Miracle-dz/UniFAR-FTUs huggingface dataset:
Question Classification in English-Chinese Cross-Language Question Answering: An Integrated Genetic Algorithm and Machine Learning Approach

Question classification plays an important role in cross-language question answering (CLQA) systems, while question Informer plays a key role in enhancing question classification for factual question answering. In this paper, we propose an integrated genetic algorithm (GA) and machine learning (ML) approach for question classification in English-Chinese cross-language question answering. To enhance question informer prediction, we use a hybrid method that integrates GA and conditional random fields (CRF) to optimize feature subset selection in a CRF-based question informer prediction model. The proposed approach extends cross-language question classification by using the GA-CRF question informer feature with support vector machines (SVM). The results of evaluations on the NTCIR-6 CLQA question sets demonstrate the efficacy of the approach in improving the accuracy of question classification in English-Chinese cross-language question answering.
Preliminary validation of a romantic attachment orientation measure from the California Adult Q-Sort

ABSTRACT We present data on the preliminary validation of a measure of romantic attachment orientation from the California Adult Q-Sort (CAQ). The CAQ is found in several longitudinal data sets, and researchers can use the CAQ to answer questions about changes in romantic attachment across the lifespan. Expert raters nominated CAQ items that were characteristic of attachment anxiety and avoidance. In a sample of observers and targets, we compared ratings based on composites of these CAQ items to self- and observer-reports from a widely used scale of adult attachment. These expert-generated measures of CAQ-attachment orientation correlated highly with ECR measures of attachment orientation, suggesting that items from the CAQ can reliably measure an individual’s attachment orientation.
Pricing American Option Based on a Chebyshev Approximation of the Early Exercise Boundary

In this paper we introduce a new method of pricing an American call option by approximating its early exercise boundary based on Chebyshev polynomial functions. We implement the method to price options under the standard assumptions of the Black-Scholes model, for European options, and under stochastic volatility. For the latter, we provide an integral representation which unbundles the American call option price to Arrow-Debreu security prices. Numerical results indicate that our method is an effective alternative to other exercise boundary approximation methods under both the standard assumptions of the Black-Scholes and stochastic volatility.
Unified Performance Analysis of Mixed Radio Frequency/Free-Space Optical Dual-Hop Transmission Systems

The mixed radio frequency (RF)/free-space optical (FSO) relaying is a promising technology for coverage improvement, while there lacks unified expressions to describe its performance. In this paper, a unified performance analysis framework of a dual-hop relay system over asymmetric RF/FSO links is presented. More specifically, we consider the RF link follows generalized κ-μ or η-μ distributions, while the FSO link experiences the gamma-gamma distribution, respectively. Novel analytical expressions of the probability density function and cumulative distribution function are derived. We then capitalize on these results to provide new exact analytical expressions of the outage probability and bit error rate (BER). Furthermore, the outage probability for high signal-to-noise ratios and the BER for different modulation schemes are deduced to provide useful insights into the impact of system and channel parameters of the overall system performance. These accurate expressions are general, since they correspond to generalized fading in the RF link and account for pointing errors, atmospheric turbulence, and different modulation schemes in the FSO link. The links between derived results and previous results are presented. Finally, numerical and Monte-Carlo simulation results are provided to demonstrate the validity of the proposed unified expressions.
Automatic Opinion Analysis Based on SVM and Distance-Weighted Computing

This paper presents an SVM and distance-weighted computing based approach to analyze opinion, which includes two aspects: opinion automatic detection and opinion holder automatic extraction. We established a series related resources to opinion analysis, such as opinion-bearing verb set, orientation indicating adverb set etc. Based on these related resources, we accomplish the detection of opinions via SVM. On the basis of opinion automatic detection, we use opinion-bearing verb as a key indicator and apply distance-weighted computing method to extract the opinion holder (or holders) of the given sentence. With expanding the holders to noun phrases by pattern matching, the results of opinion holder automatic extraction are further improved.
Phosphate in early chronic kidney disease: Associations with clinical outcomes and a target to reduce cardiovascular risk

