Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy-preserving source separation for distributed data using independent component analysis

Building good feature representations and learning hidden source models typically requires large sample sizes. In many applications, however, the size of the sample at an individual data holder may not be sufficient. One such application is neuroimaging analyses for mental health disorders - there are many individual research groups, each with a moderate number of subjects. Pooling such data can enable efficient feature learning, but privacy concerns prevent sharing the underlying data. We propose a model for private feature learning in which the data holders share differentially private views of their respective datasets to enable collaborative learning of a joint feature map. We give an example of such an algorithm for independent component analysis (ICA) - a popular blind source separation algorithm used in neuroimaging analyses. Our algorithm is a differentially private version of the recently proposed distributed joint ICA algorithm. We evaluate the performance of this method on simulated functional magnetic resonance imaging (fMRI) data.
A Content-Based Algorithm for Blog Ranking

Due to the different structure and content character of blogs, the traditional ranking algorithm of Web page turns to be insufficient. To solve this problem, a novel ranking algorithm of blog is presented. The algorithm considers both the link analysis and the content analysis of the blog, It helps mining more implicit features of blog, such as common topics, to improve the satisfaction of the users. The experimental results show that the proposed method can improve retrieval performance obviously.
Privacy Preserving Frequent Pattern Mining on Multi-cloud Environment

As the age of big data evolves, outsourcing of data mining tasks to multi-cloud environments has become a popular trend. To ensure the data privacy in outsourcing of mining tasks, the concept of support anonymity was proposed to hide sensitive information about patterns. Existing methods that tackle the privacy issues, however, do not address the related parallel mining techniques. To fill this gap, we refer to a pseudo-taxonomy based technique, called as k-support anonymity, and improve it into multi-cloud environments. This has several advantages. First, outsourcing to multi-cloud environments can meet the requirement of great computational resources in big data mining, and also parallelize the mining tasks for better efficiency. Second, the data that we send out to a cloud can be partial. An assaulter who gets the data in one cloud can never re-construct the original data. That means it is more difficult for an assailant to violate the privacy in outsourced data. Experimental results also demonstrated that our approaches can achieve good protection and better computation efficiency.
Limit distributions for multitype branching processes of $m$-ary search trees

A particular continuous-time multitype branching process is consid- ered, it is the continuous-time embedding of a discrete-time process which is very popular in theoretical computer science: the m-ary search tree (m is an integer). There is a well-known phase transition: when m ≤ 26, the asymptotic behavior of the process is Gaussian, but for m ≥ 27 it is no more Gaussian and a limit W of a complex-valued martingale arises. Thanks to the branching property it appears as a solution of a smoothing equation of the type Z L = e −λT (Z (1) + ··· + Z (m) ), where λ ∈ C, the Z (k) are independent copies of Z and T is a R+-valued random variable, independent of the Z (k) . This distributional equation is extensively stud- ied by various approaches. The existence and unicity of solution of the equation are proved by contraction methods. The fact that the distribution of W is abso- lutely continuous and that its support is the whole complex plane is shown via Fourier analysis. Finally, the existence of exponential moments of W is obtained by considering W as the limit of a complex Mandelbrot cascade.
Identifying the best feature combination for sentiment analysis of customer reviews

Opinions are increasingly available in form of reviews and feedback at websites, blogs, and microblogs which influence future customers. From human perspective, it is difficult to read all the opinions and summarize them which require an automated and faster opinion mining to classify the reviews. In this paper different features namely, N-gram features, POS based features and features based on the lexicon SentiWordNet, have been investigated. The Support Vector Machines (SVM) classifier has been modeled with presence as feature representation for classification of the reviews into positive and negative classes thereby identifying the best feature combination. Results of Experiments conducted on smart phone reviews for different feature combinations have been presented. A highest accuracy up till 92% and 95% has been obtained for small and large datasets, respectively.
Interactive Movie Recommendation Through Latent Semantic Analysis and Storytelling

Recommendation has become one of the most important components of online services for improving sale records, however visualization work for online recommendation is still very limited. This paper presents an interactive recommendation approach with the following two components. First, rating records are the most widely used data for online recommendation, but they are often processed in high-dimensional spaces that can not be easily understood or interacted with. We propose a Latent Semantic Model (LSM) that captures the statistical features of semantic concepts on 2D domains and abstracts user preferences for personal recommendation. Second, we propose an interactive recommendation approach through a storytelling mechanism for promoting the communication between the user and the recommendation system. Our approach emphasizes interactivity, explicit user input, and semantic information convey; thus it can be used by general users without any knowledge of recommendation or visualization algorithms. We validate our model with data statistics and demonstrate our approach with case studies from the MovieLens100K dataset. Our approaches of latent semantic analysis and interactive recommendation can also be extended to other network-based visualization applications, including various online recommendation systems.
Body Mass Index Reference Curves for Children Aged 0-18 Years in Shaanxi, China

