This work studies the aggregation operators on the set of all possible membership degrees of typical hesitant fuzzy sets , which we refer to as H , as well as the action of H-automorphisms which are defined over the set of all finite non-empty subsets of the unitary interval . In order to do so , the partial order = < " H , based on @a-normalization , is introduced , leading to a comparison based on selecting the greatest membership degrees of the related fuzzy sets . Aggregate operators are a useful class of operators in relational databases . In this paper , we examine spatial aggregate operators over regions . When no relation referenced by a query is fragmented , an optimal algorithm is given to decide which relation and its copy is to be partitioned into fragments , how the relation is to be partitioned and where the fragments are to be sent for processing . The conditions for efficient processing of aggregate operators are also discussed . Furthermore , a linear programming method is developed to solve the MCDM problems with probabilistic linguistic information , and a case study about the evaluation of hospitals is conducted to illustrate the proposed method . Finally , some comparative analyses with the aggregation operator-based method and the probabilistic linguistic preference ranking organization method for enrichment evaluation ( PL-PROMETHEE ) method are made to show the advantages of the proposed method . Most preferred ordered weighted average ( MP-OWA ) operator is a new kind of neat ( dynamic weight ) OWA operator in the aggregation operator families . It considers the preferences of all alternatives across the criteria and provides unique aggregation characteristics in decision making . In addition , the information can be studied under a fuzzy approach . This paper proposes an index construction method using the notion of consistency in aggregation operator families to ensure robustness , in addition to considering a hierarchical structure with different level of importance of input information . Two applications are presented , a measure of social and advanced uses of the Internet considering attitudinal , demographic , social and technological factors , and a measure of the child ’s family environment considering different aspects . The interface language deals with the temporal and spatial aspects of multimedia information retrieval and delivery , in addition to the usual capabilities provided by the ordinary database languages . EVA has five classes of operations , namely : operations for querying and updating ( i.e. editing ) the multimedia information , operations for screen management , temporal operators , operators for specifying rules and constraints , and aggregation ( computational ) operators . EVA , an extension of the query language Varqa , is a functional language whose notation is based on that of conventional set theory . The Choquet technique is here extendedto the case where the integrand , just like the measure , is a non-additive function taking values on subsets itself . The resultingaggregation operator is an extension of the Choquet integral : the former coincides with the latter whenever the integrand isadditive . Four such extensions are provided , two of which areobtained by means of the M¨obius inversion of the integrandand the ( fuzzy ) measure with respect to which integration isperformed . In this paper we introduce a new parametrized variation of record linkage relying on the Mahalanobis distance , and a supervised learning method to determine the optimum simulated covariance matrix for the linkage process . We evaluate and compare our proposal with other studied parametrized and not parametrized variations of record linkage , such as weighted mean or the Choquet integral , which determines the optimal fuzzy measure . URL http://agop2011.ciselab.org/proceedings [ 13 ] Source URL : https://www.iiia.csic.es/en/node/54955 Links [ 1 ] https://www.iiia.csic.es/en/staff/daniel-abril [ 2 ] https://www.iiia.csic.es/en/staff/guillermo-navarro-arribas [ 3 ] https://www.iiia.csic.es/en/staff/vicenÃ§-torra [ 4 ] https://www.iiia.csic.es/en/staff/bernard-de-baets [ 5 ] https://www.iiia.csic.es/en/bibliography?f[author]=1996 [ 6 ] https://www.iiia.csic.es/en/bibliography?f[author]=1997 [ 7 ] https://www.iiia.csic.es/en/bibliography?f[keyword]=936 [ 8 ] https://www.iiia.csic.es/en/bibliography?f[keyword]=497 [ 9 ] https://www.iiia.csic.es/en/bibliography?f[keyword]=465 [ 10 ] https://www.iiia.csic.es/en/bibliography?f[keyword]=935 [ 11 ] https://www.iiia.csic.es/en/bibliography?f[keyword]=934 [ 12 ] https://www.iiia.csic.es/en/bibliography?f[keyword]=470 [ 13 ] http://agop2011.ciselab.org/proceedings At the first stage , Analytic Hierarchy Process ( AHP ) is used to provide priorities ratings for the available control alternatives . In the second step , the Choquet integral operator is employed for the aggregation of the partial scores obtained for the different alternatives according to each criterion in order to deal with the existing interactions between the criteria . An industrial case study from a manufacturing enterprise is provided to illustrate the application of the suggested approach . For rice pest management , decision makers base on not only experience but also information synthesized from data sources to make plans for controlling rice pests , these information are key inputs to reveal potential global rice pestrisk levels at different spatial-temporal scales and a rice pestrisk index is synthesized from these information can support stakeholders understand global rice pest risk levels of areas before decision making process is made . This paper introduces a method to build a synthetic rice pest risk index and designs decision maker agents with ability of aggregating information from other agents , OWA based method and Choquet integral based method are experimented on decision maker agents to determine global rice pest risk index , a case study is carried out on risk assessment of brown plant hoppers in the Mekong Delta region to explore feasibility of these methods in rice pest management applications . When this latter appears , the process can not be a simple problem due to complexity to model such synergy relations . To deal with these issues , a Choquet integral based method has been developed to determine the most appropriate ranking of alternatives . Therefore , a fuzzy measure that models considerations of an expert in the problem domain must be identified . This restriction on the adopted scale may be considered as a limitation in some practical problems . In order to overcome this limitation , we propose a new Choquet integral formulation , specific for sorting problems , that constructs a common scale from heterogenous scales using the framework of the FlowSort method . We also introduce a new outranking degree based on the Choquet integral preference model , which allows the modeling of interacting criteria in FlowSort . Therefore , the proposed approach can be seen either as an extension of FlowSort for problems with interacting criteria or as a new Choquet integral formulation for multi-criteria sorting problems with heterogeneous scales . Numerical examples attest that the proposed approach is conceptually simple to be implemented and acknowledge the importance of considering interaction between criteria when it exists . Although linear models have gained considerable attention and recommendation , contemporary literature contains strong evidence in support of nonlinear noncompensatory models . In this paper , the authors first demonstrate how fuzzy measures , which are defined on subsets of decision attributes , and their Choquet-integral formulation , which exhibits both compensatory and noncompensatory properties , have meaningful behavioral interpretations within the context of new-product screening . Then , they show how to address the complex problem of building such measures by applying a learning algorithm that relies on methods of judgment analysis . Our definition is based on the mass assignment approach from Dempster-Shafer theory of evidence . The generalized belief function is totally monotone and it has Choquet integral representation w.r.t . a classical belief function . Our approach is based on mass assignments used in the Dempster – Shafer theory of evidence . A generalised belief function is totally monotone and it has Choquet integral representation with respect to a unique belief measure on Boolean events . This paper aims at presenting the state-of-the-art of Choquet integral in quantifying the uncertainty in financial economics . Not only Choquet integral becomes a suitable model for defining financial coherent risk measures in the investment context , it seems also possible to use Choquet integral calculations as a means for asset pricing . We address also utility aspect of Choquet integral risk measures . Second , we set up a framework for pricing derivatives written on an underlying asset traded on an illiquid market . To this end we have extended the Choquet integral definition to account for interval payoffs of the underlying asset . The price interval we obtain may be interpreted as a bid‐ask price quoted by the intermediary issuing the derivative security . The conventional weighted mean can not deal with this aspect , and compromise operators are more suitable . In this sense , the operators of the Choquet integral family are interesting and are applied to evaluate the suppliers ’ performance for an industrial case . Both approaches have in common the use of theoretical principles of mathematical and spatial modelling and the methods included in the geographic information systems ( GIS ) . These procedures involve a wide variety of methods ranging from a simple statistical test of differences between two files ( the occurrence of the phenomenon in the landscape and in archaeological sites ) , the determination of the significance rate of such a difference , the determination of the correlation rate through the creation of predictive models combining properly made layers by the usage of corresponding aggregation functions to the very validation of the predictive models . A number of these practices are common for creating archaeological predictive models for both bases . How to deposit the data is a challenge . An usual strategy is to save the abstract of the data , such as using aggregation functions to preserve the features of the original data with much smaller space . A key problem , however is that such information can be distorted by the presence of illegitimate traffic , e.g. botnet recruitment scanning , DDoS attack traffic , etc . Much work in social choice theory takes individual preferences as uninvestigated inputs into aggregation functions designed to reflect considerations of fairness . Advances in experimental and behavioural economics show that fairness can also be an important motivation in the preferences of individuals themselves . A new class of the artificial neuron models is described in this work . These models are based on assumptions : ( 1 ) contributions of synapses are summing with the help of certain aggregation operation ; ( 2 ) contribution of synaptic clusters are computed with the help of another aggregation operation on the set of simple synapses . These models include a big part of known functional models of neurons . A method of generating a computer program based on an SQL query includes receiving a SQL query , including a reference to a first data set stored at a first data source , and including a reference to a second data set stored at a second data source different from the first data source ; determining that the SQL query includes two or more commands , the commands including a first union-type operation , and a first aggregation operation ; determining that the SQL query describes that the first union-type operation shall be applied to at least a portion of data from the first data set , and applied to at least a portion of data from the second data set ; determining that the SQL query describes that the first aggregation operation shall be applied to data resulting from the first union-type operation ; and generating the computer program . This paper aims at elaborating an integrated decision aiding tool regarding the selection of combined transport alternatives according to actual road freight transport situations . Based upon a wide range of criteria covering partly the European transport policies , the integrated tool is made up of two aggregation steps . Firstly , aggregation methods integrate the economic , environmental , safety and logistics related performances of transport scenarios into four global scores . The non-linear algorithms aggregate data only in certain steps , such as conjugate gradient , which are linear in the data . We introduce a new and highly efficient VSS ( Verifiable Secret-Sharing ) protocol in a special but widely-applicable model that allows secret-shared arithmetic operations in such aggregation steps to be done over small fields ( e.g. 32 or 64 bits ) . There are two major advantages : ( 1 ) in this framework private arithmetic operations have the same cost as normal arithmetic and ( 2 ) the scheme admits extremely efficient zero-knowledge ( ZK ) protocols for verifying properties of user data . The non-linear algorithms aggregate user data only in certain steps , such as conjugate gradient , which are linear in per-user data . We introduce a new and highly efficient VSS ( Verifiable Secret-Sharing ) protocol in a special but widely-applicable model that allows secret-shared arithmetic operations in such aggregation steps to be done over small fields ( e.g. 32 or 64 bits ) , so that private arithmetic operations have the same cost as normal arithmetic . Verification of user data is required to prevent a malicious user from biasing the computation . We compare the performance of four ( 4 ) different special-purpose pattern recognition methods designed for trading systems in forex markets . Each of thesemethodshasbeenproperlydesignedtoexploithighlycompute-intensive aggregation calculations of complex but efficient distributed SQL queries on the relational databases holding the market data . Specifically , we have implemented several methods for discovering hidden price patterns in forex marketstime-seriesdatathatsignaltheshort-termdirectionofpricechangesof the market . We applied the data mining technique to medical near-miss cases collected by a foundation related to the Japanese Health , Labor and Welfare Ministry , and extracted information such as pairs of confusing medicines , the cause of near-miss cases in some situations , which can not be obtained by simple aggregation calculations and descriptive statistics . We also introduce the results of text mining applied to the free-description data regarding the backgrounds and causes of near-miss cases and their counter measures . However , securing WSNs with traditional cryptographic mechanism is insufficient because of the existing limited resources and the lack of tamper resistant hardware . In this paper , we propose a Reputation-based Secure Data Aggregation for WSNs ( RSDA ) that integrates aggregation functionality with the advantages provided by a reputation system to enhance the network lifetime and the accuracy of the aggregated data . We bind symmetric secret keys to geographic locations and assign these keys to sensor nodes based on their locations . These predicate patterns are obtained from a detailed empirical study of large real-world question-sets . In addition , the system also incorporates a specific non-pattern based approach which make use of aggregation-like operations , to generate questions that involve superlatives ( e.g. , highest mountain , largest river etc . ) . We have tested the applicability and efficacy of the proposed methods by generating MCQs from several online available ontologies , and verified our results in a classroom setup — incorporating real students and domain experts . Backlog is allowed . Aggregation of operations is invariable for the entire planning period , the composition of the batch and intensities of processing operations are invariable within an interval , but can vary from interval to interval . As the objective function we use the planned profit . Our graph encoder is an extension to GraphSAGE with two major distinctions . First , we non - trivially generalize it to cope with both directed and undirected graphs by splitting original node into forward nodes ( a node directs to ) and backward nodes ( direct to a node ) according to edge direction and applying two distinct aggregation functions to these types of nodes . Second , we exploit two different schemes ( pooling - based and supernode - based ) to reassemble the learned node embeddings to generate graph embedding , which is not studied in GraphSAGE . The conventional method of aggregating the satisfaction of transport projects with respect to multiple attributes is commonly some variant of Simple Additive Weighting ( SAW ) , which involves the sum of products of standardized outcomes of projects with respect to attributes and attribute importance weights . It is suggested that alternative forms of aggregation might be more useful , in particular , the Ordered Weighted Averaging ( OWA ) operator introduced by Yager ( 1988 ) . Attribute importance weights and satisfaction of attributes by projects may be aggregated prior to aggregation via an OWA operator . Therefore , a multi-stage methodology is proposed and linguistic Z-numbers are applied to describe the assessment information . Moreover , the weighted averaging ( WA ) aggregation operator , the ordered weighted averaging ( OWA ) aggregation operator and the hybrid weighted averaging ( HWA ) aggregation operator are developed to fuse the input arguments under the linguistic Z-number environment . Then , using the max-score rule and the score-accuracy trade-off rule , three qualitative portfolio models are presented to allocate the optimal assets . In this paper , we present an alternate/supporting approach based on comparison of the players ’ overall talent class viz‐a‐viz the corresponding performances of the player against the 28 aspects of talent from cricketing perspective . The Ordered Weighted Averaging Aggregation ( OWA ) operator was used to aggregate the opinion of experts for assessment of talent classes in cricket . The normative data obtained for a cricket enthusiast was then compared using normalized adequacy coefficient with aggregated opinions of experts . This uncertainty is modeled by specifying a scenario set containing K distinct processing time scenarios . The ordered weighted averaging aggregation ( OWA ) operator , whose special cases are the maximum and Hurwicz criteria , is applied to compute the best schedule . Some new positive and negative approximation results concerning the problem are shown . This paper seeks to advance the theory and practice of the dynamics of complex networks in relation to direct and indirect citations . It applies social network analysis ( SNA ) and the ordered weighted averaging operator ( OWA ) to study a patent citations network . So far the SNA studies investigating long chains of patents citations have rarely been undertaken and the importance of a node in a network has been associated mostly with its number of direct ties . We have adopted link mining approach to recommend books using Ordered Ranked Weighted Averaging ( ORWA ) aggregation operator . ORWA is a modified form of Ordered Weighted Aggregated averaging ( OWA ) operator , a multi criteria decision making procedure . The weight generation using guided quantifier does not take into account the value of the voters , here , rankers which recommend the products , i.e. universities ' ranking . The proposed system uses appropriate preprocessed X-ray images of passenger ’s luggage and designs to detect