waashk/dblp huggingface dataset:
semantic role labeling systems for arabic using kernel methods. the design and evaluation of microprocessor architectures is a difficult and time-consuming task. although small, hand-coded microbenchmarks can be used to accelerate performance evaluation, these programs lack the complexity to stress increasingly complex architecture designs. larger and more complex real-world workloads should be employed to measure the performance of a given design or to evaluate the efficiency of various design alternatives. these applications can take days or weeks if run to completion on a detailed architecture simulator. in the past, researchers have applied machine learning and statistical sampling methods to reduce the average number of instructions required for detailed simulation. others have proposed statistical simulation and workload synthesis techniques, which can produce programs that emulate the execution characteristics of the application from which they are derived but have a much shorter execution period than the original. however, these existing methods are difficult to apply to multi-threaded programs and can result in simplifications that miss the complex interactions between multiple, concurrently running threads. this study focuses on developing new techniques for accurate and effective multi-threaded workload synthesis, which can significantly accelerate architecture design evaluation of multi-core processors. we propose to construct synchronized statistical flow graphs that incorporate inter-thread synchronization and sharing behavior to capture the complex characteristics and interactions of multiple threads. moreover, we develop thread-aware data reference models and wavelet-based branching models to generate accurate memory access and dynamic branch statistics. experimental results show that a framework integrated with the aforementioned models can automatically generate synthetic programs that maintain characteristics of original workloads but have significantly reduced runtime.
contradictions and justifications: extensions to the textual entailment task. an extensive simulation study of various combinations of resistive bridges and crosstalk has been performed and several notable properties that have significant implications for test development have been discovered. scenarios have been identified where a combination of a bridge at one site and a crosstalk at a separate site in its transitive fanout (or vice versa) can cause slowdown/speed-up whose magnitude significantly exceeds the sum of the slow-down/speed-up, caused by each effect in isolation. it has also been identified that a test vector generated for crosstalk may in fact be invalidated due to the presence of a weak bridge at the crosstalk site. the properties discovered, provide the motivation for a more analytical study that will eventually lead to the proposed framework for test development.
can you summarize this? identifying correlates of input difficulty for multi-document summarization. a novel adaptive smoothing approach is proposed for noise removal and feature preservation where two distinct measures are simultaneously adopted to detect discontinuities in an image. inhomogeneity underlying an image is employed as a multiscale measure to detect contextual discontinuities for feature preservation and control of the smoothing speed, while local spatial gradient is used for detection of variable local discontinuities during smoothing. unlike previous adaptive smoothing approaches, two discontinuity measures are combined in our algorithm for synergy in preserving nontrivial features, which leads to a constrained anisotropic diffusion process that inhomogeneity offers intrinsic constraints for selective smoothing. thanks to the use of intrinsic constraints, our smoothing scheme is insensitive to termination times and the resultant images in a wide range of iterations are applicable to achieve nearly identical results for various early vision tasks. our algorithm is formally analyzed and related to anisotropic diffusion. comparative results indicate that our algorithm yields favorable smoothing results, and its application in extraction of hydrographic objects demonstrates its usefulness as a tool for early vision.
solving relational similarity problems using the web as a corpus. many asset tracking applications demand long-lived, low-cost, and continuous monitoring of a large number of items, which has posed a significant challenge to today's rfid design. in order to satisfy these requirements, we propose to adopt transmit-only tags without a receiver, which can offer both low power and low cost. in spite of their great potential, such a platform faces many challenges since it cannot sense the channel, causing the collisions among tag transmissions to be high. it is thus crucial to employ effective multi-user detection schemes at the tag reader to extract valid information from collided signals. traditional detection schemes, such as successive cancellation, cannot be directly applied to the targeted system. firstly, due to the simplicity of receiver-less transmit-only tags, there is no mechanism for feedback to the tags that is traditionally needed for accurate multi-user detection. more importantly, these schemes impose serious processing and memory requirements on the underlying system, which makes real-time tracking impossible. in this study, we address these challenges by performing a statistical estimation of the signal amplitude, and by dividing the received signal sequence (from all the tags) and assigning each block to one reader. we also adopt an online learning mechanism so that readers can anticipate the tags that belong to them. we show that the proposed detection algorithm can achieve low detection error under realistic system conditions.