Objectives: Health care professionals have recommended the use of age-related body mass index (BMI) to evaluate obesity in children. Until now, no age-related reference curves for BMI have been reported in China. Presented here are age-related BMI percentile curves for children aged 0~18 years in Shaanxi, China, 1995. Methods: The Third Nationwide Growth Survey was performed in 1995 and from this survey, data of the Shaanxi population were retrieved to construct the age-related BMI percentile curves. A total of 27,200 healthy children aged 0~18 years were examined for height and weight, using the standardized methods. The λ-median-coefficient of variation (LMS) method was used for curve fitting; all analyses were carried out on the basis of different sexes and areas through a special program for LMS method. Results: Median BMI increased steeply in early life, with a peak at 8 months, then declined, and then leveled off at about 6 years. The age of adiposity rebound for urban children was about two years earlier than that for rural children and one year earlier for boys than for girls. After adiposity rebound, BMI increased more rapidly in girls than in boys, and the increase in urban children was more rapid than that in rural children. As the onset of puberty, female BMI became higher than that of males, and the difference between boys and girls was larger for rural children than for urban children. The 95th, 50th and 5th percentiles for Shaanxi children were lower than those of comparable American children. Cut-off points for obesity was lower than those of international averages, suggesting the nutrition status of Shaanxi children is lower than that of children in developed countries, and has not reached the international average level. Conclusions: Using the LMS method, we constructed age-related BMI percentile curves for Shaanxi children aged 0~18 years, the first for Chinese children. Percentile curves and cut-off points for obesity can be used as a reference for assessing the nutrition status of Shaanxi children aged 0~18 years. The identified gender and residency differences may serve as guides to an understanding of the cause and prevention of obesity.
Investigating the aetiology of and evaluating the impact of the Men C vaccination programme on probable meningococcal disease in England and Wales

The aims were to (1) investigate the aetiology of probable meningococcal disease, where a clinical diagnosis is made in the absence of laboratory data, and (2) evaluate the impact of the Men C vaccination programme in England and Wales. Multiple linear regression analyses were carried out using data reported to Enhanced Surveillance of Meningococcal Disease (ESMD) and laboratory reports of isolates of organisms causing symptoms that mimic meningococcal disease. Confirmed meningococcal disease appeared to be a significant predictor of probable disease. Thus, an additional reduction in meningococcal disease attributable to the serogroup C vaccination campaign was evident in probable disease over and above that observed in confirmed cases alone. Enteroviruses were a significant contributor to cases of probable meningitis and influenza appeared to be a significant contributor to probable cases of septicaemia. This analysis confirms the success seen following the Men C vaccination campaign and gives an indication of the aetiologies of other causes of probable meningitis and septicaemia reported to ESMD.
Staying in School: Assessing the Role of Access, Availability and Opportunity Cost Staying in School: Assessing the Role of Access, Availability and Opportunity Cost

This paper investigates the role of contextual factors outside the household in determining whether or not a child attends basic education in Tajikistan. By combining data from the Tajikistan Living Standard Survey with data from a parallel community survey, aggregated census data at the jamoat (village) level, and spatial data, a series of variables are constructed which characterize the environment where the child lives. These variables serve as proxies for the accessibility and availability of school services, quality of education, opportunity cost of education in terms of the opportunities for income generating activities forgone, and level of economic development in the communities. Applying multilevel modelling techniques, the results show that contextual factors have a strong effect on school attendance. Accessibility of service and higher quality of school have a positive effect, however a high opportunity cost to education in a community exerts a negative effect on school attendance. Abstract This paper investigates the role of contextual factors outside the household in determining whether or not a child attends basic education in Tajikistan. By combining data from the Tajikistan Living Standard Survey with data from a parallel community survey, aggregated census data at the jamoat (village) level, and spatial data, a series of variables are constructed which characterize the environment where the child lives. These variables serve as proxies for the accessibility and availability of school services, quality of education, opportunity cost of education in terms of the opportunities for income generating activities forgone, and level of economic development in the communities. Applying multilevel modelling techniques, the results show that contextual factors have a strong effect on school attendance. Accessibility of service and higher quality of school have a positive effect, however a high opportunity cost to education in a community exerts a negative effect on school attendance.
Language, literacy and identity practices influencing acculturation in immigrant/migrant Nepalese families:An Ethnographic study

