Miracle-dz/UniFAR-FTUs huggingface dataset:
Language Universals and Linguistic Typology: Syntax and Morphology

Since its first publication, "Language Universals and Linguistic Typology" has become established as the leading introductory account of one of the most productive areas of linguistics-the analysis, comparison, and classification of the common features and forms of the organization of languages. Adopting an approach to the subject pioneered by Greenberg and others, Bernard Comrie is particularly concerned with syntactico-semantic universals, devoting chapters to word order, case making, relative clauses, and causative constructions. His book is informed throughout by the conviction that an exemplary account of universal properties of human language cannot restrict itself to purely formal aspects, nor focus on analysis of a single language. Rather, it must also consider language use, relate formal properties to testable claims about cognition and cognitive development, and treat data from a wide range of languages. This second edition has been revised and updated to take full account of new research in universals and typology in the past decade, and more generally to consider how the approach advocated here relates to recent advances in generative grammatical theory.
Machine Tongues X: Constraint Languages

Before long, programming as we now know it will be a rare activity. Even for builders of complex software systems, manually constructing an algorithm-describing a sequence of steps for a computer to perform in achieving a single result-will be uncommon. Current methods of programming, in which programmers spend their time writing algorithms and debugging flawed realizations of them, will give way to more powerful tools that foster system design and perform most coding tasks automatically. Constraint languages and programming environments will help the designer produce a lucid description of a problem domain, and then of particular situations and problems in it. Early versions of these languages were given descriptions of "real world" domain constraints, like the operation of electrical and mechanical parts. More recently, I have automated a vocabulary for describing musical jazz phrases, using constraint language as a jazz improviser. General constraint languages will handle all of these domains. Once the model is in place, the system will connect built-in code fragments and algorithms to answer questions about situations; that is, to help solve problems. Bugs will surface not in "code," but in designs themselves. Debugging systems will explain why a situation is impossible or underconstrained; and, as always, designers will decide that their ideas were flawed and change them. Not every programmer shares this vision or sees it as a realistic goal. But since the early 1960s, computer scientists have demonstrated increasingly powerful and flexible versions: constraint languages for assistance in describing and solving problems, rather than programming. From Ivan Sutherland's original SketchPad system to Software Arts's recently introduced TK! Solver, constraint languages have presented a seductive alternative to the conventional, algorithmic style. This article describes the principles behind constraint languages; their applications, especially to musical problems; and research that will some day make constraint languages more general-purpose and efficient.
The holographic fuzzy learning for credit scoring

The holographic fuzzy classifier has been developed. Stimulus symmetrization based on optimal transformation has been implemented as a set of C functions. Stimulus-response associations are both learned and expressed in one noniterative mapping using holographic network. An initial network determines the most influential fields of input vectors. The reduced vectors are expanded to higher order statistics prior to input to the cortex cell. The classifier deals with financial, biomedical, power and geophysical problems. The loan and credit scoring application is described. Two real-world financial databases have been analyzed, one from America and the other from Europe. Classification in terms of good vs bad clients has been performed. It leads to the elimination of 88% of financial losses while reducing the good client prospective by only 13%.
ISAR target recognition based on manifold learning

In this paper, the idea of manifold learning is introduced into inverse synthetic aperture radar (ISAR) target recognition, a new method based on locality preserving projections (LPP) algorithm and k-nearest neighbour classification for ISAR target recognition is proposed. Firstly, the LPP algorithm is used to reduce the dimension of ISAR image, and then three kinds of aircraft target are classified by k-nearest neighbour classification in the low-dimensional subspace. The simulated experimental results suggest that the proposed method has the capability of finding the low-dimensional manifold structure embedded in the high-dimensional ISAR image space controlled by few parameters, such as attitude angle, scale and position, etc., and better classification performance is acquired comparing to PCA and LDA. (4 pages)
Partial Sparse Channel Estimation Based on Optimal SL0 Algorithm

Abstract In this paper we establish the partial sparse channel and make the channel estimation by optimal SL0 algorithm. In the channel estimation, derive the theoretical model of compressed sensing and verify the feasibility of channel estimation algorithm. The model can reconstruct the original signal more accurately and suppress the interference more effectively. The simulation and analytical results show that optimal SL0 algorithm is better than other algorithms in MSE, reconstruction precision, matching degree of signal and the compute complexity, thereby validating the proposed algorithm and channel model in wireless communications.
BIOLOGICAL CONSTRAINTS ON CONNECTIONIST MODELLING

