Miracle-dz/UniFAR-FTUs huggingface dataset:
A FUZZY C-MEANS ALGORITHM AND SENTIMENT-LEXICONS-BASED MULTI-DIMENSIONAL VECTORS OF A SOKAL & SNEATH-IV COEFFICIENT USED FOR ENGLISH SENTIMENT CLASSIFICATION

Sentiment classification has long been the subject of research and there are many applications and many studies to service communities, commerce, politics, etc. In this research, we have proposed a new model for Big Data sentiment classification in the parallel network environment – a Cloudera system with Hadoop Map (M) and Hadoop Reduce (R). Our new model has used a Fuzzy C-Means Algorithm (FCM) with sentiment-lexicons-based multi-dimensional vectors and 3,000,000 documents of our training data set for document-level sentiment classification in English. First, we calculate the sentiment scores of English terms (verbs, nouns, adjectives, adverbs, etc.) by using a SOKAL & SNEATH-IV coefficient (SSIVC) through a Google search engine with AND operator and OR operator. Then, we transfer all the documents of both the testing data set and the training data set into many multi-dimensional vectors which are identified by using the sentiment lexicons. Finally, we implement the proposed model in both a sequential environment and a distributed system. Our new model can classify sentiment of millions of English documents based on many English documents in the parallel network environment. However, we tested our new model on our testing data set (including 5,500,000 English reviews, 2,750,000 positive and 2,750,000 negative) and achieved 87.82% accuracy. The results of this work can be widely used in applications and research of the English sentiment classification.
Social Media Video Content Diversity Visualization

In this paper diversity visualization of video content posted on Social Media is efficiently performed, by proposing an unsupervised intelligent video analysis scheme. The proposed scheme assumes several different videos, posted by several different social media users. Its aim is to provide an overall compact view of the diverse video content people share. Similarly, it is like providing a summary of the total posted visual information (for a specific time instance or interval), so that users can take an idea of what is happening outside their micro-world. Towards this direction, each video is analyzed and key-frames are extracted based on a correlation measure and a social computing algorithm. The final summary is created by extracting the most uncorrelated frames among all key-frames, so that the diversity of the visualized content is kept. Experimental results are presented, to denote the full potential of the proposed scheme, its advantages as well as important issues for future work. Key-Words: Social Media, Social Computing, Video Summarization, Content Diversity, Correlation
L G ] 1 0 A pr 2 01 9 Programmatically Interpretable Reinforcement Learning

We present a reinforcement learning framework, called Programmatically Interpretable Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable agent policies. Unlike the popular Deep Reinforcement Learning (DRL) paradigm, which represents policies by neural networks, PIRL represents policies using a high-level, domain-specific programming language. Such programmatic policies have the benefits of being more easily interpreted than neural networks, and being amenable to verification by symbolic methods. We propose a new method, called Neurally Directed Program Search (NDPS), for solving the challenging nonsmooth optimization problem of finding a programmatic policy with maximal reward. NDPS works by first learning a neural policy network using DRL, and then performing a local search over programmatic policies that seeks to minimize a distance from this neural “oracle”. We evaluate NDPS on the task of learning to drive a simulated car in the TORCS car-racing environment. We demonstrate that NDPS is able to discover human-readable policies that pass some significant performance bars. We also show that PIRL policies can have smoother trajectories, and can be more easily transferred to environments not encountered during training, than corresponding policies discovered by DRL.
Automatic MRI Prostate and Central Gland Segmentation using Holistically Nested Networks with Short Connections

Accurate and automated prostate and central gland segmentation on MR images is essential for aiding the prostate cancer diagnosis system. This work presents a 2D orthogonal volumetric deep learning method to automatically segment the whole prostate and central gland from T2-weighted axial MR images. The method also generates high density 3D surfaces from the low-resolution (z-axis) MR images. In the past research works, most methods focus on axial image alone, for example, 2D based segmentation of the prostate from each 2D slice. Those methods suffer the problems of over-segmenting or under-segmenting the prostate at apex and base, which is the major contribution of errors. The proposed method leverages the volumetric context to effectively reduce the apex and base segmentation ambiguities. It also overcomes the jittering or laddering surface artifacts when constructing a 3D surface from 2D segmentation or 3D U-Net segmentation approaches. The experimental results demonstrate that the proposed method can achieve 92.4%±3% Dice similarity coefficient (DSC) for prostate and DSC of 90.1%±4.6% for central gland without trimming any ending contours at apex and base. Our experiments illustrate the feasibility and robustness of the 2D volumetric Holistically Nested Networks with Short Connections method for MR prostate and central gland segmentation. The proposed method achieves a competitive performance with the current literature methods.
Prosodic Organization and Focus Realization in Taiwan Mandarin

