waashk/dblp huggingface dataset:
better alignments = better translations? we present a new modeling method to evaluate the performance indices of traffic control systems where each intersection has two or four phases. the method is based on the stochastic timed petri net models of isolated intersections. it considers one direction of traffic along a street each time, and the interactions between traffic of different directions are partially approximated by statistical models. by so doing, it dramatically reduces the computing complexity that other petri net-based methods suffer due to the consideration of detailed interactions among all directions of traffic. an example is presented to show the potential application of the new method
mining wiki resources for multilingual named entity recognition. in this paper, a novel passive reduced-order macro-modeling algorithm is proposed for structure dynamics in mems systems that are described by discrete models through the use of finite element methods (fem). in the proposed scheme, the system of equations given by the fem formulation are converted to state-space form such that the state-space equations are compatible with passive krylov subspace methods based on congruent transformations. as an example, modeling, simulation, and experimental studies are presented for a butterfly gyro developed at the imego institute. our simulation results for this example are in very good agreement with previous publications using traditional modeling techniques
a joint model of text and aspect ratings for sentiment summarization. for a signal distributed in rd with an arbitrary density we find the limit distribution as the cell size goes to zero of the quantization noise in the case of a redundant system and the pulse-code modulation scheme. the dimension the support of the limiting distribution equals the largest number of non-comeasurable vectors in the frame and the limit distribution is uniform over its support. this gives the necessary and sufficient conditions for the asymptotic form of the white noise hypothesis.
credibility improves topical blog post retrieval. we present a new global approach to the variable-ordering problem in pseudo-symmetric binary decision diagrams (psbdds). it is based on analyzing symmetric relations between functions' variables. we group all function's variables into symmetry chains and then order the chains based on symmetries. to further reduce the size and the number of levels in psbdds, we introduce a hierarchical decomposition method, based on multiple symmetries. comparing to the previously published results, we substantially reduced the numbers of levels (delay) in psbdds, and for many non-symmetric benchmark functions we did not have to use any repeated variables. results for mcnc benchmark functions are presented
resolving personal names in email using context expansion. we show that even if every node or edge in an n-node butterfly network fails independently with some constant probability, p, it is still possible to identify a set of &theta;(n) nodes between which packets can be routed in any permutation in o(logn) steps, with high probability. although the analysis as complicated, the routing algorithm itself is relatively simple
which are the best features for automatic verb classification. in large-deformation diffeomorphic metric mapping (lddmm), the diffeomorphic matching of images are modeled as evolution in time, or a flow, of an associated smooth velocity vector field v controlling the evolution. the initial momentum parameterizes the whole geodesic and encodes the shape and form of the target image. thus, methods such as principal component analysis (pca) of the initial momentum leads to analysis of anatomical shape and form in target images without being restricted to small-deformation assumption in the analysis of linear displacements. we apply this approach to a study of dementia of the alzheimer type (dat). the left hippocampus in the dat group shows significant shape abnormality while the right hippocampus shows similar pattern of abnormality. further, pca of the initial momentum leads to correct classification of 12 out of 18 dat subjects and 22 out of 26 control subjects
you talking to me? a corpus and algorithm for conversation disentanglement. this paper presents a frequency-estimation algorithm for the adpll designs instead of traditional binary frequency-search algorithm. with the proposed adpll architecture and synchronization process, the lock time can be optimized to two cycles. as the reference clock varies or frequency multiplication switches, lock time holds in two reference clock cycles. an implementation of proposed adpll design is realized in umc 0.18 mum 1p6m cmos technology with core area of 520times530 mum2. the pll has the frequency range of 140 mhz to 1030 mhz with 22ps dco resolution
a single generative model for joint morphological segmentation and syntactic parsing. this paper presents a method for recovering 3d facial shape from single image via learning the relationship between the 2d intensity images and the 3d facial shapes. with a coupled training set, the intensity images and their corresponding facial shapes make up two vector spaces respectively. but only the correlated components in both spaces are useful for inference, so there must be embedded hidden subspaces in each space which preserve the inter-space correlation information. thus by learning the projection onto hidden subspaces based on maximum correlation criteria and optimizing the linear transform between the hidden spaces, 3d facial shape is inferred from the intensity image. the effectiveness of the method is demonstrated on both synthesized and real world data.
