waashk/dblp huggingface dataset:
the tradeoffs between open and traditional relation extraction. an effective method for fuzzy image enhancement was presented by russo, which was controlled by tuning of one parameter. the fixed parameter was used for the entire image resulting in over blurring or sharpening of image features in some parts of the image. on the basis of these, in this paper, we applied russo's algorithm on different kinds of noise and proposed an efficient method to obtain the parameter values adaptively. firstly, each pixel went through a phase of smoothing and then followed by a phase of combining smoothing and sharpening; secondly, to effectively enhance image, each pixel local feature was adaptively assigned different parameter values by evaluating pixel local features through a fuzzy membership function; finally, compared with russopsilas method and other methods, experimental results indicated that the proposed method could achieve better performance than traditional methods for the enhancement of images corrupted with impulse and gaussian noise.
learning bigrams from unigrams. clustering web search result is a promising way to help alleviate the information overload for web users. in this paper, we focus on clustering snippets returned by google scholar. we propose a novel similarity function based on mining domain knowledge and an outlier-conscious clustering algorithm. experimental results showed improved effectiveness of the proposed approach compared with existing methods.
word clustering and word selection based feature reduction for maxent based hindi ner. a robot, which is composed of adaptive mobile mechanism, is developed for the purpose of performing the internal inspection tasks of pipelines. adaptability and efficiency are the basic considerations for this robot. based on these concepts, a prototype is designed and fabricated. the proposed adaptive mobile mechanism equipped with one actuator can perform two working modes, a normal working mode and an assistant enhanced mode. robot under the normal working mode is used for moving in pipe or monitoring the inner surface of the pipe. on the other hand, robot under the assistant enhanced mode will produce a larger torque to help itself surmount an obstacle in the pipe without any other driving actuator. this special feature is achieved by applying a power transmission mechanism. the rotation problem of the stator is solved according to the calculation results of the robot kinematics. basic experiments have been conducted to testify the adaptability and efficiency of the robot.
maxsim: a maximum similarity metric for machine translation evaluation. this paper presents our study over the effect of complementary energy feedback on virtual slope walking, while virtual slope walking is our new biped gait generation method inspired by passive dynamic walking. the energy feedback strength is defined and the walking is modeled as a step-to-step function. the jacobi matrix eigenvalues of the function are calculated together with the basin of attraction. from the analysis, we find the characteristic of complementary energy feedback is being effective on a fast gait but weak on a slow one. by making use of the complementary energy feedback in walking experiment, our robot achieves speed change from 1.5 leg/s to 4.1 leg/s.
soft syntactic constraints for hierarchical phrased-based translation. distributed and heterogeneous environments present significant challenges to complex software systems, which must operate in the context of continuously changing loads, with partial or out-of-date information on resource capabilities. a distributed query processor (dqp) can be used to access and integrate data from distributed sources, as well as for combining data access with data analysis. however in heterogeneous environments, statically constructed query plans may commit a query evaluator to following significantly suboptimal strategies. as such, there is considerable interest in using adaptive query processors (aqps) in such settings to provide self-optimizing behaviour. however, with many possible adaptive strategies available, it is important that aqps can be constructed in a systematic and efficient manner. this paper outlines an approach to the development of aqps in which adaptive behaviour is implemented using cooperating monitoring, assessment and response components. it is shown how this decomposition has been applied in the development of an adaptive dqp system for service-based grids, which reallocates load at query runtime, thereby supporting self-optimization.
regular tree grammars as a formalism for scope underspecification. copyright protection of digital contents, as one of the most important application of digital watermarks, works by watermark verification. in traditional watermark authentication schemes, a prover exposed a watermark to be present in a digital data to the possible dishonest verifier. however, a potential attacker is able to spoil or remove the watermark entirely once classified information like the watermark or the embedding key is known. some of previous schemes proposed as solutions have not really achieved desirable results. their lack of security and validity is the most serious problem. in this paper, we propose a secure watermark verification scheme based on zero knowledge protocols and public-key encryption scheme in order to solve this problem. there is no secret information that can be used for removing watermark disclosed during the verification process. it has considerable advantages over previously proposed schemes in terms of security and validity
sentence simplification for semantic role labeling. video encoding now is being implemented in various computing platforms with different computing capability, the requirement on the encoding complexity is also different according to different applications. as the most computation-intensive part of video encoding, the me (motion estimation) should have a scalable complexity. this paper proposes a me algorithm with fine-granular scalable complexity, a more important feature of the proposed algorithm is that it seeks for the complexity-distortion optimization. the given computation budget will be allocated to each mb (macroblock) in one frame. each mb will consume its allocated computation by a hybrid search pattern. experimental results show that the proposed algorithm can get a better computation-distortion performance than the existing me algorithms
multi-task active learning for linguistic annotations. collaborative filtering is a key technique in recommender system and applied widely in e-commerce. in reality, due to data sparseness, similarity of users is computed wrongly, which results that really similar users maybe filtered out while false similar users are exploited to produce recommendation. in this paper, two pre-filling methods based on community, respectively simple pre-filling based on community (pfci) and pre-filling based on community association (pfcii) are presented to overcome the sparsity. if user-item pair is null, its rating is pre-filling by using our method before traditional collaborative filtering is executed. the experiment shows better performance of our methods.
