Miracle-dz/UniFAR-FTUs huggingface dataset:
Using deductive databases technology for approximate query answering in partially complete databases

The Closed-World Assumption (CWA) on relational databases expresses that an atom not in the database is false. A more appropriate assumption for sound but partially incomplete databases, is the Local ClosedWorld Assumption (LCWA), a local form of the CWA, expressing that the database is complete in a certain area. A database for which local closed world assumptions are specified is called a locally complete database. By specifying these areas where the predicates of the database are complete –which we call windows of expertise of a database predicate–, users can retrieve more informative answers than using standard SQL queries, i.e. obtain answers for the so-called possible and certain queries. Unfortunately, it turns out that this query answering process is an expensive task (unless P=NP): it amounts to co-NP and NP for checking whether a tuple is a possible or certain answer, respectively. In this presentation we introduce a deductive databases approach for approximating query answers in locally complete databases. The idea is, given a relational database and a set of LCWAs expressions, reconstruct an approximation of the logic theory that the database conveys, by means of a deductive database under stable model semantics. The users can then directly query the deductive database and retrieve the answers. As we will show in the presentation, this approach allows to answer queries in polynomial time. Moreover, based on previous work of the authors [3, 1, 2], we also assert that for important classes of queries –conjunctive for certain answers and disjunctive for possible answers– and windows of expertise, this approach is optimal, i.e. it returns the exact answers to queries. Thus, we obtain a class of queries and locally closed databases for which certain and possible query answering is tractable after all. We will then address the problem of domain independent and safe queries in locally closed databases. The specific question we pose here is for which type of queries and windows of expertise in a locally closed database, the set of answers do not depend on the underlying domain and is finite. To answer this question we turn to the literature on safe queries in relational databases and domain independence on query answering mechanisms of logic programming, which combined provide the basic machinery in which our results are founded.
Implicit product feature extraction through regularized topic modeling

To solve the implicit product feature extraction task in product opinion mining,we proposed a novel regularized topic modeling framework based on the classical topic modeling through the analysis of the distribution of opinion words for different product features in reviews and also the assumption of topic dependency of opinion words.In the new framework,we took into consideration the opinionated information by defining a regularizer based on the similarity in opinion word usage of different reviews.The basic idea of the regularization was that if two reviews were similar in the usage of opinion words,they were more likely to comment on the same features.The qualitative and quantitative experiments both show that the novel framework outperforms classical topic modeling algorithms in accuracy and thus indicate the effectiveness of the regularization.
Research on Named Entity Recognition in Sports Events Filed

In order to accurately recognize the complex sports events named entities in Chinese text,this paper presents a method of named entity recognition based on cascaded conditional random fields.In the proposed method,simple named entities are firstly recognized by lower model and then complex named entities nesting simple sports events named entity such as event name,team name and venue name are recognized by higher model.In open test on large-scale corpus,its F-measure of event name,team name and venue name is 97.09%,97.81% and 98.03%.
Kajian Algoritma Peningkatan Kontras Citra Dengan Fast Hue Dan Range Preserving Histogram Equalization Specification

Faktor pencahayaan yang kurang saat suatu citra diakuisisi membuat citra menjadi gelap. Untuk memperbaiki tingkat kecerahan kontras citra, beberapa metode telah dilakukan seperti Fast Hue and Range Preserving Histogram Equalization Specification yang meliputi Algoritma Naik and Murthy, algoritma Optimal Range-Preserving Enhancement, algoritma Multiplicative Color Enhancement dan algoritma Additive Color Enhancement. Pada tahap awal dilakukan proses perataan histogram (Histogram Equalization (HE)). Namun dari beberapa referensi belum dapat ditentukan algoritma yang lebih baik dalam proses peningkatan kontras tersebut. Skenario pengujian dilakukan dengan menurunkan nilai lightness dari suatu citra, memproses citra gelap dengan algoritma yang dibahas, dan mengukur perbedaan citra hasil algoritma dengan citra asli menggunakan Structural Similarity Index (SSIM). Hasil pengujian menunjukkan bahwa ? nilai SSIM tertinggi didapatkan dengan menggunakan algoritma Optimal Range-Preserving Enhancement dan algoritma Multiplicative Color Enhancement. Pada algoritma Optimal Range-Preserving Algorithm, nilai SSIM tertinggi diperoleh dengan menggunakan nilai Lamda (? ») di atas 0.6.
New Induction Motor Synchronous Angular Frequency Estimation Method Based on Dual Second Order Generalized Integrator-frequency Locked Loop

