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Synthesizing voice with the help of machine learning techniques has made rapid progress over the last years. Given the current increase in using conferencing tools for online teaching, we question just how easy (i.e. needed data, hardware, skill set) it would be to create a convincing voice fake. We analyse how much training data a participant (e.g. a student) would actually need to fake another participants voice (e.g. a professor). We provide an analysis of the existing state of the art in creating voice deep fakes and align the identified as well as our own optimization techniques in the context of two different voice data sets. A user study with more than 100 participants shows how difficult it is to identify real and fake voice (on avg. only 37% can recognize a professor’s fake voice). From a longer-term societal perspective such voice deep fakes may lead to a disbelief by default.
In 2015, Google engineer Alexander Mordvintsev presented DeepDream as technique to visualise the feature analysis capabilities of deep neural networks that have been trained on image classification tasks. For a brief moment, this technique enjoyed some popularity among scientists, artists, and the general public because of its capability to create seemingly hallucinatory synthetic images. But soon after, research moved on to generative models capable of producing more diverse and more realistic synthetic images. At the same time, the means of interaction with these models have shifted away from a direct manipulation of algorithmic properties towards a predominance of high level controls that obscure the model's internal working. In this paper, we present research that returns to DeepDream to assess its suit-ability as method for sound synthesis. We consider this research to be necessary for two reasons: it tackles a perceived lack of research on musical applications of DeepDream, and it addresses DeepDream's potential to combine data driven and algorithmic approaches. Our research includes a study of how the model architecture, choice of audio data-sets, and method of audio processing influence the acoustic characteristics of the synthesised sounds. We also look into the potential application of DeepDream in a live-performance setting. For this reason, the study limits itself to models consisting of small neural networks that process time-domain representations of audio. These models are resource-friendly enough to operate in real time. We hope that the results obtained so far highlight the attractiveness of Deep-Dream for musical approaches that combine algorithmic investigation with curiosity driven and open ended exploration.
This paper describes the authors' first experiments in creating an artificial dancer whose movements are generated through a combination of algorithmic and interactive techniques with machine learning. This approach is inspired by the time honoured practice of puppeteering. In puppeteering, an articulated but inanimate object seemingly comes to live through the combined effects of a human controlling select limbs of a puppet while the rest of the puppet's body moves according to gravity and mechanics. In the approach described here, the puppet is a machine-learning-based artificial character that has been trained on motion capture recordings of a human dancer. A single limb of this character is controlled either manually or algorithmically while the machine-learning system takes over the role of physics in controlling the remainder of the character's body. But rather than imitating physics, the machine-learning system generates body movements that are reminiscent of the particular style and technique of the dancer who was originally recorded for acquiring training data. More specifically, the machine-learning system operates by searching for body movements that are not only similar to the training material but that it also considers compatible with the externally controlled limb. As a result, the character playing the role of a puppet is no longer passively responding to the puppeteer but makes movement decisions on its own. This form of puppeteering establishes a form of dialogue between puppeteer and puppet in which both improvise together, and in which the puppet exhibits some of the creative idiosyncrasies of the original human dancer.
Generative machine learning models for creative purposes play an increasingly prominent role in the field of dance and technology. A particularly popular approach is the use of such models for generating synthetic motions. Such motions can either serve as source of ideation for choreographers or control an artificial dancer that acts as improvisation partner for human dancers. Several examples employ autoencoder-based deep-learning architectures that have been trained on motion capture recordings of human dancers. Synthetic motions are then generated by navigating the autoencoder's latent space. This paper proposes an alternative approach of using an autoencoder for creating synthetic motions. This approach controls the generation of synthetic motions on the level of the motion itself rather than its encoding. Two different methods are presented that follow this principle. Both methods are based on the interactive control of a single joint of an artificial dancer while the other joints remain under the control of the autoencoder. The first method combines the control of the orientation of a joint with iterative autoencoding. The second method combines the control of the target position of a joint with forward kinematics and the application of latent difference vectors. As illustrative example of an artistic application, this latter method is used for an artificial dancer that plays a digital instrument. The paper presents the implementation of these two methods and provides some preliminary results.
