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In this study, 30 mental states were suggested to 50 theatre actors. They designed a specific physical form for each mental state. In this process, 2000 images from actors’ gestures were collected. Images was surveyed by 321 accidental audiences; and among all images, images achieving the highest rate were selected. Reliability and validity of selected images were evaluated by a software designing for mind-reading's test though body. This study achieved its results mentioned below through preparation, design, implementation, analysis, record of results of a mind-reading’s test.
According to statistical results, the body has an ability to convey the mental state. We can figure out individuals’ mental states through body forms. Based on Baron-Cohen’s study, 30 body forms were determined. The relation between mind-reading through body and eyes was analyzed. Selected images for designing a mind-reading’s test through body were identified, and its reliability and validity were confirmed.
In this study, the first archive of images representing mental states through actors’ bodies has been designed and prepared. In comparison to men, women have a better function in understanding of mental states. Furthermore, bodies can represent mental states better than eyes.

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The aim of this study was to investigate the effect of cognitive functions on verbal fluency. For this purpose, some cognitive functions and verbal fluency in adult patients with right hemisphere damage were evaluated. The methodological nature of this study is descriptive-analytical. The statistical population of the study consists of 18 adult patients with right hemisphere damage and 18 healthy adults. Selective attention test, memory test, clock drawing test (visual neglect) and verbal fluency test were used to evaluate cognitive functions and linguistic skill of the subjects. Research data were analyzed using descriptive and inferential statistics. The findings showed that there is a significant difference between performance of adult patients with right hemisphere damage and healthy adults in the cognitive tests (P< 0.05). Likewise, in the verbal fluency test, there is a significant difference between performance of adult patients with right hemisphere damage and healthy adults (P< 0.05). In addition, the research findings showed that there is a correlation between cognitive functions and verbal fluency. Also, the results showed that damage to the right hemisphere of brain can lead to disorders in cognitive functions of patients with right hemisphere damage. Furthermore, it seems that impairment in cognitive functions can cause problems in language skills.
 

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Despite the abundance of research on the language teachers’ pedagogical knowledge base (PKB), there is a scarcity of studies probing into the teachers’ individual differences and how they relate to the teachers’ instructional effectiveness. To address this gap, we investigated the association of language teachers’ pedagogical knowledge and their instructional efficacy, shedding light on the similarities and differences in the knowledge base of the teachers. Through administering a context-specific self-efficacy test, eight teachers were selected based on their scores and put into two groups. Afterwards, a 90-minute instructional session of each teacher was video-recorded and later used in a stimulated-recall interview with the teacher. The verbal reports were transcribed verbatim and subjected to thematic content analysis to identify the teachers’ pedagogical thoughts. The results indicated significant differences between the groups, with the high efficacy group reporting an average of 4.18 thoughts-per-minute in contrast to 2.85 thoughts-per-minute reported by the low efficacy group. Five of the dominant knowledge categories were common between the two groups, though with varying frequencies and ranking. The findings offer implications for attending to the construct of self-efficacy and its sources in teacher professional development, as well as the socio-cognitive and emotional side of teacher preparation and development.

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This study applies artificial neural networks (ANNs) to assess the impact of climate factors on the collaborative development of agriculture and logistics in Zhejiang, China. The ANN model investigates how average temperature and rainfall from 2017-2022 influence crop yield, water usage, energy demand, logistics efficiency, and economic growth at yearly and seasonal scales. By training the neural network using temperature and rainfall data obtained from ten weather stations, alongside output indicators sourced from statistical yearbooks, the ANN demonstrates exceptional precision, yielding an average R² value of 0.9725 when compared to real-world outputs through linear regression analysis. Notably, the study reveals climate-induced variations in outputs, with peaks observed in crop yield, water consumption, energy usage, and economic growth during warmer summers that surpass historical norms by 1-2°C. Furthermore, the presence of subpar rainfall ranging from 20-30 mm also exerts an influence on these patterns. Seasonal forecasts underscore discernible reactions to climatic factors, especially during the spring and summer seasons. The findings underscore the intricate relationship between environmental and economic factors, indicating progress in agricultural practices but vulnerability to short-term climate fluctuations. The study emphasizes the necessity of adapting supply management to address increased water demands and transitioning to clean energy sources due to rising energy consumption. Moreover, optimizing logistics requires strategic seasonal infrastructure planning.

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Context-dependent modeling is a well-known approach to increase modeling accuracy in continuous speech recognition. The most common way to implement this approach is via triphone modeling. Nevertheless, the large number of such models results in several problems in model training, whilst the robust training of such models is often hardly obtained. One approach to solve this problem is via parameter tying. In this paper, clustering has been carried out on HMM state parameters and the states allocated to any cluster are tied to decrease the overall number of system parameters and achieve robust training. Two types of groupings, one based on the final trained model set parameters and their inter-model distances and the other based on the training data and a decision tree, have been carried out. In the implementation of the later, a decision tree based on the acoustic properties of the Persian (Farsi) language and the phonetic similarities and differences has been designed. The results obtained have shown the usefulness of both the approaches. However, the second approach has the advantage of making the estimation of unseen model parameters possible.

