Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting Opinion Targets and Opinion Words from Online Reviews with Graph Co-ranking

Extracting opinion targets and opinion words from online reviews are two fundamental tasks in opinion mining. This paper proposes a novel approach to collectively extract them with graph coranking. First, compared to previous methods which solely employed opinion relations among words, our method constructs a heterogeneous graph to model two types of relations, including semantic relations and opinion relations. Next, a co-ranking algorithm is proposed to estimate the confidence of each candidate, and the candidates with higher confidence will be extracted as opinion targets/words. In this way, different relations make cooperative effects on candidates’ confidence estimation. Moreover, word preference is captured and incorporated into our coranking algorithm. In this way, our coranking is personalized and each candidate’s confidence is only determined by its preferred collocations. It helps to improve the extraction precision. The experimental results on three data sets with different sizes and languages show that our approach achieves better performance than state-of-the-art methods.
Evaluation on reliability and validity of Chinese Walkable Environment Scale for urban community residents

Objective To evaluate the reliability and validity of the Chinese Walkable Environment Scale for urban community residents and to provide scientific basis for the use of the scale in China.Methods An intercept convenient sample of 360 community residents(20 years old) from two neighborhoods with different walkable environment were interviewed.The internal consistent reliability,test-retest reliability,split-half reliability,content validity,and construct validity of the scale were examined.Results The Cronbach'α coefficient,the intraclass correlation coefficient(ICC),and the Spearman correlation coefficient of the items of the whole scale were 0.807,0.945,and 0.721,respectively.The Pearson correlation coefficient between the individual scale and the whole scale ranged from 0.395 to 0.840. Except for access to services dimension,the correlation coefficient between the other four dimensions and the whole scale was all above 0.5(P0.01),indicating the scale had a good content validity.The factor analysis indicated that the eigenvalues of the four common factors were 2.957,2.607,2.271,and 1.572,respectively;the contributing rate of squares were 17.392%,15.333%,13.357%,and 9.248%,respectively;the contributing rate of cumulative sums of squares was 55.33%. And almost all items' factor loadings were higher than 0.4 except the items of B4 and D2,indicating that the scale had a good construct validity.Conclusion The Chinese Walkable Environment Scale for urban community residents has good reliability and validity and could be used as an effective tool for assessing urban community walkable environment in the country.
Analysis of intrapatient heterogeneity uncovers the microevolution of Middle East respiratory syndrome coronavirus

Genome sequence analysis of Middle East respiratory syndrome coronavirus (MERS-CoV) variants from patient specimens has revealed the evolutionary dynamics and mechanisms of pathogenesis of the virus. However, most studies have analyzed the consensus sequences of MERS-CoVs, precluding an investigation of intrapatient heterogeneity. Here, we analyzed non–consensus sequences to characterize intrapatient heterogeneity in cases associated with the 2015 outbreak of MERS in South Korea. Deep-sequencing analysis of MERS-CoV genomes performed on specimens from eight patients revealed significant intrapatient variation; therefore, sequence heterogeneity was further analyzed using targeted deep sequencing. A total of 35 specimens from 24 patients (including a super-spreader) were sequenced to detect and analyze variants displaying intrapatient heterogeneity. Based on the analysis of non–consensus sequences, we demonstrated the intrapatient heterogeneity of MERS-CoVs, with the highest level in the super-spreader specimen. The heterogeneity could be transmitted in a close association with variation in the consensus sequences, suggesting the occurrence of multiple MERS-CoV infections. Analysis of intrapatient heterogeneity revealed a relationship between D510G and I529T mutations in the receptor-binding domain (RBD) of the viral spike glycoprotein. These two mutations have been reported to reduce the affinity of the RBD for human CD26. Notably, although the frequency of both D510G and I529T varied greatly among specimens, the combined frequency of the single mutants was consistently high (87.7% ± 1.9% on average). Concurrently, the frequency of occurrence of the wild type at the two positions was only 6.5% ± 1.7% on average, supporting the hypothesis that selection pressure exerted by the host immune response played a critical role in shaping genetic variants and their interaction in human MERS-CoVs during the outbreak.
A set approach to the simultaneous localization and map building - application to underwater robots

This paper proposes a set approach for the simultaneous localization and mapping (SLAM) in a submarine context. It shows that this problem can be cast into a constraint satisfaction problem which can be solve efficiently using interval analysis and propagation algorithms. The efficiency of the resulting propagation method is illustrated on the localization of submarine robot, named Redermor. The experiments have been collected by the GESMA (Groupe d’Etude Sous-Marine de l’Atlantique) in the Douarnenez Bay, in Brittany.
Chinese Question Classification Based on Ensemble Learning

