Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithms and agenda-setting in Wikileaks’ #Podestaemails release

ABSTRACT In the month before the 2016 U.S. Presidential election, Wikileaks released 37 serialized batches of e-mails authored by former Clinton campaign manager John Podesta. Each release was announced using a unique PodestaEmail related hashtag (#PodestaEmails2, #PodestaEmails3, etc.). In total, Podesta e-mail related hashtags hit town-wide, country-wide, or worldwide Trending topics lists a total of 1,917 times, remaining on Trending Topic lists everyday within the U.S. for 30 days before election day. In this article, we discuss how Wikileaks’ release methodology increased the potential reach of Podesta E-mail related content. We describe how Wikileaks’ tweets spoke to two audiences: Twitter users and Twitter algorithms. In serializing its content and using new hashtags for each release, Wikileaks increased the potential persistence, visibility, spreadability, and searchability of this content. By creating the possibility for this content to remain persistently visible on the Trending Topics list, Wikileaks was able to potentially realize a greater degree of agenda-setting than would have been possible through singular hashtag use.
Preregistered Replication of the Auditory Deviant Effect: A Robust Benchmark Finding

Short-term memory of visually presented lists of items is disrupted by auditory distraction. The auditory deviant effect refers to the finding that a sequence in which a single auditory event deviates from all other auditory objects disrupts serial recall more than a sequence without such a deviant. The changing-state effect refers to the finding that auditory changing-state sequences with changes from one auditory distractor item to the next disrupt the immediate serial recall of verbal items more than steady-state sequences consisting of distractor repetitions. One purpose of the present study is to perform a preregistered replication of the auditory deviant effect as well as (for the purpose of comparison) the changing-state effect and to provide reference data sets for the auditory deviant effect and the changing-state effect in the benchmarks repository with a large sample of participants and trials. Both effects were robustly obtained over the course of two sessions in which participants were tested. We also explored the relationship between auditory distraction and personality, and found auditory distraction to be unrelated to extraversion, neuroticism, and psychoticism.
HBD: Towards Efficient Reactive Rule Dispatching in Software-Defined Networks

Most types of Software-Defined Networking (SDN) architectures employ reactive rule dispatching to enhance real-time network control. The rule dispatcher, as one of the key components of the network controller, generates and dispatches the cache rules with response for the packet-in messages from the forwarding devices. It is important not only for ensuring semantic integrity between the control plane and the data plane, but also for preserving the performance and efficiency of the forwarding devices. In theory, generating the optimal cache rules on demands is a knotty problem due to its high theoretical complexity. In practice, however, the characteristics lying in real-life traffic and rule sets demonstrate that temporal and spacial localities can be leveraged by the rule dispatcher to significantly reduce computational overhead. In this paper, we take a deep-dive into the reactive rule dispatching problem through modeling and complexity analysis, and then we propose a set of algorithms named Hierarchy-Based Dispatching (HBD), which exploits the nesting hierarchy of rules to simplify the theoretical model of the problem, and trade the strict coverage optimality off for a more practical but still superior rule generation result. Experimental result shows that HBD achieves performance gain in terms of rule cache capability and rule storage efficiency against the existing approaches.
A Method of Text Classifier for Focused Crawler

This paper combines DF and CHI to select features of web pages related to the area of teaching Chinese as a second language(TCSL). A classifier is first constructed based on two-step topic similarity measurement by the title and the main text.The classifier is then applied to crawling web pages related to TCSL,and the results show substantial improvements on efficiency and recall rate compared with traditional methods.Now this classifier has been deployed for data collection for a big TCSL corpus in actual practice.
Radiative correction in approximate treatments of electromagnetic scattering by point and body scatterers

The transition-matrix ($T$-matrix) approach provides a general formalism to study scattering problems in various areas of physics, including acoustics (scalar fields) and electromagnetics (vector fields), and is related to the theory of the scattering matrix ($S$-matrix) used in quantum mechanics and quantum field theory. Focusing on electromagnetic scattering, we highlight an alternative formulation of the $T$-matrix approach, based on the use of the reactance matrix or $K$-matrix, which is more suited to formal studies of energy conservation constraints (such as the optical theorem). We show in particular that electrostatics or quasi-static approximations can be corrected within this framework to satisfy the energy conservation constraints associated with radiation. A general formula for such a radiative correction is explicitly obtained, and empirical expressions proposed in earlier studies are shown to be special cases of this general formula. This work therefore provides a justification of the empirical radiative correction to the dipolar polarizability and a generalization of this correction to any types of point or body scatterers of arbitrary shapes, including higher multipolar orders.
Grundkurs funktionale Programmierung mit Scala