Cross-linguistically, the way that focus is marked through prosody can depend on a variety of factors, including local constraints on prosodic organization or the position of a word within the larger focus constituent. Here we report on a production study that explores the possible influence of prosodic organization and position on focus realization in Taiwan Mandarin. The materials consisted of sentences in which the syntactic subject consisted of a monosyllabic numeral, classifier, and noun. The context was manipulated to elicit narrow information focus (i.e., using wh-questions) on either the numeral, the noun, or the entire NP. The resulting target syllables were then analyzed in terms of their F0 characteristics, duration, and amplitude. The results revealed clear asymmetries in how the numeral and noun were realized in their corresponding singleword narrow focus condition versus in the NP focus condition, though confiding to the intrinsic tone-patterns (e.g., Tone 1 versus Tone 3 words showed different contours). Moreover, the classifier did not always conform to its expected status when external to the focus constituent. These results thus show that focus effects in Taiwan Mandarin cannot be explained in terms of single parameter acoustic enhancement on individual focused words, and they suggest that focus marking in that variety is conditioned by independent constraints on local prosodic structure.
Seasonal Dynamics of Riverine Zooplankton Functional Groups in Turkey: Kocaçay Delta as a Case Study

River ecosystems are among the most affected habitats globally by human activities, such as the release of industrial, agricultural and domestic pollutants to the rivers. However, how affected zooplankton functional groups in rivers are largely unknown. In the present study zooplankton functional and taxonomic structure were investigated seasonally in relation to environmental parameters between 2013 and 2015 in Kocaçay Delta located on the South of the Marmara Region. The environmental parameters (e.g. water temperature, conductivity, dissolved oxygen, pH, chlorophylla and nutrients) were measured at four sampling station covering estuarine and riverine area. A total of 44 taxa of zooplankton were identified and mostly dominated by cosmopolite microphagous rotifers reflecting the proper intervals of environmental parameters for their habitat choices. Among rotifers Microcodides chlaena and Keratella serrulata were the new records for the study site and Turkish rotifer list. The only selective filter feeder was Bosmina longirostris in Kocacay Delta. The zooplankton functional group, microphagous, showed seasonal variations and affected by chlorophyll-a content. We suggested that trait-based approaches would be a useful tool to assess the degree of environmental disturbance and interaction between trophic levels.
A Novel Method for Tracking Tensor-based Regions of Interest in Large-Scale , Spatially-Dense Turbulent Combustion Data

In visualization of tensor data associated with turbulent combustion simulations, the datasets we have to process and visualize are typically enormous in size and continue to grow exponentially. The large amount of data may severely affect the manipulating speed, thus posing a major challenge to interactive visualization [2]. Even worse, tensor datasets tend to be very dense leading to clutter and occlusion problems. To address these issues, feature extraction is an emerging method. Typically, only a small percentage of data is of interest, thus making the effective visualization of very large datasets possible. Feature extraction also helps the users highlight and focus on regions of interest. We introduce an approach for the segmentation, visualization and tracking of regions of interest in large scale tensor field datasets generated by computational turbulent combustion simulations. We use canopy clustering followed by a K-means algorithm to partition and cluster the tensor field components. The resulting clusters are tracked through multiple timesteps. Interactive, hardware-
THE MEANING OF PROOF IN MATHEMATICS EDUCATION

Perhaps it is a sign of the maturity of research into the teaching and learning of proof and proving that we are beginning to reflect on what it is we are researching, and whether, as a community, we are successful in communicating our work to each other. Three papers have been presented in the past seven years exploring the different meaning for “proof” and “proving” used in different research communities within mathematics education. In this paper I will continue this exploration and attempt to create a common terminology through which these meanings can be interpreted.
Ensemble of small models for estimating potential abundance of Caucasian grouse (Lyrurus mlokosiewiczi) in Iran

The need to obtain information on population size to inform management actions for conservation is imperative. Despite this, reliable data on Caucasian grouse abundance is scarce in Iran. The goal of this study was to explore the potential distribution of Caucasian grouse using an ensemble of small models with outstanding performance for modelling rare species’ distributions to estimate the potential population size in Iran. We fitted an ensemble of small models with generalized boosted model (GBM) and maximum entropy (MaxEnt), and then built a final ensemble prediction by averaging across these two ensembles of small models.
A Role Concept and Design Space for Social VR Application

