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The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding source code is more challenging. Recent research has shown, how such detection can be achieved by deep learning methods. However, that particular approach is limited to the identification of only 4 types of vulnerabilities. Subsequently, we analyze to what extent we could cover the identification of a larger variety of vulnerabilities. Therefore, a supervised deep learning approach using recurrent neural networks for the application of vulnerability detection based on binary executables is used. The underlying basis is a dataset with 50,651 samples of vulnerable code in the form of a standardized LLVM Intermediate Representation. The vectorised features of a Word2Vec model are used to train different variations of three basic architectures of recurrent neural networks (GRU, LSTM, SRNN). A binary classification was established for detecting the presence of an arbitrary vulnerability, and a multi-class model was trained for the identification of the exact vulnerability, which achieved an out-of-sample accuracy of 88% and 77%, respectively. Differences in the detection of different vulnerabilities were also observed, with non-vulnerable samples being detected with a particularly high precision of over 98%. Thus, the methodology presented allows an accurate detection of 23 (compared to 4) vulnerabilities.
Socially assistive robots (SARs) are becoming more prevalent in everyday life, emphasizing the need to make them socially acceptable and aligned with users' expectations. Robots' appearance impacts users' behaviors and attitudes towards them. Therefore, product designers choose visual qualities to give the robot a character and to imply its functionality and personality. In this work, we sought to investigate the effect of cultural differences on Israeli and German designers' perceptions and preferences regarding the suitable visual qualities of SARs in four different contexts: a service robot for an assisted living/retirement residence facility, a medical assistant robot for a hospital environment, a COVID-19 officer robot, and a personal assistant robot for domestic use. Our results indicate that Israeli and German designers share similar perceptions of visual qualities and most of the robotics roles. However, we found differences in the perception of the COVID-19 officer robot's role and, by that, its most suitable visual design. This work indicates that context and culture play a role in users' perceptions and expectations; therefore, they should be taken into account when designing new SARs for diverse contexts.
Seit mehr als 40 Jahren wiederholen sich Diskussionen und Kontroversen über Sinn und Unsinn von Informationstechnik (IT) in Bildungseinrichtungen. Wurde bislang über das Arbeiten an und mit PC, Laptop oder Tablet debattiert, drehen sich aktuelle Diskussionen verstärkt um netzbasierte Anwendungen mit Rückkanal für Schülerdaten. Das Schüler*innenverhalten wird per Software ausgewertet, um Lehrinhalte automatisiert und „individualisiert“ anzupassen. Ergänzt werden solche Lernprogramme um Anwendungen der sogenannten „Künstliche Intelligenz“ (KI), die als „Lernbegleiter“ fungieren und zumindest perspektivisch fehlende Lehrkräfte ersetzen (sollen). Damit werden technische Systeme in Schulen etabliert, von denen nicht einmal mehr die Entwickler wissen, was diese Algorithmen genau tun.
Das erfordert einen kritisch-reflektierenden Diskurs. Dafür vertritt Ralf Lankau im vorliegenden Aufsatz die These, dass essenzielle Elemente der Bildung, wie die Erziehung zu Selbstbewusstsein, Reflexion und einer kritischen Bürgerschaft, mit solchen Lernprogrammen verloren gehen.
Beuys-Gespräch
(2022)