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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.
Privacy is the capacity to keep some things private despite their social repercussions. It relates to a person’s capacity to control the amount, time, and circumstances under which they disclose sensitive personal information, such as a person’s physiology, psychology, or intelligence. In the age of data exploitation, privacy has become even more crucial. Our privacy is now more threatened than it was 20 years ago, outside of science and technology, due to the way data and technology highly used. Both the kinds and amounts of information about us and the methods for tracking and identifying us have grown a lot in recent years. It is a known security concern that human and machine systems face privacy threats. There are various disagreements over privacy and security; every person and group has a unique perspective on how the two are related. Even though 79% of the study’s results showed that legal or compliance issues were more important, 53% of the survey team thought that privacy and security were two separate things. Data security and privacy are interconnected, despite their distinctions. Data security and data privacy are linked with each other; both are necessary for the other to exist. Data may be physically kept anywhere, on our computers or in the cloud, but only humans have authority over it. Machine learning has been used to solve the problem for our easy solution. We are linked to our data. Protect against attackers by protecting data, which also protects privacy. Attackers commonly utilize both mechanical systems and social engineering techniques to enter a target network. The vulnerability of this form of attack rests not only in the technology but also in the human users, making it extremely difficult to fight against. The best option to secure privacy is to combine humans and machines in the form of a Human Firewall and a Machine Firewall. A cryptographic route like Tor is a superior choice for discouraging attackers from trying to access our system and protecting the privacy of our data There is a case study of privacy and security issues in this thesis. The problems and different kinds of attacks on people and machines will then be briefly talked about. We will explain how Human Firewalls and machine learning on the Tor network protect our privacy from attacks such as social engineering and attacks on mechanical systems. As a real-world test, we will use genomic data to try out a privacy attack called the Membership Inference Attack (MIA). We’ll show Machine Firewall as a way to protect ourselves, and then we’ll use Differential Privacy (DP), which has already been done. We applied the method of Lasso and convolutional neural networks (CNN), which are both popular machine learning models, as the target models. Our findings demonstrate a logarithmic link between the desired model accuracy and the privacy budget.
In den letzten Jahren haben Recommender Systeme zunehmend an Bedeutung gewonnen. Diese Systeme sind meist für Bereiche des E-Commerce konzipiert und berücksichtigen oftmals nicht den aktuellen Kontext der nutzenden Person. Recommender Systeme können allerdings nicht nur im E-Commerce zum Einsatz kommen, sondern finden ihren Anwendungszweck auch im Gesundheitswesen. Ziel dieser Bachelorarbeit ist es, ein Recommender System zu entwickeln, das den aktuellen Kontext der nutzenden Person (Chatverlauf, demografische Daten) besser berücksichtigen kann. Dazu befasst sich diese Arbeit mit der Konzeption und prototypischen Umsetzung eines kontextsensitiven Recommender Systems für einen bereits existierenden Chatbot aus dem Gesundheitswesen. Das in dieser Arbeit konzipierte und entwickelte Recommender System soll Mitarbeitende aus dem Gesundheits- und Sozialwesen entlasten und ihnen hilfreiche sowie thematisch sinnvolle Informationen zur Verfügung stellen. Basierend auf festgelegten Anforderungen wurde ein Konzept für das Recommender System entwickelt und zu Teilen als Prototyp umgesetzt. Abschließend wurde der Prototyp im Hinblick auf die Anforderungen evaluiert. Zudem fand eine technische Evaluation und eine Evaluation mithilfe von Anwendenden statt, welche den implementierten Prototypen bereits existierenden Systemen gegenüberstellte. Die von dem Prototyp empfohlenen Textausschnitte erzielten in der Evaluation mit nutzenden Personen eine thematisch signifikant höhere Übereinstimmung mit den Chatdaten.