In order to solve the dc drift and the initial value problems of the pure integrator in the voltage-model-based flux observer, the low-pass filter(LPF) is often used. However, the LPF may produce magnitude error and phase error in the estimated flux. Thus, a compensation block is often used to eliminate these errors. The performance of the LPF-based flux estimation method often highly depends on the accuracy of the synchronous angular frequency. We recently presented a new synchronous angular frequency estimation method based on second-order generalized integrator frequency-locked loop. However, the dynamic performance of this method is not very satisfactory. This paper presents a detailed theoretical analysis on the dynamic errors of this method. Further, an improved method based on dual second-order generalized integrator frequency-locked loop is presented. The properties and performance of the new synchronous angular frequency estimation method are analyzed in comparison to the former one, demonstrating that it has improved dynamic response. An induction motor speed control system is built based on the vector control and controlled by TMS320F2812 DSP. Simulation and experimental results prove the effectiveness of the proposed method.
Terminology Extraction: Towards Resource Minimization (Extraction terminologique : vers la minimisation de ressources) [in French]

The article presents the method which aims to minimize the use of external resources for the terminology extraction task and to make this task less langage dependent. For that purpose, the method builds simplified morphological and morphosyntactic resources directly from a lemmatized corpus. These endogenous resources are used both in filters, which refine the statistical calculations, and in patterns for polylexical terms identification. The method was tested on two comparable corpora in chemistry and in telecommunication in French and in English. The precision observed on the first 100 monolexical terms fluctuates between 71% and 87% for French and between 44% and 69% in English ; for polylexical terms the precision was 69-78% in French and 69-85% in English depending on the domain. Mots-cles : extraction terminologique, ressources endogenes, apprentissage automatique.
Research on Virtual Storage System in Cloud Computing

Data storage is important for cloud computing, the virtual storage system is a new method for cloud storage which combines virtualization technology and network storage. A virtual storage system based on NAS is proposed, requirement analysis and design objects are discussed, and system structure and resource management processing are presented. At last a virtual storage system based on SAN and NAS is provided which is more efficient, flexible and realizes the real integration for storage information.
3D SEGMENTATION OF GLIOMA FROM BRAIN MR IMAGES USING SEEDED REGION GROWING AND FUZZY C-MEANS CLUSTERING

This thesis presents two algorithms for brain MR image segmentation. The images used are axial MR images of the human brain. The images show a glioma. The objective is to segment the tumour and edema surrounding it from the images. Initially the images are pre-processed by contrast adjustment. Segmentation is performed by two algorithms: seeded region growing and fuzzy c-means clustering. After the images are segmented, the volumes of the segmented regions are measured. The segmentation is done in MATLAB. Finally the results are rendered in 3D in AMIRA.
Cooperative Game Theory-Based CAC Strategy for Mobile Communication Systems

Since the radio spectrum is a very scarce resource,the call admission control(CAC) is one of the most important parts in radio resource management of mobile communication systems.With the aim at resolving the problem of streaming a medium's over-use of resource,a cooperative game theory-based CAC strategy is proposed in this paper.The players are the services in connection,and a new service requests to access the system.The base station is the external power,which ensures the protocol operation.The results show that the capture effect of resource caused by streaming media is eased by adopting the CAC strategy;therefore,the access fairness is guaranteed.It is very significant to improve the performance of practical system applications.
A New Type of Functional-Link Neural Network With Dynamic Structure and its Application to System Indentification

The paper introduced the development of the new type of artificial neural network and its application to non-linear system identification.This is the functional-link neural network with internal dynamic elements.The internal dynamic elements are auto-regressive moving average filters with local activation feedback and local output feedback,respectively.The optimization of the parameters about the network is decided by genetic algorithms.Experimental results demonstrate a better capability of generalization of the suggested neural network in comparison with the functional-link net with static structute and external dynamic elements,used so far to perform system identification.
Exploring the impact of innovation guidance on user participation in online communities: A mixed methods investigation of cognitive and affective perspectives

In recent years, many online communities have launched opinion-gathering activities to promote user participation in innovation and improve the quality of new products. The current methods for online innovation activities can be divided into two categories: cognitive guidance and affective guidance. However, the studies on online communities have mainly focused on user engagement motivations, and little attention has been paid to investigating the impact and underlying mechanism of innovation guidance on user participation at the linguistic level. This study first collected secondary data from NetEase.com and conducted an econometric model to explore the impact of cognitive guidance and affective guidance on users’ participation in online innovation activities. Subsequently, we investigated the impact mechanism of different innovation guidance methods on user participation through two experiments, here by drawing on mental simulation theory. The experimental results showed that outcome simulation and process simulation imposed a dual mediating effect of innovation guidance on user participation. In addition, we also found that product types moderate the dual mediating effect of outcome simulation and process simulation. The findings can deepen and expand the research on user participation while providing practical implications for companies and platforms as they attempt to promote user participation in innovation activities.
Analysis of the Hierarchical Chinese Functional Chunk Bank