the prohibited items like Pistol , Knife , Explosive materials , scissors , and handguns of different size and orientation etc The X-ray imaging is an important technology in many fields , from non-intrusive inspection of delicate objects , to weapons detection at security checkpoints . In this work we will detect the object by ordered weighted averaging method(OWA ) OWA operator provides a parameterized family of aggregation operators , including well-known operators such as maximum , minimum , arithmetic mean , k-order statistics and median . This paper deals with multicriteria decision‐making problems in which the criteria are partitioned into q categories , and a prioritization relationship exists over categories . We aggregate the criteria in the same priority category by a weighted OWA ( ordered weighted averaging ) operator and introduce two averaging operators , a generalized prioritized averaging operator and a generalized prioritized OWA operator . In the case with one criterion in each priority category , the two operators reduce to the prioritized averaging operator and the prioritized OWA operator as proposed by Yager . The fuzzy operator defined as the so-called Weighted OWA ( WOWA ) aggregation offers a well-suited approach to this problem . The WOWA aggregation , similar to the classical ordered weighted averaging ( OWA ) , uses the preferential weights assigned to the ordered values ( i.e. to the worst value , the second worst and so on ) rather than to the specific criteria . This allows one to model various preferences with respect to the risk . Analyzing the LES status of rapid urbanization areas has a significant impact on decision-making of urban development . Based on the integration of ordered weighted averaging ( OWA ) and sensitivity analysis , this paper focuses on hightening the understanding of indictor weights ’s influences to LES evaluation and assisting in making a suitable assignment of weights in Chengdu Plain Economic Zone . The results highlight how the statu of LES , in 2014 , is closely linked to the administrator ’s attitude and corresponding indictor weights . The problem of aggregating multiple criteria to obtain a globalizing objective function is of special interest when the number of Pareto solutions becomes considerably large or when a single , meaningful solution is required . Ordered weighted average or ordered median operators are very useful when preferential information is available and objectives are comparable since they assign importance weights not to specific objectives but to their sorted values . In this paper , ordered weighted average optimization problems are studied from a modeling point of view . This perspective provides a strong theoretical basis for the standardization of factors and their subsequent aggregation . In this context , a new aggregation operator that accommodates and extends the Boolean and WLC approaches is discussed : the Ordered Weighted Average . A case study of industrial allocation in Nakuru , Kenya is employed to illustrate the different approaches . This paper describes an evolutionary algorithm applied to tuning of parameters of license plate detection systems . We consider both simple and compound detection systems , where the latter ones consist of multiple simple systems fused by some aggregation operation ( weighted sum or ordered weighted average ) . With the structure of a system given by a human and fixed , we perform an evolutionary search in the space of possible parameter combinations . In particular , we substitute the WA by three operators from the Ordered Weighted Average ( OWA ) family of operators because it permits to decide the degree of andness/orness that is used during the aggregation . The OWAWA ( Ordered Weighted Average Weighted Average ) , WOWA ( Weighted Ordered Weighted Average ) and IOWA ( Induced Ordered Weighted Average ) operators are studied . They are capable to combine the importance given to each criterion with the conjunctive/disjunctive requirement applied in the definition of the outranking relation . Fuzzy integral is a general aggregation operator , which encompasses many common aggregation operators likeweighted mean , ordered weighted mean , weighted minimumand maximum , etc . In classifier combining , it can be usedto aggregate the outputs of the individual classifiers in theteam with respect to a fuzzy measure , based on the classifierconfidences . This paper presented a new software-based pathway of process mining approach for extending a flexible WQI ( Water Quality Index ) that would deal with uncertainties derived from missing data occurrence in short- and long-term assessments . The methodology is based on integration of four multi-criteria group decision-making models coupled with fuzzy simulation including AHP ( Analytical Hierarchy Process ) , fuzzy OWA ( Ordered Weighting Average ) , TOPSIS ( Technique for Order Preference by Similarity to Ideal Solution ) , and fuzzy TOPSIS that were used for data mining and group consensus evaluation .. Examining the methodology on groundwater resources being supplied for drinking in Shiraz , Iran showed high integrity , accuracy , and proximity-to-real interpretation of water quality . This work also finds the effect of counting ThSV and the ThSV resistive and capacitive parasitic on utilization of timer control . Followed by the work on parameters that reflect changes the generally wire measurement size wise and timer energy in three dimension timer system model and lastly develop a 2 stair three dimension timer hierarchy fusion method based on three dimension technique [ i.e. technique of Means and Medians ] which determine the best possible count of ThSV to be utilized in the three dimension timer hierarchy . Hence the whole energy expenditure is reduced . A fuzzy c‐medians variant is proposed for the generation of fuzzy term sets with one half overlap . The proposed variant is modified from the original algorithm mainly in two areas . The effect of data size used for the generation of fuzzy terms is also discussed . A generalized s‐shaped function with a tunable parameter along with its complement ( i.e. , a generalized z‐shaped function with a tunable parameter ) is developed to fit all term sets generated by the fuzzy c‐medians variant using various m values . © 2002 John Wiley & Sons , Inc. The terms are combined in a weighted sum to generate an indicator variable that is then compared to a detection threshold . An FIR band-pass filter , consisting of a cascaded series of running medians and means prefiltered the raw ECG signal to reduce high-frequency noise as well as low-frequency base line drift . The technique appears to be exceedingly robust , correctly detecting even aberrant QRS complexes in noise-corrupted ECGs.<<ETX > > Finally , we present the results of extensive experimentation showing that our new heuristic outperforms all others in accuracy and , especially , in running time : the heuristic typically returns solutions within 1 % of optimal and runs in seconds to minutes even on genomes with 25'000 genes -- in contrast , MGR can take days on instances of 200 genes and can not be used beyond 1'000 genes . ConclusionFinding good rearrangement medians , in particular inversion medians , had long been regarded as the computational bottleneck in whole-genome studies . Our new heuristic for inversion medians , ASM , which dominates all others in our framework , puts that issue to rest by providing near-optimal solutions within seconds to minutes on even the largest genomes . Because the four-via routing algorithm does not take the requirement of no timing delay in multi-chip module ( MCM ) clock routing into consideration , there will be a skew among the clock pins . This paper presented a new method , which combines the Method of Means and Medians and v4R algorithm , to balance the skew of clock net , and thus imoprove the v4R algorithm . The forward search algorithm is made up of three steps . The rst step addresses the choice of an initial subset of the observations that will be used with a robust estimation procedure ( least medians of squares or LMS ) to provide an initial model t and a “ good ” estimate of the residual error . If the model contains p parameters , the initial subset will be the one subset out of the nCp potential subsets that minimizes the sum of squared residuals from the LMS t. If there are too many potential subsets , the choice will be made after examining a large sample of subsets of size p. Distribution-free methods are considered for comparing k1 treatments to k2 controls in the one-way layout . Using normal scores statistics , one-sided tests are developed for k1 treatment versus k2 control comparisons of medians . Using Savage statistics , analogous results are developed for comparing scale parameters of the treatments to the controls with non-negative data . A Bayesian procedure for forecasting S-shaped growth is introduced and compared to classical methods of estimation and prediction using three variants of the logistic functional form and annual times series of the diffusion of music compact discs in twelve countries . The Bayesian procedure was found not only to improve forecast accuracy , using the medians of the predictive densities as point forecasts , but also to produce intervals with a width and asymmetry more in accord with the outcomes than intervals from the classical alternative . While the analysis in this paper focuses on logistic growth , the problem is set up so that the methods are transportable to other characterizations of the growth process . The sub-optimality approximation problem is at the core of online stochastic optimization algorithms and it can also be used for solution repair and approximate filtering of optimization constraints . This paper formalizes the problem and presents sub-optimality approximation algorithms for metric TSPs , packet scheduling , and metric k-medians that run faster than the