Miracle-dz/UniFAR-FTUs huggingface dataset:
Attitudes to English in the Kaleidoscopic Iranian Context: Second, Foreign or International?

Today’s world seems to be quite overwhelmingly concerned with communication as an incontestable aspect of which is communicating with people lacking a shared linguistic background. A common language (or lingua franca) would, therefore, be needed which can cut across all local, national, and regional linguistic boundaries. This study aimed at exploring the attitudes of 400 bilingual and monolingual college students and EFL learners. It investigated English language status in the linguistically diverse context of Iran. For the purpose of the study, a standardized questionnaire containing 37 Likert-type items was distributed. Descriptive statistics were employed for data analysis revealing that learners from different linguistic backgrounds had significantly different reactions and attitudes to the issues such as: General perception to English, Status of English, text and content matter comprehensibility through English, job prospects, official status, culture learning, integrating with American or British cultures, religion and foreign language learning, English ownership, Adherence to British English, American English or English as an international language.
Algorithm of multi-PSO co-evolution based on GA and PSO

Firstly introduced genetic algorithms and particle swarm optimization algorithms,based on which an algorithm of multi- pso co-evolution is proposed.Four benchmark function are tested and shown that the performance of the MPSOE3 algorithm is better than basic PSO algorithm.Lastly extended the algorithm and given a model of multi-swarm co-evolution,which is based on computational intelligence.
Latinx Immigrant Youth’s Indirect and Direct Disclosures About Their Family-Undocumented Experiences, Received Emotional Support, and Depressive Symptoms

Drawing upon sensitive interaction systems theory, this study investigated Latinx immigrant youth’s indirect and direct disclosures about their family-undocumented experiences (i.e., informing others of their own or family’s undocumented experiences, including details surrounding their undocumented situation) to a teacher(s) or a friend(s). Furthermore, this study examined how such disclosures relate to subsequent disclosure and to received emotional support across an academic year. Received emotional support, in turn, was theorized to predict youth’s depressive symptoms. Survey data were collected in the beginning, middle, and end of the 2015-2016 academic year from 410 Latinx family-undocumented students (ninth-12th grades). Latinx students reported more indirect than direct disclosures about their family-undocumented experiences, potentially as a way to seek support. They also reported greater indirect and direct disclosure about their family-undocumented experiences to a friend(s) compared with a teacher(s). Emotional support did not significantly mediate the associations between disclosure and depressive symptoms; however, indirect and direct disclosures to a friend(s) positively related to depressive symptoms within various waves.
The lifetime soundtrack ‘on the move’: Music, autobiographical memory and mobilities

Music can easily become associated with many activities in everyday life, often recorded within personal memory archives, sometimes maintaining a personal meaning across the lifetime. This article convenes on the interactions between music, memory and mobilities, as it explores the recollection of musical memories that incorporate both music mobilities and corporeal mobilities related to personal travel and movement. Drawing on empirical interview data, this research seeks to understand how music can both accompany and signify acts of ‘being mobile’, where unique conditions of time and space provide for the formation of musical memories. Concurrently, the article considers how autobiographical remembering is affected by the dual activities of music listening and travel, through the conceptual lens of the ‘lifetime soundtrack’. Contrasting with previous research, the memory narratives presented here reveal diversity in the way music, memory and mobilities are simultaneously enacted, and looks to the long-term significance such memories may hold for individuals.
Programming Environments for Children: Creating a Language that Grows with you

Author(s): Hill, Charlotte | Advisor(s): Franklin, Diana | Abstract: Recent efforts have increased the number of elementary and middle schools teaching computer science — but do they have the right tools for the job? Elementary school teachers are usually responsible for teaching all subjects, and often do not have a background or training in computer science. Fourth through sixth grade students are still developing their math and reading skills as well as learning how to type and use computers. Fortunately, computer science is one of the only domains that can adapt to meet the needs of the user. Unlike math or physics, computer science has few constants; computers, languages, and development environments have changed over the last decades and will continue to evolve. How can programming languages and environments better meet the needs of upper elementary classes learning computer science? This paper looks at designing block-based programming environments for upper elementary school students as a part of a larger research study on early computer science education. Block-based programming environments let children create complex, visual programs without worrying about compiling or syntax errors. This paper describes the research studies completed in the design and implementation of block-based programming environments created alongside the development of KELP-CS, a computational thinking curriculum for 4th — 6th grade. Both the programming environment and curriculum were piloted in schools across California as part of a design-based research project.
The Gulf Information War| News Coverage of the Gulf Crisis in the Turkish Mediascape: Agendas, Frames, and Manufacturing Consent