Strings P
(2021)
Strings is an audiovisual performance for an acoustic violin and two generative instruments, one for creating synthetic sounds and one for creating synthetic imagery. The three instruments are related to each other conceptually , technically, and aesthetically by sharing the same physical principle, that of a vibrating string. This submission continues the work the authors have previously published at xCoAx 2020. The current submission briefly summarizes the previous publication and then describes the changes that have been made to Strings. The P in the title emphasizes, that most of these changes have been informed by experiences collected during rehearsals (in German Proben). These changes have helped Strings to progress from a predominantly technical framework to a work that is ready for performance.
The transition from college to university can have a variety of psychological effects on students who need to cope with daily obligations by themselves in a new setting, which can result in loneliness and social isolation. Mobile technology, specifically mental health apps (MHapps), have been seen as promising solutions to assist university students who are facing these problems, however, there is little evidence around this topic. My research investigates how a mobile app can be designed to reduce social isolation and loneliness among university students. The Noneliness app is being developed to this end; it aims to create social opportunities through a quest-based gamified system in a secure and collaborative network of local users. Initial evaluations with the target audience provided evidence on how an app should be designed for this purpose. These results are presented and how they helped me to plan the further steps to reach my research goals. The paper is presented at MobileHCI 2020 Doctoral Consortium.
Loneliness, an emotional distress caused by the lack of meaningful social connections, has been increasingly affecting university students who need to deal with everyday situations in a new setting, especially those who have come from abroad. Currently there is little work on digital solutions to reduce loneliness. Therefore, this work describes the general design considerations for mobile apps in this context and outlines a potential solution. The mobile app Noneliness is used to this end: it aims to reduce loneliness by creating social opportunities through a quest-based gamified system in a secure and collaborative network of local users. The results of initial evaluations with the target audience are described. The results informed a user interface redesign as well as a review of the features and the gamification principles adopted.
During the periods of social isolation to contain the advance of COVID-19 in 2020 and 2021, educational institutions have had the challenge to adopt technological strategies not only to ensure continuity in students’ classes, but also to support their mental health in a period of uncertainty and health risks. Loneliness is an emotional distress caused by the lack of meaningful social connections; it has increasingly affected young adults worldwide during the pandemic's social isolation and still bears psychological effects in the current post-pandemic period. In the light of this challenge, the Nonenliness App was developed as a way to bring together university communities to address issues related to loneliness and mental health disorders through a gamified and social online environment. In this paper, we present the app and its main functionalities (Beta version) and discuss the preliminary results of a pilot clinical study conducted with university students in Germany (N = 12) to verify the app's efficacy and usability, alongside the challenges faced and the next steps to be taken regarding the platform's improvement.
We aim to debate and eventually be able to carefully judge how realistic the following statement of a young computer scientist is: “I would like to become an ethical correctly acting offensive cybersecurity expert”. The objective of this article is not to judge what is good and what is wrong behavior nor to present an overall solution to ethical dilemmas. Instead, the goal is to become aware of the various personal moral dilemmas a security expert may face during his work life. For this, a total of 14 cybersecurity students from HS Offenburg were asked to evaluate several case studies according to different ethical frameworks. The results and particularities are discussed, considering different ethical frameworks. We emphasize, that different ethical frameworks can lead to different preferred actions and that the moral understanding of the frameworks may differ even from student to student.
The paper will focus on the activities of the International Year of Light and Optical Technologies 2015 (IYL) with their impact in life, science, art, culture, education and outreach as well as the importance in promoting the objectives for sustainable development. It describes our activities carried out in the run-up to or during the IYL, as well as reports on the generic projects that led to the success of the IYL. The success of the IYL is illustrated by examples and statistics. Relating to the potential and success of the IYL, the impact and the genesis of the International Day of Light (IDL) is presented. Impressions from the opening ceremony of the IYL in Paris at UNESCO headquarters and the Inaugural Ceremony of the IDL will then be covered. A second focus is placed on the interdisciplinary media projects realized by the students of our university dedicated to these events. Finally, an analysis of the impact and legacy of IYL and IDL will be presented.