Virtual Reality has been increasing in popularity in the last years. However, viewers can feel isolated using head-mounted displays. Since the content of VR applications is often suitable for social experiences, it is important to know if the use of head-mounted displays is appropriate for enabling shared VR experiences and how non-HMD users can be involved. Even if viewers are in the same virtual environment simultaneously, they do not automatically see the same field of view, since they can freely choose the viewing direction. Our goal is to explore which components are needed and which values are available to efficiently support social VR experiences. We present and discuss a role concept and a design space for social VR application and highlight directions for future work. Author
Master Thesis

The purpose of this thesis is to investigate the role of networking for social entrepreneurs. The thesis conducts three case studies of Swedish social entrepreneurs and investigates the role of networking for these entrepreneurs in executing their businesses. In the study, a literature review combining traditional network theory and entrepreneurship theory with recent research on social entrepreneurship is presented. The literature review is used as background for the analysis by providing an understanding of the motivations for and functions of networking for network actors. The study shows that networking plays a role in enabling the social entrepreneur to take on a multifaceted role set. Hence, the role of networking can be seen as helping the social entrepreneur to be present and active in different roles simultaneously, which helps the social entrepreneur in managing the tension inherent in running a commercial business.
Solution of inverse problem for reservoir permeability

A precise knowledge of permeability is crucial for accurate simulation of reservoir performance and enhanced reservoir management. At present permeability is measured by taking a core sample to a lab and measuring flow rates, but this is both expensive and less accurate because there can be significant problems with measurement error, and the value is measured only at the point of the sample. The best way to resolve this problem is to obtain in-situ permeability with the aid of reservoir characteristics by means of solving differential equations which govern the fluid flow in porous media.
1 Relative Values for Asset Sales

This study provides evidence that the outcome for shareholders resulting from asset sales is determined at the time of transaction by the value for the asset sold. Assets sold above market value are followed by positive and significant abnormal returns over the following three months; these returns are magnified in firms where the balance of power in external governance favors shareholders. Abnormal returns following undervalued asset sales are insignificant from zero, indicating value-preservation. Value-preservation when the assets are sold below market value is more likely for firms that are approaching financial constraints and for firms with quality internal governance. This evidence is documented for apartment REITs where the volume of comparable transactions is sufficient for estimation of expected market values at the time of sale.
Improved approach to weighting terms in Web Text

This paper uses vector space model as the description of the Web text,analyses and improves the traditional formula TF*IDF.First,we explore the feature of the Web pages which are written in HTML and describe the situation information of the terms in Web text.Second,we use generalized information theory as the theory base to introduce the quadratic entropy mutual information into the formula.The experiment shows the feasibility and the validity of this method.
01 Planar Development of Digital Free-Form Surfaces

Three are the main goals of this chapter. First, a detailed review of differential-geometry criteria for the developability of free-form surfaces is presented. Then, tools measuring accuracy of planar developments are introduced and analyzed. These tools are a prerequisite for evaluating the numerous methods/approaches proposed for generating flat developments of digital free-form surfaces whose level of involvement in many areas of CAD/CAM and Computer Graphics is constantly increasing. Finally, some of the most efficient surface flattening methods are analyzed and categorized followed by discussion of representative examples.
Real-time activity recognition on a mobile companion robot

Recently, there has been an increasing research effort in supporting people by mobile robots in home environments. In this scope, activity recognition can tremendously enhance the social interaction skills of a robot by taking into account the user’s state. Additionally, the system can adapt to the user’s preferences and habits or detect deviations from daily routines. This paper presents a novel real-time activity recognition system on a mobile robot. The system continuously tracks the pose and motion of the user and combines them with structural knowledge like the current room or objects in proximity. All extracted features are modeled as probability distributions and processed by Bayesian Networks to reason about different activities. First experimental results on real data show the usefulness of our approach.
Network Traffic Time Series Generation abs ( DFT ) Normal DDoS TestEvaluationTraining KSVD Algorithm Basic Matching Pursuit SOM Coefficients Dictionary Normal Behavior Modeling Step Dictionary Generation Step Feature Extraction Step Basic Matching Pursuit

