IMPROVING THE EFFECTIVENESS OF TEACHING STUDENTS A FOREIGN LANGUAGE BASED ON A COMMUNICATIVE METHODOLOGY

In the context of globalization and integration in the country into the international community, the modernization of foreign language teaching in domestic higher education is taking place. The development of international contacts in the country causes a need for specialists of various profiles who are well-versed in foreign languages, but their training does not always give the desired results. Practice has shown that the grammatical translation method used in the last few decades to teach a foreign language to specialists of various profiles, in which the emphasis is on learning rules and translating texts, rather than on communication, has not fully justified itself. Having generally good grammar knowledge, and skills in the field of translation, graduates of non-linguistic universities have difficulties in communication with foreigners in the course of professional activities, and the question about the level of knowledge of foreign language professionals are increasingly responsible read and translate with a dictionary, which actually means no possession and failure to carry out practical communication in a foreign language.

أحكام المحاكم السودانية في قضايا الأحوال الشخصية لغير المسلمين في السودان

أحكام المحاكم السودانية في قضايا الأحوال الشخصية لغير المسلمين في السودان The study aims at attaining learning rules of non-Muslims private affairs status in Sudanese courts and how their cases are looked into. In this study, the researcher followed the analytic historical method which comes into four Themes. Important findings: 1. The Sudanese legislation has put rules organizing the non-Muslim problems in the Sudan. 2. Despite the residence of non-Muslims in Sudan for stable settlement but their favor is still to their beliefs, and the Islamic Sharia laws, is not applied on them except by agreement of the two conflicting parties. 3. In case of not finding a text in Sharia, justice and sound feeling is applied on them, then stable custom from their religion beliefs

Higher-order learning

We design a novel experiment to study how subjects update their beliefs about the beliefs of others. Three players receive sequential signals about an unknown state of the world. Player 1 reports her beliefs about the state; Player 2 simultaneously reports her beliefs about the beliefs of Player 1; Player 3 simultaneously reports her beliefs about the beliefs of Player 2. We say that beliefs exhibit higher-order learning if the beliefs of Player k about the beliefs of Player $$k-1$$ k - 1 become more accurate as more signals are observed. We find that some of the predicted dynamics of higher-order beliefs are reflected in the data; in particular, higher-order beliefs are updated more slowly with private than public information. However, higher-order learning fails even after a large number of signals is observed. We argue that this result is driven by base-rate neglect, heterogeneity in updating processes, and subjects’ failure to correctly take learning rules of others into account.

Evolving Hebbian Learning Rules in Voxel-based Soft Robots

<div>According to Hebbian theory, synaptic plasticity is the ability of neurons to strengthen or weaken the synapses among them in response to stimuli. It plays a fundamental role in the processes of learning and memory of biological neural networks. With plasticity, biological agents can adapt on multiple timescales and outclass artificial agents, the majority of which still rely on static Artificial Neural Network (ANN) controllers. In this work, we focus on Voxel-based Soft Robots (VSRs), a class of simulated artificial agents, composed as aggregations of elastic cubic blocks. We propose a Hebbian ANN controller where every synapse is associated with a Hebbian rule that controls the way the weight is adapted during the VSR lifetime. For a given task and morphology, we optimize the controller for the task of locomotion by evolving, rather than the weights, the parameters of the Hebbian rules. Our results show that the Hebbian controller is comparable, often better than a non-Hebbian baseline and that it is more adaptable to unforeseen damages. We also provide novel insights into the inner workings of plasticity and demonstrate that "true" learning does take place, as the evolved controllers improve over the lifetime and generalize well.</div>