semantic class learning from the web with hyponym pattern linkage graphs. given two sets da and db of multidimensional objects, a spatial region r, and a critical distance dc, an optimal-nearest- neighbor (onn) query retrieves outside r, the object in db with maximum optimality. let car (sp,p) be the cardinality of the subset sp of objects in da which locate within r and are enclosed by the vicinity circle centered at p with radius dc. then, an object o is said to be better than another one o' if (i) car (so,o) = car (so,o'), or (ii) when car (so,o) = car (so',o') the sum of the weighted distance from each object in so to o is smaller than the sum of the weighted distance between every object in so' and o'. this type of queries is quite useful in many decision making applications. in this paper, we formalize the onn query, develop the optimality metric, and propose several algorithms for finding optimal nearest neighbors efficiently. our techniques assume that both da and db are indexed by r-trees. extensive experiments demonstrate the efficiency and scalability of our proposed algorithms using both real and synthetic datasets.
a discriminative latent variable model for statistical machine translation. existing algorithms for learning bayesian network require a lot of computation on high dimensional itemsets which affects accuracy especially on limited datasets and takes up a large amount of time. to address the above problem, we propose a novel bayesian network learning algorithm mrmrg, max relevance-min redundancy greedy. mrmrg algorithm is a variant of k2 which is a well- known bn learning algorithm. we also analyze the time complexity of mrmrg. the experimental results show that mrmrg algorithm has much better efficiency and accuracy than most of existing algorithms on limited datasets.
semi-supervised convex training for dependency parsing. future cmp designs that will integrate tens of processor cores on-chip will be constrained by area and power. area constraints make impractical the use of a bus or a crossbar as the on-chip interconnection network, and tiled cmps organized around a direct interconnection network will probably be the architecture of choice. power constraints make impractical to rely on broadcasts (as token-cmp does) or any other brute-force method for keeping cache coherence, and directory-based cache coherence protocols are currently being employed. unfortunately, directory protocols introduce indirection to access directory information, which negatively impacts performance. in this work, we present dico-cmp, a novel cache coherence protocol especially suited to future tiled cmp architectures. in dico- cmp the role of storing up-to-date sharing information and ensuring totally ordered accesses for every memory block is assigned to the cache that must provide the block on a miss. therefore, dico-cmp reduces the miss latency compared to a directory protocol by sending coherence messages directly from the requesting caches to those that must observe them (as it would be done in brute-force protocols), and reduces the network traffic compared to token-cmp (and consequently, power consumption in the interconnection network) by sending just one request message for each miss. using an extended version of gems simulator we show that dico-cmp achieves improvements in execution time of up to 8% on average over a directory protocol, and reductions in terms of network traffic of up to 42% on average compared to token-cmp.
learning document-level semantic properties from free-text annotations. today, the scale of high performance computing (hpc) systems is much larger than ever. some hpc systems consist of thousands or even tens of thousands of processors. the larger scale leads to a challenge that how to deal with process failures. the most important programming tool for hpc is mpi (message passing interface). there are some existing methods to deal with fault-tolerance, such as mpich-v, starfish, mpi/ft and so on, using the mpi context. most of them do the checkpoint on disk. in this paper, some erasure codes, which used in raid systems usually, are applied to deal with the fault-tolerance in-memory. based on fault-tolerance-mpi (ft-mpi) platform, raid4, raid5, rdp and x-code are implanted to do the checkpoint in-memory. the experimental results show that rdp is feasible for double-fault-tolerance in-memory.
