Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative Web Service Quality Prediction via Exploiting Matrix Factorization and Network Map

Quality of services (QoS) is an important concern in Web service recommendation or selection. Predicting QoS values of Web services based on their historical QoS records is an effective way to acquire Web service QoS, and thus has attracted considerable research interests. Recently, matrix factorization (MF), a well-known model-based collaborative filtering (CF) technique, has been successfully applied to the Web service QoS prediction. It is generally believed that MF can significantly outperform traditional memory-based CF techniques. However, previous work seldom considered the influence of the underlying network on Web service QoS when adopting MF for Web service QoS prediction. Hence, the prediction performance is not good enough. In this paper, we propose a network-aware Web service QoS prediction approach by integrating MF with the network map. By employing the network map, network distances between service users can be measured and neighborhoods of users are identified. Then, the traditional MF model is revamped by incorporating the constraint term that neighbor users are likely to perceive similar QoS of Web services. Experiments conducted on two real-world Web service datasets indicate that our approach outperforms previous MF and CF-based approaches in prediction accuracy.
Performance Enhanced Multiset Similarity Joins

The amount of data produced on a daily basis is growing at an exponential rate. One method of filtering through this data is the use of similarity joins, or methods that are used to identify similar data. Such algorithms are used for a variety of applications ranging from plagiarism detection to marketing. These methods are typically time-consuming and computationally expensive. This paper proposes an efficient three-stage MapReduce algorithm named Adept Similarity Join (ASJ) for multisets. The main novelty in ASJ is to integrate suffix filtering with positional filtering when performing similarity joins, in addition to incorporating prefix and size filtering. The proposed algorithm is compared to the state-of-the-art Strategic and Suave processing for performing similarity joins using MapReduce (SSS) algorithm, which it outperforms by lowering the number of redundant comparisons. Experimental results on a Twitter dataset demonstrate that the proposed ASJ algorithm provides about a 25% to 40% decrease in execution time and 100x reduction in memory usage compared to SSS.
Further Evidence of Increasing Diversity of Plasmodium vivax in the Republic of Korea in Recent Years

Background Vivax malaria was successfully eliminated from the Republic of Korea (ROK) in the late 1970s but re-emerged in 1993. Two decades later as the ROK enters the final stages of malaria elimination, dedicated surveillance of the local P. vivax population is critical. We apply a population genetic approach to gauge P. vivax transmission dynamics in the ROK between 2010 and 2012. Methodology/Principal Findings P. vivax positive blood samples from 98 autochthonous cases were collected from patients attending health centers in the ROK in 2010 (n = 27), 2011 (n = 48) and 2012 (n = 23). Parasite genotyping was undertaken at 9 tandem repeat markers. Although not reaching significance, a trend of increasing population diversity was observed from 2010 (HE = 0.50 ± 0.11) to 2011 (HE = 0.56 ± 0.08) and 2012 (HE = 0.60 ± 0.06). Conversely, linkage disequilibrium declined during the same period: IAS = 0.15 in 2010 (P = 0.010), 0.09 in 2011 (P = 0.010) and 0.05 in 2012 (P = 0.010). In combination with data from other ROK studies undertaken between 1994 and 2007, our results are consistent with increasing parasite divergence since re-emergence. Polyclonal infections were rare (3% infections) suggesting that local out-crossing alone was unlikely to explain the increased divergence. Cases introduced from an external reservoir may therefore have contributed to the increased diversity. Aside from one isolate, all infections carried a short MS20 allele (142 or 149 bp), not observed in other studies in tropical endemic countries despite high diversity, inferring that these regions are unlikely reservoirs. Conclusions Whilst a number of factors may explain the observed population genetic trends, the available evidence suggests that an external geographic reservoir with moderate diversity sustains the majority of P. vivax infection in the ROK, with important implications for malaria elimination.
Combining minimization and generation for combinatorial testing

Combinatorial Test Design (CTD) is an effective test planning technique that reveals faults resulting from feature interactions in a system. The standard application of CTD requires manual modeling of the test space, including a precise definition of restrictions between the test space parameters, and produces a test suite that corresponds to new test cases to be implemented from scratch. Interaction-based Test-Suite Minimization (ITSM) is a complementary approach to standard CTD, which reduces a given test suite without impacting its coverage of feature interactions. ITSM requires much less modeling effort, and does not require a definition of restrictions or generation of new test data. On the other hand, it does not improve the coverage obtained by the given test suite. In this work, we introduce Minimization Generation CTD (MG-CTD). MG-CTD is a combination of CTD with ITSM for addressing situations in which CTD is impractical, and ITSM is insufficient. In MG-CTD, one can define a subset of the parameters that can be freely assigned, as in CTD. The other parameter combinations must be selected from an existing set, as in ITSM. MG-CTD is suitable when for some parts of the test space it is easy to specify restrictions and generate new test data, while for others it is not. MG-CTD can be viewed as an enhancement of ITSM, and always achieves better interaction coverage than ITSM. We discuss the trade-offs between CTD, ITSM and MG-CTD, and present an efficient implementation which is based on binary decision diagrams. We then present some of the measures that one should take when implementing such an approach, in order to achieve the best possible coverage in the final result. Finally, we demonstrate MG-CTD on three real-life case studies.
Development and Psychometric Analysis of Internet Addiction Predict Test for Adolescents