The authors set the focus in this paper on the description of polarization with the help of the Jones calculus and the application of polarization in photography. Furthermore, the effect of the circular polarization filter is described by using the Jones calculus. Also, an enhancement of artistic and creative possibilities in photography through quantization or parametrization by the Jones matrices is presented.
Teaching and learning concepts that are adapted to the constantly evolving requirements due to rapid technological progress are essential for teaching in media photonics technology. After the development of a concept for research-oriented education in optics and photonics, the next step will be a conceptual restructuring and redesign of the entire curriculum for education in media photonics technology. By including typical research activities as essential components of the learning process, a broad platform for practical projects and applied research can be created, offering a variety of new development opportunities.
Due to the increasing aging of the population, the number of elderly people requiring care is growing in most European countries. However, the number of caregivers working in nursing homes and on daily care services is declining in countries like Germany or Italy. This limits the time for interpersonal communication. Furthermore, as a result of the Covid-19 pandemic, social distancing during contact restrictions became more important, causing an additional reduction of personal interaction. This social isolation can strongly increase emotional stress. Robotic assistance could contribute to addressing this challenge on three levels: (1) supporting caregivers to respond individually to the needs of patients and residents in nursing homes; (2) observing patients’ health and emotional state; (3) complying with high hygiene standards and minimizing human contact if required. To further the research on emotional aspects and the acceptance of robotic assistance in care, we conducted two studies where elderly participants interacted with the social robot Misa. Facial expression and voice analysis were used to identify and measure the emotional state of the participants during the interaction. While interpersonal contact plays a major role in elderly care, the findings reveal that robotic assistance generates added value for both caregivers and patients and that they show emotions while interacting with them.
DE\GLOBALIZE
(2022)
The artistic research cycle DE\GLOBALIZE is a media ecological search movement for the terrestrial. After examining matters of fact in India (2014-18), matters of concern in Egypt (2016-2019) and matters of care in the Upper Rhine (2018-22), the focus turns toward matters of violence in the Congo (2022). From matter to mater, mother-earth, the garden to exploitation. From science, water and climate to migration, oppression and extermination.
The long-term research is accessible through interactive web documentation. The platform serves as a continuous media-archaeological archive for a speculative ethnography. The relational structure of the videographic essay is enabling the forensic processing of single documents in the sense of the actor-network theory.
The subject of the presentation at IFM is a field trip to the Congo planned for March 2022, which will focus on the ambivalence of violence and care in collaboration with local artists. The field trip is based on the postcolonial reflection luderitzcargo by the author from 1996, in which a freight container was transformed into a translocal cinema in Namibia.
Through the journey to Congo, a group of media artists, a psychotherapist, a theater dramaturg, a filmmaker and a philosopher intend to explore the political, technological and psycho-geographic borders. By artistic interventions with locals, we want to interfere with relational string figures as part of the new Earth Politics. They are focusing on the displaced consumption of resources which are hard-fought and guarantee prosperity in the global north. The so-called ghost acreages are repressed and justified as part of a civilizational mission. With this trip, we want to confront our self-lies with the ones of our hosts. We want to confront ourselves with the foreign, the dark and the displaced ghosts within ourselves. In the presentation at the #IFM2022 Conference, the platform DE\GLOBALIZE will be problematized itself as an example of epistemic violence for the ethnographic memory of (Western) knowledge.
We are not the missionaries but the perplexed travellers. In our search movement, we are dealing with psychoanalysis, video, performance and trance. As disoriented white men we try the reversal of Black Skin and White Mask by Franz Fanon without blackfacing. We will not only care about the sensitivity of our skin but that of our g/hosts and the one of mother earth.
Inner Congo
(2023)
This research-creation project, part of the DE\GLOBALIZE artistic research cycle presented at the #IFM2022 Conference, investigates the complexities of Congo violence, care, and colonialism. Drawing on Michel Serres' metaphor of the great estuaries, the study explores the topology of interactive documentaries, blending theory, emotion, and personal experiences. Accessible through the interactive web documentation at http://deglobalize.com, the platform offers a media-archaeological archive for speculative ethnography, enabling the forensic processing of single documents in line with actor-network theory.