Many researchers interested in connectionist models accept that such models are "neurally inspired" but do not worry too much about whether their models are biologically realistic. While such a position may be perfectly justifiable, the present paper attempts to illustrate how biological information can be used to constrain connectionist models. Two particular areas are discussed. The first section deals with visual information processing in the primate and human visual system. It is argued that speed with which visual information is processed imposes major constraints on the architecture and operation of the visual system. In particular, it seems that a great deal of processing must depend on a single bottum-up pass. The second section deals with biological aspects of learning algorithms. It is argued that although there is good evidence for certain coactivation related synaptic modification schemes, other learning mechanisms, including back-propagation, are not currently supported by experimental data.
A Domain Ontology-Based Knowledge Organization Model for Complex Product Design

Complex product design is a knowledge-intensive process that involves various aspects of technical knowledge and experience. This paper contends that the conventional Product Data Management (PDM) system has some inherent limitations due to its original intention to manage processes and results, not to manage and reuse knowledge. Therefore, to overcome the shortage that knowledge cannot be discovered and reused effectively in PDM systems, a “Domain Ontology - Index Knowledge – Data Resource” (OKD) three-layer mapping model is proposed to organize DK. The key idea behind our approach is a flexible ontology-based schema that domain ontology is used as semantic model and index knowledge as data indexing to enable the capture and reuse of problem-solving knowledge to improve PDM’s performance. Finally, conclusion is made.
A numerical study of the effect of sea breeze circulation on photochemical pollution over a highly industrialized peninsula

Numerical simulations compared with measurements are used to investigate the effect of sea breeze circulation on the ozone accumulation over a highly industrialized peninsula in southern Italy, where high levels of ozone concentration are often registered. A frequent meteorological phenomenon in this region during weak summer synoptic conditions is the development of complex sea breeze systems from the coastlines, with convergence areas within the peninsula. A case study characterized by strong winds alternating with sea breeze circulations was selected. The simulations show that during weak synoptic conditions, sea breezes transport ozone and its precursors over land from the sea, as well as from the coastlines where the largest industrialized districts are localized. The overlapping breezes lead to ozone accumulation in the area where sea breeze convergence occurs. This may explain the high values of ozone registered close to the sea breeze convergence lines. The comparison between predictions and experimental data indicates that the numerical system successfully reproduces both weather and ground level ozone concentration in different meteorological conditions, resulting in a fundamental tool for both scientific comprehension of the evolution of air contaminants and interpretation of the monitoring data. Copyright © 2009 Royal Meteorological Society
Preliminary results of a Chinese address segmentation algorithm based on self-organizing neural network

In this paper we present preliminary results and lessons learned in an effort to design an automatic Chinese address segmentation algorithm using self-organizing neural network (SONN). The SONN design is composed of an input layer and an output layer, fully connected with random initial weights. Unparsed addresses are transformed into activation patterns on the input layer by activating corresponding neurons simultaneously. The training process is controlled by the Hebbian learning rule and the "k winners take all" (KWTA) competing function. After the self-organization process, each unparsed address will be represented by k (multiple) winners with each winner representing a sub-pattern of the address. The goal of the algorithm is to make these sub-patterns correspond to the word segments of the addresses. Two tests were carried out using a prototype system implementing the algorithm. The results show that the current design can achieve the purpose conditionally and we will discuss the limitations of the SONN for actual applications and the possible improvements of the model will be also discussed.
The effect of weather conditions on the Oxford–Cambridge University Boat Race

The sensitivity of Oxford–Cambridge University Boat Race finishing times to changing weather conditions was assessed over the period 1949–2006. Predictors of race times included tide height and river discharge as well as standard weather variables (temperature, humidity, wind speed and wind direction). As the Race is rowed against the flow of the River Thames, quicker races are favoured when a low river discharge is combined with a high tide and a SE wind. Wind speed and direction exert a much greater influence on race times than temperature and humidity. The zonal (W–E) wind speed explained 28.5% of the year‐to‐year variations in finishing times between 1967 and 2005, with races taking place under a westerly wind being on average 1 min and 27 s slower than races rowed under easterly conditions. The combined effects of the wind, river discharge and tide height accounted for 42.9% of the inter‐annual variance in race times over a 39 year period. Races rowed under cooler conditions tended to be slightly slower. The results are discussed in the context of the biometeorological literature and the hydrodynamics of the Boat Race course. Copyright © 2008 Royal Meteorological Society
Intra-Frame Image Coding By Adaptive Vector Quantization With Variable Block Sizes