which words are hard to recognize? prosodic, lexical, and disfluency factors that increase asr error rates. measurement of visual quality is of fundamental importance for numerous video processing applications. in this paper, we proposed a full-reference color video quality assessment based on quaternion singular value decomposition(qsvd), which takes into account the luminance, chrominance and gradient information of each pixel and treats them as a whole. the algorithm is tested on the video quality experts group (vqeg) phase i fr-tv test dataset. experiments show that it has good correlation with perceived video quality.
classification of semantic relationships between nominals using pattern clusters. we study the application of rissanen's (1989) principle of minimum description length (mdl) to the problem of wavelet denoising and compression for natural images. after making a connection between thresholding and model selection, we derive an mdl criterion based on a laplacian model for noiseless wavelet coefficients. we find that this approach leads to an adaptive thresholding rule. while achieving mean-squared-error performance comparable with other popular thresholding schemes, the mdl procedure tends to keep far fewer coefficients. from this property, we demonstrate that our method is an excellent tool for simultaneous denoising and compression. we make this claim precise by analyzing mdl thresholding in two optimality frameworks; one in which we measure rate and distortion based on quantized coefficients and one in which we do not quantize, but instead record rate simply as the number of nonzero coefficients
an unsupervised approach to biography production using wikipedia. in this paper, we propose a set of human perceptual cues used jointly to automatically detect image orientation. the cues used are: orientation of faces, position of the sky, brighter regions, and textured objects, and symmetry. we combine these cues in a bayesian framework, and the photo acquiring model has been considered carefully as the prior knowledge of the image orientation. results on more than a thousand different images provide a compelling argument that our approach is a viable one.
unsupervised learning of narrative event chains. we have developed a software system, phi-psi, on the connection machine that uses a parallel algorithm to retrieve and use information from a database of 112 known protein structures (selected from the brookhaven protein databank) to predict the structures of other proteins. the &#x03c6; and angles of each amino acid (the angles each amino acid forms with its immediate neighbors) in a protein are used to represent its 3-d structure. phi-psi's algorithm is based on the idea of memory-based reasoning (mbr) [10] and extends it to include a recursive procedure to refine its initial prediction and a &#x0201c;window&#x0201d; of varying sizes to look at different contexts of an input. phi-psi has been tested with all the available data. initial results show that it performs better than distribution-based guesses for most of the &#x03c6; and angle values.
chinese-english backward transliteration assisted with mining monolingual web pages. facial expression animation considering wrinkle formation is an aspiring goal and a challenging task. this paper presents a new geometric wrinkle model that is defined according to facial muscle anatomy for efficient simulation of dynamic wrinkles within expressions. our method is applied to an anatomy-based face model with a multi-layer structure of skin, muscles, and skull. the location and orientation of the wrinkles are automatically determined based on muscle contraction and its influence on the skin. corresponding to two types of facial muscles, the geometric wrinkle model governs evolution of wrinkle amplitude in the local deformed face regions. it provides intuitive parameters for easy control over wrinkle characteristics by taking into account the properties of real wrinkles. results show that this method enables realistic wrinkles synchronized with facial movements to be dynamically simulated and rendered at an interactive rate.
simple semi-supervised dependency parsing. we propose xseed, a synopsis of path queries for cardinality estimation that is accurate, robust, efficient, and adaptive to memory budgets. xseed starts from a very small kernel, and then incrementally updates information of the synopsis. with such an incremental construction, a synopsis structure can be dynamically configured to accommodate different memory budgets. cardinality estimation based on xseed can be performed very efficiently and accurately. extensive experiments on both synthetic and real data sets show that even with less memory, xseed could achieve accuracy that is an order of magnitude better than that of other synopsis structures. the cardinality estimation time is under 2% of the actual querying time for a wide range of queries in all test cases.
generating impact-based summaries for scientific literature. the essence of counter inference is intelligence competition and critical decision making that will result in chained reactions in an open system environment. intelligent agents are capable not only of carrying out negotiations but also playing games. a negotiation could have win-win (or lose-lose) outcomes while a game may result in win-lose situations. the paper proposes counter inference as a unified reasoning paradigm to allow intelligent agents to execute reasoning tasks in either cooperative or non-cooperative ways. based on logic theory, we discuss a framework of counter inference. some examples of counter inference are also given to illustrate its reasoning scenario
learning to rank answers on large online qa collections. in a typical manufacturing system, jobs are released from a production planning stage to a shop floor, where they are allocated to resources like machines. an optimal or near optimal schedule is generally found for those jobs. in reality, the execution of this schedule is often interrupted by dynamic events like unexpected incoming new jobs, machine breakdowns, etc. a rapid recovery or a self-organized feature of the interrupted schedule is desirable for manufacturing resource management. this paper proposes an ant-colony inspired approach to recover the interrupted schedule through a self-organization mechanism. the experiments show that this mechanism significantly improves the quality of the recovered schedules.
