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Trojanized software packages used in software supply chain attacks constitute an emerging threat. Unfortunately, there is still a lack of scalable approaches that allow automated and timely detection of malicious software packages and thus most detections are based on manual labor and expertise. However, it has been observed that most attack campaigns comprise multiple packages that share the same or similar malicious code. We leverage that fact to automatically reproduce manually identified clusters of known malicious packages that have been used in real world attacks, thus, reducing the need for expert knowledge and manual inspection. Our approach, AST Clustering using MCL to mimic Expertise (ACME), yields promising results with a 𝐹1 score of 0.99. Signatures are automatically generated based on characteristic code fragments from clusters and are subsequently used to scan the whole npm registry for unreported malicious packages. We are able to identify and report six malicious packages that have been removed from npm consequentially. Therefore, our approach can support the detection by reducing manual labor and hence may be employed by maintainers of package repositories to detect possible software supply chain attacks through trojanized software packages.
The corporate landscape is experiencing an increasing change in business models due to digitization. An increasing availability of data along the business processes enhance the opportunities for process automation. Technologies such as Robotic Process Automation (RPA) are widely used for business process optimization, but as a side effect an increase in stand-alone solutions and a lack of holistic approaches can be observed. Intelligent Process Automation (IPA) is said to support more complex processes and enable automated decision-making, but due to the lack of connectors makes the implementation difficult. RPA marketplaces can be a bridging technology to help companies implement Intelligent Process Automation. This paper explores the drivers and challenges for the adoption of RPA marketplaces to realize IPA. For this purpose, we conducted ten expert interviews with decision makers and IT staff from the process automation sector.
In this paper, modeling of piston and generic type gas compressors for a globally convergent algorithm for solving stationary gas transport problems is carried out. A theoretical analysis of the simulation stability, its practical implementation and verification of convergence on a realistic gas network have been carried out. The relevance of the paper for the topics of the conference is defined by a significance of gas transport networks as an advanced application of simulation and modeling, including the development of novel mathematical and numerical algorithms and methods.
Technical aspects are brought into focus thinking of inclusion opportunities and exclusion risks in digital learning scenarios. However, focussing on technical limitations is not sufficient. This contribution describes another important field of inclusion, namely psychological personality traits. In a longitudinal study at the Hochschule Bonn-Rhein-Sieg (H-BRS), University of Applied Sciences, we accompanied a civil law lecture of a bachelor's degree programme, which had been digitalized because of COVID-19, with empirical Scholarship of Teaching and Learning methods for two semesters. N=55 students from the first measured semester and N=35 from the second one rated different digital teaching methods used in the developed digital learning scenario. Their personality traits according to the five-factor model were measured by using a validated psychometric short-scale (BFI-10). Moderate to large empirical effects of the students' personality traits on the assessments of different digital teaching methods, used in the digital learning scenario, could be observed. Neuroticism values influences the perceptions of the course difficulty and the preference for using an instant messenger as a central communication platform, where students can interact with fellows and lecturers in a way the students are used to in their daily life. High conscientiousness predicts a more regular execution of the weekly tasks given throughout the semester, while higher values in extraversion are associated with a preference for synchronous video conference sessions and active webcams. Higher agreeableness is associated with rating the learning atmosphere as more constructive while low values are associated with perceiving more negative consequences due to the reduced contact to fellows based on COVID-19 restrictions. Correlations between the dimension openness and any ratings of digital teaching methods could not be observed. With this insight into our students' personality traits, we were able to match the digital teaching methods used in our digital learning scenario to the psychological needs of our students, which resulted in a higher inclusion level and a reduction of exclusion risks.
ProtSTonKGs: A Sophisticated Transformer Trained on Protein Sequences, Text, and Knowledge Graphs
(2022)
While most approaches individually exploit unstructured data from the biomedical literature or structured data from biomedical knowledge graphs, their union can better exploit the advantages of such approaches, ultimately improving representations of biology. Using multimodal transformers for such purposes can improve performance on context dependent classication tasks, as demonstrated by our previous model, the Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs (STonKGs). In this work, we introduce ProtSTonKGs, a transformer aimed at learning all-encompassing representations of protein-protein interactions. ProtSTonKGs presents an extension to our previous work by adding textual protein descriptions and amino acid sequences (i.e., structural information) to the text- and knowledge graph-based input sequence used in STonKGs. We benchmark ProtSTonKGs against STonKGs, resulting in improved F1 scores by up to 0.066 (i.e., from 0.204 to 0.270) in several tasks such as predicting protein interactions in several contexts. Our work demonstrates how multimodal transformers can be used to integrate heterogeneous sources of information, paving the foundation for future approaches that use multiple modalities for biomedical applications.