This thesis is an ethnographic study which investigates the practices of language maintenance and ethnic identity affiliation for immigrant/migrant Nepalese families with primary to middle school children in the United Kingdom. The thesis investigates the connection between two larger fields of research; language maintenance and ethnic identity affiliation. Previously, research investigated one area or the other. After initial interviews with the family members it was determined from investigation into past exposure and present participation in the various practices of both ethnic identity and language maintenance that deeper investigation needed to occur. Finally, it was determined that in the prior research none had examined differences between family members and reasons for these variations. The thesis presents an ethnographic comparative case study analysis of three Nepalese families using a three-tiered macro, micro, and internal theoretical framework in conjunction with a newly redefined acculturation continuum. Observation, which was participatory, occurred in various contexts during a nine month period. In conjunction with these observation sessions, semi-structured interviews regarding present participation in language maintenance and ethnic identity practices, and historical narrative interviews investigating prior exposure to home country, ethnic, cultural, and social literacy practices occurred. In addition, data analysis from the observations used a language maintenance practice checklist based on three types of practices (social language, social literacy, and peripheral ritualised practices) thus discovering whether shifts or loss occurred in families. This analysis revealed a newly termed practice of peripheral ritualised practices. Furthermore, observations discovered that it was not only exposure to home country experience and ethnic customs which play a significant role in shaping social and ethnic identity construction but more informatively the age of exposure. Consequently, the researcher was able to examine not only shifts from children via pre-adolescences to teens, but to assess differences between siblings, an area that had previously not been researched. Finally, the study noted that for children, pre-adolescence and adolescences there is a balance between globalised practices and ethnic practices which need further future investigation.
AIS Electronic

Online product review networks help to transmit information that customers can use to evaluate products in Internet commerce. These networks frequently include an explicit social component allowing consumers to view both how community members have rated individual product reviews and the social status of individual reviewers. We analyze how these social factors impact consumer responses to consumer review information. We use a new dataset collected from Amazon.com’s customer reviews of books. This dataset allows us to control for the degree to which other community members found the review helpful, and the reputation of the reviewer in the community. We find that more helpful reviews and highlighted reviews have a stronger impact on sales than other reviews do. We also find that reviewer information has a stronger impact on less popular books than on more popular books. These results suggest that the dynamics of reputation communities make it harder for self-interested parties to manipulate reviews versus an environment where all reviews are treated equally.
A fast MAP adaptation technique for gmm-supervector-based video semantic indexing systems

We propose a fast maximum a posteriori (MAP) adaptation technique for a GMM-supervectors-based video semantic indexing system.The use of GMM supervectors is one of the state-of-the-art methods in which MAP adaptation is needed for estimating the distribution of local features extracted from video data. The proposed method cuts the calculation time of the MAP adaptation step. With the proposed method, a tree-structured GMM is constructed to quickly calculate posterior probabilities for each mixture component of a GMM. The basic idea of the tree-structured GMM is to cluster Gaussian components and approximate them with a single Gaussian. Leaf nodes of the tree correspond to the mixture components, and each non-leaf node has a single Gaussian that approximates its descendant Gaussian distributions. Experimental evaluation on the TRECVID 2010 dataset demonstrates the effectiveness of the proposed method. The calculation time of the MAP adaptation step is reduced by 76.2% compared to that of a conventional method and resulting accuracy (in terms of Mean average precision) was 10.2%.
Technical Analysis and Prediction : A Neural Network Approach to the Italian Stock Market

The paper analyses the relationship between common technical analysis indicators and the returns of an index for the period considered. It is expected to find correlation between indicators and index prices, as well as showing clear patterns and potential strategies for investment analysis and portfolio management. As an innovative methodology a mixed analysis is carried out, trying to combine classic signals offered by the indicators with the power of neural networks. The neural network plays an important role in that allows for an accurate regression with efficient error minimization, while giving indications about the concentration of results obtained around some reference values. Through a simple hidden-layer, back-propagation algorithm, regressions give interesting result, in term of the forecasting potential of the analyzed indicators. The final step of the project is to conclude about results and summarize the indication coming from the multivariate stage analysis, commenting on the power of the indicators to reveal potential investment opportunities.
Renewed Geoarchaeological Investigations of Mwanganda's Village (Elephant Butchery Site), Karonga, Malawi