forest reranking: discriminative parsing with non-local features. this paper proposes a novel approach for multi-view multi-pose object detection using discriminative shapebased exemplars. the key idea underlying this method is motivated by numerous previous observations that manually clustering multi-view multi-pose training data into different categories and then combining the separately trained two-class classifiers greatly improved the detection performance. a novel computational framework is proposed to unify different processes of categorization, training individual classifier for each intra-class category, and training a strong classifier combining the individual classifiers. the individual processes employ a single objective function that is optimized using two nested adaboost loops. the outer adaboost loop is used to select discriminative exemplars and the inner adaboost is used to select discriminative features on the selected exemplars. the proposed approach replaces the manual time-consuming process of exemplar selection as well as addresses the problem of labeling ambiguity inherent in this process. also, our approach fully complies with the standard adaboost-based object detection framework in terms of real-time implementation. experiments on multi-view multi-pose people and vehicle data demonstrate the efficacy of the proposed approach.
task-oriented evaluation of syntactic parsers and their representations. the pervasiveness of computer networks in our economic system has increased our vulnerability to systems-based attacks. these attacks are addressed in this minitrack, which covers issues related to anticipating, detecting, mitigating and preventing them. in particular, the minitrack focuses on (1) cyber-threats and information security, which includes assaults on computer systems themselves as well as fraud or other actions taken through the use of computers, (2) emergent risks in operations, which comprise heretofore-underestimated risks from the introduction of technology and technology-based infrastructure, (3) compliance and prevention, (4) information sharing (public regulation or private, confidential information pooling of risks and disclosure might be an interesting option), and (5) modeling and theory building of security topics.
evaluating roget's thesauri. majority-logic decoding is attractive for three reasons: (1) it can be simply implemented; (2) the decoding delay is short; and (3) its performance, while suboptimal, is always superior to bounded distance decoding. for these reasons, majority-logic decodable cyclic codes are very suitable for error control in high speed digital data transmission systems. among the majority-logic decodable codes, the one-step decodable codes can be most easily implemented; they employ a single majority-logic gate. in this paper we study a class of one-step majority-logic decodable cyclic codes. first, we describe these codes in a simple manner. second, a way of finding the orthogonal polynomials for decoding these codes is presented. third, we show that for a given error correction capability, the ratio of the number of parity digits to the code length goes to zero as the code length increases. for error correction capabilities of the form 2k-1 or 2k, we determine the dimensions of the codes exactly.
automatic editing in a back-end speech-to-text system. in this paper, we propose an approach for body detection and tracking with a hierarchical and salient-half-body-prior scheme and apply it in real dynamic scenes. the human region is firstly located by an svm classifier with histogram of oriented gradients (hog). two image observations, motion foreground and edge distance map, are then applied for body detection. in detail, the torso part is first roughly initialized, and then parts are localized in a salient-prior half-body sampling scheme by maximizing body configuration probability with both parts likelihood and body geometry constraints. for body tracking, the color-texture appearance templates of parts extracted in detection phase are further used in tracking process. the approach has been tested on soccer and skating videos and got good results.
generalized expectation criteria for semi-supervised learning of conditional random fields. service oriented architecture (soa), web services, asynchronous javascript and xml (ajax), and other new web technologies have revolutionized the use of geospatial web applications. the geobrain project has built a powerful online extensible and scalable geospatial analysis system based on soa. this system utilizes web services and ajax to increase the interactive capabilities of user interfaces and improve the user experience. it provides geosciences community a highly interoperable way of accessing open geospatial consortium (ogc) web services for geospatial data discovery, retrieval, visualization and analysis. it also leverages web service standards to enable geospatial services discovery, selection, negotiation and invocation to be used in making more informed decisions.
