Background:Prisoners are at risk of mental disorders. Therefore attention to mental health of prisoners is important.Objectives:This study aimed determine to the prevalence of mental disorders among Kashan prisoners.Patients and Methods:This cross sectional study was carried out in Kashan prison (Iran). 180 Subjects were selected by using stratified random sampling and evaluated with Symptoms Check List-90-Revised (SCL-90-R) questionnaire and clinical interview based on Diagnostic Statistical Manual of Disease-IV (DSM-IV) check list by two psychiatrists. Data were analyzed by SPSS-16 software and Chi square, Kolmogrov Smirnov, Mann-whiteny and Leven tests.Results:The mean age of prisoners was 31.9 ± 8.96. The prevalence of psychiatric disorders in prisoners was 43.4 %. The most frequent disorders were major depressive disorders (27.9 %), Post traumatic Stress Disorder (17.4%) and substance use disorder (17.4 %). 28.3% of prisoners had personality disorders, that the most prevalent were antisocial personality and borderline personality. The comorbidity of psychiatric disorders was (36 %) in axis I. Suicidal thoughts there were in 44.6 % of prisoners. History of head trauma in Prisoners with psychiatric disorders was (52.2 %). There was significant difference between head trauma and psychiatric disorders (P = 0.05). Significant difference was between marital status and duration of imprisonment with psychiatric disorders P < 0.05. There was not significant difference between type of crime and educational level with psychiatric disorders.Conclusions:About half of all prisoners suffered from psychiatric disorders; therefore treatment psychiatric disorder in this group is essential for prevention of crime. Prisoners are at risk of mental disorders. Therefore attention to mental health of prisoners is important.

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Identifying the topic (domain) of each user's utterance in opendomain conversational systems is a crucial step for all subsequent language understanding and response tasks. In particular, for complex domains, an utterance is often routed to a single component responsible for that domain. Thus, correctly mapping a user utterance to the right domain is critical. To address this problem, we introduce ConCET: a Concurrent Entity-aware conversational Topic classifier, which incorporates entity-type information together with the utterance content features. Specifically, ConCET utilizes entity information to enrich the utterance representation, combining character, word, and entity-type embeddings into a single representation. However, for rich domains with millions of available entities, unrealistic amounts of labeled training data would be required. To complement our model, we propose a simple and effective method for generating synthetic training data, to augment the typically limited amounts of labeled training data, using commonly available knowledge bases as to generate additional labeled utterances. We extensively evaluate ConCET and our proposed training method first on an openly available human-human conversational dataset called Self-Dialogue, to calibrate our approach against previous state-of-the-art methods; second, we evaluate ConCET on a large dataset of human-machine conversations with real users, collected as part of the Amazon Alexa Prize. Our results show that ConCET significantly improves topic classification performance on both datasets, including 8-10% improvements over state-of-the-art deep learning methods. We complement our quantitative results with detailed analysis of system performance, which could be used for further improvements of conversational agents.

Predicting user satisfaction in conversational systems has become critical, as spoken conversational assistants operate in increasingly complex domains. Online satisfaction prediction (i.e., predicting satisfaction of the user with the system after each turn) could be used as a new proxy for implicit user feedback, and offers promising opportunities to create more responsive and effective conversational agents, which adapt to the user's engagement with the agent. To accomplish this goal, we propose a conversational satisfaction prediction model specifically designed for open-domain spoken conversational agents, called ConvSAT. To operate robustly across domains, ConvSAT aggregates multiple representations of the conversation, namely the conversation history, utterance and response content, and system-and user-oriented behavioral signals. We first calibrate ConvSAT performance against state of the art methods on a standard dataset (Dialogue Breakdown Detection Challenge) in an online regime, and then evaluate ConvSAT on a large dataset of conversations with real users, collected as part of the Alexa Prize competition. Our experimental results show that ConvSAT significantly improves satisfaction prediction for both offline and online setting on both datasets, compared to the previously reported stateof-the-art approaches. The insights from our study can enable more intelligent conversational systems, which could adapt in real-time to the inferred user satisfaction and engagement.

Objectives: Mental health is one of the most important public health issues, because of its major contribution in decrease of global burden of diseases. This study investigates the prevalence rate of mental disorders in General population aged 18 years and over in (kashan city, Iran 2005). Methods: This was a descriptive cross sectional study that was conducted via classified-randomized sampling. At stage one, by using the general health Questionnaire (N= 1800) and stage two by using clinical interview based on checklist DSM-IV by two psychiatrists;546 ones among 606 questionable people participated and 60 ones did not participated. Data were analyzed using Spss v. 16 and OR, CI and Chi-square test. Results: The overall prevalence of mental disorders was 29.2% (women 35/5% and men 21.2%). The most prevalent disorders were mood disorders and Anxiety disorders respectively (9.3%),(4.7%).In 505 subject with mental disorders, major depression (8.2%) and generalized anxiety disorder (7.2%) were the most prevalent and 7.8% of people had one mental disorder at least. The prevalence was higher than in adult aged 56-65 years (35.8%), widow (35.8%), illiterate (42.8%) and unemployed (38.8%) in this study. Significant difference was found between genders, education, marital status variables with mental disorder.

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