstone SDK plug-ins or associated applications back to LICENSOR, with a description and unit, integration, systems test data/results. 1.2.3 Modifications, parts, and associated tests can be submitted to the LICENSOR for potential integration into the LICENSOR maintained Licensed Software.
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Related Works. The design and development of the trustworthy agreement protocol has several requirements that must be considered. Therefore, the structure of CMCC and the security technology will be discussed in this section.
Related Works. Generalized Consensus is a generalization of the consen- sus problem defined in terms of command structure, or sim- ply c-struct [12]. By using different c-structs, Generalized Consensus reduces do different agreement problems. As ex- plained in [12], one can define c-structs for traditional con- sensus, total order broadcast, generic broadcast, etc. The command histories presented in Section 2 are, in fact, an instance of c-structs and were first presented in [12]. Generalized Paxos [12] (Gen Paxos) is an extension of Fast Paxos that solves Generalized Consensus. Gen and Fast Paxos are the only other agreement protocols we are aware of that have classic and fast modes. Gen Paxos can be further extended with multicoordinated mode to improve availability [4]. We call the resulting protocol Multicoordi- nated Paxos (XX Xxxxx). The Generic Broadcast algorithm of Section 3.2 is a simplified version of MC Paxos. There are three generic broadcasts algorithms in the lit- erature to which we can compare our protocol: GB+ [17], AGB [1], and OptGB [19]. In specific, GB+ is roughly equivalent to the fast mode only version of our protocol. We say roughly because the fast mode always ignores con- flicts between messages that have been spontaneously or- dered, while GB+ only ignores conflicts of messages deliv- ered distant in time. Likewise, AGB may be able to ignore some conflicts but, in general it falls back to atomic broad- cast when conflicting proposals happen. Differently from GB+, AGB tolerates the crash of any minority of acceptors and, hence, requires three steps to deliver messages in the absence of conflicts and three more otherwise. The authors of AGB have also described a variation of the algorithm that tolerates less than one third of failures but decides in two or four steps.) The protocol of [19] combines the fast and the single-coordinated mode to terminate in two steps. It also combines the drawbacks of both rounds, requiring bigger acceptor quorums and relying on a correctly elected leader. In general, these protocols consider the crash stop model, do not have a generalized version, cannot switch execution modes, and all resort to Consensus or Atomic Broadcast to solve conflicts. What is more, Gen and MC Paxos may re- sort to coordinated recovery and, in the case of Multicoordi- nated Paxos, an uncoordinated classic recovery which may gradually lower the requirement for spontaneous ordering until the instance is decided.
Related Works. In this section, we will introduce works that have been done to impart in- terpretability to word embeddings. In the early works, Xxxxxx et al.(2012) suggested a variant of sparse matrix factorization, called Non-Negative Sparse Embedding (NNSE), which can generate highly interpretable word repre- sentations [29]. Based on NNSE, Jang and Myaeng introduced a method analyzing dimensions characterizing categories by linking concepts with types using HyperLex datasets [48] and comparing dimension values within concept groups with the average of dimension values within category groups [17]. Ma- trix factorization techniques were also used to extend Skip-Gram models [26] by applying a projected gradient from non-negative matrix factorization (NMF) [22] to modify the learning process of Skip-Gram [24]. Thus the modi- fied model encourages learning of interpretable word embeddings since NMF only forbid combinations of addition operations over subtraction operations, which result in an embedding that is part-based [24]. Other work [20] also invested in the interpretability topic by extending current embedding models of Glove [34]. Via mapping group label information obtained from Rogets Thesaurus, the words belonging to certain groups, are encouraged to learn an elevated amount in the dimensions that correspond to the group [20]. Other works make use of pre-trained embeddings and apply post-processing tech- niques to acquire embeddings with more interpretability. Researches [53, 33] used matrix transformation methods on pre-trained embeddings. The ap- proach in [53] utilized canonical orthogonal transformations to map current embeddings to a new vector space where the meanings of components are more interpretable. Similar to the work of [53], Park, Bak and Oh(2017) proposed an approach that rotates pre-trained embedding by minimizing the complexity function so that the dimensions after rotation become more interpretable [33]. Another type of methods apply sparse encoding techniques on word embeddings and map them to sparse vectors [45, 2]. [43] directly analyzes dimensions of embeddings using eigenvector analysis.
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