Many consumers fail to identify online sponsored content as advertising. This is an ethical problem because consumers need to know when they are exposed to advertising so they can raise counterargu...

The ethical treatment of cancer patientsparticipating in clinical trials requiresthat patients are well-informed about thepotential benefits and risks associated withparticipation. When patients enrolled in phaseI clinical trials report that their chance ofbenefit is very high, this is often taken as evidence of a failure of the informed consent process. We argue, however, that some simple themes from the philosophy of language may make such a conclusion less certain. First, the patient may receive conflicting statements from multiple speakers about the expected (...) outcome of the trial. Patients may be reporting the message they like best. Second, there is a potential problem of multivocality. Expressions of uncertainty of the frequency type can be confused with expressionsof uncertainty of the belief type. Patients may be informed using frequency-type statements and respond using belief-type statements. Third, each speech episode involving the investigator and the patient regarding outcomes may subservemultiple speech acts, some of which may beindirect. For example, a patient reporting ahigh expected benefit may be reporting a beliefabout the future, reassuring family members,and/or attempting to improve his or her outcome by apublic assertion of optimism. These sources oflinguistic confusion should be considered injudging whether the patient's reported expectation isgrounds for a bioethical concern that there hasbeen a failure in the informed consent process. (shrink)

Statʹi, razmyshlenii︠a︡, ocherki -- Pisʹma v redakt︠s︡ii -- Perepiska s chlenami Soi︠u︡za svobodomysli︠a︡shchikh -- Pisʹma k M.I. Barskoĭ -- Perepiska mezhdu K.P. Krizhanovskim i I.G. Khusidom -- Iz materialov dela K.P. Krizhanovskogo.

Children’s exposure to the marketing of energy-dense nutrient-poor (EDNP) foods is a public health concern and marketing investment is known to be shifting to non-broadcast media, such as the Internet. This paper examines the perceptions of parents and children on ethical aspects of food marketing to which children are exposed. The research used qualitative methods with parent-child (aged between 8–13 years), from South Australia. Thirteen parent-child pairs participated in this research. Ethical concerns raised by parents and children included, the marketing (...) of EDNP foods, pester power and family conflict and the use of powerful techniques via the Internet. Their views on rights and responsibilities represented a complex mixture of idealistic and pragmatic positions. They appeared to be caught within the tensions of problematizing unhealthy food marketing to children, both as a social problem and as an individual problem. Their dilemmas are not dissimilar to the broader policy debate in Australia on the matter of food marketing to children. The stale-mate on statutory regulations to protect children from exposure to EDNP food marketing could be advanced by stronger use of ethical arguments to protect children from harmful exploitation and to protect parents from forces that undermine their authority. (shrink)

Introduction of deep neural networks to the machine translation research ameliorated conventional machine translation systems in multiple ways, specifically in terms of translation quality. The ability of deep neural networks to learn a sensible representation of words is one of the major reasons for this improvement. Despite machine translation using deep neural architecture is showing state-of-the-art results in translating European languages, we cannot directly apply these algorithms in Indian languages mainly because of two reasons: unavailability of the good corpus and (...) Indian languages are morphologically rich. In this paper, we propose a neural machine translation (NMT) system for four language pairs: English–Malayalam, English–Hindi, English–Tamil, and English–Punjabi. We also collected sentences from different sources and cleaned them to make four parallel corpora for each of the language pairs, and then used them to model the translation system. The encoder network in the NMT architecture was designed with long short-term memory (LSTM) networks and bi-directional recurrent neural networks (Bi-RNN). Evaluation of the obtained models was performed both automatically and manually. For automatic evaluation, the bilingual evaluation understudy (BLEU) score was used, and for manual evaluation, three metrics such as adequacy, fluency, and overall ranking were used. Analysis of the results showed the presence of lengthy sentences in English–Malayalam, and the English–Hindi corpus affected the translation. Attention mechanism was employed with a view to addressing the problem of translating lengthy sentences (sentences contain more than 50 words), and the system was able to perceive long-term contexts in the sentences. (shrink)