Miracle-dz/UniFAR-FTUs huggingface dataset:
EXTRACTION OF RETINAL BLOOD VESSEL USING ARTIFICIAL BEE-COLONY OPTIMIZATION

Retinal blood vessel Extraction in retinal images allows early diagnosis of disease and is useful in detecting ocular disorders and helps in laser surgery. Automating this process provides several benefits including minimizing subjectivity and eliminating a painstaking. This paper proposes an automated retinal blood vessel segmentation approach based on Fuzzy C-Means (FCM) clustering and then performed extraction using Artificial Bee-colony (ABC) to improve the accuracy of segmented image. FCM allocate the values of membership to the pixels instead of separating the pixels as in hard clustering problem and the clustering is optimized using ABC swarm based optimization algorithm, finally the system classify the images according to the level of damage in blood vessel using support vector machine (SVM). The performance was evaluated on DRIVE database and an accuracy of 96.35% was obtained.
BEFORE AND AFTER THE FALL : NEW ZEALAND ECONOMISTS ON THE POST-WAR UNE ~ LOYMENTEXPERmNCE

What explanations did economists advance for New Zealand's remarkable experience of full employment between 1938 and 1980? What causes did they suggest for the breakdown and subsequent massive rise in unemployment? This paper categorises and critically surveys material on these questions as part of a wider project on New Zealand's post-war unemployment experience. This paper, part of a larger project on the creation and demise of full employment over the post-war perio<L examines what New Zealand economists have said about full employment in New Zealan<L why full employment broke down and why unemployment rose so steeply afterwards. Consideration of this literature aims to distil the collective wisdom of previous New Zealand researchers to enable a better story of our post-war unemployment experience to be told and to generate a set of hypotheses on full employment and its breakdown which can then be examined empirically in later work. In terms of coverage, my survey is 1epresentative rather than comprehensive. Adopting a representative approach eliminates repetition. 2 The following questions arise: • to what cause did economists attribute New Zealand's remarkably low unemployment over much of the post-war
Expanding Training Datasets by Generalization of Linguistic Structures

We convert existing training datasets into the ones closed under linguistic generalization operations to expand infrequent cases. We transfer the definition of least general generalization from logical formulas to linguistic structures, from words to phrases, sentences, speech acts and discourse trees. The main advantage of the resultant frameworks is explainability and learnability from a small set of samples. Learning via generalization of linguistic structures turned out to be well suited for industrial linguistic applications with limited training datasets.
Investigation of One-Way Delay Variation in Substrate and Slice Measurements over a European-wide Future Internet Platform

Virtualization in networks is becoming very popular, because it offers the possibility to address varying demands for network resources in a flexible and cost-efficient way. By combining network hardware, software and network functionalities into virtual topological network units, resources can be shared by providing the illusion of real network environments. While such virtual environments offer the security of an isolated network slice and the possibility for customized platforms to support innovative network technologies without the disruption of general network traffic, the processing of the virtual components introduces a certain amount of performance overhead that needs to be investigated. This paper describes experimental measurements conducted in the Future Internet platform NOVI in order to determine the impact of network virtualization on the IP performance metric One-Way Delay Variation (OWDV). The European-wide measurements are unique as they were set up in a directly comparable implementation over substrate and slice. It is shown that the average median one-way delay variation over a measurement link in a virtual slice compared to the physical substrate increased by at least a factor > 10 for the 75 percentile of all measured values and factors ≥ 20 for higher percentiles. Keywords—Virtualization; network measurements; virtualized & physical infrastructures; Future Internet platforms.
Do Saliency Models Detect Odd-One-Out Targets? New Datasets and Evaluations

Recent advances in the field of saliency have concentrated on fixation prediction, with benchmarks reaching saturation. However, there is an extensive body of works in psychology and neuroscience that describe aspects of human visual attention that might not be adequately captured by current approaches. Here, we investigate singleton detection, which can be thought of as a canonical example of salience. We introduce two novel datasets, one with psychophysical patterns and one with natural odd-one-out stimuli. Using these datasets we demonstrate through extensive experimentation that nearly all saliency algorithms do not adequately respond to singleton targets in synthetic and natural images. Furthermore, we investigate the effect of training state-of-the-art CNN-based saliency models on these types of stimuli and conclude that the additional training data does not lead to a significant improvement of their ability to find odd-one-out targets.
WP / 19 / 40 SAMA Working Paper : Testing for Causality between Oil Prices and Money Supply in Saudi Arabia June 2019