a cascaded linear model for joint chinese word segmentation and part-of-speech tagging. traditional reversible cellular automata (rca) is fit for cryptography for its rules being an affine function, i.e. one reversible ca's rule can be applied in encryption process while another counterpart rule can be applied in decryption process. however, there is few number of traditional rca; for example, there are only six reversible rules in elementary ca. this property caused it cannot meet large key-space of cryptography. in this paper, an extended rca is proposed aimed to be applied in cryptography. analysis indicates that the extended rca can be obtained by a pair of complementary traditional rule. furthermore, in order to improve the complexity of ca's dynamics, which is a crux in cryptography, traditional ca model is replaced by multi-granularity cellular automata (mgca). in mgca, different granularity cells have their own rules. based on mgca and rca a cryptography algorithm that is proposed which called mgrca. in mgrca, cells have different granularity and can adjust their granularity dynamically by "split-recombination" behavior during the process of encryption and decryption. unlike des, the length of block in mgrca can be adjusted by user. the analysis results show that the cryptosystem can resist brute attack and differential attack, and also has high security. the hardware that shared by encryption and decryption caused the cryptosystem has a strong practicability.
ad hoc treebank structures. in this paper, we propose a robust approach for recognition of text embedded in natural scenes. instead of using binary information as most other ocr systems do, we extract features from intensity of an image directly. we utilize a local intensity normalization method to effectively handle lighting variations. we then employ gabor transform to obtain local features, and use the linear discriminant analysis (lda) for selection and classification of features. the proposed method has been applied to a chinese sign recognition task. the system can recognize a vocabulary of 3755 level i chinese characters in the chinese national standard character set gb2312-80 with various print fonts. we tested the system on 1630 test characters in sign images captured from the natural scenes, and the recognition accuracy was 92.46%. we have integrated the system into our automatic chinese sign translation system.
large scale acquisition of paraphrases for learning surface patterns. in recent years, inspired by the emerging web services standard and peer-to-peer technology, a new federated service providing (fsp) system paradigm has attracted increasing research interests. many existing systems have either explicitly or implicitly followed this paradigm. instead of exchanging files, peers in fsp systems share their computation resources in order to offer domain-specific services. in this paper, we focused on the coordination problem of how to self-organize the service group structures in response to the varying service demand. we presented our solution in the form of a coordination mechanism, which includes a labor-market model, a recruiting protocol, and a policy-driven decision architecture. peers make their service providing decisions based on their local policies, which can be added, removed, or modified by users. a general methodology is introduced in this paper to facilitate policy design. specifically, a heuristic inspired by the extremal optimization technique is utilized to handle potential inconsistencies among policies. a stimulus-response mechanism was further applied to make the decision process adjustable. experiments under five application scenarios verified our ideas and demonstrated the effectiveness of our coordination mechanism.
efficient multi-pass decoding for synchronous context free grammars. there has been ongoing debate regarding the use of voltage overscaling along with error resilience techniques for ultra low power operation of scaled cmos logic. the issue is whether to build enough design margin into future electronic systems so that errors do not impact the quality of service of the end application or to allow errors to occur and correct them using error tolerance mechanisms. specific signal processing algorithms have been shown to be inherently tolerant to errors. however, large general purpose processors experience virtually zero errors under supply voltage scaling up to a certain scaling level and then exhibit &#x201c;massive errors&#x201d; or &#x201c;complete breakdown&#x201d;. the problem is made worse by the fact that low power design methodologies force devices to be sized in such a way as to make a large number of circuit paths &#x201c;critical&#x201d;. under all of the above constraints, what is the best way to build low power systems of the future using deeply scaled cmos technologies? is the use of voltage overscaling along with error resilience techniques realistic? can we allow errors to occur and compensate for them with high confidence? under what conditions will design guardbanding be absolutely necessary? if we do let errors occur periodically, will customers buy the associated products and is there a marketplace for such error-resilient ics?