Using a data set of 2,968 articles collected from 22 different newspapers in Turkey, this article maps media responses to the ongoing Gulf Crisis. In doing so, we deploy a pioneering methodology derived from natural language processing and correspondence analysis to test whether categorical variables such as political affiliation, ownership, and ideological outlook had any impact on how a news publication covered the Gulf Crisis. In the results and interpretation sections, we attempt to connect our findings to broader discussions on agenda setting, framing, and building consent. Based on our analysis, we propose the following conclusions: (a) Political affiliation, ownership structure, and the ideological outlook all had unique effects on how a publication covered the Gulf Crisis, (b) the progovernment press embarked on a campaign to sway public opinion about the government’s decision to side with Qatar. The dimensions of this campaign strongly resembled an executive act of consent manufacturing, and (c) corporate-owned news organizations were the driving force shaping both the public agenda and the dominant framing of the Gulf Crisis in the Turkish mediascape.
Are Different Forms of Repetitive Negative Thinking Associated With Interpretation Bias in Generalized Anxiety Disorder and Depression?

Worry and rumination, two forms of repetitive negative thinking (RNT), are prevalent in generalized anxiety disorder (GAD) and depression. Cognitive processing biases, especially the tendency to draw negative conclusions from ambiguous information (interpretation bias), may maintain worry and rumination. Yet the relationship between interpretation bias and both forms of RNT has not been explored in clinical versus nonclinical samples. In this cross-sectional study, participants with GAD (n = 72), depression (n = 79), or neither disorder (n = 71) completed two tasks assessing interpretation bias, measures of worry and rumination, and reported negative thought intrusions during a behavioral task. Interpretation bias was associated with higher levels of worry, rumination, and negative thought intrusions. Both clinical groups generated significantly more negative interpretations than healthy comparison participants. These findings link interpretation bias to worry and rumination and establish the need for research investigating the causal role of interpretation bias in maintaining RNT.
The Effects of Emotional Exhaustion and Depersonalization on Personal Accomplishment in Pharmaceutical Industry

Bu calismanin amaci, ilac endustrisinde satis departmani calisanlarinin kisisel basari hissiyatlarinin duygusal tukenmislik ve duyarsizlasmadan etkilenip etkilenmedigini arastirmaktir. Tukenmislik sen-dromunun ic dinamikleri cercevesinde, kisisel basari hissini olusturan mekanizma arastirma sorusunu olusturmaktadir. Mevcut literaturde tukenmisligin ic boyutlari arasindaki iliskiye dair bir uzlasmaya varilmamistir. Bu arastirma, kisisel basari, duygusal tukenme ve duyarsizlasma arasindaki istatistiksel olarak anlamli iliskileri aciklayarak mevcut literature onemli bir katki saglamistir. Bu arastirma nicel bir calismadir. Arastirmada kullanilan veriler ilac sektorunde faaliyet gosteren isletmelerin satis departmani calisanlarindan elde edilmistir. Olcum araci olarak kullanilan anketlerde bulunan maddelerin tamami bes dereceli Likert tipi olcek ile katilimcilardan toplanarak istatistiksel analize ve degerlendirmeye tabi tutulmustur. Hipotez testleri kismi en kucuk kareler yapisal esitlik modellemesi (PLS-SEM) yontemi ile gerceklestirlmistir. Bulgular, duygusal tukenme ile kisisel basari arasinda, duyarsizlasma ile kisisel basari arasinda negatif ve anlamli bir iliski oldugunu gostermektedir. Sonuc olarak, bu arastirma calisanlarin kisisel basari hissini arttirmak icin bazi pratik yonetimsel oneriler ortaya koymustur.
Research on analyzing sentiment of texts based on k-nearest neighbor algorithm

In order to identify polarity of sentiment on web texts,by analyzing the text structure and the characteristics of expressing sentiment in texts,a method based on K-nearest algorithm is proposed.In this method,sentiment of a text is divided into local sentiment and global sentiment.Local sentiment can be determined by conditional random field models,and the K-nearest neighbor algorithm is used to compute global sentiment of the text.Experimental results show that compared with traditional machine learning methods,this method can analyze sentiment on multi-level and is fine granularity,and can effectively improve accuracy of sentiment analysis.
Node dynamics on graphs : dynamical heterogeneity in consensus, synchronisation and final value approximation for complex networks