In this paper, a method of Chinese question classification based on ensemble learning is proposed. Words and bi-gram are extracted from question as classic features and feature classifiers are constructed. The classifiers are kinds of simple classifier and their generalization ability are not strong enough, but they are a qualified base learner for ensemble because of their low computational cost, and the generalization feasibility improved with the help of ensemble learning. We translate and modify the UIUC question set and TREC2001 question set as Chinese question set. Experimental results on the corpus show that the proposed method can achieve good performance, the classification precision reaches 87.6%, under the fine grained question types.
Enhancement of Web Proxy Caching Using Random Forest Machine Learning Technique

The Random Forest Tree is an ensemble learning method for Web data classification. In this study, we attempt to improve the performance of the traditional Web proxy cache replacement policies such as LRU and GDSF by integrating machine learning technique for enhancing the performance of the Web proxy cache. Web proxy caches are used to improve performance of the web. Web proxy cache reduces both network traffic and response time. In the first part of this paper, a supervised learning method as Random Forest Tree classifier (RFT) to learn from proxy log data and predict the classes of objects to be revisited or not. In the second part, a Random Forest Tree classifier (RFT) is incorporated with traditional Web proxy caching policies to form novel caching approaches known as RFT-LRU and RFT-GDSF. These proposed RFT-LRU and RFT-GDSF significantly improve the performances of LRU and GDSF respectively.
Working Paper No . 381 Reinventing Fiscal Policy

There has been a major shift within macroeconomic policy over the past two decades or so in terms of the relative importance given to monetary policy and to fiscal policy, with the former gaining considerably in importance, and the latter being so much downgraded that it is rarely mentioned. Monetary policy has focused on the setting of interest rates as the key policy instrument, along with the adoption of inflation targets and the use of monetary policy to target inflation. The Central Bank sets its discount rate 2 with a view to achieving the set inflation target, but the discount rate can be considered as set relative to an "equilibrium rate" so that the problem of aggregate demand deficiency appears to be effectively dispensed with.3 This can be seen in the operation of Taylor's rule for the setting of the discount rate (Taylor 1993). In Arestis and Sawyer (2003a), we critically examine the significance of this shift in terms of monetary policy, which led us to question the effectiveness of monetary policy. In the same paper we also explore the role of fiscal policy, and argue that within the "new consensus" there is barely mention of fiscal policy.4 We strongly suggest there that fiscal policy should be reinstated, and conclude that "fiscal policy remains a potent tool for offsetting major changes in the level of aggregate demand" (p. 19). This paper aims to consider further that particular conclusion (see also Arestis and Sawyer 1998).
Improvement in InSAR phase unwrapping using external DEM

Interferometric synthetic aperture radar (InSAR) technique is widely used for DEM generation and regeneration. Phase unwrapping is one of the most crucial operation in DEM generation through InSAR process. Difficulty in phase unwrapping arises due to the phase error in the interferogram, derived from interferometric pairs. This phase error can occurs due to various factors like system noise, decorrelation for non-simultaneous image acquisitions, image misregistration, geometric decorrelation and atmospheric phase screen (APS) difference between acquired InSAR pairs. In this paper, we present an improved phase unwrapping approach which reduces the phase errors introduced due to decorrelation. Phase errors are reduced by using simulated phase. Simulated phase generated through orbit model of InSAR pair in slant range plane, by using external coarse DEM. Branch-cut and least-square algorithms are used for phase unwrapping in the process of DEM generation. A comparative analysis on the results of both the algorithms is also presented. Results of both phase unwrapping algorithms of direct and blending of simulated phase are compared. Our procedure improves the phase unwrapping significantly that gives improved DEM.
Optimization of Probabilistic Argumentation with Markov Decision Models

One prominent way to deal with conflicting viewpoints among agents is to conduct an argumentative debate: by exchanging arguments, agents can seek to persuade each other. In this paper we investigate the problem, for an agent, of optimizing a sequence of moves to be put forward in a debate, against an opponent assumed to behave stochastically, and equipped with an unknown initial belief state. Despite the prohibitive number of states induced by a naive mapping to Markov models, we show that exploiting several features of such interaction settings allows for optimal resolution in practice, in particular: (1) as debates take place in a public space (or common ground), they can readily be modelled as Mixed Observability Markov Decision Processes, (2) as argumentation problems are highly structured, one can design optimization techniques to prune the initial instance. We report on the experimental evaluation of these techniques.
On Consumers' Causal Attribution Mechanism in Product-harm Crises