Evolving Hebbian Learning Rules in Voxel-based Soft Robots

<div>According to Hebbian theory, synaptic plasticity is the ability of neurons to strengthen or weaken the synapses among them in response to stimuli. It plays a fundamental role in the processes of learning and memory of biological neural networks. With plasticity, biological agents can adapt on multiple timescales and outclass artificial agents, the majority of which still rely on static Artificial Neural Network (ANN) controllers. In this work, we focus on Voxel-based Soft Robots (VSRs), a class of simulated artificial agents, composed as aggregations of elastic cubic blocks. We propose a Hebbian ANN controller where every synapse is associated with a Hebbian rule that controls the way the weight is adapted during the VSR lifetime. For a given task and morphology, we optimize the controller for the task of locomotion by evolving, rather than the weights, the parameters of the Hebbian rules. Our results show that the Hebbian controller is comparable, often better than a non-Hebbian baseline and that it is more adaptable to unforeseen damages. We also provide novel insights into the inner workings of plasticity and demonstrate that "true" learning does take place, as the evolved controllers improve over the lifetime and generalize well.</div>

Learning rules for automatic identification of implicit aspects in Portuguese

This sentiment analysis work is focused on the task of identifying aspects, emphasizing the so-called implicit aspects, i.e., those that are not explicitly mentioned in the texts. For this, we analyzed frequency-based methods, adapted rules from the English language to Portuguese, and developed a method that learns new rules through corpus analysis.

Beyond gradients: Noise correlations control Hebbian plasticity to shape credit assignment

Two key problems that span biological and industrial neural network research are how networks can be trained to generalize well and to minimize destructive interference between tasks. Both hinge on credit assignment, the targeting of specific network weights for change. In artificial networks, credit assignment is typically governed by gradient descent. Biological learning is thus often analyzed as a means to approximate gradients. We take the complementary perspective that biological learning rules likely confer advantages when they aren't gradient approximations. Further, we hypothesized that noise correlations, often considered detrimental, could usefully shape this learning. Indeed, we show that noise and three-factor plasticity interact to compute directional derivatives of reward, which can improve generalization, robustness to interference, and multi-task learning. This interaction also provides a method for routing learning quasi-independently of activity and connectivity, and demonstrates how biologically inspired inductive biases can be fruitfully embedded in learning algorithms.

Autonomously Learning About Meaningful Actions from Exploratory Behaviour

<p>The thesis addresses the problem of creating an autonomous agent that is able to learn about and use meaningful hand motor actions in a simulated world with realistic physics, in a similar way to human infants learning to control their hand. A recent thesis by Mugan presented one approach to this problem using qualitative representations, but suffered from several important limitations. This thesis presents an alternative design that breaks the learning problem down into several distinct learning tasks. It presents a new method for learning rules about actions based on the Apriori algorithm. It also presents a planner inspired by infants that can use these rules to solve a range of tasks. Experiments showed that the agent was able to learn meaningful rules and was then able to successfully use them to achieve a range of simple planning tasks.</p>

Autonomously Learning About Meaningful Actions from Exploratory Behaviour

<p>The thesis addresses the problem of creating an autonomous agent that is able to learn about and use meaningful hand motor actions in a simulated world with realistic physics, in a similar way to human infants learning to control their hand. A recent thesis by Mugan presented one approach to this problem using qualitative representations, but suffered from several important limitations. This thesis presents an alternative design that breaks the learning problem down into several distinct learning tasks. It presents a new method for learning rules about actions based on the Apriori algorithm. It also presents a planner inspired by infants that can use these rules to solve a range of tasks. Experiments showed that the agent was able to learn meaningful rules and was then able to successfully use them to achieve a range of simple planning tasks.</p>

A Normative Account of Confirmation Bias During Reinforcement Learning

Reinforcement learning involves updating estimates of the value of states and actions on the basis of experience. Previous work has shown that in humans, reinforcement learning exhibits a confirmatory bias: when the value of a chosen option is being updated, estimates are revised more radically following positive than negative reward prediction errors, but the converse is observed when updating the unchosen option value estimate. Here, we simulate performance on a multi-arm bandit task to examine the consequences of a confirmatory bias for reward harvesting. We report a paradoxical finding: that confirmatory biases allow the agent to maximize reward relative to an unbiased updating rule. This principle holds over a wide range of experimental settings and is most influential when decisions are corrupted by noise. We show that this occurs because on average, confirmatory biases lead to overestimating the value of more valuable bandits and underestimating the value of less valuable bandits, rendering decisions overall more robust in the face of noise. Our results show how apparently suboptimal learning rules can in fact be reward maximizing if decisions are made with finite computational precision.