Through an experimental analysis of the relation between the length and the structure of functional chunks and the performance of the parser,this paper reveals that it is the long,complex-structured functional chunks that are the major difficulty in parsing.Therefore,this paper proposes a new hierarchical functional chunk scheme and automatically generates the new functional chunk bank from Tsinghua Chinese Tree Bank(TCT). Further extensive researches about the length and structure distribution of the new chunk bank indicates that the new functional chunks bear short length and simple structure,which will help to improve the performance of functional chunk parser.
The relationship between transposable elements and ecological niches in the Greater Cape Floristic Region: A study on the genus Pteronia (Asteraceae)

Non-coding repetitive DNA (repeatome) is an active part of the nuclear genome, involved in its structure, evolution and function. It is dominated by transposable elements (TEs) and satellite DNA and is prone to the most rapid changes over time. The TEs activity presumably causes the global genome reorganization and may play an adaptive or regulatory role in response to environmental challenges. This assumption is applied here for the first time to plants from the Cape Floristic hotspot to determine whether changes in repetitive DNA are related to responses to a harsh, but extremely species-rich environment. The genus Pteronia (Asteraceae) serves as a suitable model group because it shows considerable variation in genome size at the diploid level and has high and nearly equal levels of endemism in the two main Cape biomes, Fynbos and Succulent Karoo. First, we constructed a phylogeny based on multiple low-copy genes that served as a phylogenetic framework for detecting quantitative and qualitative changes in the repeatome. Second, we performed a comparative analysis of the environments of two groups of Pteronia differing in their TEs bursts. Our results suggest that the environmental transition from the Succulent Karoo to the Fynbos is accompanied by TEs burst, which is likely also driving phylogenetic divergence. We thus hypothesize that analysis of rapidly evolving repeatome could serve as an important proxy for determining the molecular basis of lineage divergence in rapidly radiating groups.
Integrating Exponential Dispersion Models to Latent Structures

Latent variable models have two basic components: a latent structure encoding a hypothesized complex pattern and an observation model capturing the data distribution. With the advancements in machine learning and increasing availability of resources, we are able to perform inference in deeper and more sophisticated latent variable models. In most cases, these models are designed with a particular application in mind; hence, they tend to have restrictive observation models. The challenge, surfaced with the increasing diversity of data sets, is to generalize these latent models to work with different data types. We aim to address this problem by utilizing exponential dispersion models (EDMs) and proposing mechanisms for incorporating them into latent structures. In Chapter 2, we show that the density of steep EDMs can be expressed with a Bregman divergence. Based on this relationship, we parametrize families of steep EDMs for various data types. We then use these families in the mixture model setting and propose an expectation-maximization algorithm (AdaCluster) that can identify the underlying distribution of each attribute in a heterogeneous data set. In Chapter 3, we generalize hierarchical Poisson factorization, a Bayesian nonnegative matrix factorization model, by compounding the original Poisson output with EDMs. We show that the proposed model is particularly effective for large data sets with extreme sparsity and arbitrary data distribution. In Chapter 4, we use the compound-Poisson-EDM structure within the context of missing data. We show that an arbitrary data-generating model with EDM output— such as Gaussian mixture model, probabilistic matrix factorization, Poisson mixture model or linear regression model—can be coupled with a Poisson factorization encoding the missing-data pattern through compounding. In particular, we argue that the heteroscedastic impact of missing-data pattern on the dispersion of observation variable can be captured with the proposed model. iii
Context-Aware Recommender Systems in the E-commerce Domain

Recommender systems help users explore a large data set by proposing items in that data set that the system expects to be of interest to that user. The use of context, information that describes in which situation a user interacts with the recommender system, has shown to increase the effectiveness of recommender systems in several domains. In this research, we evaluate the effectiveness of recommender systems that use such contextual information, so-called context-aware recommender systems, in a domain that has not been addressed in literature: the e-commerce domain. In this domain, the goal of the recommender system is to activate users users to buy products in the data set. We explore six very different e-commerce data sets that contain contextual information and we show that the recommender’s effectiveness improves when contextual information is used in the recommendation process. We also find that it is difficult to predict which type of context will increase the effectiveness on which data set, and that not all users always benefit from using a context-aware recommender system. Finally, we also investigate the impact on revenue: when contextual information leads to an increase in effectiveness, the revenue generated by the recommender system is also increased.
Individual differences in nonnative lexical tone perception: Effects of tone language repertoire and musical experience