Causal Attribution,which plays a dominant role in consumers' cognition of product-harm crises,not only determines the crises' accountability and its degree but also functions as an important guidance to crisis response.Based on Cues Utilization Theory,this essay chooses self-monitoring and brand image respectively as personality and situational variables,constructs the model of consumers' causal attribution mechanism,which is then tested with Structural Equation Modeling(SEM),and discovers two ways of consumers' causal attribution: personality attribution and situational attribution.Finally,the findings are discussed and some suggestions on management are given.
Exit Tales: How Precarious Workers Navigate Bad Jobs

Why do some workers quit undignified “bad jobs,” while others persist in them? We know a great deal about how people find employment, along with what they do at work. But we have few studies documenting the lived experience of quitting a bad job. Recent structural transformations, such as the demise of Fordism and the curtailment of welfare, have surely recalibrated the strategies by which precarious individuals navigate the labor market. This article, an ethnography that follows a single cohort of call center employees over nine months, documents four main pathways through which such workers leave versus stay in their jobs. It argues that the emergent class of precarious workers is not homogenous. Gender, race, and age intersect with class to shape how one experiences a given bad job.
XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis

This paper presents our contribution to the Se-mEval 2016 task 5: Aspect-Based Sentiment Analysis. We have addressed Subtask 1 for the restaurant domain, in English and French, which implies opinion target expression detection, aspect category and polarity classiﬁcation. We describe the different components of the system, based on composite models combining sophisticated linguistic features with Machine Learning algorithms
Local Thresholding in General Network Graphs

Local thresholding algorithms were first presented more than a decade ago and have since been applied to a variety of data mining tasks in peer-to-peer systems, wireless sensor networks, and in grid systems. One critical assumption made by those algorithms has always been cycle-free routing. The existence of even one cycle may lead all peers to the wrong outcome. Outside the lab, unfortunately, cycle freedom is not easy to achieve. This work is the first to lift the requirement of cycle freedom by presenting a local thresholding algorithm suitable for general network graphs. The algorithm relies on a new repositioning of the problem in weighted vector arithmetics, on a new stopping rule, whose proof does not require that the network be cycle free, and on new methods for balance correction when the stopping rule fails. The new stopping and update rules permit calculation of the very same functions that were calculable using previous algorithms, which do assume cycle freedom. The algorithm is implemented on a standard peer-to-peer simulator and is validated for networks of up to 80,000 peers, organized in three different topologies representative of major current distributed systems: the Internet, structured peer-to-peer systems, and wireless sensor networks.
Adaptation to New Legal Procedures in Rural China : Integrating Survey and Ethnographic Data

Jingming and the villagers of Li Village for their kindness and cooperation. 2 Introduction This chapter portrays the implementation of a fully integrated mixed method research design aimed to capture the ongoing process of social change in mainland China. We approach the study of social change by combining the investigation of existing social structures in China with the exploration of Chinese peoples' perceptions about the function and role of social relationships in the process of adaptation to new social practices. Our specific focus for the study of social change is the recent establishment of the rule of law in China, which is perhaps one of the most sweeping social reforms in the history of the country. The Chinese legal system was one of the social institutions that received a major overhaul by the Chinese government as part of its economic reform package initiated in the late 1970s. In 1992 the National People's Congress formally recognized that a sound market economy must be based on the rule of law and expected civil courts to provide legal services to all citizens alongside criminal courts (Potter 2001; Wang 2000). This decision has provided Chinese people with the option of settling civil disputes through formal adjudication by a judge at court in addition to the traditional practice of mediation based on the principles of reciprocity assisted by a local authority. How do Chinese citizens navigate these new opportunities for social behavior? Since norms and laws are important components of culture, our research wants to understand the significance of offering conflict resolution at court as an agent for social change and economic development. We are interested to learn how Chinese people experience this social change. In particular, we want to know how changes in the procedures of the Chinese justice system influence individual choices to engage in this alternative mode for solving conflicts. Hence, the guiding question for our research went beyond the exploration of who uses adjudication at court and why. Taking the implications of the changed rules of conduct into account and linking them 3 to existing cultural practices, we ask: What explains the likelihood of taking a case to court? Experiences during previous research projects conducted in mainland China and extensive literature review familiarized us with the specific characteristics of the Chinese social order. As the next section of this chapter introduces in detail, the traditional Chinese social system, rooted in the …
Value-Sensitive Hybrid Information Flow Control for a JavaScript-Like Language

