Lexical Choices and Ideology in Translation: A Case Study of 'The old Man and the Sea

The present research aims at conducting a critical study of the novel 'The Old Man and the Sea' written by Ernest Hemingway (1976) and its two translated versions in Persian; one rendered by Faramarzi (2006) the other by Shahin (1979). The researchers apply a comparative lexical analysis proposed by Newmark (1988) and Venuti (1995). An attempt has been made to reveal the ideology behind the original sample words and to show how translators and the effect thereof handle it. The data of this research consists of 10 ideological laden terms selected randomly among 45 words from the original text and the corresponding Persian translations. The results of this study suggest a significant difference between the two Persian translations and the original novel. It revealed that one of the translators has attempted to 'domesticate' his translation while another has been attentive to 'foreignize' it. As for implication, it seems necessary to note that translational decisions made by actual translators under different socio-cultural and ideological settings in real life and real situations should be considered. The perlocutionary consequences resulted from adoption of such decisions are of importance

Hippocampal representations of homing based on path integration

The ability to plan and execute a journey to a specific destination is essential for the survival of individuals of different species. Purposeful navigation can be achieved using landmark-based navigation and path integration. During path integration, the animal integrates self-motion information to estimate its location. Path integration is needed when external landmarks, such as visual landmarks, odour or auditory cues, are unavailable or when the animal visits unfamiliar environments. The study of the neuronal basis of path integration has been hampered by the lack of behavioural paradigm assessing path integration that allows simultaneous in vivo electrophysiological recordings in freely moving animals. Lesion studies have shown that the hippocampus and parahippocampal area are involved in path integration but the firing activity of the spatially selective cells, such as place cells, during path integration is unknown. Here, we developed a new behavioural paradigm (Automated Path Integration or AutoPI) to study homing behaviour based on path integration. In this task, a mouse finds a movable lever on the arena, presses it and returns to its home base to collect a food reward. Using the AutoPI task, we could record the firing pattern of the neurons in a large arena and investigate their spatial properties during homing behaviour. We used silicon probes to record the activity of hippocampal pyramidal cells when mice were running in AutoPI. By comparing the firing activity of neurons in the AutoPI task and during random foraging, we detected a complete reorganisation of hippocampal ensembles. We also found that several hippocampal pyramidal cells were firing when the animal was close to the lever (lever-anchored cells), independently of the lever's location on the arena. The spatial stability of lever-anchored cells was reduced during the trials with inaccurate homing. Moreover, the firing activity of lever-anchored cells also predicted the homing direction of the mice. These findings describe how hippocampal neurons with object-anchored firing fields contribute to homing behavior based on path integration

Acoustic model selection for recognition of regional accented speech

Accent is cited as an issue for speech recognition systems. Our experiments showed that the ASR word error rate is up to seven times greater for accented speech compared with standard British English. The main objective of this research is to develop Automatic Speech Recognition (ASR) techniques that are robust to accent variation. We applied different acoustic modelling techniques to compensate for the effects of regional accents on the ASR performance. For conventional GMM-HMM based ASR systems, we showed that using a small amount of data from a test speaker to choose an accent dependent model using an accent identification system, or building a model using the data from N neighbouring speakers in AID space, will result in superior performance compared to that obtained with unsupervised or supervised speaker adaptation. In addition we showed that using a DNN-HMM rather than a GMM-HMM based acoustic model would improve the recognition accuracy considerably. Even if we apply two stages of accent followed by speaker adaptation to the GMM-HMM baseline system, the GMM-HMM based system will not outperform the baseline DNN-HMM based system. For more contemporary DNN-HMM based ASR systems we investigated how adding different types of accented data to the training set can provide better recognition accuracy on accented speech. Finally, we proposed a new approach for visualisation of the AID feature space. This is helpful in analysing the AID recognition accuracies and analysing AID confusion matrices

Contextual Factors in the Adoption of Social Software: a Case Study

Over the past decade, social computing has emerged immensely as a phenomenon among distributed communities. The benefits of social systems depend on a large part on the existence of an active user community who use it continuously to deploy and share information. However, while certain systems have enjoyed tremendous success (Facebook, twitter), others have experienced modest adoption at best. It is not clear what factors contribute to the rise and fall of these systems. This paper is a report on our experience with the deployment of a social software tool and our attempts to identify the major barriers to its adoption. We first introduce the system, Gleanr [6], and describe our research methodology. Based on our findings, we propose a set of contextual factors for successful adoption of such tools. While small-scale, our study might provide some insight on how to design social software systems with better chances of wide adoption

Exploring Trust in Personal Learning Environments

The design of effective trust and reputation mechanisms for personal learning environments (PLEs) is believed to be a promising research direction. In this paper, we propose a 4-dimensional trust model that complies with the specific requirements of PLEs. Trust is explored in four dimensions: trustor, trustee, context and visibility. The importance of these four dimensions is investigated through a number of scenarios. The model is implemented in a PLE platform named Graaasp. Preliminary evaluation of usefulness is conducted through a user study and some interesting findings are discussed in the end

Trust-aware Privacy Control for Social Media

Due to the huge exposure of personal information in social media, a challenge now is to design effective privacy mechanisms that protect against unauthorized access to social data. In this paper, a trust model for social media is first presented. Based on the trust model, a trust-aware privacy control protocol is proposed, that exploits the underlying inter-entity trust information. The objective is to design a fine-grained privacy scheme that ensures a user’s online information is disclosed only to sufficiently trustworthy parties

Acoustic model selection using limited data for accent robust speech recognition

This paper investigates techniques to compensate for the effects of regional accents of British English on automatic speech recognition (ASR) performance. Given a small amount of speech from a new speaker, is it better to apply speaker adaptation, or to use accent identification (AID) to identify the speaker’s accent followed by accent-dependent ASR? Three approaches to accent-dependent modelling are investigated: using the ‘correct’ accent model, choosing a model using supervised (ACCDIST-based) accent identifi- cation (AID), and building a model using data from neighbouring speakers in ‘AID space’. All of the methods outperform the accentindependent model, with relative reductions in ASR error rate of up to 44%. Using on average 43s of speech to identify an appropriate accent-dependent model outperforms using it for supervised speaker-adaptation, by 7%