efficient, feature-based, conditional random field parsing. this paper proposes a mobile, intelligent agent-based framework that allows buyers and sellers to perform business at remote locations. a business participant can generate a mobile, intelligent agent via a mobile device and dispatch the agent to the internet to do business. the proposed framework brings in several advantages: first, it provides great convenience for traders as business can be conducted anytime and anywhere. secondly, the user is freed from the time-consuming task of finding and negotiating with appropriate traders. thirdly, it addresses the problem of limited and expensive connection time for mobile users: a trader can disconnect her mobile device from its server after launching a mobile agent. later on, she can reconnect and call back the agent for results, therefore minimizing the connection time. finally, by complying with the standardization body of fipa, this flexible framework increases the interoperability between agent systems and provides high design scalability.
optimal $k$-arization of synchronous tree-adjoining grammar. location service provides position of mobile destination to source node so that position based routing can be applied. several quorum-based location services have been proposed. these location service protocols suffer from complex structure with high overhead, or host location foreknowledge, and difficulty in quorum construction. to overcome those deficiencies, we propose a novel quorum-based location service. in the proposed scheme, location databases are stored in the network nodes themselves, which form a self-organizing virtual backbone network. quorum system is constructed on the virtual backbone network in conformity to irregular grid rule. every mobile node will notify its location update information to irregular grid quorum system so that other hosts can obtain the mobile node location through irregular grid quorum system. the simulation results show that our proposed location service leads to high success rate, appropriate quorum size and good load.
multilingual harvesting of cross-cultural stereotypes. we present a framework for acquiring and restoring images of warped documents to their original planar shapes. image capturing of warped documents often results in warped images. most digital restoration approaches make use of 2d image processing methods which are dependent on the content of the images. some recent work attempts to remove this constraint by working directly on the 3d shape of the warped documents. in our framework, we improve on this recent approach to make it more efficient and stable. first we capture the warped surface representation through a set of active lighting from a laser range scanner onto the document surface. this gives us an accurate 3d representation of the document shape. next by using a stick constraint and a stable numerical integrator, we can digitally flatten the 3d model to a planar shape. this simple approach avoids potential instability problem and is fast. restoration results for a few types of warped documents are demonstrated and a significant improvement over the original images can be observed.
applying a grammar-based language model to a simplified broadcast-news transcription task. in this paper, we present the approach and architecture of target: two-way web service router gateway, for two-way web service interaction crossing enterprise domain and firewall. it provides a full support for asynchronous outbound operation and event notification in communication services. target addresses an acute issue for internet applications that today's enterprise nats and firewalls only allow outbound http request from the inside to the outside and block any request from the outside to the inside, which is a serious problem for two-way web services. target is a generic solution to allow two-way web service interaction to traverse legitimately through nats and strictly configured firewalls; and it is based on two-way soap message tunneling, service local registry, and service routing to bridge two-way web service interaction. a research target system has been implemented and applied to real time communication services, e.g. conferencing. extensive experiments on target are performed, and its performance with various sizes of soap messages is studied. the applicability and feasibility of target for two-way web service interaction is verified
correcting misuse of verb forms. designing effective image priors is of great interest to image super-resolution (sr), which is a severely under-determined problem. an edge smoothness prior is favored since it is able to suppress the jagged edge artifact effectively. however, for soft image edges with gradual intensity transitions, it is generally difficult to obtain analytical forms for evaluating their smoothness. this paper characterizes soft edge smoothness based on a novel softcuts metric by generalizing the geocuts method . the proposed soft edge smoothness measure can approximate the average length of all level lines in an intensity image. thus, the total length of all level lines can be minimized effectively by integrating this new form of prior. in addition, this paper presents a novel combination of this soft edge smoothness prior and the alpha matting technique for color image sr, by adaptively normalizing image edges according to their alpha-channel description. this leads to the adaptive softcuts algorithm, which represents a unified treatment of edges with different contrasts and scales. experimental results are presented which demonstrate the effectiveness of the proposed method.
collecting a why-question corpus for development and evaluation of an automatic qa-system. new electronic technologies for signal analysis raise the possibility of sampling very rapidly, with a time-varying density, and determining empirically both the sampling rate and the window width as the signal evolves in time. these opportunities also point to the possibility of sequentially sampling in a time-varying way in more traditional problems. motivated by these ideas, we establish a sampling formula, valid in cases where both sampling rate and window width may be varied. the formula states that, in terms of the ways in which these quantities should alter with time, optimal performance is achieved when the window width is inversely proportional to squared sampling rate, and sampling rate is directly proportional to squared bias.