The impact of oil price shocks on a country’s economy has been well studied in the economic literature. However, until now, articles analyzing the impact of these shocks on the Saudi Arabian economy have been relatively sparse. This paper attempts to shed some light on this important topic by examining the causal relationship between oil prices and an important monetary variable, the M3 (broad-based) money supply. Monthly data going back to 1982 were used in this study, which employs unit root tests and Granger causality analysis to test whether there is a causal relationship or not. No discernable causality relationship was found; this lack of such link leads the authors to conclude that this may be due to the prudent and stable fiscal and monetary policy on the part of the Kingdom’s government and its central bank, the Saudi Arabian Monetary Authority (SAMA).
AutoPropp : Toward the Automatic Markup , Classification , and Annotation of Russian Magic Tales

In this paper, I describe the current state of my program of research that uses the R environment and programming language for machine learning and statistics (Feinerer et al, 2008) to automatically analyze and/or parse texts in PFTML (Proppian Fairy Tale Markup Language) (Malec, 2005; Lendvai et al, 2010a; Lendvai et al, 2010b), an XML grammar that can be used for the semantic markup of folk narratives. R can be applied by researchers to manipulate and analyze motifs in tandem with "functions", the suband supra-sentential structures as described by Vladimir Propp in his Morphology of the Folktale (1928) in his analysis of Russian (magic) tales (Afanas'ev, 1945). These methods may be applied to corpora beyond the domain of folklore, but a limited formulaic corpus provides an interesting use case for these methods. The core idea is that this research program would use a corpus that was annotated in PFTML format as a training set to automate the process of annotating folkloric texts. I describe the conceptual framework, the required software packages, the basic steps of preprocessing text (annotation, stemming, removing whitespace and stopwords, importing data into the R environment) and discuss my progress on this project and the questions that I have encountered along the way.
30TH INTERNATIONAL COSMIC RAY CONFERENCE Observation of AE Aquarii with the MAGIC telescope

In August 2005, the MAGIC telescope observed the cataclysmi c variable AE Aquarii. Observations were done during four consecutive nights within e context of a quasi-simultaneous multiwavelength campaign covering the radio, optical, UV, and Xray ranges. The analysis of these data has revealed no evidence for any steady or pulsed γ-ray emission. Upper limits have been caclulated for steady emission at the level of ∼ 8 × 10cms above 340GeV and∼ 4 × 10cms for coherent pulsed emission at the first harmonic of the white dw arf frequency.
ConvoBot : A Conversational Bot via Deep Q-Learning and Query Simulation

This paper looks at combining advances in deep reinforcement learning with natural language processing techniques to train a conversational chatbot. The primary aims are to build a deep Q network (DQN) chatbot model, a novel architecture for user simulation, and finally integrating the two components into a reinforcement learning (RL) pipeline for training the DQN chatbot by conversing with the user simulator in a database query setting. While prior work has defined rule-based user simulators as well as simplistic chatbot agents, our primary contributions will be in training a DQN chatbot as well as developing a novel architecture for user simulation, the State-Action2Action model (SA2A) that aims to help with addressing cases where there is a lack of available data. Deploying a DQN chatbot rather than a simple single-layer neural network has shown improvements both in average reward as well as success rate in correctly responding to user queries. The SA2A model has also shown promise as an effective user simulation module.
A Gravity Model Analysis of the Benefits of Economic Integration in the Pacific Rim

A modified gravity model of international trade is used to evaluate determi­ nants of flows of commodities most often traded in the Pacific Rim. It is shown that the gravity model can be reparameterized effectively by using time series and cross section data rather than using cross section data alone. Documented evidence indicates that all independent variables including income, export and * Correspondence Address: Department of Economics, South Carolina State University, Orangeburg, SC 29117,U.S.A. , (Fax)803-536-8066, (E-mail) karemera@scsu.edu. The authors wish to thank Won Koo and Stephen Devadoss for constructive suggestions on earlier drafts. We also thank session participants at the Western Economic Associa­ tion International conference organized in Seattle, Washington in July 1996. The paper benefitted form conversation with Jeffrey Bergstrand and Benjamin Kim. Emmanuel Onunkwo, and Viceola Sykes patiently read the manuscript. The usual disclaimer applies. ©1999 Institute for International Economics, Sejong Institution. All rights reserved. import unit values, exchange rates, and membership in regional trade groups are major determinants of trade flows in the Pacific Rim. Specifically, member­ ship in the ASEAN significantly increased trade creation among members as well as fostered trade diversion from members to nonmembers. However, the impact of membership in the NAFTA on trade flows in the Pacific Rim is limit­ ed and appears to be commodity specific. (JEL Classifications: F15, F14, F02) <
Learning Cross-lingual Word Embeddings with Universal Concepts

