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Most Brain-inspired Visual Object Recognition Models(BVORMs) do not consider local and global reciprocal con-nections in visual pathway. We addressed this weakness and implemented an attention modulation mechanism based on feed-back connections in BVORMs, where feature-selectivity is shaped and modulated by categorization of objects based on theirvisual features. This modification is inspired by the top-down neuromodulatory signals that make changes in post-synapticactivities of the feature-selective neurons. We also incorporated an implicit memory unit in BVORMs to accumulate recentHebbian synaptic plasticity’s of the neurons in each task. This mechanism guides the top-down feature-based attention modula-tion to retrieve the interrelated feature-selectivity pattern for each task.HMax and CNN models were used as two BVORMs andtested on a visual categorization problem: natural versus artificial objects in CALTECH-256. Based on experimental results,our proposed modifications not only increased their biological-plausibility but also significantly improved their categorizationaccuracies compared to the original models.

This research was conducted to design a model to influence the strategic orientation and management of export market orientation on export performance in business environment transformations. The method of this research is Grounded theory. For this purpose, the topic was investigated by interviewing experts who were selected by a purposeful method of judgment from the managers and marketing experts of 52 sample exporting companies in Isfahan province in 2020. The validity of the interview questions was assessed with the Binominal test and the reliability was assessed with the Cronbach's alpha test. The research results indicate that; Strategic export orientation, export market orientation, and export performance are the three main pillars of exports in the trans formatives. To direct an effective export strategy in the current situation, six key factors of information systems strategy, customer-oriented engineering strategy, increasing innovation strategy, Competitive orientation strategy, learning orientation strategy, and cost control strategy must be included. Moreover, the results of the analysis aimed at extracting four components for export market orientation, including; Virtual marketing, distribution through capillary networks, video branding, and health brand led. Furthermore, the results of content analysis for export performance include 2 components financial and operational. Examining the export performance in transformations should be considered as the contribution of the article in the management of knowledge in the field. [ABSTRACT FROM AUTHOR]

Purpose: This research aims to enhance understanding of online retailing through electronic channels (such as mobile devices) and touch points of electronic channels (such as mobile shopping software) from the customer's perspective. Methods: This research is applied for the purpose and descriptive in terms of survey and library data collection methods. The statistical population for this research consists of students at Tehran Azad University. The community consists of more than 4000 people, so according to Morgan's table, the number of samples should be 384. Sampling has been conducted using the available method, and data analysis has been performed using the LSD test. The dependent variable in online shopping is purchase intention, while the independent variables include usefulness, ease of use, pleasure, privacy, and satisfaction. Findings: The research findings indicate that customers currently use eight different devices, including laptops/notebooks, personal computers (PCs), smartphones, tablets, internet-equipped TVs, and in-store kiosks. Additionally, the research findings revealed that purchasing devices can be categorized into four electronic channel categories from the perspective of online retail customers. The floors are categorized as A, B, C, and D, and the coordinates of each are listed in the article. The research findings indicate that both the technological quality and the situational benefits of the context influence consumers' use of electronic channels. Also, customers engage in online shopping through various electronic channels (sets of Internet-enabled devices, such as mobile devices) and multi-channel touchpoints (digital shopping formats, such as mobile shopping apps). Conclusion: The results indicate that there is no significant difference between the first cluster (A), which involves shopping using personal computers (PCs), laptops, and netbooks, and the second cluster (B), which involves shopping using smartphones and tablets, in terms of usefulness, ease of use, shopping pleasure, privacy, satisfaction, and purchase intention. There is no significant difference between the first cluster (A) and the third cluster (C), i.e., Internet TV (IE TV), in terms of usefulness, ease of use, shopping pleasure, satisfaction, and purchase intention. There is a significant difference between the first cluster (A) and the fourth cluster (D), specifically in-store kiosks, in terms of usefulness, ease of use, shopping pleasure, privacy, satisfaction, and purchase intention. There is a significant difference between the second cluster (B) and the fourth cluster (D) in terms of usefulness, ease of use, shopping enjoyment, privacy, satisfaction, and purchase intention. Also, there is no significant difference between the third cluster (C) and the fourth cluster (D) in terms of usefulness, ease of use, shopping enjoyment, privacy, satisfaction, and purchase intention. [ABSTRACT FROM AUTHOR]

