Miracle-dz/UniFAR-FTUs huggingface dataset:
Aspect Clustering Combined N-gram for Reviews

With the increase in popularity of e-commerce, more and more customer reviews are available online, it's usu- ally hard to go through each of them. Latent Dirichlet Allocation (LDA) was used to mine product aspect. Considering the weakness of standard LDA when processing review text, we defined the product aspect model, and predefined aspects for different domains, and proposed aspect model combined N-gram based on sentence, which can automate aspect cluster- ing. Our experimental results show that the proposed model can cluster mostly aspects and recognize representative words for the aspect with more than one word, and achieve better sentence-level aspect precision than previously proposed as- pect models.
A Particle Filtering Framework for Failure Prognosis

Bayesian estimation techniques are finding application domains in machinery fault diagnosis and prognosis of the remaining useful life of a failing component/subsystem. This paper introduces a methodology for accurate and precise prediction of a failing component based on particle filtering and learning strategies. This novel approach employs a state dynamic model and a measurement model to predict the posterior probability density function of the state, i.e., to predict the time evolution of a fault or fatigue damage. It avoids the linearity and Gaussian noise assumption of Kalman filtering and provides a robust framework for long-term prognosis while accounting effectively for uncertainties. Correction terms are estimated in a learning paradigm to improve the accuracy and precision of the algorithm for long-term prediction. The proposed approach is applied to a crack fault and the results support its robustness and superiority.Copyright © 2005 by ASME
"Supercrip" vs human interest: Examining stereotypes towards paralympians following the viewing of Canadian paralympic committee videos

The media typically portrays Paralympians by emphasizing their superhuman qualities (i.e. a supercrip portrayal) or the characteristics of their disability (i.e. a human interest portrayal). While these portrayals may be of interest to people without physical disabilities (PD), they are perceived negatively by people with PD. No studies have examined the effect of different types of media portrayals on disability stereotypes. Therefore, the objective of this study was to compare the effect of these two portrayals on stereotype perceptions of individuals with and without PD. Participants (n=148 with PD; n=180 without PD) watched two Canadian Paralympic Committee videos, in counterbalanced order, that presented the same Paralympian using either a supercrip or human interest portrayal. After each video, participants rated Paralympians on measures of warmth and competence, two indicators of stereotypes. A 2(disability status) x 2(video) x 2(warmth and competence) mixed model ANOVA demonstrated that people without PD rated the human interest portrayal higher in warmth (M=4.12; SD=0.64) than those with PD (M=3.89; SD=0.80; p=.014), suggesting increased presence of stereotypes towards this portrayal amongst those without PD. Furthermore, regardless of group, warmth scores were significantly higher following viewing of the human interest portrayal (M=4.01; SD=.73) compared to ratings after viewing the supercrip video (M=3.87; SD=. 77; p
Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos.
Assessing the Accuracy of High Resolution Digital Surface Models Computed by PhotoScan® and MicMac® in Sub-Optimal Survey Conditions

For monitoring purposes and in the context of geomorphological research, Unmanned Aerial Vehicles (UAV) appear to be a promising solution to provide multi-temporal Digital Surface Models (DSMs) and orthophotographs. There are a variety of photogrammetric software tools available for UAV-based data. The objective of this study is to investigate the level of accuracy that can be achieved using two of these software tools: Agisoft PhotoScan ® Pro and an open-source alternative, IGN © MicMac ® , in sub-optimal survey conditions (rugged terrain, with a large variety of morphological features covering a range of roughness sizes, poor GPS reception). A set of UAV images has been taken by a hexacopter drone above the Riviere des Remparts, a river on Reunion Island. This site was chosen for its challenging survey conditions: the topography of the study area (i) involved constraints on the flight plan; (ii) implied errors on some GPS measurements; (iii) prevented an optimal distribution of the Ground Control Points (GCPs) and; (iv) was very complex to reconstruct. Several image processing tests are performed with different scenarios in order to analyze the sensitivity of each software package to different parameters (image quality, numbers of GCPs, etc.). When computing the horizontal and vertical errors within a control region on a set of ground reference targets, both methods provide rather similar results. A precision up to 3–4 cm is achievable with these software packages. The DSM quality is also assessed over the entire study area comparing PhotoScan DSM and MicMac DSM with a Terrestrial Laser Scanner (TLS) point cloud. PhotoScan and MicMac DSM are also compared at the scale of particular features. Both software packages provide satisfying results: PhotoScan is more straightforward to use but its source code is not open; MicMac is recommended for experimented users as it is more flexible.
A Key Management Scheme for Manually Deployed Wireless Sensor Networks Based on Dual Directional Hash Chains