GRUNDKURS FUNKTIONALE PROGRAMMIERUNG MIT SCALA // Funktionale Programmierung erlebt eine Renaissance – viele Programmiersprachen nutzen inzwischen Ideen der funktionalen Programmierung, etwa zur Parallelisierung von Software. Und es gibt jetzt auch eine neue, moderne funktionale Programmier sprache: Scala. Sie ist mit Java kompatibel und genießt nicht zuletzt deshalb große Aufmerksamkeit. Ausreichend Gründe also, um sich mit den Grundlagen der funktionalen Programmierung auseinanderzusetzen.
A job server for parallel and concurrent execution of Möbius simulators

Möbius is an extensible modeling environment used to validate the reliability, availability, security, and performance of computer systems. It provides graphical editors through which users can construct system models or compositions of several models, and it provides interfaces for finding the metrics of the modeled systems. It supports a large set of modeling formalisms through an abstract functional interface, which allows it to support new formalisms without requiring modification of existing formalisms. It offers several solvers that work with any modeling formalism; one of those solvers is a tool for executing simulations over a network. However, that tool does not resemble a service, because the Möbius graphical user interface launches and collects data from the simulators directly, and simulators cannot be executed remotely if they exist on different subnets. This thesis presents the Remote Job Server (RJS), an extension to the Möbius modeling environment that supports the remote execution of simulators. It provides more flexibility in the networking topology, and it features a tree hierarchy in which each node in the tree represents an RJS instance that serves a specific purpose. One or more client nodes, which serve as leaves in the tree, connect to a manager node, the root of the tree. Part of the manager’s role is to route commands through zero or more forward nodes to the worker nodes, which also serve as leaves in the tree. The manager also dispatches simulation jobs, aggregates simulation data from the worker nodes, and reports simulation results to the client. The workers execute the jobs assigned by the manager; that typically involves compiling of Möbius projects and running of simulators on the compiled project. The client is implemented as a graphical user interface that is integrated with Möbius so that the user can seamlessly develop the experiment studies and run the simulations from a single interface.
The Role of Perceived Parental Autonomy Support in Academic Achievement of Asian and Latino American Adolescents.

Introduction. Driven by an overarching conceptual framework adapted from SelfDetermination Theory, this study tested the direct and indirect effects of perceived parental academic autonomy support vs. academic planning control on the interrelated variables of adolescents’ self-esteem, academic motivation, and academic achievement, using multisample structural equation modeling. Method. A sample of 271 Asian American and 218 Latino American high school students were recruited in Southern California to respond to a questionnaire composed of a battery of adapted and self-constructed scales. Results. In the Asian American sample, perceived parental academic autonomy support had significant direct positive effects on academic achievement, self-esteem, academic motivation, and indirect effects on academic motivation mediated by self-esteem, while perceived parental academic planning control had direct negative effects on self-esteem and academic achievement. However, in the Latino American sample, perceived parental academic autonomy support had positive indirect effects on academic achievement mediated by academic motivation and self-esteem, and perceived academic planning control had negative direct effects on both self-esteem and academic motivation. Discussion and Conclusion. Plausible explanations for the different pathways in the two cultural groups were presented from the cultural perspectives. The potentiality of using parental academic autonomy support as a predictor for academic achievement was also discussed.
Simulated annealing for selecting optimal initial seeds in the K-means algorithm

Explores the applicability of simulated annealing, a probabilistic search method, for finding optimal partition of the data. A new formulation of the clustering problem is investigated. In order to obtain an optimal partition, a search is undertaken to locate optimal initial seeds, such that the K-means algorithm converges to optimal partition. Search space involved in this process is continuous, so discretization is done and simulated annealing is employed for locating optimal initial seeds. Experimental results substantiate the proposed method. Results obtained with the selected data sets are presented.
Culturally Diverse Discharge Planning and Education