optimal or approximation algorithms . It also presents results on the hardness/easiness of sub-optimality approximations . It is shown that any real weighted median is equivalent to an integer weighted median . As a consequence , a sensitivity analysis can always be performed to represent real weighted medians by finite precision weighted medians . This result proves very useful if one realizes that optimal adaptive weighted medians are usually real-valued . In this paper , we design and implement a system , iHEED , in which node clustering is integrated with multi-hop routing for TinyOS . We consider simple data aggregation operators , such as AVG or MAX . We use a simple energy consumption model to keep track of the battery consumption of cluster heads and regular nodes . In First International Conferen e on Computational Logi , DOOD-2000 Stream , July 2000 . Index , 29 * + > > , 37 * ->- > , 36 + > > , 37 ->- > , 36 expunge , 30 flLoadedModule/1 , 19 flThisModule/1 , 18 abolish all tables , 39 , 46 aggregates avg , 36 olle tbag , 36 olle tset , 36 ount , 36 max , 36 min , 36 multi-valued methods , 36 sum , 36 aggregation aggregate operator , 35 grouping , 35 anonymous oid , 38 anonymous variable , 4 arithmeti expression , 13 atomdata , 7 isa , 8 signature , 7 atomi formula in F-logi , 6 attribute inheritable , 32 non-inheritable , 32 ba ktra kable update , 44 btdelete , 44 btdeleteall , 44 bterase , 44 bteraseall , 44 btinsert , 44 btinsertall , 44 base part of predi ate , 40 bform , 26 , 49 boolean method inheritable , 37 bulk delete , 43 bulk insert , 42 anoni term , 64 hara ter list , 10 lass , 8 instan e , 8 sub lass , 8 losure axioms , 60 omment , 11 ompiler dire tive , 48 arguments , 26 , 48 equality , 48 expunge , 48 firstorderall , 30 , 48 firstorder , 30 , 48 hilogtable , 39 index , 48 op , 12 , 48 table , 38 equality , 31 uts in FLORA-2 , 40 utting a ross tables , 40 data atom , 7 debugging , 56 delete bulk , 43 derived part of predi ate , 40 do n't are variable , 4 dynami module , 32 equality , 31 es aped hara ter , 9 F-mole ule , 8 flDump/1 , 53 FLIP , 1 flNoTra e , 56 floating number , 10 68 INDEX 69 FLORID , 1 flP2H/2 , 29 flTra e , 56 flTra eLow , 56 HiLog , 26 translation , 27 unifi ation , 27 HiLog to Prolog onversion , 29 hilogtable , 39 I/O port-based , 51 stream-based , 51 Id-term , 6 inheritan e behavioral , 32 non-monotoni , 32 stru tural , 32 insert bulk , 42 integer , 10 loading files , 18 logi al expressions , 12 meta signature of a predi ate , 26 meta-programming , 28 meta-unifi ation , 29 method , 7 boolean , 37 inheritable , 37 multi-valued , 7 pro edural , 39 s alar , 7 self , 23 set-valued , 7 single-valued , 7 module , 15 expunge , 30 flLoadedModule/1 , 19 flThisModule/1 , 18 flora(modulename ) , 21 prolog(modulename ) , 19 ontents , 15 name , 15 Prolog , 16 , 19 rules for , 17 system , 16 , 21 , 50 user , 16 ompilation of , 18 referen e to , 16 mole ule logi expressions , 13 obje t value , 24 truth value , 24 multi-valued methods aggregation , 36 non-ba ktra kable update , 41 delete , 42 deleteall , 42 erase , 42 eraseall , 42 insert , 41 insertall , 41 number , 10 obje t base part of , 40 derived part of , 40 obje t onstru tor , 6 obje t identifier , 6 oid , 6 anonymous , 38 operators , 11 pre eden e level , 11 type , 11 pat h rules , 63 path expression , 22 in rule body , 22 in rule head , 24 predi ate base part of , 40 derived part of , 40 first-order , 30 HiLog , 30 predi ate meta signature , 26 Prolog module , 16 , 19 Prolog to HiLog onversion , 29 INDEX 70 signature in F-logi , 7 spying , 56 string , 10 sub lass , 8 symbol , 9 synta ti obje t , 26 , 49 system module , 16 , 21 , 50 table , 38 tabling , 38 tra ing , 56 triehandle , 62 type he king , 48 update , 40 ba ktra kable , 44 non-ba ktra kable , 41 user module , 16 variable anonymous , 4 do n't are , 4 well-founded semanti s , 35 For this reason , this paper is aimed at introducing a new nonaveraging operator named < inline-formula><tex-math notation="LaTeX">$C_{F_1F_2}$</tex-math></inline-formula>-integral , which is a generalization of the Choquet-like Copula-based integral ( CC-integral ) . < inline-formula><tex-math notation="LaTeX">$C_{F_1F_2}$</tex-math></inline-formula>-integrals present the desired properties of an aggregation-like operator since they satisfy appropriate boundary conditions and have some kind of increasingness property . We show that < inline-formula><tex-math notation="LaTeX">$C_{F_1F_2}$</tex-math></inline-formula>-integrals , when used to cope with classification problems , enhance the results of the previous averaging generalizations of the Choquet integral and provide competitive results ( even better ) when compared with state-of-the-art FRBCSs . The H2TLCA aggregation operator and H2TLCG aggregation operator can describe dependencies between criteria in decision-making problem solving . Moreover all aggregation operation operators have the properties of idempotency , boundedness and monotonicity , and the H2TLCA aggregation operator and H2TLCG aggregation operator are also verified to be symmetric functions . In addition , the H2TLPWA aggregation operator and H2TLCA aggregation operator are employed to settle multicriteria decision-making problems with hesitant 2-tuple linguistic terms . There in to , the parameters for aggregation operator are the decision maker 's special preferences for some alternatives . On these bases , the correlation coefficient and aggregation operator integrated approach is constructed , to determine the most favorable alternative under the MCDM with interval-valued intuitionistic fuzzy numbers . Finally , we apply an example to show the validity of the proposed decision making approach . The probability of a particular candidate as being the answer is then computed by aggregating the probabilities of all document tokens which appear in and renormalizing over the candidates : where is the set of positions where a token in appears in the document . This aggregation operation is the same as the pointer sum attention applied in the AS Reader kadlec2016text . Finally , the candidate with maximum probability is selected as the predicted answer : During the training phase , model parameters of GA are updated w.r.t . To train the target model , we use inputs from the target dataset and outputs from collaborator models . We train each STM on its dataset for one epoch , and change the target STM as follows : where denotes concatenation and denotes an aggregation operation such as max pooling or concatenation . We used weighted max pooling for the aggregation operation .
This work studies the aggregation operators on the set of all possible membership degrees of typical hesitant fuzzy sets , which we refer to as H , as well as the action of H-automorphisms which are defined over the set of all finite non-empty subsets of the unitary interval . In order to do so , the partial order = < " H , based on @a-normalization , is introduced , leading to a comparison based on selecting the greatest membership degrees of the related fuzzy sets .
Aggregate operators are a useful class of operators in relational databases . In this paper , we examine spatial aggregate operators over regions .
When no relation referenced by a query is fragmented , an optimal algorithm is given to decide which relation and its copy is to be partitioned into fragments , how the relation is to be partitioned and where the fragments are to be sent for processing . The conditions for efficient processing of aggregate operators are also discussed .
Furthermore , a linear programming method is developed to solve the MCDM problems with probabilistic linguistic information , and a case study about the evaluation of hospitals is conducted to illustrate the proposed method . Finally , some comparative analyses with the aggregation operator-based method and the probabilistic linguistic preference ranking organization method for enrichment evaluation ( PL-PROMETHEE ) method are made to show the advantages of the proposed method .
Most preferred ordered weighted average ( MP-OWA ) operator is a new kind of neat ( dynamic weight ) OWA operator in the aggregation operator families . It considers the preferences of all alternatives across the criteria and provides unique aggregation characteristics in decision making .
In addition , the information can be studied under a fuzzy approach . This paper proposes an index construction method using the notion of consistency in aggregation operator families to ensure robustness , in addition to considering a hierarchical structure with different level of importance of input information . Two applications are presented , a measure of social and advanced uses of the Internet considering attitudinal , demographic , social and technological factors , and a measure of the child ’s family environment considering different aspects .
The interface language deals with the temporal and spatial aspects of multimedia information retrieval and delivery , in addition to the usual capabilities provided by the ordinary database languages . EVA has five classes of operations , namely : operations for querying and updating ( i.e. editing ) the multimedia information , operations for screen management , temporal operators , operators for specifying rules and constraints , and aggregation ( computational ) operators . EVA , an extension of the query language Varqa , is a functional language whose notation is based on that of conventional set theory .
The Choquet technique is here extendedto the case where the integrand , just like the measure , is a non-additive function taking values on subsets itself . The resultingaggregation operator is an extension of the Choquet integral : the former coincides with the latter whenever the integrand isadditive . Four such extensions are provided , two of which areobtained by means of the M¨obius inversion of the integrandand the ( fuzzy ) measure with respect to which integration isperformed .