Vector quantization is a new coding technique which has enjoyed much success in its brief history. Its most attractive features are a high compression ratio and a simple decoder. Thus, it shows great promise in applications using a single-encoder with a multiple decoder such as videotext and archiving. However, some problems have arisen, most notably edge degradation, the so-called "block effect". A novel method is described in this paper which alleviated this problem without much increase in computational effort. An index has been devised, the activity index, based upon measurements on the input image data; it is used to classify image areas into two groups, active and nonactive. For nonactive areas, large block size is used, while for active areas the block size is small. Two codebooks are generated corresponding to each of the two groups of blocks formed. Using this adaptive vector quantization scheme the results obtained show that the edge features are well preserved upon reconstruction at the decoder.
Extension of an Adaptive Distributed Routing Algorithm to Mixed Media Networks

By modeling the broadcast portion of a mixed media network as a fully connected point-to-point network with link capacities varying as functions of the traffic rate it is possible to extend an adaptive distributed routing algorithm that was originally developed for point-to-point ground networks. Additional modifications for improved dynamic performance at the satellite interface message processors are also considered.
Use of the knowledge management framework as a tool for innovation capability audit

As effective innovation management needs to balance efficient coordination and the proper spending of limited resources on the one hand and creative chaos on the other hand, it is of critical importance to understand the whole structure of relationships and implications of processes involved. Success of innovation processes in relation to knowledge management has become a popular research topic in the last decade. In this way, we learned that early economic-oriented insights about importance of Ramp;D expenditure are not enough to explain the difference between innovation success and failure. It was found that more important than R&D expenditure are different factors such as people, information and communication technology, knowledge management processes, culture, organisation structure, management systems, process of assets allocation. As theoretical and empirical work in knowledge and innovation management has brought a large number of sub-theories and fragmented solutions, it is difficult for an organisation to know how to put all the key success factors together. For this reason, I propose a holistic knowledge management framework, which in a systemic way includes and connects all relevant organisation elements and its sub-dimensions in short, middle, and long time frame. Based on this, I then developed an innovation capability audit model and a related self-assessment questionnaire.
Real time recurrent neural networks for time series prediction and confidence estimation

This paper explores two established techniques for doing time series modeling and prediction of mean and variance. The first method is an explicit method used to establish the embedding dimension of the time series, define the sensitivity of each variable and come up with a systematic decision of the delay of inputs for future prediction. The second method makes use of recurrent networks to implicitly derive models with "adaptive" time delays for the mean and variance predictions of a given time series. The recurrent system gives better prediction performance on artificial chaotic signals as well as real world exchange rate data in terms of mean squared error criterion and requires no laborious determination of the number of inputs.
Anthony McEnery and Zhonghua Xiao. Help or Help to: What do Corpora have to Say?

1 HELP is a frequent verb of English, with a distinctive syntax, that has generated ongoing debate amongst language researchers. As such it is a verb that is often given some prominence in textbooks and grammars (e. Leech & Svartvik 1972: 841). For example, all of the authors who provide a poorer account of HELP maintain the choice of a full or bare infinitive after HELP is determined by a semantic distinction between the two – this is not the case (cf. section 4). In this paper we will take a corpus-based approach to improve the description of the verb and to test claims made about the verb in the literature. We will also explore variation in that description between two major varieties of English, British English (BrE) and American English (AmE). In addition, we will investigate how HELP has varied diachronically and by register in these varieties. First, however, the claim that HELP is a frequent verb of English with distinctive syntactic properties must be justified. HELP is one of the most frequent words in the English language, ranking as 245 th in the word frequency list of the British National Corpus (BNC). 2 When its inflected forms helped, helps and helping are included, there are 528.62 instances of HELP per million words. When we look at the most frequent verbs (lemmatized) in the BNC, HELP rises to 72 nd in the word frequency list. 2 Furthermore, HELP is the only verb that can both control either a full infinitive or a bare infinitive and occur either with or without an intervening noun phrase (NP), 3 as in the following examples cited from the BNC: (1) (a) HELP to V Perhaps the book helped to prevent things from getting even worse. (b) HELP NP to V I thought I could help him to forget. (c) HELP V Savings can help finance other Community projects. (d) HELP NP V We helped him get to his feet and into a chair. In this paper, we will examine the factors that may potentially influence a language user's choice of a full infinitive or a bare infinitive as the object or object complement of HELP. 4 Our work is based on the relative frequencies of HELP in six corpora, as shown in Figure 1. All of these corpora are used to explore the potential syntactic and semantic conditions that may be relevant to …
Logging based Coordinated Checkpointing in Mobile Distributed Computing Systems