Here we consider a range of Laplacian-based dynamics on graphs such as dynamical invariance and coarse-graining, and node-speci ic properties such as convergence, observability and consensus-value prediction. Firstly, using the intrinsic relationship between the external equitable partition (EEP) and the spectral properties of the graph Laplacian, we characterise convergence and observability properties of consensus dynamics on networks. In particular, we establish the relationship between the original consensus dynamics and the associated consensus of the quotient graph under varied initial conditions. We show that the EEP with respect to a node can reveal nodes in the graph with increased rate of asymptotic convergence to the consensus value as characterised by the second smallest eigenvalue of the quotient Laplacian. Secondly, we extend this characterisation of the relationship between the EEP and Laplacianbased dynamics to study the synchronisation of coupled oscillator dynamics on networks. We show that the existence of a non-trivial EEP describes partial synchronisation dynamics for nodes within cells of the partition. Considering linearised stability analysis, the existence of a nontrivial EEP with respect to an individual node can imply an increased rate of asymptotic convergence to the synchronisation manifold, or a decreased rate of de-synchronisation, analogous to the linear consensus case. We show that high degree 'hub' nodes in large complex networks such as Erdős-Rényi, scale free and entangled graphs are more likely to exhibit such dynamical heterogeneity under both linear consensus and non-linear coupled oscillator dynamics. Finally, we consider a separate but related problem concerning the ability of a node to compute the inal value for discrete consensus dynamics given only a inite number of its own state values. We develop an algorithm to compute an approximation to the consensus value by individual nodes that is ε close to the true consensus value, and show that in most cases this is possible for substantially less steps than required for true convergence of the system dynamics. Again considering a variety of complex networks we show that, on average, high degree nodes, and nodes belonging to graphs with fast asymptotic convergence, approximate the consensus value employing fewer steps.
Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems

This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.
Image classification using a set of labeled and unlabeled images

Image classification into meaningful classes is essentially a supervised pattern recognition problem. These classes include indoor, outdoor, landscape, urban, faces, etc. The recognition problem necessitates a large set of labeled examples for training the classifier. Any stratagem, which reduces the burden of labeling, is therefore very important to the deployment of such classifiers in practical applications. In this paper we show that the labeled training set can be augmented by an unlabeled set of examples in order to boost the performance of the classifier. In general, the set of unlabeled examples is not guaranteed to improve the classifier performance. We show that if the actual examples to be labeled are automatically selected through an unsupervised clustering step, the performance is more likely to improve with the unlabeled set. In this paper, we first present a modified EM algorithm, which combined labeled and unlabeled sets for training. We then apply this algorithm to image classification. Using mutually exclusive classes we show that the clustering step is crucial to the improvement in classifier performance.
Impact of Technology Devices on College Students' Stress Levels of Using Technology.

________________________________________________________________________ Jie Zhang is Associate Professor, and Moira Fallon, Professor Emeritus, in the Department of Education and Human Development at the College at Brockport State University of New York. Theresa Russo is Professor in the Department of Human Ecology at State University of New York at Oneonta. Jie Zhang can be reached at jzhang@brockport.edu. Impact of Technology Devices on College Students’ Stress Levels of Using Technology
Irony and sarcasm expression in Twitter

Information extraction and sentiment analysis of social net contents is a relatively new field, but a very promising and quickly developing one. In the current work, we discuss the possibilities of automatic detection and analysis of ironic and sarcastic messages in one of the most popular (among both users and researches) social nets Twitter. A particular trait of this research will consist in analyzing the languages content, which is rarely observed. We will focus on the material of Spanish, German and Russian languages, trying to implement the same analysis algorithms to all three as well and to define the differences.
A Framework for Opinion Mining- RootCause Monitoring the Social System WhichKeeps Tracks of Weak Points in Rural Areas

The advent of Web 2.0 and social media content has stirred much excitement and created abundant opportunities for understanding the opinions of the general public and consumers toward social events, political movements, company strategies, marketing campaigns, and product preferences. Opinion mining, a sub discipline within data mining and computational linguistics, refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online news sources, social media comments, and other user-generated content. Sentiment analysis is often used in opinion mining to identify sentiment, affect, subjectivity, and other emotional states in online text. In this paper the idea is to keep track of all the weak points in a system. A constant monitoring system will keep track of all these failure points. As more and more weak points are gathered, then the criticality ration of the problem can be calculated and the management alerted of the impending problem. We all wait for disasters to happen and then respond to it. This can also be used by traffic management and other social welfare initiatives.
Absence of posture-dependent and posture-congruent memory effects on the recall of action sentences