In this paper we introduce a new parametrized variation of record linkage relying on the Mahalanobis distance , and a supervised learning method to determine the optimum simulated covariance matrix for the linkage process . We evaluate and compare our proposal with other studied parametrized and not parametrized variations of record linkage , such as weighted mean or the Choquet integral , which determines the optimal fuzzy measure . URL http://agop2011.ciselab.org/proceedings [ 13 ] Source URL : https://www.iiia.csic.es/en/node/54955 Links [ 1 ] https://www.iiia.csic.es/en/staff/daniel-abril [ 2 ] https://www.iiia.csic.es/en/staff/guillermo-navarro-arribas [ 3 ] https://www.iiia.csic.es/en/staff/vicenÃ§-torra [ 4 ] https://www.iiia.csic.es/en/staff/bernard-de-baets [ 5 ] https://www.iiia.csic.es/en/bibliography?f[author]=1996 [ 6 ] https://www.iiia.csic.es/en/bibliography?f[author]=1997 [ 7 ] https://www.iiia.csic.es/en/bibliography?f[keyword]=936 [ 8 ] https://www.iiia.csic.es/en/bibliography?f[keyword]=497 [ 9 ] https://www.iiia.csic.es/en/bibliography?f[keyword]=465 [ 10 ] https://www.iiia.csic.es/en/bibliography?f[keyword]=935 [ 11 ] https://www.iiia.csic.es/en/bibliography?f[keyword]=934 [ 12 ] https://www.iiia.csic.es/en/bibliography?f[keyword]=470 [ 13 ] http://agop2011.ciselab.org/proceedings
At the first stage , Analytic Hierarchy Process ( AHP ) is used to provide priorities ratings for the available control alternatives . In the second step , the Choquet integral operator is employed for the aggregation of the partial scores obtained for the different alternatives according to each criterion in order to deal with the existing interactions between the criteria . An industrial case study from a manufacturing enterprise is provided to illustrate the application of the suggested approach .
For rice pest management , decision makers base on not only experience but also information synthesized from data sources to make plans for controlling rice pests , these information are key inputs to reveal potential global rice pestrisk levels at different spatial-temporal scales and a rice pestrisk index is synthesized from these information can support stakeholders understand global rice pest risk levels of areas before decision making process is made . This paper introduces a method to build a synthetic rice pest risk index and designs decision maker agents with ability of aggregating information from other agents , OWA based method and Choquet integral based method are experimented on decision maker agents to determine global rice pest risk index , a case study is carried out on risk assessment of brown plant hoppers in the Mekong Delta region to explore feasibility of these methods in rice pest management applications .
When this latter appears , the process can not be a simple problem due to complexity to model such synergy relations . To deal with these issues , a Choquet integral based method has been developed to determine the most appropriate ranking of alternatives . Therefore , a fuzzy measure that models considerations of an expert in the problem domain must be identified .
This restriction on the adopted scale may be considered as a limitation in some practical problems . In order to overcome this limitation , we propose a new Choquet integral formulation , specific for sorting problems , that constructs a common scale from heterogenous scales using the framework of the FlowSort method . We also introduce a new outranking degree based on the Choquet integral preference model , which allows the modeling of interacting criteria in FlowSort . Therefore , the proposed approach can be seen either as an extension of FlowSort for problems with interacting criteria or as a new Choquet integral formulation for multi-criteria sorting problems with heterogeneous scales . Numerical examples attest that the proposed approach is conceptually simple to be implemented and acknowledge the importance of considering interaction between criteria when it exists .
Although linear models have gained considerable attention and recommendation , contemporary literature contains strong evidence in support of nonlinear noncompensatory models . In this paper , the authors first demonstrate how fuzzy measures , which are defined on subsets of decision attributes , and their Choquet-integral formulation , which exhibits both compensatory and noncompensatory properties , have meaningful behavioral interpretations within the context of new-product screening . Then , they show how to address the complex problem of building such measures by applying a learning algorithm that relies on methods of judgment analysis .
Our definition is based on the mass assignment approach from Dempster-Shafer theory of evidence . The generalized belief function is totally monotone and it has Choquet integral representation w.r.t . a classical belief function .
Our approach is based on mass assignments used in the Dempster – Shafer theory of evidence . A generalised belief function is totally monotone and it has Choquet integral representation with respect to a unique belief measure on Boolean events .
This paper aims at presenting the state-of-the-art of Choquet integral in quantifying the uncertainty in financial economics . Not only Choquet integral becomes a suitable model for defining financial coherent risk measures in the investment context , it seems also possible to use Choquet integral calculations as a means for asset pricing . We address also utility aspect of Choquet integral risk measures .
Second , we set up a framework for pricing derivatives written on an underlying asset traded on an illiquid market . To this end we have extended the Choquet integral definition to account for interval payoffs of the underlying asset . The price interval we obtain may be interpreted as a bid‐ask price quoted by the intermediary issuing the derivative security .
The conventional weighted mean can not deal with this aspect , and compromise operators are more suitable . In this sense , the operators of the Choquet integral family are interesting and are applied to evaluate the suppliers ’ performance for an industrial case .
Both approaches have in common the use of theoretical principles of mathematical and spatial modelling and the methods included in the geographic information systems ( GIS ) . These procedures involve a wide variety of methods ranging from a simple statistical test of differences between two files ( the occurrence of the phenomenon in the landscape and in archaeological sites ) , the determination of the significance rate of such a difference , the determination of the correlation rate through the creation of predictive models combining properly made layers by the usage of corresponding aggregation functions to the very validation of the predictive models . A number of these practices are common for creating archaeological predictive models for both bases .
How to deposit the data is a challenge . An usual strategy is to save the abstract of the data , such as using aggregation functions to preserve the features of the original data with much smaller space . A key problem , however is that such information can be distorted by the presence of illegitimate traffic , e.g. botnet recruitment scanning , DDoS attack traffic , etc .
Much work in social choice theory takes individual preferences as uninvestigated inputs into aggregation functions designed to reflect considerations of fairness . Advances in experimental and behavioural economics show that fairness can also be an important motivation in the preferences of individuals themselves .
A new class of the artificial neuron models is described in this work . These models are based on assumptions : ( 1 ) contributions of synapses are summing with the help of certain aggregation operation ; ( 2 ) contribution of synaptic clusters are computed with the help of another aggregation operation on the set of simple synapses . These models include a big part of known functional models of neurons .
A method of generating a computer program based on an SQL query includes receiving a SQL query , including a reference to a first data set stored at a first data source , and including a reference to a second data set stored at a second data source different from the first data source ; determining that the SQL query includes two or more commands , the commands including a first union-type operation , and a first aggregation operation ; determining that the SQL query describes that the first union-type operation shall be applied to at least a portion of data from the first data set , and applied to at least a portion of data from the second data set ; determining that the SQL query describes that the first aggregation operation shall be applied to data resulting from the first union-type operation ; and generating the computer program .
This paper aims at elaborating an integrated decision aiding tool regarding the selection of combined transport alternatives according to actual road freight transport situations . Based upon a wide range of criteria covering partly the European transport policies , the integrated tool is made up of two aggregation steps . Firstly , aggregation methods integrate the economic , environmental , safety and logistics related performances of transport scenarios into four global scores .
The non-linear algorithms aggregate data only in certain steps , such as conjugate gradient , which are linear in the data . We introduce a new and highly efficient VSS ( Verifiable Secret-Sharing ) protocol in a special but widely-applicable model that allows secret-shared arithmetic operations in such aggregation steps to be done over small fields ( e.g. 32 or 64 bits ) . There are two major advantages : ( 1 ) in this framework private arithmetic operations have the same cost as normal arithmetic and ( 2 ) the scheme admits extremely efficient zero-knowledge ( ZK ) protocols for verifying properties of user data .
The non-linear algorithms aggregate user data only in certain steps , such as conjugate gradient , which are linear in per-user data . We introduce a new and highly efficient VSS ( Verifiable Secret-Sharing ) protocol in a special but widely-applicable model that allows secret-shared arithmetic operations in such aggregation steps to be done over small fields ( e.g. 32 or 64 bits ) , so that private arithmetic operations have the same cost as normal arithmetic . Verification of user data is required to prevent a malicious user from biasing the computation .