In this paper, an analysis of the error ellipsoid in the space of solutions of stationary gas transport problems is carried out. For this purpose, a Principal Component Analysis of the solution set has been performed. The presence of unstable directions is shown associated with the marginal fulfillment of the resistivity conditions for the equations of compressors and other control elements in gas networks. Practically, the instabilities occur when multiple compressors or regulators try to control pressures or flows in the same part of the network. Such problems can occur, in particular, when the compressors or regulators reach their working limits. Possible ways of resolving instabilities are considered.
Technological objects present themselves as necessary, only to become obsolete faster than ever before. This phenomenon has led to a population that experiences a plethora of technological objects and interfaces as they age, which become associated with certain stages of life and disappear thereafter. Noting the expanding body of literature within HCI about appropriation, our work pinpoints an area that needs more attention, “outdated technologies.” In other words, we assert that design practices can profit as much from imaginaries of the future as they can from reassessing artefacts from the past in a critical way. In a two-week fieldwork with 37 HCI students, we gathered an international collection of nostalgic devices from 14 different countries to investigate what memories people still have of older technologies and the ways in which these memories reveal normative and accidental use of technological objects. We found that participants primarily remembered older technologies with positive connotations and shared memories of how they had adapted and appropriated these technologies, rather than normative uses. We refer to this phenomenon as nostalgic reminiscence. In the future, we would like to develop this concept further by discussing how nostalgic reminiscence can be operationalized to stimulate speculative design in the present.
Taste is a complex phenomenon that depends on the individual experience and is a matter of collective negotiation and mediation. On the contrary, it is uncommon to include taste and its many facets in everyday design, particularly online shopping for fresh food products. To realize this unused potential, we conducted two Co-Design workshops. Based on the participants’ results in the workshops, we prototyped and evaluated a click-dummy smart-phone app to explore consumers’ needs for digital taste depiction. We found that emphasizing the natural qualities of food products, external reviews, and personalizing features lead to a reflection on the individual taste experience. The self-reflection through our design enables consumers to develop their taste competencies and thus strengthen their autonomy in decision-making. Ultimately, exploring taste as a social experience adds to a broader understanding of taste beyond a sensory phenomenon.
Intention: Within the research project EnerSHelF (Energy-Self-Sufficiency for Health Facilities in Ghana), i. a. energy-meteorological and load-related measurement data are collected, for which an overview of the availability is to be presented on a poster.
Context: In Ghana, the total electricity consumed has almost doubled between 2008 and 2018 according to the Energy Commission of Ghana. This goes along with an unstable power grid, resulting in power outages whenever electricity consumption peaks. The blackouts called "dumsor" in Ghana, pose a severe burden to the healthcare sector. Innovative solutions are needed to reduce greenhouse gas emissions and improve energy and health access.
We benchmark the robustness of maximum likelihood based uncertainty estimation methods to outliers in training data for regression tasks. Outliers or noisy labels in training data results in degraded performances as well as incorrect estimation of uncertainty. We propose the use of a heavy-tailed distribution (Laplace distribution) to improve the robustness to outliers. This property is evaluated using standard regression benchmarks and on a high-dimensional regression task of monocular depth estimation, both containing outliers. In particular, heavy-tailed distribution based maximum likelihood provides better uncertainty estimates, better separation in uncertainty for out-of-distribution data, as well as better detection of adversarial attacks in the presence of outliers.
Aim of this study is to investigate the effects of user experience (UX) on shopping mall customers’ intention to use a social robot. Therefore, we used a Wizard of Oz approach that enabled data collection in situ. Quantitative data was obtained from a questionnaire completed by shopping mall customers who interacted with a social robot. Data was used in a regression analysis, where user experience factors served as predictors for robot use in retail. The regression model explains up to 23.2% of the variance in customers’ intention to use a social robot. In addition, we collected qualitative data on human-robot-interactions and used the data to complement the interpretation of statistical results. Our findings suggest that only hedonic qualities significantly contribute to the prediction of customers’ intention, that shopping mall customers are reluctant to grant pragmatic qualities to social robots, and that UX evaluation in HRI requires additional predictors.