Objective: To develop Internet Addiction Predict Test (IAPT) for Adolescents. Methods: 2620 middle school students(1319 boys,1301 girls,mean of age 15.1±1.7 years) were divided into IA group and CONTROL group according to Internet Addiction Test. Items were collected with significant difference (P0.01) and higher discrimination (D0.20) between the two groups from SDQ,EPQ,SCARED,SES and ATMD,and reconciled,screened and encoded,and then a draft of IAPT was formed. IAPT draft and IAT were used to investigate 1363 students(709 boys,604 girls,mean of age 13.69±0.95 years) to verify the IAPT’s reliability and validity. Results: The model of IAPT( 5 factors and 56 items) was clear and stable and its construct validity was receptible (df/n=2.5,NFI=0.84,NNFI=0.88 RMSEA=0.046). Its reliability (Cronbach α=0.72,and re-test reliability=0.61,P0.001) was satisfactory. The correlation coefficients between predictable object (IAT) and IAPT and its factors were 0.35-0.60. The retest reliability after six months was 0.61,P0.001. The difference of IAPT and its factors between IA and CONTROL group was significant(t=6.8-13.2,P0.001;Cohen’s d=0.51-1.150.5). When the score of IAPT was 39,the Youden index was the biggest(0.62),the true positive fraction(TPF) was 0.83 and false positive fraction(FPF) was 0.21,and correct frequency was 0.80. Conclusion: The IAPT has good reliability and validity. And it could be used to predict adolescents' Internet addiction.
Mining dependency relations for query expansion in passage retrieval

Classical query expansion techniques such as the local context analysis (LCA) make use of term co-occurrence statistics to incorporate additional contextual terms for enhancing passage retrieval. However, relevant contextual terms do not always co-occur frequently with the query terms and vice versa. Hence the use of such methods often brings in noise, which leads to reduced precision. Previous studies have demonstrated the importance of relationship analysis for natural language queries in passage retrieval. However, they found that without query expansion, the performance is not satisfactory for short queries. In this paper, we present two novel query expansion techniques that make use of dependency relation analysis to extract contextual terms and relations from external corpuses. The techniques are used to enhance the performance of density based and relation based passage retrieval frameworks respectively. We compare the performance of the resulting systems with LCA in a density based passage retrieval system (DBS) and a relation based system without any query expansion (RBS) using the factoid questions from the TREC-12 QA task. The results show that in terms of MRR scores, our relation based term expansion method with DBS outperforms the LCA by 9.81%, while our relation expansion method outperforms RBS by 17.49%.
Efficient Identification of Additive Link Metrics via Network Tomography

We investigate the problem of identifying individual link metrics in a communication network from accumulated end-to-end metrics over selected measurement paths, under the assumption that link metrics are additive and constant during the measurement, and measurement paths cannot contain cycles. We know from linear algebra that all link metrics can be uniquely identified when the number of linearly independent measurement paths equals u, the number of links. It is, however, inefficient to collect measurements from all possible paths, whose number can grow exponentially in u, as the number of useful measurements (from linearly independent paths) is at most u. The aim of this paper is to develop efficient algorithms for constructing linearly independent measurement paths and calculating link metrics. We show that whenever there exists a set of u linearly independent measurement paths, there must exist a set of three pairwise independent spanning trees. We exploit this property to develop an algorithm that can construct u linearly independent, cycle-free paths between monitors without examining all candidate paths, whose complexity is quadratic in u. A further benefit of the proposed algorithm is that the generated paths satisfy a nested structure that allows linear-time computation of link metrics without explicitly inverting the measurement matrix. Our evaluations on both synthetic and real network topologies verify the superior efficiency of the proposed algorithms, which are orders of magnitude faster than benchmark solutions for large networks.
Spectral efficiency of multi-cell multi-user DAS with pilot contamination