The site of Mwanganda's Village, located along a paleochannel in northern Malawi, is one of only a few sites that have characterized the Middle Stone Age (MSA) of Malawi for decades (Clark & Haynes, ; Clark et al., ; Kaufulu, ). The Malawi Earlier‐Middle Stone Age Project has re‐examined the site using new mapping and chronometric tools in order to reinterpret the site's significance within the context of current debates surrounding human origins and the potential role the environment played in shaping human behavior. The new data do not support the previous hypothesis that the site was an elephant butchery location (contra Clark & Haynes, ; Clark et al., ; Kaufulu, ). Instead, the evidence shows successive colonization of riparian corridors by MSA hunter‐gatherers focused on exploiting localized resources during periods of generally humid climates while other lakes desiccated across Africa. We challenge the hypothesis that stable and intermediately high lake levels within the African Rift Valley System (sensu Trauth et al., ) catalyzed the evolution of regional interaction networks between 42 and 22 ka. Instead, we interpret the evidence to suggest that regional variants of technology persist into the late MSA as foragers focused on exploiting resources from local catchments.
IGBT selection method for the design of resonant inverters for domestic induction heating

The power transistor (IGBT) is one of the most important components of the inverter of domestic induction cookers. For this application the maximum junction temperature (Tjmax) of the IGBT is one of the most critical design parameters because the cooker has to decrease the output power when the junction temperature is near to Tjmax of the device. The junction temperature (Tj) of the IGBT principally depends on the power losses. This paper proposes a method to choose the IGBT based on the calculation of the temperature variation ΔTja from the power losses. Nowadays, IGBT behaviour is a trade-off between conduction and switching performance, so it is necessary to measure the IGBT power losses separately. This paper proposes a method to measure the power losses separately based on electrical measurement and the IGBT conduction characteristics. Moreover it is proposed a criterion to determinate the limits of the power losses based on eliminate the offset of device voltage and the switching area is defined from the voltage is over 2.5 V to the current is zero. Finally, the proposed method has been applied to five different IGBTs for different levels of power and with two standard pans with an electronic board of a commercial cooker Moreover this method has been verified with a thermografic camera obtaining similar results.
Incentives to Innovate with Next Generation Networks

Although the concept of a new Internet protocol has been debated by various standardization bodies and technical forums for over a decade, the economic trade-offs implied by innovation and capital investment in an updated specification remain largely unexplored. This paper discusses the economics of the next generation networks based on Internet, and provides an accessible overview of the new protocol – IPv6 (Internet Protocol version 6). Work on IPv6 took off in the early 1990s when forecasts began to indicate that Internet address space would become an increasingly limited resource, curbing the future growth of the Internet. Several parallel efforts within the Internet community began searching for solutions to the problem of restricted address availability, while providing and improving Internet scalability and security to allow for easier configuration and network management. The current Internet protocol, IPv4 (Internet Protocol version 4), can be modified to resolve some of these issues, but experts expected to achieve far more useful results from the introduction and deployment of a new specification. During the first half of 1995 most of the design process was completed and the IPv6 protocol was specified. The current specification was published in 1998 by the Internet Engineering Task Force (IETF).
Private Enforcement of Federal Environmental Law

While the role of nongovernmental organizations in environmental policy is growing rapidly, our analytical understanding of the causes and consequences of this emerging role has not kept pace. In the 1970s, Congress authorized private organizations to seek injunctions (and in some cases penalties) against firms violating the terms of their environmental operating permits. The economic models in this paper show how the varying remedies, limitations, and reimbursement procedures can affect both the level and patterns of litigation activity as well as the compliance consequences. A database containing information on 1,205 cases is used to infuse empirical content into the analysis.
Network Forensic Computing Based on ANN-PCA

The network forensic computing is faced with the question of the massive information stream analyses. Two solutions including feature extracting and classification mining were proposed. The main work includes features extracting with ANN-PCA, classification mining after features extracting in FAAR algorithm which we proposed to mine association rules based on PCA and fuzzy classification technology. The experiment indicates that the classification accuracy raise distinctly after feature extracting, and the feature quantity decrease especially U2R attack. Further more, the number of generating rules also reduces clearly.