weakly-supervised acquisition of open-domain classes and class attributes from web documents and query logs. the classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. in gene expression data, the number of genes is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds); that is, the data dimension is large compared to the number of data points (such data is said to be undersampled). to cope with performance and accuracy problems associated with high dimensionality, it is commonplace to apply a preprocessing step that transforms the data to a space of significantly lower dimension with limited loss of the information present in the original data. linear discriminant analysis (lda) is a well-known technique for dimension reduction and feature extraction, but it is not applicable for undersampled data due to singularity problems associated with the matrices in the underlying representation. this paper presents a dimension reduction and feature extraction scheme, called uncorrelated linear discriminant analysis (ulda), for undersampled problems and illustrates its utility on gene expression data. ulda employs the generalized singular value decomposition method to handle undersampled data and the features that it produces in the transformed space are uncorrelated, which makes it attractive for gene expression data. the properties of ulda are established rigorously and extensive experimental results on gene expression data are presented to illustrate its effectiveness in classifying tissue samples. these results provide a comparative study of various state-of-the-art classification methods on well-known gene expression data sets
using conditional random fields to extract contexts and answers of questions from online forums. middleware components are becoming increasingly important as applications share computational resources in distributed environments. one of the main challenges in such environments is to achieve scalability of concurrency control. existing concurrency protocols lack scalability. scalability enables resource sharing and computing with distributed objects in systems with a large number of nodes. we have designed and implemented a novel, scalable and filly decentralized middleware concurrency control protocol. our experiments on a linux cluster indicate that an average number of three messages is required per lock request on a system with as many as 120, which is a logarithmic asymptote. at the same time, the response time for the requests scales linearly with the increase in concurrency level. a comparison to another scalable concurrency protocol shows that our protocol results in significantly superior asymptotic savings in message overhead and response time for large number of nodes. while our approach follows the specification of general corba concurrency services for large-scale data and object repositories, the principles are applicable to any distributed concurrency services and transaction models. the results of this work impact scalability for distributed computing facilities ranging from embedded computing with distributed objects over peer-to-peer computing environments to arbitrating accesses in very large database environments.
enhancing performance of lexicalised grammars. genetic algorithms (gas) are increasingly being applied to large scale problems. the traditional mpi-based parallel gas require detailed knowledge about machine architecture. on the other hand, mapreduce is a powerful abstraction proposed by google for making scalable and fault tolerant applications. in this paper, we show how genetic algorithms can be modeled into the mapreduce model. we describe the algorithm design and implementation of gas on hadoop, an open source implementation of mapreduce. our experiments demonstrate the convergence and scalability up to 105 variable problems. adding more resources would enable us to solve even larger problems without any changes in the algorithms and implementation since we do not introduce any performance bottlenecks.
lexicalized phonotactic word segmentation. in this paper an area signal control model based on temporal planning is presented. every activity of area signal control is modeled using planning domain definition language, and then the domain model of area signal control is established. aiming at the representation problem of resource and time in the area signal control model, we extended the basic activity model by adding resource constraint and temporal constraint, and temporal planning based domain model for area signal control is established finally.
phrase chunking using entropy guided transformation learning. this paper presents a quick group search optimizer with passive congregation (qgsopc) for optimum design of pin connected structure. the algorithm is based on the quick group search optimizer (qgso). the qgsopc algorithm is verified and compared with the hpso, gso and qgso algorithm used for the designs of two planar and spatial truss structures. the results show that the qgsopc algorithm not only has preferable convergence rate and accuracy, but also it has the best stability of convergence rate and accuracy. it is desired for qgsopc to be used more effectively for practical structural optimal design problems.
summarizing emails with conversational cohesion and subjectivity. chip-multiprocessors (cmps) have been revealed as the most promising way of making efficient use of current improvements in integration scale. nowadays, commercial cmp releases integrate at most 8 processor cores onto the chip. however, 16 or more processor cores are expected to be offered in near future dense-cmp (d-cmp) systems. in this way, these architectures impose new design restrictions, and some topics, such as the cache-coherence problem, must be reviewed. in this paper we present an exhaustive performance evaluation of two recently proposed d-cmp architectures, making special emphasis on the solution to the cache-coherence problem that each one of them introduces. the shared bus fabric architecture (sbf) features a snoop cache-coherence protocol and is based on a high-performance bus fabric interconnection network. the second architecture follows a directory-based approach and integrates a bi-dimensional mesh as the interconnection network. our results show that the performance achieved by the sbf architecture is hard-limited by the bandwidth restrictions of the bus fabric. on the other hand, the directory-based architecture outperforms the sbf one, but presents some performance inefficiencies due to the additional indirection that the directory structure stored in the l2 cache level introduces
em can find pretty good hmm pos-taggers (when given a good start). choosing a cotton variety involves making comparisons over many trials and across an increasingly large number of available varieties. to aid in this process, a computerized tool has been developed at the texas tech university. this tool, called cotwiz, based on the cotton variety selection model, allows multiple comparisons of varieties across a number of trial locations. cotwiz based on the cotton variety selection model and uses cotton ovt data to make comparisons that are imported by the user.