Wireless sensor networks (WSNs) are usually deployed in unattended adversary or even hostile environments. Great difficulties and challenges of bootstrapping network security stem from the particular nature of WSNs. Key management mechanism is a basic key problem of them. To the case of manually deployed WSNs, we introduce a key distribution scheme based on dual directional hash chains and honeycomb-like network topology. The analysis and evaluation show that our scheme can effectively weaken the threat of node capture, be resilience against node replication or node forgery. Besides of good security properties, the scheme promises good node addition or network extension ability.
A Study on the Application of Data Mining in the Patent Information Analysis for Company

The emergence of knowledge economy has enabled the competition among companies to transform into science and technology (S&T) competition and technology innovation ability competition. While as the largest source of the technical information, patent information contains a large number of technology development opportunities, so how to collect and analyze the useful information from the complicated and massive patent information and take full advantage of them for companies is of great significance in enhancing the technology innovation ability of companies. This paper attempts to establish a basic process of patent information analysis based on data mining (DM) techniques, so as to illustrate new ideas of analyzing patent information: Firstly, collect the patent information related to the analyzing technology field from the patent databases, and then utilize text mining techniques to extract the keywords from the patent data, finally, we use the data mining methods to mine and analyze the data deeply, with the purpose of obtaining the competitive intelligence of potential value and providing an information support for technology innovation activities of companies.
The power of tree projections: local consistency, greedy algorithms, and larger islands of tractability

Enforcing local consistency is a well-known technique to simplify the evaluation of conjunctive queries. It consists of repeatedly taking the semijion between every pair of (relations associated with) query atoms, until the procedure stabilizes. If some relation becomes empty, then the query has an empty answer. Otherwise, we cannot say anything in general, unless we have some information on the structure of the given query. In fact, a fundamental result in database theory states that the class of queries for which---on every database---local consistency entails global consistency is precisely the class of acyclic queries. In the last few years, several efforts have been made to define structural decomposition methods isolating larger classes of nearly-acyclic queries, yet retaining the same nice properties as acyclic ones. In particular, it is known that queries having bounded (generalized) hypertree-width can be evaluated in polynomial time, and that this structural property is also sufficient to guarantee that local consistency solves the problem, as for acyclic queries. However, the precise power of such an approach was an open problem: Is it the case that bounded generalized hypertree-width is also a necessary condition to guarantee that local consistency entails global consistency? In this paper, we positively answer this question, and go beyond. Firstly, we precisely characterize the power of local consistency procedures in the more general framework of tree projections, where a query Q and a set V of views (i.e., resources that can be used to answer Q) are given, and where one looks for an acyclic hypergraph covering Q and covered by Q---all known structural decomposition methods are just special cases of this framework, defining their specific set of resources. We show that the existence of tree projections of certain subqueries is a necessary and sufficient condition to guarantee that local consistency entails global consistency. In particular, tight characterizations are given not only for the decision problem, but also when answers restricted to variables covered by some view have to be computed. Secondly, we consider greedy tree-projections that are easy to compute, and we study how far they can be from arbitrary tree-projections, which are intractable in general. Finally, we investigate classes of instances not included in those having tree projections, and which can be easily recognized and define either new islands of tractability, or islands of quasi-tractability.
I-vector based deep neural network acoustic model adaptation using multilingual language resource

I-vector adaptation of DNN-HMM acoustic models has shown clear performance improvement for speech recognition. In this paper, we study this technique on Babel task. we use Swahili as target language (training data of 50 hours) and another 6 languages as multilingual resources to train i-vector extractors respectively. Our study shows that i-vector extractors trained with more multilingual data only produce slightly improved results. Moreover, we compared two i-vectors adaptation methods, 1) concatenate i-vectors with spectral features; 2) predict a bias term adding it to spectral features from i-vectors using a NN. When DNN is trained from scratch, the two methods perform similarly. However, only the second method is appropriate in a cross-lingual transfer learning scenario. We investigate it as well, and results show further word error rate reduction can be gained.
A NEW IMAGE CLUSTERING AND COMPRESSION METHOD BASED ON FUZZY LOGIC AND DISCRETE COSINE TRANSFORM