There is an intimate association between mineral and bone disorders in chronic kidney disease (CKD) and the extensive burden of cardiovascular disease (CVD) in this population. High phosphate levels in CKD have been associated with increased all‐cause mortality and cardiovascular morbidity and mortality. Observational studies have also shown a consistent relationship between serum phosphate in the normal range and all‐cause and cardiovascular mortality, left ventricular hypertrophy (LVH) and decline in renal function. Furthermore, fibroblast growth factor‐23 (FGF‐23), a phosphaturic hormone, increases very early in the course of CKD and is strongly associated with death and CVD, including LVH and vascular calcification. Few studies have addressed outcomes using interventions to reduce serum phosphate in a randomized controlled fashion; however, strategies to address cardiovascular risk in early CKD are imperative and phosphate is a potential therapeutic target. This review outlines the epidemiological and experimental evidence highlighting the relationship between excess phosphate and adverse outcomes, and discusses clinical studies required to address this problem.
A parallel and modular multi-sieving neural network architecture for constructive learning

Presents a parallel and modular multi-sieving neural network (PMSN) architecture for constructive learning. This PMSN architecture is different from existing constructive learning networks such as the cascade correlation architecture. The constructing element of the PMSNs is a compound modular network rather than a hidden unit. This compound modular network is called a sieving module (SM). In the PMSN, a complex learning task is decomposed into a set of relatively simple subtasks automatically. Each of these subtasks is solved by a corresponding individual SM and all of these SMs are processed in parallel.
Semi-supervised Support Vector Rainfall Estimation Using Satellite Images

In this paper we introduce the use of semi-supervised support vector machines for rainfall estimation using images obtained from visible and infrared NOAA satellite channels. Two experiments were performed, one involving traditional SVM and other using semi-supervised SVM (S 3 VM). The S 3 VM approach outperforms SVM in our experiments , with can be seen as a good methodology for rainfall satellite estimation, due to the large amount of unla-beled data.
Literacy and Voting Behavior: A Statistical Analysis Based on the 1985 Young Adult Literacy Survey.

This paper explores the contribution of selected literacy, demographic, and sociopolitical variables on registering to vote and on voting, using as a database a subset of the respondents to the Young Adult Literacy Survey (YALS), administered in 1985 on a stratified national sample of 21through 25-yearolds. Three sets of independent variables were explored: demographic characteristics, political participation, and literacy practices and proficiencies. When a bivariate probit regression model with sample selection was applied to these data, only the amount of hard news read (e.g., governmental affairs) and years of education were significant predictors of voting, given registration. For registration, these variables, plus ethnicity/race, television viewing, and the degree to which one keeps up with governmental affairs were all significant predictors. We then conclude that increased education as well as increased attention to hard news reading within school and adult education curricula could lead to increased propensities to vote.
Fiscal Deficits and Stock Prices in India: Empirical Evidence

The study aims at examining how fiscal deficits affect the performance of the stock market in India by using annual data from 1988–2012. The study makes use of Ng-Perron unit root tests to check the non-stationarity property of the series; the Auto Regressive Distributed Lag (ARDL) bounds test and a Vector Error Correction Model (VECM) for testing both short and long run dynamic relationships. The variance decomposition (VDC) is used to predict the exogenous shocks of the variables. The findings of the bounds test reveal that the estimated equation and the series are co-integrated. The ARDL results suggest a long run negative relationship exists between budget deficit and stock prices and do not show any significant relationship in the short run. The VECM result shows that fiscal deficits influence the stock price only in the short run. The results of the Variance Decomposition show that stock price movement in the long run is mostly explained by shocks of fiscal deficits. The study implies that the government must adopt appropriate macroeconomic policies to reduce budget deficit, which will result in stock market growth and in turn will lead to the financial development of the country.
“My School Is Where My Friends Are”: Interpreting the Drawings of Children With Cancer

ABSTRACT Thousands of children are diagnosed with cancer each year and are unable to participate in normative developmental and cultural contexts such as school. Little is known about how children with cancer perceive attending school in alternative settings, such as the home and hospital, while receiving treatment. This study coded the drawings of 10 children with cancer to explore how they perceive these school experiences; adapted from previous developmental and medical literature, drawings were analyzed for expressions across developmental domains and representations of the illness experience. It was found that participants primarily represented social perceptions of their school experiences; physical and illness-related representations were varied and often absent from the drawings. Participants perceived schooling through a social lens, suggesting the need for interventions to maximize social interactions with teachers and peers in this population. Teachers working with children with cancer should maximize socialization opportunities during hospitalization and treatment to promote continued development. In addition, policymakers and health care administrators should fund and implement interactive and educational group programs for children with cancer and apply techniques to help these students remain connected with classmates in the community even during cancer therapy.
A feature extraction method using base phrase and keyword in Chinese text