When shopping online, it is usually not possible to view products in the same way as you are used to when shopping offline. With augmented reality (AR), it is not only possible to view the product in detail, but also to view it at home in the real environment. Such an AR application sets stimuli that can affect the users and their purchase decision and Word-of-mouth intention. In this work, we assume that when viewing a product in AR, not only affective internal states but also cognitive perception processes have an impact on purchase decision and Word-of-mouth intention. While positive affective reactions have already been studied in the context of AR, this paper will also describe inner cognitive perception processes, using the construct of AR authenticity. To test these assumptions, a study was conducted with 155 participants. The results show that both the purchase intention and the Word-of-mouth intention are influenced by the constructs of positive affective reactions and AR authenticity.
The paper describes the implementation of practical laboratory settings in a virtual environment. With the entry of VR glasses into the mass market, there is a chance to establish educational and training applications for displaying some teaching materials and practical works. Therefore our project focuses on the realization of virtual experiments and environments, which gives users a deep insight into selected subfields of Optics and Photonics. Our goal is not to substitute the hand on experiments rather to extend them. By means of VR glasses, the user is offered the possibility to view the experiment from several angles and to make changes through interactive control functions. During the VR application, additional context-related information is displayed. By using object recognition, the specific graphics and texts for the respective object are loaded and supplemented at the appropriate place. Thus, complex facts are supported in an informative way. The prototype is developed using the Unity Engine and can thus be exported to different platforms and end devices. Another major advantage of virtual simulations to the real situation is the high degree of controllability as well as the easy repeatability. With slight modifications, entire experiments can be reused. Our research aims to acquire new knowledge in the field of e-learning in association with VR technology. Here we try to answer a core question of the compatibility of the individual media components.
Currently, immersive technologies are enjoying great popularity. This trend is reflected in technological advances and the emergence of new products for the mass market, such as augmented reality glasses. The range of applications for immersive technologies is growing with more efficient and affordable technologies and student adoption. Especially in education, the use will improve existing learning methods. Immersive application use visual, audio and haptic sensors to fully engage the user in a virtual environment. This impression is reinforced with the help of realistic visualizations and the opportunity for interaction. In particular, Augmented reality is characterized by a high degree of integration between reality and the inserted virtual objects. An augmented interactive simulation for the determination of the specific charge of an electron will be used as an example to demonstrate how such immersion can be created for users. A virtual Helmholtz coil is used to measure and calculate the e/m constant. The voltage at the cathode for generating the electron beam, but also the voltage of the homogeneous magnetic field for deflecting the electron beam, can be variably controlled by haptic user input. Based on these voltages, an immersive virtual electron beam is calculated and visualized. In this paper, the authors present the conceptual steps of this immersive application and address the challenges associated with designing and developing an augmented and interactive simulation.
This work documents the rising acceptance of social robots for healthcare as well as their growing economic potential from 2017 to 2021. The comparison is based on two studies in the active assisted living (AAL) community. We first provide a brief overview of social robotics and a discussion of the economic potential of social health robots. We found that, despite the huge potential for robotic support in healthcare and domestic routines, social robots still lack the functionality to access that potential. At the same time, the study exemplifies a rise in acceptance: all health-related activities are more accepted in 2021 when in 2017, most of them with high statistical significance. When investigating the economic perspective, we found that persons are aware of the influence of cultural, spiritual, or religious beliefs. Most experts (57%), having a European background, expect the state or the government to be the key driver for establishing social robots in health and significantly prefer leasing or renting a social health robot to buying one. Nevertheless, we speculate that it might be a global financial elite which is first to adopt social robots.
Public educational institutions are increasingly confronted with a decline in the number of applicants, which is why competition between colleges and universities is also intensifying. For this reason, it is important to position oneself as an institution in order to be perceived by the various target groups and to differentiate oneself from the competition. In this context, the brand and thus its perception and impact play a decisive role, especially in view of the desired communication of the institution's own values and its self-image, the brand identity. To this end, emotions serve as an approach to creating positive stimulation and brand loyalty.