In two experiments with large samples of participants, we explored contextual memory effects associated with body posture, which was considered a physical and proprioceptive context and, therefore, potentially relevant to the encoding and retrieval of information. In Experiment 1 (N = 128), we studied the effect of context dependence on memory by manipulating the body posture adopted by the participants during the incidental encoding and subsequent recall of a series of action sentences not intrinsically associated with particular body postures (e.g., “to put on a pair of glasses”, “to look at a postcard”). Memory performance was not affected by context manipulation, as reflected by the absence of significant differences between remembering while in the posture adopted at study or in a different posture. Experiment 2 (N = 85) was designed to analyze context congruency memory effects, and for that purpose we manipulated the participants' body posture during the recall of sentences that described actions usually performed in body postures that were congruent or incongruent with the posture of the participants (e.g., recalling the sentence “to travel by taxi” while sitting or while standing). A content-neutral posture (lying) was used for the incidental encoding phase. Memory performance was not affected by contextual congruency at the time of recall, as evidenced by the lack of significant differences between recalling in a posture congruent with the content to be recalled and recalling in an alternative posture. Bayesian analyses supported the strength of null findings in the two experiments, adding to the evidence that, when taken together, the results in this study clearly failed to show contextual memory effects of body posture on the recall of action-related verbal statements.
Taxonomic Work as Information Work: Design for Semantic Refactoring

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. Copyright is held by the owner/author(s). CSCW ’16, February 27 March 02, 2016, San Francisco, CA, USA Abstract Taxonomy is the branch of science concerned with classifying organisms: drawing the line between cats and dogs, fish and fowl, animals and vegetables. Modern taxonomic work is built on a hundreds-year-old tradition of qualitative research and description. There are aspects of this work that illustrate the pervasiveness and difficulty of a particular kind of qualitative data wrangling, which we call semantic refactoring: the review, normalization, and re-engineering of semantic structures. Because taxonomic work is conducted over long time spans, the processes underlying semantic refactoring become more visible. An examination of taxonomic data practices may inform our understanding of how (and if) collections of qualitative data scale, particularly when collaboratively created.
Hidden carbon costs of the “everywhere war”: Logistics, geopolitical ecology, and the carbon boot‐print of the
 US
 military

This paper examines the US military's impact on climate by analysing the geopolitical ecology of its global logistical supply chains. Our geopolitical ecology framework interrogates the material‐ecological metabolic flows (hydrocarbon‐based fuels, water, sand, concrete) that shape geopolitical and geoeconomic power relations. We argue that to account for the US military as a major climate actor, one must understand the logistical supply chain that makes its acquisition and consumption of hydrocarbon‐based fuels possible. Our paper focuses on the US Defense Logistics Agency – Energy (DLA‐E), a large yet virtually unresearched sub‐agency within the US Department of Defense. The DLA‐E is the primary purchase‐point for hydrocarbon‐based fuels for the US military, as well as a powerful actor in the global oil market. After outlining our geopolitical ecology approach, we detail the scope of the DLA‐E's operations, its supply chain, bureaucratic practices, and the physical infrastructure that facilitates the US military's consumption of hydro‐based carbons on a global scale. We show several “path dependencies” – warfighting paradigms, weapons systems, bureaucratic requirements, and waste – that are put in place by military supply chains and undergird a heavy reliance on carbon‐based fuels by the US military for years to come. The paper, based on comprehensive records of bulk fuel purchases we have gathered from DLA‐E through Freedom of Information Act requests, represents a partial yet robust picture of the geopolitical ecology of American imperialism.
Parameterized Transformation Schema for a Non-Functional Properties Model in the Context of MDE

The integration between design models of software systems and analytical models of non-functional properties is an ideal framework on which lay the foundation for a deep understanding of the architectures present in software systems and their properties. In order to reach this integration, this chapter proposes a parameterized transformation for a model of performance properties derived from a system model in the MDE context. The idea behind a parameterized term is to leave open the transformation framework to adopt future improvements and make the approach reusable. The authors believe that this kind of integration permits the addition of analysis capabilities to the software development process and permits an early evaluation of design decisions