Generalized lambda distribution (GLD) is a very flexible distribution that has many applications in estimating the distribution of real data. Its flexibility is due to its λs parameters that each of them somehow determine the form of distribution. Since the shape of distribution is very sensitive to the changes of each λs, the smallest error in estimating each parameter results in an error in other parameters, and then resulting in a total error in estimating the data distribution. One of the most accurate and widely used methods for estimating GLD parameters is the momentum method, which requires integral calculations. This paper uses Gaussian quadrature to integrate GLD equations. The results show that in terms of both percentile index and momentum comparison, the new method leads to more accurate calculation of the GLD parameters. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.robot.2007.11.002 Byline: Borhan Beigzadeh (a)(b), Majid Nili Ahmadabadi (b), Ali Meghdari (a), Adel Akbarimajd (b) Abstract: In this paper, we aim at an integrated approach to Dynamic Biped Walking (DBW) and Dynamic Object Manipulation (DOM) at an abstract level. To this end, we offer a unified and abstract concept with a dual interpretation as a DOM and as a DBW system. We validate the proposed approach by using a set of simulations on an illustrative case study and show how it can be used in modeling as well as design of planning and control algorithms for DOM and DBW systems. In the case study, we describe the proposed approach and show its dual interpretation by identifying the relations between 2D dynamic object manipulation of a disc using two planar manipulators and 2D dynamic object locomotion of lower part of a biped robot. More specifically, having obtained the equations of DOM, we change the boundary conditions of the problem in such a way that both radius and mass of the disc tend to infinity. Simultaneously, both size and mass of the manipulators' base, i.e. the planet earth, tend to some values in the order of human body mass and dimension. Regarding these changes, we can transform DOM into DBW and vice versa. To test the proposed approach, a simple control strategy is introduced to handle impact between the manipulators (legs) and the object (the earth). In addition, a motion planning system is designed in such a way that the manipulators (legs) catch and throw the manipulated object (the earth) in appropriate configurations. Author Affiliation: (a) Centre of Excellence in Design, Robotics, and Automation (CEDRA), ME Department, Sharif University of Technology, Tehran, Iran (b) Control and Intelligent Processing Centre of Excellence, Robotics and AI Laboratory, ECE Department, University of Tehran, Tehran, Iran Article History: Received 12 July 2007; Revised 30 October 2007; Accepted 13 November 2007

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.robot.2007.01.007 Byline: Adel Akbarimajd (a), Majid Nili Ahmadabadi (a), Borhan Beigzadeh (b) Abstract: Dynamic manipulation of polygonal objects by an array of one degree of freedom arms is studied from kinematics and planning points of view. In the studied manipulation method, an object is manipulated to its goal configuration by a sequence of juggles. A kinematic model of an object throwing task is driven and a method for object manipulation by a sequence of throws is proposed. The method, called Backward Throws Method (BTM), is based on throwing an object backward towards the arm pivot. Based on the developed model, a planning algorithm is proposed for BTM. In addition, the only existing similar method to BTM, which is based on forward throws-named FTM in this paper-is reformulated for implementation by a series of arms and compared with the proposed method. Analytical investigations, simulation results, and experimental outcomes show that BTM meets the desired requirements. Moreover, in comparison to FTM, BTM requires fewer number of throws and lower release velocity. In addition, the object's maximum height of flight is much lower in BTM which results in lower catching impact. According to the experimental results, although the proposed method has no feedback from object position, accumulated position error is very small. This fact is directly related to the attained decrease in catching impact which causes small object slippage and rebound on catching. Furthermore, there is no restriction on the arm geometry in BTM while in FTM an arm with negative offset is needed. Author Affiliation: (a) Control and Intelligent Processing Center of Excellence, Robotics and AI Lab., ECE Department, University of Tehran, Iran (b) Center of Excellence in Design, Robotics and Automation (CEDRA), ME Department, Sharif University of Technology, Iran Article History: Received 30 May 2006; Revised 11 January 2007; Accepted 25 January 2007

Cooperation in learning (CL) can be realized in a multiagent system, if agents are capable of learning from both their own experiments and other agents' knowledge and expertise. Extra resources are exploited into higher efficiency and faster learning in CL as compared to that of individual learning (IL). In the real world, however, implementation of CL is not a straightforward task, in part due to possible differences in area of expertise (AOE). In this paper, reinforcement-learning homogenous agents are considered in an environment with multiple goals or tasks. As a result, they become expert in different domains with different amounts of expertness. Each agent uses a one-step Q-learning algorithm and is capable of exchanging its Q-table with those of its teammates. Two crucial questions are addressed in this paper: "How the AOE of an agent can be extracted?" and "How agents can improve their performance in CL by knowing their AOEs?" An algorithm is developed to extract the AOE based on state transitions as a gold standard from a behavioral point of view. Moreover, it is discussed that the AOE can be implicitly obtained through agents' expertness in the state level. Three new methods for CL through the combination of Q-tables are developed and examined for overall performance after CL. The performances of developed methods are compared with that of IL, strategy sharing (SS), and weighted SS (WSS). Obtained results show the superior performance of AOE-based methods as compared to that of existing CL methods, which do not use the notion of AOE. These results are very encouraging in support of the idea that "cooperation based on the AOE" performs better than the general CL methods. Index Terms--Area of expertise (AOE), cooperative Q-learning agents, cooperative Q-learning using AOE, extraction of AOE, multiagent systems (MASs).