0 x N 2 π v 1) (2y cos N 2 π u 1) (2x cos y) f(x, ) ( ) ( v) (u, C v u α α ABSTRACT This paper presents a new approach to image compression based on fuzzy clustering. This new approach includes prefiltering, fuzzy logic image enhancing and obtaining of cluster centers by performing the zig-zag method in discrete cosine transform coefficients. After applying the new method on sample images at different number of clusters, Better compression ratio, performing time and good validity measure was observed.
SafeMatch: Energy-efficient private matching in mobile social networks

This paper proposes an energy-efficient private matching for secure two-party computation, called SafeMatch, in mobile social networks (MSNs). The proposed SafeMatch utilizes locality sensitive hashing (LSH) to sample the social data and protects social samples with cryptographic hash functions and one-time padding. Given acceptable tolerance rate and error rate, our proposed SafeMatch provides lightweight computation cost regardless the size of social data set. To guarantee the soundness of the proposed SafeMatch, we define its privacy and prove its security. Empirical experiment results on smart phones show that SafeMatch achieves orders of magnitude improvement in energy consumption compared to the homomorphic encryption based protocol.
Industry Evidence on the Effects of Government Spending

This paper investigates industry-level effects of government purchases in order to shed light on the transmission mechanism for government spending on the aggregate economy. We begin by highlighting the different theoretical predictions concerning the effects of government spending on industry labor market equilibrium. We then create a panel data set that matches output and labor variables to shifts in industry-specific government demand. The empirical results indicate that increases in government demand raise output and hours, but lower real product wages and productivity. Markups do not change as a result of government demand increases. The results are consistent with the neoclassical model of government spending, but they are not consistent with the New Keynesian model of the effects of government spending.
RSS-Based Ranging by Multichannel RSS Averaging

This letter shows that the accuracy of Radio Signal Strength (RSS)-based ranging can be increased by averaging RSS samples collected on different RF channels. Starting from a multi-cluster propagation model, the analysis shows that Multi Channel RSS Average (MCRA) can indeed reduce the component of the RSS variability due to self-interference produced by strong and quasi-static multipath replicas of the received signal, while it is ineffective against fading components caused by obstacles blockage. The theory is backed up by a set of experimental results obtained in different scenarios that confirm the capability of MCRA to effectively reduce RSS variability and increase RSS-based ranging accuracy.
Optimal scheduling for jobs with progressive deadlines

This paper considers the problem of server-side scheduling for jobs composed of multiple pieces with consecutive (progressive) deadlines. One example is server-side scheduling for video service, where clients request flows of content from a server with limited capacity, and any content not delivered by its deadline is lost. We consider the simultaneous goals of 1) minimizing overall loss, and 2) differentiating loss fractions across classes of flows in proportion to relative weights. State-of-the-art policies, like Discriminatory Processor Sharing and Weighted Fair Queueing, use a fixed static proportional allocation of service rate and fail to achieve both goals. The well-known Earliest Deadline First policy minimizes overall loss, but fails to provide proportional loss across flows, because it treats packets as independent jobs. This paper introduces the Earliest Progressive Deadline First (EPDF) class of policies. We prove that all policies in this broad class minimize overall loss. Furthermore, we demonstrate that many EPDF policies accurately differentiate loss fractions in proportion to class weights, satisfying the second goal.
Whole-microwave system modeling for brain imaging

In this paper, we present the results of a whole-system modeling of a microwave measurement prototype for brain imaging, consisting of 160 ceramic-loaded antennas working around 1 GHz. The modelization has been performed using open source FreeFem++ solver. Quantitative comparisons were performed using commercial software Ansys-HFSS and measurements. Coupling effects between antennas are studied with the empty system (without phantom) and simulations have been carried out with a fine numerical brain phantom model issued from scanner and MRI data for determining the sensitivity of the system in realistic configurations.
Efficiently finding bitangents

We propose a new algorithm for the detection of bitangents in planar curves. It is much faster than an algorithm used by Rothwell et al. (1995). Careful comparisons of the stability of detection using artificially transformed curves show that the new algorithm is as stable as the one proposed by Rothwell et al. An example for the use of the algorithm for object search is given.
Explaining the Fall and Rise in the Tax Cost of Marriage: The Effect of Tax Laws and Demographic Trends, 1984-97