grounded language modeling for automatic speech recognition of sports video. this paper investigates effective human team cooperation and incorporated the findings into multi-agent teamwork. we describe how to give agents the same cooperative capabilities, observability and proactivity, that humans use. we show how agents can use observation of the environment and of teammates' actions to estimate the teammates' beliefs without generating unnecessary messages; we also show how agents can anticipate information needs among the team members and proactively communicate the information. finally, we present two experiments that explore the effectiveness and the scalability of the use of observability and proactivity.
hypertagging: supertagging for surface realization with ccg. in inter-picture coding, block-based frequency transform is usually carried out on the predicted errors for each interblock to remove the spatial correlation among them. however, it can not always do well since the predicted errors in some inter-blocks have marginal or diagonal correlation. a good solution is to omit transform operations for the predicted errors of those inter-blocks with low correlation before quantization operation. the same phenomenon also can be observed in fine grain scalability (fgs) layer coding. in this paper, an adaptive prediction error coding method in spatial and frequency domain with lower complexity is considered for fgs layer coding. transform operation is only needed when there are non-zero reconstructed coefficients in spatially co-located block in base layer. the experimental results show that compared with fgs coding in jsvm, higher coding efficiency can be achieved with lower computational complexity at decoder since inverse transform is no longer needed for those predicted errors coded in spatial domain at encoder.
a generic sentence trimmer with crfs. in an optical wdm mesh network, different protection schemes (such as dedicated or shared protection) can be used to improve the service availability against network failures. however, in order to satisfy a connections service-availability requirement in a cost-effective and resource-efficient manner, we need a systematic mechanism to select a proper protection scheme for each connection request while provisioning the connection. in this paper, we propose to use connection availability as a metric to provide differentiated protection services in a wavelength-convertible wdm mesh network. we develop a mathematical model to analyze the availabilities of connections with different protection modes (i.e., unprotected, dedicated protected, or shared protected). in the shared-protection case, we investigate how a connection's availability is affected by backup resource sharing. the sharing might cause backup resource contention between several connections when multiple simultaneous (or overlapping) failures occur in the network. using a continuous-time markov model, we derive the conditional probability for a connection to acquire backup resources in the presence of backup resource contention. through this model, we show how the availability of a shared-protected connection can be quantitatively computed. based on the analytical model, we develop provisioning strategies for a given set of connection demands in which an appropriate, possibly different, level of protection is provided to each connection according to its predefined availability requirement, e.g., 0.999, 0.997. we propose integer linear programming (ilp) and heuristic approaches to provision the connections cost effectively while satisfying the connections' availability requirements. the effectiveness of our provisioning approaches is demonstrated through numerical examples. the proposed provisioning strategies inherently facilitate the service differentiation in optical wdm mesh networks.
bayesian learning of non-compositional phrases with synchronous parsing. with the efforts to understand protein structure, many computational approaches have been made recently. among them, the support vector machine (svm) methods have been recently applied and showed successful performance compared with other machine learning schemes. however, despite the high performance, the svm approaches suffer from the problem of understandability since it is a black-box model. to overcome this limitation, this study attempted to combine the svm with the association rule based classifier which can present the meaningful explanation about the prediction. to perform this task, a new association rule based classifier (pcpar) was devised based on the existing classifier, cpar, to handle the sequential data. pcpar creates the patterns by merging the generated rules and then classifies the sequential data based on the pattern match. the experimental result presents the following: with sequential data, the pcpar scheme shows better performance with respect to the accuracy and the number of generated patterns than cpar method whether applied alone or combined with svm. the combined scheme of svmpcpar generates more compact patterns than the combined scheme of svm with decision tree, svm dt, with similar performance. these patterns are easily understandable and biologically meaningful
adaptive language modeling for word prediction. we present the development and tuning of a topic-adapted language model for word prediction, which improves keystroke savings over a comparable baseline. we outline our plans to develop and integrate style adaptations, building on our experience in topic modeling to dynamically tune the model to both topically and stylistically relevant texts.