This study sought to understand the effects of tone language repertoire and musical experience on nonnative lexical tone perception and production. Thirty-one participants completed a tone discrimination task, an imitation task, and a musical abilities task. Results showed that a larger tone language repertoire and musical experience both enhanced tone discrimination performance. However, the effects were not additive, as musical experience was associated with tone discrimination performance for single-tone language speakers, but such association was not seen for dual-tone language speakers. Furthermore, among single-tone language speakers, but not among dual-tone language speakers, musical experience and musical aptitude positively correlated with tone discrimination accuracy. It is thus concluded that individuals with varying extents of tone language experience may adopt different strategies when performing tone discrimination tasks; single-tone language speakers may draw on their musical expertise while dual-tone language speakers may rely on their extensive tone language experience instead.
Customer Classification And Prediction Based On Data Mining Technique

Due to high competition in the business field, it is essential to consider the customer relationship management of the enterprise. Here analyze the massive volume of customer data and classify them based on the customer behaviours and prediction. The classifier will predict the customers belongs to which class that should have highest posterior probability. The valuable customer information accumulated by commercial banks, which is used to identify customers and provide decision support. The data pre- processing techniques like data cleaning and data reduction can be applied for data preparation and the dates were converted into a numerical form. A data model is generated based upon the history of the customers in the bank. Then the sample data is classified by using the Naive Bayesian classification algorithm and placed them into the appropriate class based upon the posterior probability and based upon the posterior probability the percentage of loan sanction risk for the customers can be predicted.
Genetic control of the lateral petal shape and identity of asymmetric flowers in mungbean (Vigna radiata L.)

Broad diversity of flowers in Fabaceae provides a good system to investigate development and evolution of floral symmetry in higher plants. Many studies have demonstrated a conserved mechanism controlling development of zygomorphic flower during last decades. However, the molecular basis of how asymmetric flower established is largely unknown. In this study, we characterized mutants named keeled wings (kw) in mungbean (Vigna radiata L.), which is a legume species with asymmetric flowers. Compared to those in the wild type plants, the lateral petals were ventralized in the kw mutants. Map-based cloning showed that KW was VrCYC3 gene in mungbean, the ortholog of Lotus japonicus CYC3 (LjCYC3) and Pisum sativum CYC3 (PsCYC3). In addition, another two CYC-like genes named VrCYC1 and VrCYC2 were identified from mungbean genome. The three CYC-like genes displayed distinct expression patterns in dorsal, lateral and ventral petals. It was found that VrCYC3 was located in nucleus. Further analysis showed that VrCYC3 had transcription activity and could interact with VrCYC1 and VrCYC2 in yeast cell. Moreover, the deletion of two amino acid residues in the R domain of VrCYC3 protein could decrease its interaction with VrCYC1 and VrCYC2 proteins. Our results suggest that LjCYC3/VrCYC3 orthologs play conserved roles determining the lateral petal shape and identity of zygomorphic flower as well as asymmetric flower in Papilionoideae.
Leapfrog:Optimal Opportunistic Routing in Probabilistically Contacted Delay Tolerant Networks

Delay tolerant networks(DTNs) experience frequent and long lasting network disconnection due to various reasons such as mobility,power management,and scheduling.One primary concern in DTNs is to route messages to keep the end-to-end delivery delay as low as possible.In this paper,we study the single-copy message routing problem and propose an optimal opportunistic routing strategy—Leapfrog Routing—for probabilistically contacted DTNs where nodes encounter or contact in some fixed probabilities.We deduce the iterative computation formulate of minimum expected opportunistic delivery delay from each node to the destination,and discover that under the optimal opportunistic routing strategy,messages would be delivered from high-delay node to low-delay node in the leapfrog manner.Rigorous theoretical analysis shows that such a routing strategy is exactly the optimal among all possible ones.Moreover,we apply the idea of Reverse Dijkstra algorithm to design an algorithm.When a destination is given,this algorithm can determine for each node the routing selection function under the Leapfrog Routing strategy.The computation overhead of this algorithm is only O(n~2) where n is the number of nodes in the network.In addition,through extensive simulations based on real DTN traces,we demonstrate that our algorithm can significantly outperform the previous ones.