Distributed Denial of Service (DDoS) attacks have become one of the most significant problems that affects the user satisfaction by degrading the availability of on-line services. Although intrusion detection systems provide effective mechanism for discriminating various DDoS attacks, they become impotent of detection when bogus packets similar to normal ones are dispatched by the attacker. One idea is to model the normal behavior of the network traffic using time series representation of that traffic together with advanced statistical analysis techniques such as frequency domain analysis for detecting the occurrence frequency (energy) of each basic element in time series. However, frequency domain analysis may become inadequate if the original frequency features are used for the detection anomalies. Therefore, in this work, we propose a hybrid approach that employs frequency domain analysis with sparse representation model to find discriminative characteristics for anomaly-based DDoS detection. The proposed algorithm distinguish abnormal traffic from the normal one based on the energy of time series for the number of packets feature, which is extracted from the time series data by using the sparse representation model. Experimental results show that performance of the proposed algorithm provides better DDoS detection results than the state-of-the-art time-series based approaches in the literature.
Finally, a Polymorphic Linear Algebra Language

Many diﬀerent data analytics tasks boil down to linear algebra primitives. In practice, for each diﬀerent type of workload, data scientists use a particular specialised library. In this paper, we present Pilatus , a polymorphic iterative linear algebra language, applicable to various types of data analytics workloads. The design of this domain-speciﬁc language (DSL) is inspired by both mathematics and programming languages: its basic constructs are borrowed from abstract algebra, whereas the key technology behind its polymorphic design uses the tagless ﬁnal approach (a.k.a. polymorphic embedding/object algebras). This design enables us to change the behaviour of arithmetic operations to express matrix algebra, graph algorithms, logical probabilistic programs, and diﬀerentiable programs. Crucially, the polymorphic design of Pilatus allows us to use multi-stage programming and rewrite-based optimisation to recover the performance of specialised code, supporting ﬁxed sized matrices, algebraic optimisations, and fusion. 2012 ACM Subject Classiﬁcation Software and its engineering → Domain speciﬁc languages; Computing methodologies → Linear algebra algorithms
Title Seasonal dynamics and interactions among Baltic Sea prokaryotic and eukaryotic plankton assemblages Running title High-frequency planktonic dynamics and interactions

One of the main goals of microbial ecology is to identify the mechanisms that regulate patterns in community structure at temporal scales compatible with populations’ turnover times across complete seasonal cycles. Here, we examined high-frequency temporal dynamics of marine plankton from a sampling effort covering 2011-2013, roughly twice weekly, comprising 144 samples. Bacterial and eukaryotic communities were profiled by 16S and 18S high-throughput sequencing, respectively. Interestingly, we found that no combination of the measured environmental parameters could predict a significant proportion of the variation in population dynamics of bacterioplankton, and even less so for eukaryotic plankton. Large differences in physicochemical conditions and community composition typical of temperate climates mean that different regimes can quickly succeed each other over the year, with the relative importance of different drivers changing equally rapidly. Nevertheless, our approach revealed interesting recurrent co-occurrence patterns across distinct environmental changes. Hence, we could make abundance predictions for more than half of the most frequent OTUs based on interactions with other OTUs. These results suggests that a complex set of biotic interactions are contributing to temporal patterns among planktonic assemblages despite rapid changes in environmental conditions.
The Logic of EU Policy-Making on ( Irregular ) Migration : Securitisation or Risk ?

In the academic literature it has become common-place to assume that migration is treated as a security issue in the EU, and that EU migration policy has become “securitised”. However, countervailing tendencies exist: some observers have recently recognised a certain liberal turn in EU migration policy, and securitisation is sometimes applied in the academic literature rather loosely, i.e. to phenomena and developments that may rather follow a different logic, the logic of risk. The paper develops an operationalisation for distinguishing between the two logics empirically. Subsequently we probe the logic of security/securitisation and the logic of risk empirically through a qualitative content analysis of EU documents on (irregular) migration. Our findings suggest that, while there is evidence for both concepts, the risk logic has dominated the EU discourse in this field in recent years. In addition, it seems that different types of migrants are treated according to different logics in the EU documentation.
WACSgen v 1 . 0 User guide and description of the model

This document presents the stochastic weather generator WACSgen with its associated models, methods and algorithms. WACSgen is a unisite stationary multivariate weather generator for daily climate variables based on weather-states that uses a Markov chain for modeling the succession of (an unlimited number of) weather states. Conditionally to the weather states, the multivariate variables are modeled using the family of Complete skew-normal distributions. It is described in Flecher et al. (2010). Since this rst paper, the model has slightly changed, in particular as regards the modeling of the temporal autocorrelation. This document provides is organized in three, relatively independent, parts. The rst part is a classical user guide. It provides a work ow description of WACSgen. The second part describes in details the model, the methods, the associated algorithms and the mathematics involved. The third part is a description of the main functions and variables in WACSgen, thus providing a more detailed account of the implementation as a set of R functions.