This paper documents changes in the tax consequence of marriage over the period 1984 to 1997. Reversing the impact of the 1986 Tax Reform Act, tax acts in 1990 and 1993 are found to increasingly tax marriage. Our decomposition of different components show that, altogether, tax laws explain most (55-60 percent) of the change in the tax cost of marriage between 1984 and 1997. Our decompositions also show that the non-tax changes are almost exclusively driven by the changing labor market attachment of married women (as measured by their share of family earnings) and not by family size or total family income.
Real-time scalability of nested spin locks

When a real-time system is realized on a shared-memory multiprocessor, the worst-case performance of interprocessor synchronizations is one of the most important issues. In this paper, scalability of the maximum execution times of critical sections guarded by nested spin locks is discussed. With the simplest method, the maximum execution times become O(n/sup m/), where n is the number of contending processors and m is the maximum nesting level of locks. In this paper, we propose an algorithm with which this order can be reduced to O(n/spl middot/e/sup m/) and demonstrate its effectiveness when m=2 through performance measurements.
Toward Incremental Knowledge Correction for Agents in Complex Environments

In complex, dynamic environments, an agent's domain knowledge will rarely be complete and correct. Existing deliberate approaches to domain theory correction are signi cantly restricted in the environments where they can be used. These systems are typically not used in agent-based tasks and rely on declarative representations to support non-incremental learning. This research investigates the use of procedural knowledge to support deliberate incremental error correction in complex environments. We describe a series of domain properties that constrain the error correction process and that are violated by existing approaches. We then present a procedural representation for domain knowledge which is su ciently expressive, yet tractable. We develop a general framework for error detection and correction and then describe an error correction system, IMPROV, that uses our procedural representation to meet the constraints imposed by complex environments. Finally, we test the system in two sample domains and empirically demonstrate that it satis es many of the constraints faced by agents in complex and challenging environments.
Improvement of Automated POST Case Success Rate Using Support Vector Machines

generation as a classiﬁcation problem. Considering the problem in this fashion, a classiﬁer can be ﬁt to the random repetitions data and then be used to predict the success or failure of a new repetition prior to its submission to POST. If the classiﬁer is accurate enough, in can be used as a ﬁlter for new repetitions to improve the probability of convergence. This paper ﬁrst addresses the feasibility of ﬁtting a classiﬁer to the random repetitions data. Random repetitions data for a simple example problem is produced and Support Vector Machines (SVM) are utilized to classify the data within the vehicle and control vector space. The classiﬁers are ﬁt to random repetitions datasets of varying size in order to test the prediction accuracy. The SVM are shown to produce very high prediction accuracies for the given example problem, even when utilizing a relatively small amount of data from the random repetitions. This ultimately allows for a relatively short execution of random repetitions prior to execution of cases through the SVM. A comparison is given to show the time savings of this combined approach versus running straight random repetitions. In addition, the example problem is expanded to include execution of the graph method from reference [4]. This allows for a full comparison between the SVM and random repetitions approaches in terms of surrogate model ﬁtting. The example problem shows the ability of the SVM to produce more optimal seed points than the random reps method due to its increased case pass rate. Illustration of the diﬀerences in surrogate model ﬁtting between the techniques ultimately leads to the conclusion that the SVM approach can produce a surrogate model with higher accuracy in a shorter time frame than the straight random repetitions method.
Localizing Content Management Application for Spontaneous Textual Utterances in Natural Language.

The multilinguisation of content management services is an important but difficult problem and very few services do it. In fact it depends on the translation situation: types and level of possible accesses, available resources, linguistic competences of participants in the multilinguisation of application. Several strategies of multilinguisation are then possible (by translation, by internal or external localization etc.). We illustrate this study by a real case of linguistic porting (Arab to French) of an E-commerce application deployed in Jordan, using texts of spontaneous SMS for buying and selling second-hand cars. In spite the long distance between Arabic and French, the localization methods used give good results because of the proximity of the two sublanguages of Arabic and French.
The (un)reliability of NVD vulnerable versions data: an empirical experiment on Google Chrome vulnerabilities

NVD is one of the most popular databases used by researchers to conduct empirical research on data sets of vulnerabilities. Our recent analysis on Chrome vulnerability data reported by NVD has revealed an abnormally phenomenon in the data where almost vulnerabilities were originated from the first versions. This inspires our experiment to validate the reliability of the NVD vulnerable version data. In this experiment, we verify for each version of Chrome that NVD claims vulnerable is actually vulnerable. The experiment revealed several errors in the vulnerability data of Chrome. Furthermore, we have also analyzed how these errors might impact the conclusions of an empirical study on foundational vulnerability. Our results show that different conclusions could be obtained due to the data errors.