The feature extraction is the most key technology of text categorization. The word is used as the feature in the traditional text classification, and its effect for the text classification is evidence. The feature extraction method using base phrase and keyword changes the feature extraction of Chinese text from syntax and semantic further. In the first, analyzing the feature of baseNP and basedVP, and then make some words into baseNP and baseVP which accord to the rules of phrase, give WSD to other words in the finally. The paper proposes a stepwise feature extraction from word to phrase. The experiment results show that this method can perform much better than traditional feature extraction method, it can improve the text classification precision and recall.
Filter placement on a pipelined architecture

In this paper, we explore the problem of mapping filtering query services on chains of heterogeneous processors. Two important optimization criteria should be considered in such a framework. The period, which is the inverse of the throughput, measures the rate at which data sets can enter the system. The latency measures the response time of the system in order to process one single data set entirely. We provide a comprehensive set of complexity results for period and latency optimization problems, with proportional or arbitrary computation costs, and without or with communication costs. We present polynomial algorithms for problems whose dependence graph is a linear chain (hence a fixed ordering of the filtering services). For independent services, the problems are all NP-complete except latency minimization with proportional computation costs, which was shown polynomial in [6].
Context-aware search personalization with concept preference

As the size of the web is growing rapidly, a well-recognized challenge for developing web search engines is to optimize the search result towards each user's preference. In this paper, we propose and develop a new personalization framework that captures the user's preference in the form of concepts obtained by mining web search contexts. The search context consists of both the user's clickthroughs and query reformulations that satisfy some specific information need, which is able to provide more information than each individual query in a search session. We also propose a method that discovers search contexts by one-pass of raw search query log. Using the information of the search context, we develop eight strategies that derive conceptual preference judgment. A learning-to-rank approach is employed to combine the derived preference judgments and then a Context-Aware User Profile (CAUP) is created. We further employ CAUP to adapt a personalized ranking function. Experimental results demonstrate that our approach captures accurate and comprehensive user's preference and, in terms of Top-N results quality, outperforms those existing concept-based personalization approaches without using search contexts.
Borders and cytoarchitecture of the perirhinal and postrhinal cortices in the rat

Cytoarchitectonic and histochemical analyses were carried out for perirhinal areas 35 and 36 and the postrhinal cortex, providing the first detailed cytoarchitectonic study of these regions in the rat brain. The rostral perirhinal border with insular cortex is at the extreme caudal limit of the claustrum, consistent with classical definitions of insular cortex dating back to Rose ([ 1928 ] J. Psychol. Neurol. 37:467–624). The border between the perirhinal and postrhinal cortices is at the caudal limit of the angular bundle, as previously proposed by Burwell et al. ([ 1995 ] Hippocampus 5:390–408). The ventral borders with entorhinal cortex are consistent with the Insausti et al. ([ 1997 ] Hippocampus 7:146–183) description of that region and the Dolorfo and Amaral ([ 1998 ] J. Comp. Neurol. 398:25–48) connectional findings. Regarding the remaining borders, both the perirhinal and postrhinal cortices encroach upon temporal cortical regions as defined by others (e.g., Zilles [ 1990 ] The cerebral cortex of the rat, p 77–112; Paxinos and Watson [ 1998 ] The rat brain in stereotaxic coordinates). Based on cytoarchitectonic and histochemical criteria, perirhinal areas 35 and 36 and the postrhinal cortex were further subdivided. Area 36 was parceled into three subregions, areas 36d, 36v, and 36p. Area 35 was parceled into two cytoarchitectonically distinctive subregions, areas 35d and 35v. The postrhinal cortex was divided into two subregions, areas PORd and PORv. These regional definitions of perirhinal areas 35 and 36 and the postrhinal cortex were confirmed by new empirical analyses of previously reported quantitative connectional data (Burwell and Amaral [ 1998a ] J. Comp. Neurol. 398:179–205). J. Comp. Neurol. 437:17–41, 2001. © 2001 Wiley‐Liss, Inc.
Visualization with hierarchically structured trees for an explanation reasoning system

This work is concerned with an application of drawing hierarchically structured trees. The tree drawing is applied to an explanation reasoning system. The reasoning is based on synthetic abduction (hypothesis) that gets a case from a rule and a result. In other words, the system searches a proper environment to get a desired result. In order that the system may be reliably related to the amount of rules which are used to get the answer, we visualize a process of reasoning to show how rules have concern with the process. Since the process of reasoning in the system makes a hierarchically structured tree, the visualization of reasoning is a drawing of a hierarchically structured tree. We propose a method of visualization that is applicable to the explanation reasoning system.
Novel Algorithm for Constructing Support Vector Machines Classification Ensemble