The spectral efficiency of multi-cell multi-user distributed antenna system with imperfect channel state information (CSI) is studied in this paper. First, considering the pilot contamination and minimum mean-squared-error (MMSE) channel estimation, the equivalent channel model is presented and the sum-rate of the uplink transmission is derived. Under a physical channel model, the equivalent channel model can be seen as a special case of the joint correlated MIMO channel model proposed by Gao [1]. Then, the closed-form approximation of the spectral efficiency is derived. The spectral efficiency of the system with large number of antennas is also studied. Finally, the theoretical results are compared with Monte-Carlo simulation, and the performances of DAS and co-located massive MIMO are also compared. It is shown that similar to the systems with perfect CSIs, DAS still has large performance gains compared with massive MIMO.
Decision support system for heart disease based on support vector machine and Artificial Neural Network

The medical diagnosis process can be interpreted as a decision making process, during which the physician induces the diagnosis of a new and unknown case from an available set of clinical data and from his/her clinical experience. This process can be computerized in order to present medical diagnostic procedures in a rational, objective, accurate and fast way. This paper presents a decision support system for heart disease classification based on support vector machine (SVM) and Artificial Neural Network (ANN). A multilayer perceptron neural network (MLPNN) with three layers is employed to develop a decision support system for the diagnosis of heart disease. The multilayer perceptron neural network is trained by back-propagation algorithm which is computationally efficient method. Results obtained show that a MLPNN with back-propagation can be successfully used for diagnosing heart disease than support vector machine.
A scene recognition method using sparse features with layout-sensitive pooling and extreme learning machine

Scene recognition aims to find a semantic explanation of a scene, i.e., it helps intelligent machines to know where they are. It can be widely applied into various tasks in computer vision and robotics. Most of pioneer methods extracted a set of low-level features and put them into classifier directly to identify scene category. But it has been proved that low-level features do not work well. Currently researchers aim to overcome the semantic gap between the low-level vision features and high-level semantic categories to improve the recognition performance. Therefore, much attention has been put on transforming low-level descriptors into richer intermediate representations. This paper proposed a novel method based on intermediate feature representation to solve the problem of recognizing the semantic category of scene image. This proposed method uses sparse coding on SIFT features and presents a spatial layout sensitive pooling method. The space layout for pooling is based on three rectangles with size of 1∗1,1∗4 and 4∗1 in each image. They are derived from inherent characteristics of the scene images by regularly dividing the image in horizontal and vertical direction. This spatial pooling strategy is easier and it can get optimal representation of scene images. Extreme learning machine (ELM) is used as a classifier. ELM has shown great ability to fit nonlinear classification boundaries. Experimental results have shown that this proposed method not only extracts lower dimension image feature but also outperforms other similar state-of-the-art methods in terms of recognition performance.
FRCA: A Fuzzy Relevance-Based Cluster Head Selection Algorithm for Wireless Mobile Ad-Hoc Sensor Networks

Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.
Quaternionic approach to the one-regular eight-band linear phase paraunitary filter banks

Besides perfect reconstruction and linear phase, regularity is a desirable essential property of filter banks for image coding as it is associated with the smoothness of the related wavelet basis. This paper shows how to constrain quaternionic factorizations of eight-band linear phase paraunitary filter banks to have the first regularity structurally imposed. The result is not very general but some facts make it notable. Firstly, these systems are a direct extension of the standard eight-point discrete cosine transform (DCT) and this facilitates practical applications. Secondly, the first regularity eliminates the DC leakage which cause visually annoying checkerboard artifact. Finally, our solution offers clear advantages over the known ones as the regularity conditions are formulated directly in terms of quaternionic lattice coefficients. Namely, both regularity and losslessness can be easily preserved regardless of coefficient quantization unavoidable in finite-precision implementations.
The Dropper Effect: Insights into Malware Distribution with Downloader Graph Analytics

Malware remains an important security threat, as miscreants continue to deliver a variety of malicious programs to hosts around the world. At the heart of all the malware delivery techniques are executable files (known as downloader trojans or droppers) that download other malware. Because the act of downloading software components from the Internet is not inherently malicious, benign and malicious downloaders are difficult to distinguish based only on their content and behavior. In this paper, we introduce the downloader-graph abstraction, which captures the download activity on end hosts, and we explore the growth patterns of benign and malicious graphs. Downloader graphs have the potential of exposing large parts of the malware download activity, which may otherwise remain undetected. By combining telemetry from anti-virus and intrusion-prevention systems, we reconstruct and analyze 19 million downloader graphs from 5 million real hosts. We identify several strong indicators of malicious activity, such as the growth rate, the diameter, and the Internet access patterns of downloader graphs. Building on these insights, we implement and evaluate a machine learning system for malware detection. Our system achieves a 96.0% true-positive rate, with a 1.0% false-positive rate, and detects malware an average of 9.24 days earlier than existing anti-virus products. We also perform an external validation by examining a sample of unlabeled files that our system detects as malicious, and we find that 41.41% are blocked by anti-virus products.
HO2IEV: Heavyweight ontology based web information extraction technique for visionless users