We consider large scale Peer-to-Peer Sensor Networks, which try to calculate and distribute the mean value of all sensor inputs. For this we design, simulate and evaluate distributed approximation algorithms which reduce the number of messages. The main difference of these algorithms is the underlying communication protocol which all use the random call model, where in discrete round model each node can call a random sensor node with uniform probability.The amount of data exchanged between sensor nodes and used in the calculation process affects the accuracy of the aggregation results leading to a trade-off situation. The key idea of our algorithms is to limit the sample size using the Finite Population Correction (FPC) method and collect the data using a distribution aggregation using Push-Pull Sampling, Pull Sampling, and Push Sampling communication protocols. It turns out that all methods show exponential improvement of Mean Squared Error (MSE) with the number of messages and rounds.
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 [1] has shown how such detection can generally be enabled by deep learning methods, but appears to be very limited regarding the overall amount of detected vulnerabilities. We analyse 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 standardised LLVM Intermediate Representation. Te 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, our proposed technical approach and methodology enables an accurate detection of 23 (compared to 4 [1]) vulnerabilities.
In the field of network security, the detection of possible intrusions is an important task to prevent and analyse attacks. Machine learning has been adopted as a particular supporting technique over the last years. However, the majority of related published work uses post mortem log files and fails to address the required real-time capabilities of network data feature extraction and machine learning based analysis [1-5]. We introduce the network feature extractor library FEX, which is designed to allow real-time feature extraction of network data. This library incorporates 83 statistical features based on reassembled data flows. The introduced Cython implementation allows processing individual packets within 4.58 microseconds. Based on the features extracted by FEX, existing intrusion detection machine learning models were examined with respect to their real-time capabilities. An identified Decision-Tree Classifier model was thus further optimised by transpiling it into C Code. This reduced the prediction time of a single sample to 3.96 microseconds on average. Based on the feature extractor and the improved machine learning model an IDS system was implemented which supports a data throughput between 63.7 Mbit/s and 2.5 Gbit/s making it a suitable candidate for a real-time, machine-learning based IDS.
Brand-related-user-generated-content allows companies to achieve several important objectives, such as increasing sales and creating higher user engagement. In this paper a research framework is developed that provides an overview of the necessary processes to successfully use brand-related-user-generated-content. The framework also helps managers to understand the main motives of users when posting brand-related-user-generated-content. Expert interviews were carried out to validate the research framework. The results from the interviews support the proposed framework. Brand-related-user-generated-content can increase purchase intention and the community engagement. From a user’s perspective the opportunity to interact with a brand and be featured on official brand channels could be seen as the main motivation for creating brand-related-user-generated-content.
Redesigning a curriculum for teaching media technology is a major challenge. Up-to-date teaching and learning concepts are necessary that meet the constant technological progress and prepare students specifically for their professional life. Teaching and studying should be characterized by a student-oriented teaching and learning culture. In order to achieve this goal, consistent evaluation is essential. The aim of the evaluation concept presented here is to generate structured information regarding the quality of content-related, didactic and organizational aspects of teaching. The exchange of opinions between students and lecturers should be encouraged in order to continuously improve the teaching and learning processes.
VR-based implementation of interactive laboratory experiments in optics and photonics education
(2022)
Within the framework of a developed blended learning concept, a lot of experience has already been gained with a mixture of theoretical lectures and hands-on activities, combined with the advantages of modern digital media. Here, visualizations using videos, animations and augmented reality have proven to be effective tools to convey learning content in a sustainable way. In the next step, ideas and concepts were developed to implement hands-on laboratory experiments in a virtual environment. The main focus is on the realization of virtual experiments and environments that give the students a deep insight into selected subfields of optics and photonics.
Complex tourism products with intangible service components are difficult to explain to potential customers. This research elaborates the use of virtual reality (VR) in the field of shore excursions. A theoretical research model based on the technology acceptance model was developed, and hypotheses were proposed. Cruise passengers were invited to test 360° excursion images on a landing page. Data was collected using an online questionnaire. Finally, data was analyzed using the PLS-SEM method. The results provide theoretical implications on technology acceptance model (TAM) research in the field of cruise tourism. Furthermore, the results and implications indicate the potential of virtual 360° shore excursion presentations for the cruise industry.