Ensemble classification has received much attention in the machine learning community and has demonstrated promising capabilities in improving classification accuracy. And Support vector machines (SVMs) ensemble has been proposed to improve classification accuracy recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. In this research, a novel SVMs ensemble algorithm based on fuzzy choquet integral is proposed in this paper to deal with this problem. This method aggregates the outputs of separate component SVMs with importance of each component SVM, which is subjectively assigned as the nature of fuzzy logic. The simulating results demonstrate that the proposed method outperforms a single SVM and traditional SVMs aggregation technique via majority voting in terms of classification accuracy.
Active attentional sampling for speed-up of background subtraction

In this paper, we present an active sampling method to speed up conventional pixel-wise background subtraction algorithms. The proposed active sampling strategy is designed to focus on attentional region such as foreground regions. The attentional region is estimated by detection results of previous frame in a recursive probabilistic way. For the estimation of the attentional region, we propose a foreground probability map based on temporal, spatial, and frequency properties of foregrounds. By using this foreground probability map, active attentional sampling scheme is developed to make a minimal sampling mask covering almost foregrounds. The effectiveness of the proposed active sampling method is shown through various experiments. The proposed masking method successfully speeds up pixel-wise background subtraction methods approximately 6.6 times without deteriorating detection performance. Also realtime detection with Full HD video is successfully achieved through various conventional background subtraction algorithms.
Visual location recognition using smartphone sensors for indoor environment

Vision based localization is an essential problem for many applications, such as augmented reality and pedestrian navigation. In this paper, a visual location recognition system based on multi-sensor information is proposed. In this system, a database called visual map is established before the localization stage. The visual map contains the visual features and the corresponding positional features. The basic idea of this localization method is to match two user's query images with two database images by image retrieval and feature matching, and then we try to acquire the query camera poses by the use of the IMU and the electronic compass device on the smartphone. On the basis of the query camera poses and the positions of the matched features, a triangulation localization algorithm is introduced to determine the location of the user. The specific experiments for this visual location recognition system are performed, and the results demonstrate that this method can achieve the excellent performance in location recognition.
Exploring Knowledge Sharing among Members of a Community of Practice.

This paper presents the findings of a qualitative study that explored knowledge sharing among members of a community of practice (CoP) in a large, urban high school. Findings suggest that social relationships, informal channels, community culture, levels of trust, and spatial factors influence knowledge sharing, and that CoPs have the potential to foster an organizational culture that supports knowledge sharing. Implications for future research and practice are presented.
Blind separation of convolutive mixtures over Galois fields

The efforts of Yeredor, Gutch, Gruber and Theis have established a theory of blind source separation (BSS) over finite fields that can be applied to linear and instantaneous mixing models. In this work, the problem is treated for the case of convolutive mixtures, for which the process of BSS must be understood in terms of space-time processing. A method based on minimum entropy and deflation is proposed, and structural conditions for perfect signal recovery are defined, establishing interesting points of contact with canonical MIMO equalization. Simulation results give support to the applicability of the proposed algorithm and also reveal the important role of efficient entropy estimation when the complexity of the mixing system is increased.
Two simplified methods for linear turbo equalization

This paper proposes two simplified methods for calculating the extrinsic information of turbo equalization using linear minimum mean squared error (MMSE) criterion. Under the assumption of linear correlation, the extrinsic information for each coded bit can be easily computed by using a simplified channel model. Simulation results show that proposed methods perform very closely to the conventional method.
Information dynamics shape the sexual networks of Internet-mediated prostitution

Like many other social phenomena, prostitution is increasingly coordinated over the Internet. The online behavior affects the offline activity; the reverse is also true. We investigated the reported sexual contacts between 6,624 anonymous escorts and 10,106 sex buyers extracted from an online community from its beginning and six years on. These sexual encounters were also graded and categorized (in terms of the type of sexual activities performed) by the buyers. From the temporal, bipartite network of posts, we found a full feedback loop in which high grades on previous posts affect the future commercial success of the sex worker, and vice versa. We also found a peculiar growth pattern in which the turnover of community members and sex workers causes a sublinear preferential attachment. There is, moreover, a strong geographic influence on network structure—the network is geographically clustered but still close to connected, the contacts consistent with the inverse-square law observed in trading patterns. We also found that the number of sellers scales sublinearly with city size, so this type of prostitution does not, comparatively speaking, benefit much from an increasing concentration of people.