Checkpointing is an efficient way of implementing fault tolerance in distributed systems. Mobile computing raises many new issues, such as high mobility, lack of stable storage on mobile hosts (MHs), low bandwidth of wireless channels, limited battery life and disconnections that make the traditional checkpointing protocols unsuitable to checkpoint such systems. Checkpointing can be independent, synchronous, quasi-synchronous, or message logging based. In synchronous checkpointing, all or interacting processes need to checkpoint synchronously, extra synchronization messages are sent, some information may be piggybacked onto computation messages, blocking of processes may take place, and in case of fault, all processes are forced to rollback. It becomes difficult for multiple MHs to checkpoint synchronously due to disconnections and unreliable wireless channels. MHs are prone to frequent failures, which will require frequent rollback of all processes. In this paper, we propose a hybrid non-intrusive checkpointing protocol, where fixed hosts checkpoint synchronously and MHs checkpoint independently. The proposed scheme gives MHs autonomy in taking checkpoints and reduces the information to be piggybacked onto computation messages. An MH can recover independently by using its recent checkpoint and message log without forcing other nodes to rollback.
A 64-bit orthorectification algorithm using fixed-point arithmetic

As the cost of imaging systems have decreased, the quality and size has increased. This dynamic has made the practicality of many aerial imaging applications achievable such as cost line monitoring and vegetation indexing. Orthorectification is required for many of these applications; however, it is also expensive, computationally. The computational cost is due to oating point operations and divisions inherent in the orthorecti cation process. Two novel algorithm modi cations are proposed which signi cantly reduce the computational cost. The rst modi cation uses xed-point arithmetic in place of the oating point operations. The second replaces the division with a multiplication of the inverse. The result in an increase of 2x of the throughput while remaining within 15% of a pixel size in position.
A 98 GMACs/W 32-core vector processor in 65nm CMOS

This paper presents a high-performance dual-issue 32-core SIMD platform for image and video processing. Eight cores with a 4-ports L2 cache are connected by CIB bus as a cluster. Four clusters are connected by mesh network. The proposed hierarchical network can provide 192 GB/sintercore communication BW in average. To reduce coherence operation in large-scale SMP, an application specified protocol is proposed. Comparing with MOESI, 67.8% of L1 Cache energy can be saved in 32 cores case. It can achieve a peak performance of 375 GMACs and 98 GMACs/W in 65 nm CMOS.
Adaptive Skill Rehearsal and Experimentation Environment for Battle field First-Aid Procedure Training

Pictorial mnemonic devices represent a promising approach to improving the efficiency and efficacy of procedural training in comparison to traditional, text-based materials. However, simply trading static, textual depictions of process flows with static, visual depictions will not support the development of the rich knowledge structures necessary for procedural recall. In this paper, we describe our ongoing effort to design dynamic, adaptive microgames for battlefield first-aid training that incorporate pictorial mnemonic devices. This effort builds upon prior pictorial mnemonic development work to incorporate structured visual imagery within an adaptive training and experimentation environment. Examples from the training microgame suite developed under this effort are presented and a web-based component for conducting longitudinal training experiments is discussed. This work sets the stage for a follow-on effort to evaluate the performance benefits of adaptive, pictorial mnemonic-based training methods in the battlefield first-aid domain. It is our hope that this example will aid others in the development of game-based training materials for other procedural skill domains.
Comparisonoftheclosed-loopidenti!cation methods intermsofthebiasdistribution

Several schemes for plant model identi! cation in closed-loop operation including classical direct method, two-step identi! cation and closed-loop output error algorithms are considered. These methods are analyzed and compared in terms of the bias distribution of the estimates for the case that the noise model is estimated as well as the case that a ! xed model of noise is considered (output error structure). The problems concerning the ! ltered direct method which is often used in the iterative identi! cation and control scheme are mentioned. It is shown that these problems may be solved by the closed-loop output error identi! cation method. c ! 1998 Elsevier Science B.V. All rights reserved.
International Conference on Future Information Engineering Improve Bayesian Network to Generating Vietnamese Sentence Reduction

Sentence reduction is one of approaches for text summarization that has been attracted many researchers and scholars of natural language processing field. In this paper, we present a method that generates sentence reduction and applying in Vietnamese text summarization using Bayesian Network model. Bayesian network model is used to find the best likelihood short sentence through compare difference of probability. Experimental results with 980 sentences, show that our method really effectively in generating sentence reduction that understandable, readable and exactly grammar. © 2014. Published by Elsevier B.V. Selection and peer review under responsibility of Information Engineering Research Institute