We compare the performance of four ( 4 ) different special-purpose pattern recognition methods designed for trading systems in forex markets . Each of thesemethodshasbeenproperlydesignedtoexploithighlycompute-intensive aggregation calculations of complex but efficient distributed SQL queries on the relational databases holding the market data . Specifically , we have implemented several methods for discovering hidden price patterns in forex marketstime-seriesdatathatsignaltheshort-termdirectionofpricechangesof the market .
We applied the data mining technique to medical near-miss cases collected by a foundation related to the Japanese Health , Labor and Welfare Ministry , and extracted information such as pairs of confusing medicines , the cause of near-miss cases in some situations , which can not be obtained by simple aggregation calculations and descriptive statistics . We also introduce the results of text mining applied to the free-description data regarding the backgrounds and causes of near-miss cases and their counter measures .
However , securing WSNs with traditional cryptographic mechanism is insufficient because of the existing limited resources and the lack of tamper resistant hardware . In this paper , we propose a Reputation-based Secure Data Aggregation for WSNs ( RSDA ) that integrates aggregation functionality with the advantages provided by a reputation system to enhance the network lifetime and the accuracy of the aggregated data . We bind symmetric secret keys to geographic locations and assign these keys to sensor nodes based on their locations .
These predicate patterns are obtained from a detailed empirical study of large real-world question-sets . In addition , the system also incorporates a specific non-pattern based approach which make use of aggregation-like operations , to generate questions that involve superlatives ( e.g. , highest mountain , largest river etc . ) . We have tested the applicability and efficacy of the proposed methods by generating MCQs from several online available ontologies , and verified our results in a classroom setup — incorporating real students and domain experts .
Backlog is allowed . Aggregation of operations is invariable for the entire planning period , the composition of the batch and intensities of processing operations are invariable within an interval , but can vary from interval to interval . As the objective function we use the planned profit .
Our graph encoder is an extension to GraphSAGE with two major distinctions . First , we non - trivially generalize it to cope with both directed and undirected graphs by splitting original node into forward nodes ( a node directs to ) and backward nodes ( direct to a node ) according to edge direction and applying two distinct aggregation functions to these types of nodes . Second , we exploit two different schemes ( pooling - based and supernode - based ) to reassemble the learned node embeddings to generate graph embedding , which is not studied in GraphSAGE .
The conventional method of aggregating the satisfaction of transport projects with respect to multiple attributes is commonly some variant of Simple Additive Weighting ( SAW ) , which involves the sum of products of standardized outcomes of projects with respect to attributes and attribute importance weights . It is suggested that alternative forms of aggregation might be more useful , in particular , the Ordered Weighted Averaging ( OWA ) operator introduced by Yager ( 1988 ) . Attribute importance weights and satisfaction of attributes by projects may be aggregated prior to aggregation via an OWA operator .
Therefore , a multi-stage methodology is proposed and linguistic Z-numbers are applied to describe the assessment information . Moreover , the weighted averaging ( WA ) aggregation operator , the ordered weighted averaging ( OWA ) aggregation operator and the hybrid weighted averaging ( HWA ) aggregation operator are developed to fuse the input arguments under the linguistic Z-number environment . Then , using the max-score rule and the score-accuracy trade-off rule , three qualitative portfolio models are presented to allocate the optimal assets .
In this paper , we present an alternate/supporting approach based on comparison of the players ’ overall talent class viz‐a‐viz the corresponding performances of the player against the 28 aspects of talent from cricketing perspective . The Ordered Weighted Averaging Aggregation ( OWA ) operator was used to aggregate the opinion of experts for assessment of talent classes in cricket . The normative data obtained for a cricket enthusiast was then compared using normalized adequacy coefficient with aggregated opinions of experts .
This uncertainty is modeled by specifying a scenario set containing K distinct processing time scenarios . The ordered weighted averaging aggregation ( OWA ) operator , whose special cases are the maximum and Hurwicz criteria , is applied to compute the best schedule . Some new positive and negative approximation results concerning the problem are shown .
This paper seeks to advance the theory and practice of the dynamics of complex networks in relation to direct and indirect citations . It applies social network analysis ( SNA ) and the ordered weighted averaging operator ( OWA ) to study a patent citations network . So far the SNA studies investigating long chains of patents citations have rarely been undertaken and the importance of a node in a network has been associated mostly with its number of direct ties .
We have adopted link mining approach to recommend books using Ordered Ranked Weighted Averaging ( ORWA ) aggregation operator . ORWA is a modified form of Ordered Weighted Aggregated averaging ( OWA ) operator , a multi criteria decision making procedure . The weight generation using guided quantifier does not take into account the value of the voters , here , rankers which recommend the products , i.e. universities ' ranking .
The proposed system uses appropriate preprocessed X-ray images of passenger ’s luggage and designs to detect the prohibited items like Pistol , Knife , Explosive materials , scissors , and handguns of different size and orientation etc The X-ray imaging is an important technology in many fields , from non-intrusive inspection of delicate objects , to weapons detection at security checkpoints . In this work we will detect the object by ordered weighted averaging method(OWA ) OWA operator provides a parameterized family of aggregation operators , including well-known operators such as maximum , minimum , arithmetic mean , k-order statistics and median .
This paper deals with multicriteria decision‐making problems in which the criteria are partitioned into q categories , and a prioritization relationship exists over categories . We aggregate the criteria in the same priority category by a weighted OWA ( ordered weighted averaging ) operator and introduce two averaging operators , a generalized prioritized averaging operator and a generalized prioritized OWA operator . In the case with one criterion in each priority category , the two operators reduce to the prioritized averaging operator and the prioritized OWA operator as proposed by Yager .
The fuzzy operator defined as the so-called Weighted OWA ( WOWA ) aggregation offers a well-suited approach to this problem . The WOWA aggregation , similar to the classical ordered weighted averaging ( OWA ) , uses the preferential weights assigned to the ordered values ( i.e. to the worst value , the second worst and so on ) rather than to the specific criteria . This allows one to model various preferences with respect to the risk .
Analyzing the LES status of rapid urbanization areas has a significant impact on decision-making of urban development . Based on the integration of ordered weighted averaging ( OWA ) and sensitivity analysis , this paper focuses on hightening the understanding of indictor weights ’s influences to LES evaluation and assisting in making a suitable assignment of weights in Chengdu Plain Economic Zone . The results highlight how the statu of LES , in 2014 , is closely linked to the administrator ’s attitude and corresponding indictor weights .
The problem of aggregating multiple criteria to obtain a globalizing objective function is of special interest when the number of Pareto solutions becomes considerably large or when a single , meaningful solution is required . Ordered weighted average or ordered median operators are very useful when preferential information is available and objectives are comparable since they assign importance weights not to specific objectives but to their sorted values . In this paper , ordered weighted average optimization problems are studied from a modeling point of view .
This perspective provides a strong theoretical basis for the standardization of factors and their subsequent aggregation . In this context , a new aggregation operator that accommodates and extends the Boolean and WLC approaches is discussed : the Ordered Weighted Average . A case study of industrial allocation in Nakuru , Kenya is employed to illustrate the different approaches .
This paper describes an evolutionary algorithm applied to tuning of parameters of license plate detection systems . We consider both simple and compound detection systems , where the latter ones consist of multiple simple systems fused by some aggregation operation ( weighted sum or ordered weighted average ) . With the structure of a system given by a human and fixed , we perform an evolutionary search in the space of possible parameter combinations .
In particular , we substitute the WA by three operators from the Ordered Weighted Average ( OWA ) family of operators because it permits to decide the degree of andness/orness that is used during the aggregation . The OWAWA ( Ordered Weighted Average Weighted Average ) , WOWA ( Weighted Ordered Weighted Average ) and IOWA ( Induced Ordered Weighted Average ) operators are studied . They are capable to combine the importance given to each criterion with the conjunctive/disjunctive requirement applied in the definition of the outranking relation .