Readmission rates have been increasing over the past several years; therefore, the Affordable Care Act has required Social Security to create a program known as the Hospital Readmissions Reduction Program (HRRP). The main objective of the HRRP is for the Centers for Medicare and Medicaid Services (CMS) to reduce reimbursement to hospitals with excessive readmission rates. Extensive research has revealed several factors that are contributing to an increase in readmission rates and poor patient outcomes; one is a gap in communication and lack of cultural competency in nursing care. Another contribution to this phenomenon is poorly managed transitions of patients with chronic diseases from the hospital into the community due to inadequate discharge planning. Therefore, the aim of this project will be to encourage nursing faculty to develop curriculum for culturally diverse discharge planning and education for all levels of nursing courses, with the goal being to contribute to improved patient outcomes and decreased readmission rates among the various cultures. Utilizing Madeleine Leininger’s Theory of Culture Care and Universality as a model, a conceptual framework Culturally Diverse Discharge Planning and Education was created to assist in developing a curriculum to increase awareness and foster student nurse critical thinking skills of the discharge planning and education process. Instructors of various local Registered Nursing programs will be invited to participate in a “train the trainer” type presentation of curriculum to introduce cultural diversity for discharge planning and education into the classroom, which will be developed and delivered by using Power Point, case studies, student collaboration, and role play. Instructors will be asked at the end of the presentation to provide their opinions of the value of this curriculum and the chance they will incorporate cultural discharge planning and education into their curriculum. readmission Culturally Diverse Discharge Planning and
Proceedings of the 2011 Conference on Intelligent Data Understanding, CIDU 2011, October 19-21, 2011, Mountain View, California, USA

Energy production, distribution, and consumption play a critical role in the sustainability of the planet and its natural resources. Electric power systems have been going through major changes that are aimed to make the energy infrastructure “smarter”, scalable, and more efficient. These new generation of smart energy grids need novel computational algorithms for supporting generation of power from wide range of sources, efficient energy distribution, and sustainable consumption. This paper argues that a fundamentally distributed approach with more local flexibility is a lot more sustainable methodology compared to the traditional centralized frameworks for analyzing and processing data. It considers the problem of predicting power generation and consumption trends over a distributed smart grid. Since power generation from solar, wind, geothermal and other renewable sources are likely to be part of many households in near future, both power generation and consumption data will be generated over a wide area network. Moreover, a good part of the communication links between the household data sources and the central server are likely to be over the wireless networks with low bandwidth and high data-plan cost. Analyzing such data (some of it privacy sensitive) in a centralized is not scalable, sometimes not privacy-preserving, and often not practical because of cost-sensitivity of the applications. This paper presents a more sustainable distributed asynchronous algorithm for constructing energy demand prediction models in a smart grid by multivariate linear regression. The paper offers the algorithm, analysis, and experimental results.
THE USE OF SCAFFOLDING STRATEGY IN TEACHING READING COMPREHENSION AT THE FIRST GRADE STUDENTSOF SMA N 4 MATARAM IN ACADEMIC YEAR2017/2018

Abstract: Scaffolding is a teaching learning strategy in which the teacher and learners engage in a collaborative problem-solving activity with the support and guidance of the teacher to enable learners become increasingly independent. Scaffolding strategy can help students solved the task by their own and student can be understand the material. The purpose of this research is to know whether the use of scaffolding strategy is effective or not in teaching reading comprehension at the first grade students of SMA N 4 Mataram in academic year of 2017/2018. This study used Quasi Experimental Non-Equivalent Control Group Designs, the researcher was used pre-test and post-test. The sample of this research were all students of the first grade at SMAN 4 Mataram that consist of 78 students, 39 students of experimental group and 39 students of control group. The experimental group was taught by scaffolding strategy in teaching reading comprehension and the control group was taught without scaffolding strategy in teaching reading comprehension. The techniques of collecting data in this research used pre-test and post-test. Based on the result, the means score of students experimental group was 23,17 and the control group was 16,70. It was shows that score of experimental group was higher than control group. Whereas, the score of t-test was 2,70 and the score of t-table was 2,00 on the level of significance 0,05. this research shows that scaffolding strategy in teaching reading comprehension at the first grade students of SMAN 4 Mataram in Academic Year 2017/2018 is effective. So, based on the hypothesis that the researcher discussed before, the Alternative Hypothesis (Ha) was accepted.
Imbalanced Classification for Business Analytics

In pattern recognition, classification is a crucial task for automated data driven knowledge discovery. The objective of classification is to separate a set of data into classes or sub-categories and then to identify the classes that a new observation belongs to according to a training set of data. The mathematical model trained by a classification algorithm is termed classifier. When the class size of given examples is not equal for all classes, the classification problem is known as imbalanced (Japkowicz, 2000). For instance, in a cancer diagnostic problem the main objective is to identify individuals stricken with cancer and such events are relatively rare compared to normal cases. Imbalanced classification problems are also known as skewed class distribution problems or as small/ rare class learning problems (He & Garcia, 2009; Lemnaru & Potolea, 2012; Sun, Wong, & Mohamed, 2009). In binary classification, the class with fewer examples is known as the minority class and the other class as the majority class. In many applications (e.g. fraud detection, computer intrusion detection, oil spill detection, defect product detection), detection of minority class examples is more important than the majority class. Therefore, there is a need for efficient classification algorithms to address such problems. A preferred classification algorithm is the one that yields higher identification rate on rare events especially for applications where their misclassification yields to high losses. For instance in automated credit card fraud detection, a fraud event misclassification might result in high monetary losses for the credit card vendor. On the other side misclassification of non-fraudulent events will worsen the customer satisfaction experience. Imbalanced Classification for Business Analytics
Constrained magnetic resonance image reconstruction from incomplete frequency measurements

