1,721,387 research outputs found
Clinical psychology and psychotherapy in Italy during the second half of the 20th century
No full textThe article describes the most important events that, in the 1960s and 1970s, contributed to the development of modern clinical psychology and psychotherapy in Italy. In a conference organised in Milan in 1952 by the most authoritative Italian psychologist of the time, the Franciscan friar Agostino Gemelli, the methods and limits of clinical psychology were outlined and defined. In this way the discipline was legitimised, although it was placed under the tutelage of psychiatry. Clinical psychology eventually freed itself from this subordination, evolving in line with international trends to become one of the main fields of applied psychology, thanks to the contribution of at least four events: 1) the affirmation of psychoanalysis by the school of Cesare Musatt and as a result of the endeavours of Gemelli’s students; 2) the acceptance, on the part of the Catholic Church, of psychoanalysis as a therapeutic treatment in the face of distress and mental disturbance; 3) the scientific-cultural and political activity of Adriano Ossicini and Pier Francesco Galli, which opened the door to new psychotherapeutic theories and techniques; and 4)the closure of mental institutions (Basaglia Law, 1978) encouraged by anti-institutionalpsychiatry, and the new forms of treatment of mental illness practiced in therapeutic communities. This article reconstructs the vicissitudes of regulating the clinical psychologist and psychotherapist professions in relation to the diverse psychotherapeutic practices exercised in Italy since the 1970s
SIGFRID: Unsupervised, Platform-Agnostic Interference Detection in IoT Automation Rules
No full textSmart home technology has profoundly changed modern living by interconnecting devices, services, dataflows, and user interactions into integrated, automated environments. Homeowners can easily program smart devices using conditional IF-THEN rules, where triggers prompt corresponding actions. However, as smart homes incorporate more multifunctional devices, conflicting trigger-action rules can simultaneously control devices in inconsistent ways, causing unexpected and potentially unsafe interference situations. This article introduces Sigfrid, a novel interference detection approach using scene interaction graphs constructed through Large Language Models (LLMs). To enhance LLM reasoning, we propose a new prompt engineering methodology that integrates automated and manual editing techniques to formulate queries for deriving causal insights in the smart home domain. Interferences are identified through efficient exploration of the graph constructed from the extracted relations. We evaluate Sigfrid on real-world If-This-Then-That (IFTTT) and SmartThings rule sets, demonstrating its superiority over state-of-the-art methods by more than 21% in F1-score
Terpene Biosynthesis in Marine Molluscs: Incorporation of Glucose in Drimane Esters of Dendrodoris nudibranchs Via Classical Mevalonate Pathway
No full textThe biosynthesis of drimane esters in marine molluscs Dendrodoris limbata and Dendrodoris grandiflora has
been reinvestigated by feeding experiments with D-[14C-(U)]- and D-[6-13C]-glucose. Both experiments proved the
incorporation of the precursors in the terpenoid part of drimane esters. Use of [6-13C]-glucose also demonstrated
that the labelled material was incorporated via the classical acetate/mevalonate pathway
Towards Explainable Security for ECA Rules
No full textWith the rise in popularity of smart objects and online services, the use of Trigger-Action Platforms for the definition of custom behaviors is growing significantly. These platforms enable end-users to create Event-Condition-Action (ECA) rules for triggering actions upon event occurrences on physical devices or online services in different domains. ECA rules could easily expose end-users to security risks mainly due to their low level of knowledge and awareness. To alleviate this problem, classification models can be used for identifying possible security issues that ECA rules could inflict when triggered. However, the results produced by these classifiers may not be understood by end-users. This position paper provides first insights concerning the application of AI models for generating natural language explanations according to the identified risks of ECA rules
Towards a Classification Model for Identifying Risky IFTTT Applets
No full textWith the rapid growth of Internet-of-Things (IoT) devices, especially in the context of smart homes, we witnessed the rise of different services aimed at providing end-users with tools for the definition of custom behaviors. Among these, If-This-Than-That (IFTTT) became the most used end-user programming tool for creating event-condition-action (ECA) rules. However, while defining such rules, end-users might expose both their smart devices and personal information to security and privacy threats. This paper presents the progress achieved in the definition of a classification model based on neural networks for the identification of possible security and privacy issues within an IFTTT applet
Dendrinolide, a New Degraded Diterpenoid from the Antarctic Sponge Dendrilla membranosa
No full textA new glacian diterpenoid, named dendrinolide (5), was isolated, together with the 9,11-
dihydrogracilin A (1), from the Me2CO extract of the Antarctic sponge Dendrilla membranosa.
Its structure has been elucidated by interpretation of spectral data and comparison with the
similar product 6, previously found in the Mediterranean sponge Spongionella gracilis
Towards Explainable Security for ECA Rules
No full textWith the rise in popularity of smart objects and online services, the use of Trigger-Action Platforms for the definition of custom behaviors is growing significantly. These platforms enable end-users to create Event-Condition-Action (ECA) rules for triggering actions upon event occurrences on physical devices or online services in different domains. ECA rules could easily expose end-users to security risks mainly due to their low level of knowledge and awareness. To alleviate this problem, classification models can be used for identifying possible security issues that ECA rules could inflict when triggered. However, the results produced by these classifiers may not be understood by end-users. This position paper provides first insights concerning the application of AI models for generating natural language explanations according to the identified risks of ECA rules
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