Fuzzy integral is a general aggregation operator , which encompasses many common aggregation operators likeweighted mean , ordered weighted mean , weighted minimumand maximum , etc . In classifier combining , it can be usedto aggregate the outputs of the individual classifiers in theteam with respect to a fuzzy measure , based on the classifierconfidences .
This paper presented a new software-based pathway of process mining approach for extending a flexible WQI ( Water Quality Index ) that would deal with uncertainties derived from missing data occurrence in short- and long-term assessments . The methodology is based on integration of four multi-criteria group decision-making models coupled with fuzzy simulation including AHP ( Analytical Hierarchy Process ) , fuzzy OWA ( Ordered Weighting Average ) , TOPSIS ( Technique for Order Preference by Similarity to Ideal Solution ) , and fuzzy TOPSIS that were used for data mining and group consensus evaluation .. Examining the methodology on groundwater resources being supplied for drinking in Shiraz , Iran showed high integrity , accuracy , and proximity-to-real interpretation of water quality .
This work also finds the effect of counting ThSV and the ThSV resistive and capacitive parasitic on utilization of timer control . Followed by the work on parameters that reflect changes the generally wire measurement size wise and timer energy in three dimension timer system model and lastly develop a 2 stair three dimension timer hierarchy fusion method based on three dimension technique [ i.e. technique of Means and Medians ] which determine the best possible count of ThSV to be utilized in the three dimension timer hierarchy . Hence the whole energy expenditure is reduced .
A fuzzy c‐medians variant is proposed for the generation of fuzzy term sets with one half overlap . The proposed variant is modified from the original algorithm mainly in two areas .
The effect of data size used for the generation of fuzzy terms is also discussed . A generalized s‐shaped function with a tunable parameter along with its complement ( i.e. , a generalized z‐shaped function with a tunable parameter ) is developed to fit all term sets generated by the fuzzy c‐medians variant using various m values . © 2002 John Wiley & Sons , Inc.
The terms are combined in a weighted sum to generate an indicator variable that is then compared to a detection threshold . An FIR band-pass filter , consisting of a cascaded series of running medians and means prefiltered the raw ECG signal to reduce high-frequency noise as well as low-frequency base line drift . The technique appears to be exceedingly robust , correctly detecting even aberrant QRS complexes in noise-corrupted ECGs.<<ETX > >
Finally , we present the results of extensive experimentation showing that our new heuristic outperforms all others in accuracy and , especially , in running time : the heuristic typically returns solutions within 1 % of optimal and runs in seconds to minutes even on genomes with 25'000 genes -- in contrast , MGR can take days on instances of 200 genes and can not be used beyond 1'000 genes . ConclusionFinding good rearrangement medians , in particular inversion medians , had long been regarded as the computational bottleneck in whole-genome studies . Our new heuristic for inversion medians , ASM , which dominates all others in our framework , puts that issue to rest by providing near-optimal solutions within seconds to minutes on even the largest genomes .
Because the four-via routing algorithm does not take the requirement of no timing delay in multi-chip module ( MCM ) clock routing into consideration , there will be a skew among the clock pins . This paper presented a new method , which combines the Method of Means and Medians and v4R algorithm , to balance the skew of clock net , and thus imoprove the v4R algorithm .
The forward search algorithm is made up of three steps . The rst step addresses the choice of an initial subset of the observations that will be used with a robust estimation procedure ( least medians of squares or LMS ) to provide an initial model t and a “ good ” estimate of the residual error . If the model contains p parameters , the initial subset will be the one subset out of the nCp potential subsets that minimizes the sum of squared residuals from the LMS t. If there are too many potential subsets , the choice will be made after examining a large sample of subsets of size p.
Distribution-free methods are considered for comparing k1 treatments to k2 controls in the one-way layout . Using normal scores statistics , one-sided tests are developed for k1 treatment versus k2 control comparisons of medians . Using Savage statistics , analogous results are developed for comparing scale parameters of the treatments to the controls with non-negative data .
A Bayesian procedure for forecasting S-shaped growth is introduced and compared to classical methods of estimation and prediction using three variants of the logistic functional form and annual times series of the diffusion of music compact discs in twelve countries . The Bayesian procedure was found not only to improve forecast accuracy , using the medians of the predictive densities as point forecasts , but also to produce intervals with a width and asymmetry more in accord with the outcomes than intervals from the classical alternative . While the analysis in this paper focuses on logistic growth , the problem is set up so that the methods are transportable to other characterizations of the growth process .
The sub-optimality approximation problem is at the core of online stochastic optimization algorithms and it can also be used for solution repair and approximate filtering of optimization constraints . This paper formalizes the problem and presents sub-optimality approximation algorithms for metric TSPs , packet scheduling , and metric k-medians that run faster than the optimal or approximation algorithms . It also presents results on the hardness/easiness of sub-optimality approximations .
It is shown that any real weighted median is equivalent to an integer weighted median . As a consequence , a sensitivity analysis can always be performed to represent real weighted medians by finite precision weighted medians . This result proves very useful if one realizes that optimal adaptive weighted medians are usually real-valued .
In this paper , we design and implement a system , iHEED , in which node clustering is integrated with multi-hop routing for TinyOS . We consider simple data aggregation operators , such as AVG or MAX . We use a simple energy consumption model to keep track of the battery consumption of cluster heads and regular nodes .
In First International Conferen e on Computational Logi , DOOD-2000 Stream , July 2000 . Index , 29 * + > > , 37 * ->- > , 36 + > > , 37 ->- > , 36 expunge , 30 flLoadedModule/1 , 19 flThisModule/1 , 18 abolish all tables , 39 , 46 aggregates avg , 36 olle tbag , 36 olle tset , 36 ount , 36 max , 36 min , 36 multi-valued methods , 36 sum , 36 aggregation aggregate operator , 35 grouping , 35 anonymous oid , 38 anonymous variable , 4 arithmeti expression , 13 atomdata , 7 isa , 8 signature , 7 atomi formula in F-logi , 6 attribute inheritable , 32 non-inheritable , 32 ba ktra kable update , 44 btdelete , 44 btdeleteall , 44 bterase , 44 bteraseall , 44 btinsert , 44 btinsertall , 44 base part of predi ate , 40 bform , 26 , 49 boolean method inheritable , 37 bulk delete , 43 bulk insert , 42 anoni term , 64 hara ter list , 10 lass , 8 instan e , 8 sub lass , 8 losure axioms , 60 omment , 11 ompiler dire tive , 48 arguments , 26 , 48 equality , 48 expunge , 48 firstorderall , 30 , 48 firstorder , 30 , 48 hilogtable , 39 index , 48 op , 12 , 48 table , 38 equality , 31 uts in FLORA-2 , 40 utting a ross tables , 40 data atom , 7 debugging , 56 delete bulk , 43 derived part of predi ate , 40 do n't are variable , 4 dynami module , 32 equality , 31 es aped hara ter , 9 F-mole ule , 8 flDump/1 , 53 FLIP , 1 flNoTra e , 56 floating number , 10 68 INDEX 69 FLORID , 1 flP2H/2 , 29 flTra e , 56 flTra eLow , 56 HiLog , 26 translation , 27 unifi ation , 27 HiLog to Prolog onversion , 29 hilogtable , 39 I/O port-based , 51 stream-based , 51 Id-term , 6 inheritan e behavioral , 32 non-monotoni , 32 stru tural , 32 insert bulk , 42 integer , 10 loading files , 18 logi al expressions , 12 meta signature of a predi ate , 26 meta-programming , 28 meta-unifi ation , 29 method , 7 boolean , 37 inheritable , 37 multi-valued , 7 pro edural , 39 s alar , 7 self , 23 set-valued , 7 single-valued , 7 module , 15 expunge , 30 flLoadedModule/1 , 19 flThisModule/1 , 18 flora(modulename ) , 21 prolog(modulename ) , 19 ontents , 15 name , 15 Prolog , 16 , 19 rules for , 17 system , 16 , 21 , 50 user , 16 ompilation of , 18 referen e to , 16 mole ule logi expressions , 13 obje t value , 24 truth value , 24 multi-valued methods aggregation , 36 non-ba ktra kable update , 41 delete , 42 deleteall , 42 erase , 42 eraseall , 42 insert , 41 insertall , 41 number , 10 obje t base part of , 40 derived part of , 40 obje t onstru tor , 6 obje t identifier , 6 oid , 6 anonymous , 38 operators , 11 pre eden e level , 11 type , 11 pat h rules , 63 path expression , 22 in rule body , 22 in rule head , 24 predi ate base part of , 40 derived part of , 40 first-order , 30 HiLog , 30 predi ate meta signature , 26 Prolog module , 16 , 19 Prolog to HiLog onversion , 29 INDEX 70 signature in F-logi , 7 spying , 56 string , 10 sub lass , 8 symbol , 9 synta ti obje t , 26 , 49 system module , 16 , 21 , 50 table , 38 tabling , 38 tra ing , 56 triehandle , 62 type he king , 48 update , 40 ba ktra kable , 44 non-ba ktra kable , 41 user module , 16 variable anonymous , 4 do n't are , 4 well-founded semanti s , 35
For this reason , this paper is aimed at introducing a new nonaveraging operator named < inline-formula><tex-math notation="LaTeX">$C_{F_1F_2}$</tex-math></inline-formula>-integral , which is a generalization of the Choquet-like Copula-based integral ( CC-integral ) . < inline-formula><tex-math notation="LaTeX">$C_{F_1F_2}$</tex-math></inline-formula>-integrals present the desired properties of an aggregation-like operator since they satisfy appropriate boundary conditions and have some kind of increasingness property . We show that < inline-formula><tex-math notation="LaTeX">$C_{F_1F_2}$</tex-math></inline-formula>-integrals , when used to cope with classification problems , enhance the results of the previous averaging generalizations of the Choquet integral and provide competitive results ( even better ) when compared with state-of-the-art FRBCSs .