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‎ An orientational metaphor is a metaphor in which concepts are ‎spatially related to each other, as in the following ways: up or down, ‎in or out, front or back, on or off, deep or shallow, central or ‎peripheral. Such metaphorical orientations are not arbitrary. They ‎originate from our physical and cultural experience. An orientational ‎metaphor organizes a group or system of metaphorical concepts in ‎terms associated with spatial orientation, for instance “up-down” and ‎‎“front-back”. An example would be the fact that many metaphorical ‎concepts concerning happiness (e.g. “feeling up”, “spirits were ‎boosted”, “in high spirits”) have to do with the spatial orientation of ‎‎“up”, whereas many metaphorical concepts of unhappiness (e.g. ‎‎“feeling low”, “feeling down”, “sinking spirits”, “falling into ‎depression”) have to do with “down”. These spatial orientational ‎metaphors are so common that we often use them unconsciously. ‎Those metaphors using the spatial orientation of “up”, “forward” and ‎‎“on” seem to be associated with positive feelings and events, while ‎terms such as “down” and “back” are associated with the ‎negative. The majority of spatial orientational metaphors employed in ‎the Qur’an can be divided into those that convey a positive experience ‎or feeling and those that express a negative or less satisfactory event ‎or emotion.‎

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A parallel hybrid system of HMM and GMM modeling techniques was implemented and used in a telephony speaker verification and identification system. Spectral subtraction and Weighted Projection Measure were used to render this system more robust against additional noise. Cepstral Mean Subtraction method was also applied for the compensation of convolution noise due to transmission channel degradation and differences in the frequency response of telephone handsets. For a population of 100 speakers of FARSDIGITS1 database with a SNR of 8.8 dB, a speaker identification performance of 95.51% and a speaker verification error rate of 0.37% were obtained. Several score normalization methods in utterance and frame level and weighting of model scores were also implemented, and then compared and evaluated. It was shown that these methods improve discrimination between speakers and yield a reduction of speaker verification and identification error rates.

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The geometric distribution of states duration is one of the main performance limiting assumptions of hidden Markov modeling of speech signals. Stochastic segment models, generally, and segmental HMM, specifically, overcome this deficiency partly at the cost of more complexity in both training and recognition phases. In this paper, a new duration modeling approach is presented. The main idea of the model is to consider the effect of adjacent segments on the probability density function estimation and evaluation of each acoustic segment. This idea not only makes the model robust against segmentation errors, but also it models gradual change from one segment to the next one with a minimum set of parameters. The proposed idea is analytically formulated and tested on a TIMIT based context independent phoneme classification system. During the test procedure, the phoneme classification of different phoneme classes was performed by applying various proposed recognition algorithms. The system was optimized and the results have been compared with a continuous density hidden Markov model (CDHMM) with similar computational complexity. The results show slight improvement in phoneme recognition rate in comparison with standard continuous density hidden Markov model. This indicates improved compatibility of the proposed model with the speech nature.

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Forensic phonetics is a subfield of forensic linguistics in which acoustic information and phonetic features of phones are investigated for completing the forensic cases where one of the existing evidences is related to the guilty. One of the most important tasks of a forensic phonetician is forensic speaker recognition. For doing this, the phonetician is asked to estimate the degree of similarity between the given records of the guilty’s and the suspect’s speech, and determine whether these two sound evidences match to each other or not. The objective of this study, which was conducted on the sound data from 10 Persian native speakers of both sexes, was to investigate the possibility of using Logarithmic spectrographs of vowels as a key for forensic speaker recognition tasks. The results showed that using these logarithmic spectrographs may be a useful means with perfect reliability in the tasks related to forensic speaker recognition.  

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In this paper, we used a shape matching algorithm to recognize Farsi digits. For each sampled point on the contour of a shape, we obtain a descriptor showing the distribution of the other points of the contour, with respect to this point. Based on these descriptors, we find the corresponding points of the two contours and take the sum of their distances as a dissimilarity measure between two shapes. Then we define a geometric transformation that maps the sampled points of the one shape to the corresponding points of the other shape. The bending energy of this transform is taken as the second dissimilarity measure between two shapes. We optimized the parameters of the matching algorithm for the recognition of Farsi digits and used the method of minimum distance from the class prototypes for the recognition. In a test on a set of 1288 digits, we obtained a recognition rate of 89.9%. This result was obtained without any post processing