As the internet grows rapidly, millions of web pages are being added on a daily basis. The extraction of precise information is becoming more and more difficult as the volume of data on the internet increases. Several search engines and information fetching tools are available on the internet, all of which claim to provide the best crawling facilities. For the most part, these search engines are keyword based. This poses a problem for visually impaired people who want to get the full use from online resources available to other users. Visually impaired users require special aid to get along with any given computer system. Interface and content management are no exception, and special tools are required to facilitate the extraction of relevant information from the internet for visually impaired users. The HO2IEV (Heavyweight Ontology Based Information Extraction for Visually impaired User) architecture provides a mechanism for highly precise information extraction using heavyweight ontology and built-in vocal command system for visually impaired internet users. Our prototype intelligent system not only integrates and communicates among different tools, such as voice command parsers, domain ontology extractors and short message engines, but also introduces an autonomous mechanism of information extraction (hereafter referred to as IE) using heavyweight ontology. In this paper we designed domain specific heavyweight ontology using OWL (Web Ontology Language) for ontology modeling and PAL (Protégé Axiom Language) for axiom writing. We introduced a novel autonomous mechanism for IE by developing prototype software. A series of experiments were designed for the testing and analysis of the performance of heavyweight ontology in general, and our information extraction prototype specifically.
MONOPOD HOPPING ON ROUGH PLANETARY ENVIRONMENTS

Recent successes of missions such as the MSL and the Rosetta have increased the interest in the robotic exploration of other planets and asteroids. Although most of these missions envisage the use of rovers, legged robots have shown the potential to outperform wheeled vehicles on rough terrains in terms of speed and energy efficiency. In this paper, the x-MP controller presented in recent work, is used to evaluate the performance of a monopod robot under the effect of different gravitational fields and terrain types. The performance of the x-MP controller during regulating the robot motion on rough terrains and for the exploration of different types of planetary environments will be examined using simulations. Additionally using the Cost of Transport index, useful conclusions regarding the performance of legged robots for planetary exploration will be extracted.
Mutant p63 causes defective expansion of ectodermal progenitor cells and impaired FGF signalling in AEC syndrome

Ankyloblepharon‐ectodermal defects‐cleft lip/palate (AEC) syndrome, which is characterized by cleft palate and severe defects of the skin, is an autosomal dominant disorder caused by mutations in the gene encoding transcription factor p63. Here, we report the generation of a knock‐in mouse model for AEC syndrome (p63+/L514F) that recapitulates the human disorder. The AEC mutation exerts a selective dominant‐negative function on wild‐type p63 by affecting progenitor cell expansion during ectodermal development leading to a defective epidermal stem cell compartment. These phenotypes are associated with impairment of fibroblast growth factor (FGF) signalling resulting from reduced expression of Fgfr2 and Fgfr3, direct p63 target genes. In parallel, a defective stem cell compartment is observed in humans affected by AEC syndrome and in Fgfr2b−/− mice. Restoring Fgfr2b expression in p63+/L514F epithelial cells by treatment with FGF7 reactivates downstream mitogen‐activated protein kinase signalling and cell proliferation. These findings establish a functional link between FGF signalling and p63 in the expansion of epithelial progenitor cells and provide mechanistic insights into the pathogenesis of AEC syndrome.
Characterization of Two Second-Site Mutations Preventing Wild Type Protein Aggregation Caused by a Dominant Negative PMA1 Mutant

The correct biogenesis and localization of Pma1 at the plasma membrane is essential for yeast growth. A subset of PMA1 mutations behave as dominant negative because they produce aberrantly folded proteins that form protein aggregates, which in turn provoke the aggregation of the wild type protein. One approach to understand this dominant negative effect is to identify second-site mutations able to suppress the dominant lethal phenotype caused by those mutant alleles. We isolated and characterized two intragenic second-site suppressors of the PMA1-D378T dominant negative mutation. We present here the analysis of these new mutations that are located along the amino-terminal half of the protein and include a missense mutation, L151F, and an in-frame 12bp deletion that eliminates four residues from Cys409 to Ala412. The results show that the suppressor mutations disrupt the interaction between the mutant and wild type enzymes, and this enables the wild type Pma1 to reach the plasma membrane.