The H2TLCA aggregation operator and H2TLCG aggregation operator can describe dependencies between criteria in decision-making problem solving . Moreover all aggregation operation operators have the properties of idempotency , boundedness and monotonicity , and the H2TLCA aggregation operator and H2TLCG aggregation operator are also verified to be symmetric functions . In addition , the H2TLPWA aggregation operator and H2TLCA aggregation operator are employed to settle multicriteria decision-making problems with hesitant 2-tuple linguistic terms .
There in to , the parameters for aggregation operator are the decision maker 's special preferences for some alternatives . On these bases , the correlation coefficient and aggregation operator integrated approach is constructed , to determine the most favorable alternative under the MCDM with interval-valued intuitionistic fuzzy numbers . Finally , we apply an example to show the validity of the proposed decision making approach .
The probability of a particular candidate as being the answer is then computed by aggregating the probabilities of all document tokens which appear in and renormalizing over the candidates : where is the set of positions where a token in appears in the document . This aggregation operation is the same as the pointer sum attention applied in the AS Reader kadlec2016text . Finally , the candidate with maximum probability is selected as the predicted answer : During the training phase , model parameters of GA are updated w.r.t .
To train the target model , we use inputs from the target dataset and outputs from collaborator models . We train each STM on its dataset for one epoch , and change the target STM as follows : where denotes concatenation and denotes an aggregation operation such as max pooling or concatenation . We used weighted max pooling for the aggregation operation .
Aggregation functions for typical hesitant fuzzy elements and the action of automorphisms Algorithms for fundamental spatial aggregate operations over regions Distributed query processing in fast local networks (data base systems) A linear programming method for multiple criteria decision making with probabilistic linguistic information Parametric Extension of the Most Preferred OWA Operator and Its Application in Search Engine's Rank Social index construction method based on consistent aggregation operator families Retrieval and delivery of information in multimedia database systems Extending the Choquet integral Supervised learning using mahalanobis distance for record linkage Application of Multi-Criteria Decision Making Method for Developing a Control Plan Dynamic Evaluating Rice Pest Risk State of Decision Maker Agents in Rice Pest Management Model Discrete Choquet integral based method for criteria synergy determination An outranking Choquet integral formulation for multi-criteria sorting problems with heterogeneous scales: an extension of FlowSort for interacting criteria. Fuzzy measure theoretical approach to screening product innovations From Probabilities to Belief Functions on MV-Algebras Extension of belief functions to infinite-valued events On Choquet Integral Risk Measures Implied trees in illiquid markets: A Choquet pricing approach Evaluation of Green Suppliers Considering Decision Criteria Dependencies Selected Mathematical Principles of Archaeological Predictive Models Creation and Validation in the GIS Environment Reliable Aggregation on Network Traffic for Web Based Knowledge Discovery Axiomatic foundations for fairness-motivated preferences About \varSigma \varPi -neuron Models of Aggregating Type Processing queries containing a union-type operation Decision support system applied to combined freight transport Practical Private Computation and Zero-Knowledge Tools for Privacy-Preserving Distributed Data Mining Practical private computation of vector addition-based functions Intraday Business Model Strategies on Forex Markets: Comparing the Performance of Price Pattern Recognition Methods The Analysis of Near-Miss Cases Using Data-Mining Approach RSDA: Reputation-Based Secure Data Aggregation in Wireless Sensor Networks Automated generation of assessment tests from domain ontologies Structural-Parametric Optimization of a Complex of Intersecting Sets of Operations under Nonstationary Demand Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks FLEXIBLE AGGREGATION IN MULTIPLE ATTRIBUTE DECISION MAKING: APPLICATION TO THE KURANDA RANGE ROAD UPGRADE Linguistic Z-number weighted averaging operators and their application to portfolio selection problem An OWA‐Based Model for Talent Enhancement in Cricket Parallel Machine Scheduling under Uncertainty The value of indirect ties in citation networks: SNA analysis with OWA operator weights Ordered ranked weighted aggregation based book recommendation technique: A link mining approach Suspicious Object Detection Using OWA Generalized prioritized aggregation operators On Decision Support Under Risk by the WOWA Optimization Influence of Index Weights on Land Ecological Security Evaluation: The Case Study of Chengdu Plain Economic Zone, China Ordered weighted average combinatorial optimization: Formulations and their properties Application of fuzzy measures in multi-criteria evaluation in GIS Evolutionary Tuning of Compound Image Analysis Systems for Effective License Plate Recognition Constructing an Outranking Relation with Weighted OWA for Multi-criteria Decision Analysis Dynamic Classifier Aggregation Using Fuzzy t-conorm Integral Process Mining Approach of a New Water Quality Index for Long-Term Assessment under Uncertainty Using Consensus-Based Fuzzy Decision Support System ThSV based three dimension devise to optimize Low Energy and Reliable Timer Using MNM Technique A fuzzy c‐medians variant for the generation of fuzzy term sets Robust and computational efficient QRS detection Heuristics for the inversion median problem Balance Multi-Chip Module (MCM) Clock Skew Using Improved Four-via Routing Algorithm Applied Logistic Regression Nonparametric comparisons with k2 > 2 controls using normal scores and savage statistics A Forecasting Comparison of Classical and Bayesian Methods for Modelling Logistic Diffusion Sub-optimality Approximations Weighted medians - positive Boolean functions conversion algorithms An experimental study of routing and data aggregation in sensor networks FLORA-2: User's Manual Improving the Performance of Fuzzy Rule-Based Classification Systems Based on a Nonaveraging Generalization of CC-Integrals Named $C_{F_1F_2}$-Integrals Prioritized Aggregation Operators and Correlated Aggregation Operators for Hesitant 2-Tuple Linguistic Variables Correlation and aggregation integrated MCDM with interval-valued intuitionistic fuzzy numbers Gated-Attention Readers for Text Comprehension CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
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BigSpatial@SIGSPATIAL FUZZ-IEEE EUSFLAT Conf. (1) IEA/AIE (1) EMS CLEI SMPS IUM MCDM ISNN SDM HCI (4) PDCAT ArXiv IPMU (4) HIS ECSQARU GSKI (1) ICCCI (1) MDAI SITIS CP MASS ICNC-FSKD ACL BMC Bioinformatics
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