Secure integration of third-party code is one of the prime challenges for securing today's web. Recent empirical studies give evidence of pervasive reliance on and excessive trust in third-party JavaScript, with no adequate security mechanism to limit the trust or the extent of its abuse. Information flow control is a promising approach for controlling the behavior of third-party code and enforcing confidentiality and integrity policies. While much progress has been made on static and dynamic approaches to information flow control, only recently their combinations have received attention. Purely static analysis falls short of addressing dynamic language features such as dynamic objects and dynamic code evaluation, while purely dynamic analysis suffers from inability to predict side effects in non-performed executions. This paper develops a value-sensitive hybrid mechanism for tracking information flow in a JavaScript-like language. The mechanism consists of a dynamic monitor empowered to invoke a static component on the fly. This enables us to achieve a sound yet permissive enforcement. We establish formal soundness results with respect to the security policy of non-interference. In addition, we demonstrate permissiveness by proving that we subsume the precision of purely static analysis and by presenting a collection of common programming patterns that indicate that our mechanism has potential to provide more permissiveness than dynamic mechanisms in practice.
Just compress and relax: Handling missing values in big tensor analysis

In applications of tensor analysis, missing data is an important issue that is usually handled via weighted least-squares fitting, imputation, or iterative expectation-maximization. The resulting algorithms are often cumbersome, and tend to fail when the percentage of missing samples is large. This paper proposes a novel and refreshingly simple approach for handling randomly missing values in big tensor analysis. The stepping stone is random multi-way tensor compression, which enables indirect tensor factorization via analysis of compressed `replicas' of the big tensor. A Bernoulli model for the misses, and two opposite ends of the tensor modeling spectrum are considered: independent and identically distributed (i.i.d.) tensor elements, and low-rank (and in particular rank-one) tensors whose latent factors are i.i. d. In both cases, analytical results are established, showing that the tensor approximation error variance is inversely proportional to the number of available elements. Coupled with recent developments in robust CP decomposition, these results show that it is possible to ignore missing values without losing the ability to identify the underlying model.
A Context-Specific Mediating Schema Approach for Information Exchange between Heterogeneous Hospital Systems

Secure and seamless exchange of electronic patient-care information among heterogeneous interoperable systems is an important goal for future healthcare information systems. However, fast and efficient exchange of information between heterogeneous healthcare systems is inhibited by the variations in the data models used to define patient-care records, and the prohibitive costs associated with standardisation and software upgrades. In this paper, we analyse the factors impeding the adoption of interoperable healthcare systems and propose the use of a mediating schema-based approach that can enable interoperability and also minimise the impact of the impeding factors. The proposed mechanism uses context-specific mediating schemas for data translation and can enable the seamless exchange of electronic patient-care records between different healthcare systems. Our approach builds on existing technologies and is specifically adapted to the healthcare context. We illustrate the viability of our approach using a scenario relating to pre-hospital to hospital information exchange.
A semi-blind channel estimation method for multiuser multiantenna OFDM systems

A subspace-based blind method is proposed for estimating the channel responses of a multiuser and multiantenna orthogonal frequency division multiplexing (OFDM) uplink system. It gives estimations to all channel responses subject to a scalar matrix ambiguity and does not need precise channel order information (only an upper bound for the orders is required). Furthermore, the scalar ambiguity matrix can be easily resolved by using only one pilot OFDM block, given that the number of users is smaller than the number of symbols in the pilot symbol block. Equalization methods are discussed based on the estimated channels. By using partial knowledge of the channels, a multipath subspace method is proposed that reduces the computational complexity. Simulations show that the methods are effective and robust.
Goodput Enhancement of VANETs in Noisy CSMA/CA Channels

The growing interest in vehicular ad hoc networks (VANETs) enables decentralized traveler information systems to become more feasible and effective in Intelligent Transportation Systems (ITS). Major challenges in such network environments include varying path characteristics and vulnerable channel quality resulting from dynamic traffic conditions and the design of the road. This paper demonstrates a feasible methodology that can enhance inter-vehicle information dissemination using dynamic optimal fragmentation with rate adaptation algorithm (DORA). DORA achieves maximum goodput in wireless mobile networks by computing a fragmentation threshold and transmitting optimal sized packets with maximum transfer rates. To estimate the SNR in the model, an adaptive on-demand UDP estimator is designed to reduce estimation overhead. Several test-beds were developed to evaluate the performance of DORA in channel estimation accuracy, ad hoc network goodput, and vehicle-to-vehicle network goodput along I-85 in Atlanta, Georgia. The proposed algorithm is an energy-efficient, generic CSMA/CA MAC protocol for wireless mobile computing applications, and enhances system goodput in ad hoc networks and vehicle-to-vehicle networks without modification of the base protocols.