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In this paper, an interactive model for individual normal behaviour of drivers is presented in which the mutual effect of vehicles has been incorporated. Temporal features obtained from vehicles tracking and their motion history is utilized for generating a model of normal behaviour. Because of non-stationarity of behaviour, Hidden Markov Model has been used for interactive model. This model has three main parts. The first part is the history of antecedent trajectory which for this purpose has proposed a Centers Transition Matrix (CTM) that is some type of spatio-temporal information-data bank from motions seen in the old frames. The second part is based on the linguistic features or motion recognition of vehicles, these motions contain forward, turn right and left, lane changing to right and left motion. The third part is constituted from low level features which contain Velocity and distance to neighbor object. Also CTM is efficient in search at similar blob in image sequences and it can determine the radius and region of search. This top-down feedback caused an increment of performance of RLS tracker and object searching. In the presented system, we obtained a 81.2% membership rate to normal model. Also types of motion are recognized using HMM with a recognition rate of up to 82.7%. Prediction error is reduced on many vehicles trajectory by at least 80% using a feedback system.

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One of the main characteristics of the story in data direction is Speaker/writer method which is known as “viewpoint “. Data direction is the perspective to an object or person to become efficient by perusing a specific purpose. In semantics school of Paris based on Jacques fontanill theorem point views consists of four classes. The purpose of this research is to investigate a variety of viewpoints in the short story "peace" written by Majid Gheissari and its main issue is to answer the question “what is the application of each viewpoint for the recognition of characters, time and place in the enunciate which is done by taking notes and content analysis based on Jacques fontanill theorem. In this research we have presented how the enunciator has used all four viewpoints for identification and selecting view point according to the enunciator purpose is related to the Impact on enunciated.

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In this paper, to test the theory of Honneth, historical methods and analysis technique were used. The analysis of socio-economic forces during the 40s and 50 in the Islamic Revolution of Iran showed that Shah failed in the modernization program involved: secularism, nationalism and capitalism. Along with feeling humiliated, mis-recognition, denial and injustice by some social forces, established a major change in the approach to policy seminary. Clergymen showed a tendency to transformed orientation, and different social classes were mobilized against the regime. They felt humiliatly because of the modernization project. Because of the regime’s emphasis on nationalism, political parties and different urban middle classes including academics, writers and intellectuals felt mis- recocgnition. All of these resons led to increasing the motivation of clergymen to join the revelution. But also the economic forces played a effective role to the victory of the Islamic Revolut­ion in Iran. Traditional merchants, immigrants and poor people were the losers of the modernization project. Capitalism was unfaid and detrimental to them.They also joined the coalition. Based on these results, the recognition is one of the main causes of masses, mobalization against the regime.    

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This paper aims to study some Muslim economists’ views towards pre- suppositions of demand theory in economics, benefiting from the method of analyzing rational behaviors, as well as considering the epistemology of utility. In economics in general and Islamic economics in particular, there not only exists a sharp difference in utility - as a basis for demand theory, but also, there are some vital differences in recognizing utility. Considering the differences here influences theorization of demand in Islamic economics.

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Speech emotion can add more information to speech in comparison to available textual information. However, it will also lead to some problems in speech recognition process. In a previous study, we depicted the substantial changes of speech parameters caused by speech emotion. Therefore, in order to improve emotional speech recognition rate, in a first step, the effects of emotion on speech parameters should be evaluated and in the next steps, emotional speech recognition accuracy be improved through application of suitable parameters. The changes in speech parameters, i.e. formant frequencies and pitch frequency, due to anger and grief were evaluated for Farsi language in our former research. In this research, using those results, we try to improve emotional speech recognition accuracy using baseline models. We show that adding parameters such as formant and pitch frequencies to the speech feature vector can improve recognition accuracy. The amount of improvement depends on parameter type, number of mixture components and the emotional condition. Proper identification of emotional condition can also help in improving speech recognition accuracy. To recognize emotional condition of speech, formant and pitch frequencies were used successfully in two different approaches, namley decision tree and GMM.

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In this paper, in order to detect the number of transmitting antenna in MIMO communication systems, it is proposed that the techniques such as AIC & MDL, which have been primarily designed so as to detect the number of Gaussian sources, are applied. Then a hypothesis testing based method for recognizing the type of modulation in MIMO communication systems with block orthogonal codes is suggested; in which in order to reduce the complexity of the traditional methods, simpler likelihood functions for testing hypotheses are applied. Furthermore, because in all modulation scheme detection methods, a proper estimation of channel gain (channel matrix) is required; in this paper, a new and efficient method based on SAGE iterative algorithm for estimation of channel matrix in MIMO communication system with space-time block codes is proposed. At the end of this paper, the performance and effectiveness of all proposed modules are separately and jointly analyzed by numerical simulations.

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