Objective: Dickkopf 3 (DKK3) has been identified as a urinary biomarker. Values above 4000 pg/mg creatinine (Cr) were linked with a higher risk of short-term decline of kidney function (J Am Soc Nephrol 29: 2722–2733). However, as of today, there is little experience with DKK3 as a risk marker in everyday clinical practice. We used algorithm-based data analysis to evaluate the potential dependence of DKK3 in a cohort from a large single center in Germany.
Method: DKK3 was measured in all CKD patients in our center October 1 st 2018 till Dec. 31 2019, together with calculated GFR (eGFR) and urinary albumin/creatinine ratio (UACR). Kidney transplant patients were excluded. Until the end of follow-up Dec 31 st 2021, repeated measurements were performed for all parameters. Data analysis was performed using MD-Explorer (BioArtProducts, Rostock, Germany) and Python with multiple libraries. Linear regression models were applied in patients for DKK3, eGFR and UACR. Comparison of the models was performed with a twosided Kolmogorov-Smirnov test.
Results: 1206 DKK3 measurements were performed in 1103 patients (621 male, age 70yrs, eGFR 29,41 ml/min/1.73qm, UACR 800 mg/g). 134 patients died during follow-up. DKK3 mean was 2905 pg/mg Cr (max. 20000, 75 % percentile 3800). 121 pts had DKK3 > 4000. At the end of follow-up 7 % of patients with DKK3 < 4000 (initial eGFR 17.6) versus 39.6 % of patients with DDK3 > 4000 (initial eGFR 15.7) underwent dialysis. Compared to eGFR and UACR at baseline, DKK3 > 4000 performed best to predict eGFR loss over the next 12 months.
Conclusion: In this cohort of CKD patients, DKK3 > 4000 at baseline predicted the eGFR slope better than eGFR or UACR at baseline. DKK3 > 4000 reflected a higher risk of progression towards ESRD in patients with similar baseline eGFR levels.
Activities for rehabilitation and prevention are often lengthy and associated with pain and frustration. Their playful enrichment (hereafter: gamification) can counteract this, resulting in so-called “exergames”. However, in contrast to games designed solely for entertainment, the increased motivation and immersion in gamified training can lead to a reduced perception of pain and thus to health deterioration. Therefore, it is necessary to monitor activities continuously. However, only an AI-based system able to generate autonomous interventions could vacate the therapists’ costly time and allow better training at home. An automated adjustment of the movement training’s difficulty as well as individualized goal setting and control are essential to achieve such autonomy. This article’s contribution is two-fold: (1) We portray the potentials of gamification in the health area. (2) We present a framework for smart rehabilitation and prevention training allowing autonomous, dynamic, and gamified interactions.
Digital, virtual environments and the metaverse are rapidly taking shape and will generate disruptive changes in the areas of ethics, privacy, safety, and how the relationships between human beings will be developed. To uncover some of some of the implications that will impact those areas, this study investigates the perceptions of 101 younger people from the generations Y and Z. We present a first exploratory analysis of the findings, focusing on knowledge and self-perception. Results show that these young generations are seriously doubting their knowledge on the metaverse and virtual worlds – regarding both the definition and the usage. It is interesting to see only a medium confidence level, considering that the participants are young and from an academic environment, which should increase their interest in and the affinity towards virtual worlds. Males from both generations perceive themselves as significantly more knowledgeable than females. Regarding a fitting definition, almost 40% agreed on the metaverse as a “universal and immersive virtual world that is made accessible using virtual reality and augmented reality technologies”. Regarding the topic in general, several participants (almost 40%) considered themselves sceptics or “just” users (38%). Interestingly, generation Y participants were more likely than the younger generation Z participants to identify themselves as early adopters or innovators. In result, the considerable amount of “mixed feelings” regarding digital, virtual environments and the metaverse shows that in-depth studies on the perception of the metaverse as well as its ethical and integrity implications are required to create more accessible, inclusive, safe, and inclusive digital, virtual environments.