Introduction: Recognition of mental activities in brain-computer interface systems based on motor imagery has attracted the attention of many researchers. A Visibility graph is a powerful method for analyzing the function and connectivity of different areas of the brain. The aim of this study is to improve and develop the Visibility graph method for analyzing brain behavior and detecting motor imagery. Materials and Methods: First, brain signals including four motor imagery classes of left-handed, right-handed, foot, and tongue were transformed into three types of Visibility graphs, and important features of these graphs were extracted. Then, to reduce features, the method of analysis of variance was used. To classify the motor imagery classes, the support vector machine was used. In most investigations, graph degree distribution has been used to extract information and graph weighting. In the present study, amplitude difference distribution has been used so shorter time series are required. To analyze the function and connectivity of different areas of the brain and to obtain the direction of information flow, a new method called weighted horizontal Visibility graph-transfer entropy has been proposed. Results: Increasing the kappa value compared to other studies showed that a weighted horizontal Visibility graph is a suitable method for processing brain signals based on motor imagery. A comparison of brain graphs and the direction of information flow in the four classes of motor imagery showed a significant difference between them. Conclusion: Temporal networks provide a better understanding of brain dynamics in brain-computer interface systems based on motor imagery.

Purpose: Designing an automated emotion recognition system using biosignals has become a hot and challenging issue in many fields, including human-computer interferences, robotics, and affective computing. Several algorithms have been proposed to characterize the internal and external behaviors of the subjects in confronting emotional events/stimuli. Eye movements, as an external behavior, are habitually analyzed in a multi-modality system using classic statistical measures, and the evaluation of its dynamics has been neglected so far. Materials and Methods: This experiment intended to provide an innovative single-modality scheme for emotion classification using eye-blinking data. The dynamics of eye-blinking data have been characterized by weighted Visibility graph-based indices. The extracted measures were then fed to the different classifiers, including support vector machine, decision tree, k-Nearest neighbor, Adaptive Boosting, and random subset to complete the process of classifying sad, happy, neutral, and fearful affective states. The scheme has been evaluated utilizing the available signals in the SEED-IV database. Results: The proposed framework provided significant performance in terms of recognition rates. The highest average recognition rates of  > 90% were achieved using the decision tree. Conclusion: In brief, our results showed that eye-blinking data has the potential for emotion recognition. The present system can be extended for designing future affect recognition systems.

Introduction: Behavior contagion in social decision-making refers to the alignment of an individual’s behavior and decisions with those of others in social interactions. Despite previous studies in the field, it remains necessary to understand how brain activities are spatiotemporally organized during contagion. Since brain activities include both positive and negative fluctuations, monitoring these occurrences in a polar space using a Visibility graph ((VG)) can aid in a better understanding of this phenomenon. Methods: We subjected a healthy group to a psycho-economic task while their EEG was simultaneously recorded. The participants’ performances were compared before and after observation of others’ preferences in a dictator game task. Subsequently, two groups were differentiated based on their behavior contagion rate. Then, the (VG)s of event-related potentials (ERPs) in both contagion and non-contagion groups were compared before and after observation.  Results: Our results indicated that the (VG) features differentially change in various EEG channels. For instance, changes in clustering coefficient, modularity, and efficiency of (VG)s suggested that the number of ERP components varies after contagion, specifically at the frontal, frontocentral, centroparietal, and parietal regions.  Conclusion: This result may raise questions about the ERP analysis of contagion, particularly when using the same number and length of components (e.g. P300) for comparing ERPs before and after contagion.

Formal portfolio optimization methodologies describe the dynamics of financial instruments price with Gaussian Copula (GC). Regardless of the skewness and kurtosis of assets return rate, optimization with GC underestimates the optimal CVaR of portfolio. In the present paper, we develop an approach to portfolio optimization by introducing Lé vy processes. It focuses on describing the dynamics of assets’ log price with Variance Gamma copula ((VG)C) rather than GC. Doing a case study on three Indexes of Iran Stock Market, the best hedge positions of Total Index, Market Index and Industry Index with the performance function CVaR under (VG) model were calculated. The results indicate that (a) (VG) copula can efficiently overcome the shortcomings of Gaussian copula which underestimates the CVaR of portfolio; (b) optimal portfolio, VaR and CVaR keep stable each time one parameter of sample’ s skewness or kurtosis was changed, but the optimal portfolio change significantly when the sample’ s mean increases or decreases; (c) different copula lead to different optimal CVaR; and (d) fat-tailedness and kurtosis are extremely important in portfolio optimization framework.

Generating dynamic videos from static images and accurately modeling object motion within scenes are fundamental challenges in computer vision, with broad applications in video enhancement, photo animation, and visual scene understanding. This paper proposes a novel hybrid framework that combines convolutional neural networks (CNNs), recurrent neural networks (RNNs) with long short-term memory (LSTM) units, and generative adversarial networks (GANs) to synthesize temporally consistent and spatially realistic video sequences from still images. The architecture incorporates splicing techniques, the Lucas-Kanade motion estimation algorithm, and a loop feedback mechanism to address key limitations of existing approaches, including motion instability, temporal noise, and degraded video quality over time. CNNs extract spatial features, LSTMs model temporal dynamics, and GANs enhance visual realism through adversarial training. Experimental results on the KTH dataset, comprising 600 videos of fundamental human actions, demonstrate that the proposed method achieves substantial improvements over baseline models, reaching a peak PSNR of 35.8 and SSIM of 0.96—representing a 20% performance gain. The model successfully generates high-quality, 10-second videos at a resolution of 720×1280 pixels with significantly reduced noise, confirming the effectiveness of the integrated splicing and feedback strategy for stable and coherent video generation.

Translator's Visibility has been a widely accepted concept in the field of Translation Studies. The purpose of this study was to see whether strategies contributing to translator's Visibility, i.e. foreignization and neological translations on the textual level, use of footnotes, dedications and prefaces on the paratextual level were used by Persian translators of Silverstein's books. To find answers to the research questions, three poetry books by Silverstein named Where the Sidewalk Ends, A Light in the Attic, and Falling Up, and their two Persian translations by Hamid Khademi and Razi Khodadadi Hirmandi were analyzed.Both translators used the above said strategies, though to different degrees. They were visible in terms of foreignizing strategy with a slight difference of tendency in Where the Sidewalk Ends and Falling Up. Khademi (called Translator 2 in this Research) was found to overtake Hirmandi (referred to as Translator 1) in all the three books, with higher number of neological translations, use of paratextual elements such as footnotes, dedications, and bracketed information. The implications of using such strategies were seen to be of ideological nature, in a way that Translator 2's voice was heard louder and he exerted more power in raising himself to an authorial status.