v Acknowledgments . vii List of Figures . x List of Tables . xx List of Abbreviations . xxi List of Notation . xxii
Application of graph theory and hybrid GA-SA for operation sequencing in a dynamic workshop environment

ABSTRACTTo solve the machining operation sequencing problem in the computer aided process planning, this paper presents a hybrid genetic algorithm and simulated annealing approach for machining operation sequencing optimization in a dynamic workshop environment. The directed graph used as an explicit constraint model is formulated based on precedence constraints among machining operations, and the graph search algorithms is embedded into framework of the optimization system. The initial solutions composed of all feasible operation sequences in GA optimization stage are produced by applying a stochastic topologic sort algorithm to the OPG. Production cost calculating model is taken as the criterion to evaluate the operation sequence quantitatively. The optimization approach can make a dynamical respond to the changes of plant resources and multiple optimal/suboptimal solutions could be obtained. Finally an illustrative example for a complicated part is given, and the test results testify the feasibility an.
Complex-Valued Neural Network and Inverse Problems

It is necessary to solve inverse problems for estimating causes from observed results in various engineering fields. In particular, inverse problems have been studied in the field of mathematical science (Groetsch, 1993). The inverse problem determines the inner mechanisms or causes of an observed phenomenon. The cause is estimated from the fixed model and the given result in the inverse problem, while the result is determined from the given cause by using a certain fixed mathematical model in the forward problem. As a solution of inverse problems, the neural network based method has been proposed while other method such as the statistical method (Kaipio & Somersalo, 2005) and parametric method (Aster, Borchers, & Thurber, 2005) have also been studied. The idea of inverting network mapping was proposed by Williams (1986). Then, Linden and Kindermann proposed a method of network inversion (Linden & Kindermann, 1989). Also, the algorithms and applications of network inversion are summarized by Jansen et al. (1999). In this method, inverse problems are solved by the inverse use of the input-output relation of trained multilayer neural networks. In other words, the corresponding input is estimated from the provided output via fixed weights, after finding the forward relation by network trainABSTrACT
Perceptions and social constructions of HIV prevention in the Black Baptist church

MALIKA D. ROMAN ISLER: Perceptions and Social Constructions of HIV Prevention in the Black Baptist Church (Under the direction of Eugenia Eng) Introduction. Although the Black church (i.e., the Black Baptist church) has long exerted incomparable influence upon the health behaviors of African American communities, few have engaged in evidence-based interventions for HIV/AIDS prevention or collaborated with public health professionals on implementation. Because no research has examined the compatibility of these innovations with theological and socio-behavioral perspectives of Black church clergy and congregations, this study: 1) examines social constructions of HIV/AIDS and efforts to prevent HIV by rural and urban black Baptist churches in North Carolina; 2) identifies, compares, and contrasts key considerations about introducing 5 evidence-based HIV prevention innovations (abstinence, monogamy, condom use, voluntary counseling and testing, and prevention with positives) to Black Baptist churches; and 3) explores relevant, participant-driven HIV prevention models. Methods. This study used an exploratory qualitative design and methodological orientations of grounded theory and interpretive description. Data were collected using individual interviews and focus groups of respondents from 8 Black Baptist churches in North Carolina. A total of 1,117 interview
An Analysis of Code Clone Detection in Software's Using Metric Based Methods

Software systems are getting more unpredictable as the framework develops where keeping up such framework is an essential concern for the software commerce. Code clone is a specific type of the elements which are making software maintenance further challenging. The duplicate of code fragments and after that reuse by sticking with or without minor changes or adjustments and this kind of reuse methodology of existing code is called code cloning and the pasted code fragment (with or without alterations) is known as a clone of the first one. The significant danger of cloning is that it improves the maintenance process. Cloning is fundamentally the method for software reuse and also this becomes the fundamental need of today's surroundings. That is the reason why code cloning has been widely utilized as a part of vast software businesses. So to identify clones and refactor them is a noteworthy concern. Numerous methodologies have been created to distinguish clones. In this paper we have surveyed about clone detection its various types, clone detection process as well as previous techniques used in clone detection in brief.