mining parenthetical translations from the web by word alignment. wide-area distribution raises significant performance problems for traditional query processing techniques as data access becomes less predictable due to link congestion, load imbalances, and temporary outages. non-blocking joining query execution is a promising approach to coping with unpredictability in such environments because of reactively scheduling background processing. classical non-blocking two-way joining technique based on hash-merge (hmj), however, fail to deliver acceptable performance in such a scenario where relatively short intermittent delay exists in the gross. we have developed a fairly fine-grained hash-merge join, called hmj-fg, which has employed a replacement selection tree, allowing many disparted segments to be active in parallel. using the optimization implementation along with simulation obtained by tao, we show that hmj-fg is an effective solution for providing fast query responses to users even in the presence of the longer-term of data sources appeared as unavailability. theory and experimental results show that our technique delivers results significantly fast under unreliable network.
a probabilistic model for fine-grained expert search. peer-to-peer, or p2p sharing has gone through fast growth in recent years and drawn the interests of information systems (is) researchers. however, there is a lack of empirical study on the individual's behavior of using p2p sharing software. given the current existence of various uncertainties in p2p sharing, it is crucial to understand the factors that affect individual's usage of p2p sharing software. in this paper, we develop and empirically test a usage intention model which includes trust in the vendor of p2p sharing software and perceived risk as two major antecedent beliefs to the usage intention. several trust antecedents are also identified in the model. our preliminary results show the support for the model, and offer some important implications for software vendors in p2p sharing industry and regulatory bodies.
automatic syllabification with structured svms for letter-to-phoneme conversion. sign language recognition is to provide an efficient and accurate mechanism to transcribe sign language into text or speech. state-of-the-art sign language recognition should be able to solve the signer-independent continuous problem for practical applications. a divide-and-conquer approach, which takes the problem of continuous chinese sign language (csl) recognition as subproblems of isolated csl recognition, is presented for signer-independent continuous csl recognition. in the proposed approach, the improved simple recurrent network (srn) is used to segment the continuous csl. the outputs of srn are regarded as the states of hidden markov models (hmm) in which the lattice viterbi algorithm is employed for searching for the best word sequence. experimental results show that the srn/hmm approach has a better performance than the standard hmm.
unsupervised translation induction for chinese abbreviations using monolingual corpora. in today's information society, privacy protection has become a very important concern. in this paper we research the inference problems due to functional dependencies (fd) and multi-valued dependencies (mvd) in multilevel secure database (mls) with element classification instances. to deal with the secure problem brought by inference channels we present our fd and mvd based inference control algorithms working on the finest-grained data level which greatly improve the availability of data and minimize the information loss
distributional identification of non-referential pronouns. the computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing and large-scale problem solving over the internet. service discovery becomes an issue of vital importance in utilizing grid facilities. this paper presents rosse, a rough sets-based search engine for grid service discovery. building on the rough sets theory, rosse is novel in its capability to deal with the uncertainty of properties when matching services. in this way, rosse can discover the services that are most relevant to a service query from a functional point of view. since functionally matched services may have distinct nonfunctional properties related to the quality of service (qos), rosse introduces a qos model to further filter matched services with their qos values to maximize user satisfaction in service discovery. rosse is evaluated from the aspects of accuracy and efficiency in discovery of computing services.
robustness and generalization of role sets: propbank vs. verbnet. the advent of dna microarray technologies has revolutionized the experimental study of gene expression. clustering is the most popular approach of analyzing gene expression data and has indeed proven to be successful in many applications. our work focuses on discovering a subset of genes which exhibit similar expression patterns along a subset of conditions in the gene expression matrix. specifically, we are looking for the order preserving clusters (op-cluster), in each of which a subset of genes induce a similar linear ordering along a subset of conditions. the pioneering work of the opsm model, which enforces the strict order shared by the genes in a cluster, is included in our model as a special case. our model is more robust than opsm because similarly expressed conditions are allowed to form order equivalent groups and no restriction is placed on the order within a group. guided by our model, we design and implement a deterministic algorithm, namely opc-tree, to discover op-clusters. experimental study on two real datasets demonstrates the effectiveness of the algorithm in the application of tissue classification and cell cycle identification. in addition, a large percentage of op-clusters exhibit significant enrichment of one or more function categories, which implies that op-clusters indeed carry significant biological relevance.