Purpose: One of the tasks that court experts in the field of forensic sexology have to perform is the assessment of secured pornographic materials involving minors. Forensic experts use their specialist knowledge to answer questions posed by the procedural authorities, including whether the material may induce sexual stimulation and whether an offender may be identified as having a disorder of sexual preference in the form of pedophilia. The aim of the article is to present the possibility of using neural networks in forensic sexology. Views: Neural networks are mathematical structures whose basic elements are artificial neurons modelled on the work of biological neurons. They are used in a variety of commercial and scientific tasks. Models for classifying pornographic materials (both images and films) and for estimating the age of the minors presented in these images are introduced. Neural networks can be used to categorize pornographic materials in the context of the growing levels of sexualization of minors. Moreover, the training of neural networks to classify specific objects in the pornographic material shown in these images could allow for the differentiation between the various categories of pornographic materials involving minors. Conclusions: Neural networks can be widely used in forensic sexology as an element supporting the work of forensic experts. The presented research results seem to be very promising, but the area requires further research.
Purpose: Artificial neural networks, "artificial intelligence" or machine learning now dominate a number of areas, making many activities automatic and thus affecting the safety and comfort of life. Neural networks might provide intelligent decisions with limited human assistance. Medicine also uses artificial intelligence, also in models designed to support the therapeutic process. The aim of this article is to define the main directions of development of machine learning applications in supporting the therapeutic processes. Views: Currently, the literature distinguishes at least a few applications of new technologies of varying degrees of advancement, with machine learning at the forefront [6]. It seems that the researchers are most interested in personalizing notifications of therapeutic applications, modifying therapeutic programs in a manner adapted to the patient's problems, and conducting "intelligent" conversations with them. Conclusions: There are dangers in using machine learning methods to support the therapeutic process. Particular attention should be paid to ensuring the full privacy of the implemented applications; moreover, selling user data of this type to third parties, such as those that sell certain medications or dietary supplements, would be ethically questionable. There are no legal regulations (or a system of recommendations of relevant scientific societies) that would limit proven applications to support the therapeutic process of a given disorder in the future, and which were created solely for the financial purpose of authors who did not conduct substantive consultations.
health psychology report • volume 7(1), 9 review article An active placebo is a substance that produces side effects similar to an active ingredient while not producing the same intended therapeutic effect. The aim of this study is to review the literature on the hypothesis of the active placebo response as a mechanism of action of antidepressants. It was found that persons who expect the occurrence of side effects of a pure placebo taken under the guise of an antidepressant present a higher degree of depressive symptoms than persons who do not expect the occurrence of side effects. There are reasons to believe that the entirety or part of the difference in the effectiveness of antidepressants and placebo is due to the fact that participants of the clinical trials correctly guess which study group they have been assigned to.
Cel pracyPrzeprowadzono badanie pilotażowe celem stworzenia polskiej adaptacji zadania Stroopa do oceny preferencji pedofilnych.MetodaBadanie składało się z dwóch etapów obejmujących stworzenie materiału testowego poprzez uszeregowanie odpowiednich list słów przez sędziów kompetentnych. Drugi etap badania polegał na weryfikacji empirycznej założeń zadania Stroopa w warunku emocjonalnym wśród populacji nieklinicznej.WynikiNa podstawie oceny pięciu sędziów kompetentnych dokonano uszeregowania słów od najbardziej do najmniej pobudzających seksualnie (W Kendalla od 0.368 do 0.693). Otrzymano sześć uszeregowanych list, poproszono ponownie sędziów kompetentnych o ocenę czy stworzone listy słów będą właściwe dla badania (współczynnik trafności Lawshe'a CVR od 0.6 do 1.0). Połączono dwie kategorie słów. Otrzymano pięć uszeregowanych list, poproszono ponownie sędziów kompetentnych o ocenę czy stworzone listy słów będą właściwe dla badania (współczynnik trafności Lawshe'a CVR 1.0). Stworzona lista słów uzyskała ostatecznie akceptację wszystkich sędziów kompetentnych. Na podstawie badań eksperymentalnych przeprowadzonych na osobach z populacji nieklinicznej, wykazano, zgodnie z założeniami konstrukcyjnymi narzędzia, że średni czas reakcji na słowa o charakterze seksualnym jest dłuższy w porównaniu do słów neutralnych. Średni czas reakcji na słowa dziecięce nie różnił się istotnie od słów neutralnych.WnioskiNa podstawie badań z udziałem sędziów kompetentnych oraz przeprowadzonych badań empirycznych, stworzono wstępną polską adaptację zadania Stroopa w warunku emocjonalnym do diagnozy preferencji pedofilnych. W celu wykorzystania zadania w praktyce klinicznej, konieczne są dalsze badania z udziałem osób z preferencjami pedofilnymi.
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