Impoliteness in Power-imbalance and Power-neutral Relational Contexts: Evidence from a Persian TV Drama

 This study investigated impoliteness in relational contexts. Interlocutors analyzed the data from a Persian TV drama from two perspectives: intentionality and perception of impoliteness. Two relational contexts were identified: power-imbalance and power-neutral, each comprising two types of impoliteness: reciprocal and non-reciprocal. Reciprocal impoliteness occurred in hostile and conflictual situations where impoliteness was both intended by the speaker and perceived by the recipient as a direct face-attack. In non-reciprocal impoliteness, however, when power imbalance was due to family hierarchy, the recipient of impoliteness remained silent; in other situations, the intentional face-attack was tolerated, unless the recipient’s social identity face was directly attacked. In power-neutral situations, impoliteness was not reciprocated when intimacy existed between the interactants. It was neither intended nor perceived as face-threatening; however, direct face-attack was reciprocal in hostile situations. The findings of the present study point to the significant role of the relational context in the interpretation of impoliteness

AN INVESTIGATION INTO PRONUNCIATION PROBLEMS OF HAUSA-SPEAKING LEARNERS OF ENGLISH

AbstractThere is no dearth of publication on pronunciation problems of different L1-bacground learners in EFL contexts; however, research in ESL situations (where English is spoken outside the classroom) in general, and in the Nigerian context, in particular, is scarce. Accordingly, to fill this research gap the present study set out to investigate the pronunciation problems of Hausa speakers of English in Nigeria. To achieve the goals of the research, 60 native speakers of Hausa studying at three universities in Northern Cyprus participated in the study. The participants’ pronunciation problems of English were elicited by mean of a pronunciation test that consisted of a word list, a short paragraph, and 15 individual sentences. Moreover, 15 pictures were shown to the participants to name while being audio-recorded. All the test items contained English consonants and vowels with potential pronunciation difficulties for Hausa speakers of English.  The collected data were then transcribed and analyzed, and percentages and frequencies of pronunciation errors were computed. The results revealed that native speakers of Hausa face problems in pronouncing certain English vowels (i.e., /ᴧ/, /ᴐ:/ and /з:/) and consonants (/f/, /v/, /θ/ and /ð/). Theoretically, the findings lend support to the notion of negative transfer as all of the errors were the result of mother tongue interference. The findings are interpreted to have pedagogical implications for ESL teachers and syllabus designers in general and in Hausa-speaking communities, in particular.Keywords: Pronunciation problems, segmental phonemes, negative transfer, Hausa speakers of English

Assessing attitudes of citizens of Qazvin city towards Afghan Refugees via Cybernetics

The aim of the present study is to assess attitudes of citizens of Qazvin City towards Afghan migrants using the factor analysis technique and the Bogardus Social Distance Scale. The data were collected via a questionnaire distributed among a sample size as 384 participants. Then, the collected data were analyzed via SPSS-23 and the factor analysis technique. As a result, 30 indices were summarized by factor analysis and reduced into 7 factors presented in combinatorial meaningful factors. The contribution of each factor affecting the attitudes of citizens of Qazvin City towards Afghan refugees using the mentioned technique. The research findings indicated that those 7 factors generally explain 60.21% of the citizens’ attitudes towards Afghan migrants. The results obtained from the Bogardus Social Distance Scale showed that citizens of Qazvin City have no positive attitudes towards Afghans with the mean scores of negative answers with 77.66%. This issue indicates a great social distance between Afghan refugees and the research population.

Designing Criteria and Indices for Educational Ranking of Paramedical Sciences Schools in Iran

Background: Assessing the statue of educational services in schools of Paramedical Sciences can help authorities to plan for further promotion through identifying schools' strengths and weaknesses. Objective: To design criteria and indices for educational ranking of associate programs of Paramedical Sciences including Laboratory Sciences, Operating Room Nursing, Technology of Radiology, Anesthesiology, Nuclear Medicine, Technology of Radiotherapy and Medical Emergencies in Iran. Methods: In an expert committee, criteria used in worldwide rankings and medical education standards were reviewed. Then a set of criteria and indices which were compatible to Iran educational system was devised and their weights were defined through consensus developing methods. Each school was asked to introduce a representative to collect schools’ information and schools were visited to verify the gathered data. Then schools' scores for each criterion were calculated. Results: A set of 42 criteria sorted as a tree diagram was devised. Main branches of this tree included input branch consisting of National Entrance Examination (Weight: 2%), faculty members (weight: 18%), and facilities and equipment (weight: 20%), process branch consisting of administrations (weight: 40.5%) and support and counseling systems (weight: 4.5%) and output branch consisting of students' output (weight: 9%) and faculty members' output (weight: 6%). Conclusion: This study provided the educational strengths and weaknesses of Paramedical Sciences programs in Iran. The results can be used in devising practical strategies for qualitative and quantitative improvement . Keywords: Education, Allied health, Ranking, Stratification, School, Associate program

JITGNN: A Deep Graph Neural Network for Just-In-Time Bug Prediction

Just-In-Time (JIT) bug prediction is the problem of predicting software failure immediately after a change is submitted to the code base. JIT bug prediction is often preferred to other types of bug prediction (subsystem, module, file, class, or function-level) because changes are associated with one developer, while the entities that are predicted to be defective in other forms of bug predictions might be developed by multiple developers. JIT bug prediction can be applied when the design decisions are fresh in the developer's mind; therefore, it takes less effort to review the change and fix the potential issues. Over the years, many approaches have been proposed to tackle the JIT bug prediction problem. These methods mainly rely on the change metrics such as the size of the change, the number of modified files in the change, and the experience of the author. Little work has been done on the inclusion of the syntax and semantics of the change in JIT models. Also, although there has been extensive work on employing deep learning models for other forms of bug prediction, there are not many deep models for JIT bug prediction. None of the existing JIT models in which the changed code is included, consider the graph structure of source codes and the change codes are considered as plain text in these models. In this work, we propose a JIT model that incorporates both the content and metadata of changes leveraging the graph structure of programs. To this end, we designed and built \textit{JITGNN}, a deep graph neural network (GNN) framework for JIT bug prediction. JITGNN uses the abstract syntax trees (ASTs) of changed programs. We evaluate the performance of JITGNN on two datasets and compare it to a baseline and the state-of-the-art JIT models. Our study shows that JITGNN achieves the same AUC as the state-of-the-art model (JITLine), which does not consider the code structure of source codes, and they both have the same discriminatory power