Recent advances in generating monolingual word embeddings based on word co-occurrence for universal languages inspired new efforts to extend the model to support diversified languages. State-of-the-art methods for learning cross-lingual word embeddings rely on the alignment of monolingual word embedding spaces. Our goal is to implement a word co-occurrence across languages with the universal concepts’ method. Such concepts are notions that are fundamental to humankind and are thus persistent across languages, e.g., a man or woman, war or peace, etc. Given bilingual lexicons, we built universal concepts as undirected graphs of connected nodes and then replaced the words belonging to the same graph with a unique graph ID. This intuitive design makes use of universal concepts in monolingual corpora which will help generate meaningful word embeddings across languages via the word cooccurrence concept. Standardized benchmarks demonstrate how this underutilized approach competes SOTA on bilingual word sematic similarity and word similarity relatedness tasks.
Analysis of Vocal Ornamentation in Iranian Classical Music

In this paper we study tahrir , a melismatic vocal ornamentation which is an essential characteristic of Persian classical music and can be compared to yodeling. It is consid-ered the most important technique through which the vocalist can display his/her prowess. In Persian, nightingale’s song is used as a metaphor for tahrir and sometimes for a speciﬁc type of tahrir . Here we examine tahrir through a case study. We have chosen two prominent singers of Persian classical music one contemporary and one from the twentieth century. In our analysis we have appropri-ated both audio recordings and transcriptions by one of the most prominent ethnomusicologists, Masudiyeh, who has worked on Music of Iran [1]. This paper is the ﬁrst step towards computational modeling and recognition of different types of tahrirs . Here we have studied two types of tahrirs , mainly nashib and far¯az , and their combination through three different performance samples by two prominent vocalists. More than twenty types of tahrirs have been identi-ﬁed by Iranian musicians and music theorists. We are cur-rently working on developing a method to computationally identify these models.
STUDY AND EVALUATION OF CLASSIFICATION ALGORITHMS IN DATA MINING

– Data Mining is the process of discovering hidden or unknown patterns in huge datasets that are potentially useful and ultimately understandable. The goal of data mining is to extract useful information from huge data sets and to store it as an understandable and structured model for future use, using combined technique of statistics, machine learning and database systems. Data Mining algorithms learns from training datasets to build the model, that can be used on unknown data for prediction. Here we will discuss classification technique that is often called as supervised learning technique. Classification is used to predict the categorical class label of a given data instance , so as to classify it into one of the predefined classes. Classification is a two step process, in first step classification algorithm uses training dataset to build a classifier , and then in the second step this classifier is used to predict the class label of an unlabeled data instance . The classifier is like a function that maps a data instance to a label. The classifier that we will discuss here are Naive Bayes’, Multilayer Neural Network, Decision tree , Random forest , Support Vector Machine and K-NN. Building an accurate and an efficient classifier is an important task in data mining. We will evaluate these algorithms on various datasets using evaluation metrics such as Accuracy, Precision ,Recall,F1-Score and time by WEKA . WEKA is a machine learning tool written in Java.
Yeomans, James Thomas (2018)An exploration of support workers’ views on working in a community mental health, supported-living service: An Interpre-

Over the last three decades, evidence of mental health professionals’ negative views towards the diagnosis of borderline personality disorder has been well documented within the literature. However, research on the social care profession is limited, therefore, this study explored the views of support workers who work with service users with this diagnosis. Five semi-structured interviews were conducted, and an Interpretative Phenomenological Analysis was used to analyse the data. Three Superordinate themes emerged from the analysis: Support Worker as ‘Mother’; Service User as ‘Other’; and ‘Sad beneath the Bad’. Findings illustrate that working with service users with this diagnosis in a social care context and setting is both rewarding and challenging. Support workers report positive perceptions and experiences towards service users; contrary to the nursing literature. Where negative perceptions were expressed, efforts were made to understand the situation holistically. Implications and future research directions are discussed.
A New Approach for the Derivation of Macroscopic Balance Equations beyond the Relaxation Time Approximation

A new method for the derivation of Hydrodynamic Equations has been developed. This new approach is more general than those based on the Relaxation Time Approximation (RTA) because it reproduces the equations of irreversible thermodynamics and especially ensures Onsager's Reciprocity Relations in the region of linear transport. Comparing the transport coefficients of this newly derived set of hydrodynamic equations with the conventional ones being based on the RTA yields conditions for the validity of the RTA which can be derived from stationary Monte Carlo bulk simulations.
tual JoyStick Study of Emotional Responses and Social Motivation in Children with A utism Spectrum Disorder