unsupervised discovery of generic relationships using pattern clusters and its evaluation by automatically generated sat analogy questions. in this paper, the generalized gaussian distribution is employed first to model the dct coefficients of image data from mpeg-4 fine-granularity scalability (fgs) frame. then, according to the quantization theory, the distortion-rate function of the generalized gaussian model is analyzed and it is concluded that the derivative of the distortion-rate function first decreases, and then increases up to the boundary of 6.02 as the bit rate increases. for actual fgs coding, the derivative of actual distortion-rate function usually decreases as the rate increases, and then begins to increase slowly at a comparatively high bit rate. finally, based on above observations, a rate-distortion (r-d) model is proposed to approximate the actual distortion-rate function. experiments show that the proposed r-d model is accurate and flexible.
gestural cohesion for topic segmentation. this paper presents middleware enabling mutual and equal transfer of computing power between individuals, as in the original idea behind p2p, while also supporting large-scale distributed computation utilizing heterogeneous pcs. this goal is strongly supported by a network overlay over which peers can communicate with each other directly and bidirectionally. we made use of a general-purpose p2p library, jxta, supporting the common requirements of p2p software, including network overlay. other features of the p2p library, such as ad-hoc self-organization, discovery and grouping of peers, also support our middleware efficiently. in this paper, we propose and evaluate an application of those p2p concepts to virtual resource transfer and parallel computation with aggregated resources. however, such a p2p library imposes a certain amount of overhead on the middleware in terms of communication performance. measured communication performance and throughput of an application program shows the feasibility of the application of p2p concepts. the middleware achieves 100 &times; 106 bps communication performance and over a 20 fold increase in speed with 32 computers, even though the granularity of workunits is as fine as less than a second.
generalizing word lattice translation. the authors study the performance of online algorithms in environments where no value is obtained for the partial execution of a request. they prove that no online scheduling algorithm can have a competitive factor greater than 0.25 times the optimal. they further refine this bound by considering the effect of the loading factor. other models of task systems (for example, tasks systems consisting of many types of task requests), are considered. similar upper bounds on the competitive factor that can be made by online scheduling algorithms in these environments are proved. it is shown that the performance bound of 0.25 is tight by means of a simple online uniprocessor scheduling algorithm has a competitive factor of 1/4. the authors extend the discussion to systems with dual processors. they show that the upper bound for the dual-processor online scheduling problem is 1/2 if all tasks have the same value density. this bound is tight if the tasks all also have zero laxity
unsupervised multilingual learning for morphological segmentation. modeling background and segmenting moving objects are significant techniques for video surveillance and other video processing applications. most existing methods of modeling background and segmenting moving objects mainly operate in the spatial domain at pixel level. in this paper, we present three new algorithms (running average, median, mixture of gaussians) modeling background directly from compressed video, and a two-stage segmentation approach based on the proposed background models. the proposed methods utilize discrete cosine transform (dct) coefficients (including ac coefficients) at block level to represent background, and adapt the background by updating dct coefficients. the proposed segmentation approach can extract foreground objects with pixel accuracy through a two-stage process. first a new background subtraction technique in the dct domain is exploited to identify the block regions fully or partially occupied by foreground objects, and then pixels from these foreground blocks are further classified in the spatial domain. the experimental results show the proposed background modeling algorithms can achieve comparable accuracy to their counterparts in the spatial domain, and the associated segmentation scheme can visually generate good segmentation results with efficient computation. for instance, the computational cost of the proposed median and mog algorithms are only 40.4% and 20.6% of their counterparts in the spatial domain for background construction.
assessing dialog system user simulation evaluation measures using human judges. the existing network security assessment models have the problems of inadequate capacity of quantitative analysis and lacking for vulnerabilities correlation. to address these problems, a hierarchical network security evaluation model is proposed. the network is divided into vulnerability level, service level, equipment level and network level. the model uses attack graph to correlate the network vulnerabilities, and then calculates the probabilities of successfully exploiting the vulnerabilities. on this basis, the quantitative risks of each level are calculated. since this model much more accords with the features of network structure, it is an effectively guidance for the network administrators to develop and improve the network security policies.
searching questions by identifying question topic and question focus. the two fundamental advantages of video over still imagery are: (i) the ability capture temporal information, and (ii) the ability to acquire a continuously varying set of views of a scene. these advantages are obtained, however, at the cost of vastly increased amount of data. this paper describes an approach to video representation that is based on frame-to-frame alignment, mosaic construction, and 3d parallax recovery. the basic motivation behind our approach is to enable rapid access to the contents, while maintaining the data in a form as close to the source as possible. this representation supports a wide variety of applications that involve transmission, storage, visualization, retrieval, analysis, and manipulation of video sequences