A bstract A new virtual reality task was employed which uses preference for interpersonal distance to social stimuli to examine social motivation and emotion perception in children with A utism Spectrum Disorders. Nineteen high function children with higher functioning A utism Spectrum Disorder (HFA SD) and 23 age, gender, and IQ matched children with typical development (TD) used a joy stick to position themselves closer or further from virtual avatars while attempting to identify six emotions expressed by the avatars, happiness, fear, anger, disgust, sadness, and surprise that were expressed at different levels of intensity. The results indicated that children with HFA SD displayed significantly less approach behavior to the positive happy expression than did children with TD, who displayed increases in approach behavior to higher intensities of happy expressions. A lternatively, all groups tended to withdraw from negative emotions to the same extent and there were no diagnostic group differences in accuracy of recognition of any of the six emotions. This pattern of results is consistent with theory that suggests that some children with HFA SD display atypical social-approach motivation, or sensitivity to the positive reward value of positive social–emotional events. Conversely, there was little evidence that a tendency to withdraw from social– emotional stimuli, or a failure to process social emotional stimuli, was a component of social behavior task performance in this sample of children with HFA SD.
Dynamic Tree Based Classification of Web Queries Using B-Tree and Simple Ordinal Classification Algorithm

Queries submitted by users to search engines might be ambiguous, concise and their meaning may change over time. Web query classification is emphasized by various search engines nowadays due to the increase in the size of the web, as millions of web pages are added to it every day. Some of the current Information Retrieval (IR) systems like Library Online Public Access Catalog (OPAC), dialog system and numerous web search engines need classical Boolean approaches in addition to the current supervised methods. Document retrieval is a process which mainly involves retrieval of relevant documents for user queries and matching the results using efficient algorithms like ‘page rank’ and ‘learn to rank’ algorithms. In this paper, to retrieve more relevant documents against user query by reducing non-relevant documents, we proposed a tree based classification of web queries using Simple Ordinal Classification (SOC) and navigation of search keywords is performed dynamically with search session time of users. This method reduces the retrieval of most of the non-relevant documents and navigation cost using efficient B-tree data structure. It provides documents that match all keywords present in the user query and best resulting web pages for users with different categories of interest. We built a prototype application to evaluate the proposed approach. Our experimental results revealed that SOC has significant performance improvement with existing approaches. Subject Categories and Descriptors [H.3. 1 Content Analysis and Indexing]: [H.3. 5 Online Information Services]: [H.3. 3 Information Search and Retrieval]; Query formulation General Terms Query Classification, Web Query, Tree-based Classification, B Tree Indexing
00 84 3 v 2 [ cs . L G ] 5 O ct 2 01 9 Multiple Learning for Regression in Big Data

Regression problems that have closed-form solutions are well understood and can be easily implemented when the dataset is small enough to be all loaded into the RAM. Challenges arise when data are too big to be stored in RAM to compute the closed form solutions. Many techniques were proposed to overcome or alleviate the memory barrier problem but the solutions are often local optima. In addition, most approaches require loading the raw data to the memory again when updating the models. Parallel computing clusters are often expected in practice if multiple models need to be computed and compared. We propose multiple learning approaches that utilize an array of sufficient statistics (SS) to address the aforementioned big data challenges. The memory oblivious approaches break the memory barrier when computing regressions with closed-form solutions, including but not limited to linear regression, weighted linear regression, linear regression with Box-Cox transformation (Box-Cox regression) and ridge regression models. The computation and update of the SS arrays can be handled at per row level or per mini-batch level. And updating a model is as easy as matrix addition and subtraction. Furthermore, the proposed approaches also enable the computational parallelizability of multiple models because multiple SS arrays for different models can be computed simultaneously with a single pass of slow disk I/O access to the dataset. We implemented our approaches on Spark and evaluated over the simulated datasets. Results showed our approaches can achieve exact solutions of multiple models. The training time saved compared to the traditional methods is proportional to the number of models need to be investigated. Keywords-Big Data; Linear Regression; Weighted Linear Regression; Ridge Regression; Box-Cox Transformation
1 Counting Passengers in Railway Compartment Surveillance Video

Considering the convenience and expansibility of visual-based methods, the crowd counting via deep learning in computer vision raises more attention currently. The railway compartment, as a universal scene, also has the actual demands in passengers counting. However, the common counting methods in deep learning are unable to solve the problem due to the severe scale changing, high accurate requirement, and limited embedded devices in the actual scene. In this paper, we proposed a novel counting solution to handle the passengers counting. The solution contains a motion supervised multi-scale representation method which provides proposals against scale variation, a spatially-temporally enhanced counting which provide precise counting numbers, and a partial proposal training method which conducts methods to be utilized in reality. With the proposed solution, the passengers counting task is solved in the higher accuracy and practicable in compartment environment. In experiments, the results show that all the modules in our solution are useful and efficient in passengers counting, and the performances of our method are better than others.
Women , gender and think tanks : political influence network in Twitter 2018

During 2018 we have monitored the network of relationships of the world’s leading think tanks and some of its analysts and have added a small network of gender research centres and activists, and women analysts interested in these issues. Our representation of reality, however, gives rise to certain questions: how large is the female presence in think tanks? ; how do gender studies relate to the rest of the international relations field? ; and how does the gender focus flow within the network?