Organizations striving to achieve success in the long term must have a positive brand image which will have direct implications on the business. In the face of the rising cyber threats and intense competition, maintaining a threat-free domain is an important aspect of preserving that image in today's internet world. Domain names are often near-synonyms for brand names for numerous companies. There are likely thousands of domains that try to impersonate the big companies in a bid to trap unsuspecting users, usually falling prey to attacks such as phishing or watering hole. Because domain names are important for organizations for running their business online, they are also particularly vulnerable to misuse by malicious actors. So, how can you ensure that your domain name is protected while still protecting your brand identity? Brand Monitoring, for example, may assist. The term "Brand Monitoring" applies only to keep tabs on an organization's brand performance, reception, and overall online presence through various online channels and platforms [1]. There has been a rise in the need of maintaining one's domain clear of any linkages to malicious activities as the threat environment has expanded. Since attackers are targeting domain names of organizations and luring unsuspecting users to visit malicious websites, domain monitoring becomes an important aspect. Another important aspect of brand abuse is how attackers leverage brand logos in creating fake and phishing web pages. In this Master Thesis, we try to solve the problem of classification of impersonated domains using rule-based and machine learning algorithms and automation of domain monitoring. We first use a rule-based classifier and Machine Learning algorithms to classify the domains gathered into two buckets – "Parked" and "Non-Parked". In the project's second phase, we will deploy object detection models (Scale Invariant Feature Transform - SIFT and Multi-Template Matching – MTM) to detect brand logos from the domains of interest.
Even though the internet has only been there for a short period, it has grown tremendously. To- day, a significant portion of commerce is conducted entirely online because of increased inter- net users and technological advancements in web construction. Additionally, cyberattacks and threats have expanded significantly, leading to financial losses, privacy breaches, identity theft, a decrease in customers’ confidence in online banking and e-commerce, and a decrease in brand reputation and trust. When an attacker pretends to be a genuine and trustworthy institution, they can steal private and confidential information from a victim. Aside from that, phishing has been an ongoing issue for a long time. Billions of dollars have been shed on the global economy. In recent years, there has been significant progress in the development of phishing detection and identification systems to protect against phishing attacks. Phishing detection technologies frequently produce binary results, i.e., whether a phishing attempt was made or not, with no explanation. On the other hand, phishing identification methodologies identify phishing web- pages by visually comparing webpages with predetermined authentic references and reporting phishing together with its target brand, resulting in findings that are understandable. However, technical difficulties in the field of visual analysis limit the applicability of currently available solutions, preventing them from being both effective (with high accuracy) and efficient (with little runtime overhead). Here, we evaluate existed framework called Phishpedia. This hybrid deep learning system can recognize identity logos from webpage screenshots and match logo variants of the same brand with high precision. Phishpedia provides high accuracy with low run- time. Lastly, unlike other methods, Phishpedia does not require training on any phishing sam- ples whatsoever. Phishpedia exceeds baseline identification techniques (EMD, PhishZoo, and LogoSENSE), inaccurately detecting phishing pages in lengthy testing using accurate phishing data. The effectiveness of Phishpedia was tested and compared against other standard machine learning algorithms and some state-of-the-art algorithms. The given solutions performed better than different algorithms in the given dataset, which is impressive.
Technology advancement has played a vital role in business development; however, it has opened a broad attack surface. Passwords are one of the essential concepts used in applications for authentication. Companies manage many corporate applications, so the employees must meet the password criteria, which leads to password fatigue. This thesis addressed this issue and how we can overcome this problem by theoretically implementing an IAM solution. In this, we disused MFA, SSO, biometrics, strong password policies and access control. We introduced the IAM framework that should be considered while implementing the IAM solution. Implementing an IAM solution adds an extra layer of security.
Diese Arbeit beschäftigt sich mit der simulativen Untersuchung von Strömung und Wärmeübergang im Kontext von Vorkammerzündsystemen. Dies geschieht im Rahmen der Entwicklung eines Gasmotors mit gasgespülter Vorkammer. Entscheidene Größen für die Strömung und Arbeitsweise in einer Vorkammerzündkerze sind die Geometrie und Anordnung der Überströmbohrungen, das Vorkammervolumen und die Form der Vorkammer. Die Betrachtung wird dafür aufgeteilt in die Themen Spaltströmungen, Wärmeübergang und drallbehaftete Strömungen. Diese werden zunächst isoliert betrachtet und letztendlich in einem Anwendungsfall zusammengeführt. Für die Betrachtung von Spaltströmungen werden unterschiedliche Platten mit Bohrlöchern zu verschiedenen Drücken, Durchmessern und Plattenstärken durchströmt und der Wärmeübergang und der Drall werden mithilfe einer durch Leitbleche gelenkte Strömung in einem beheizten Rohr untersucht. Die Zusammenführung der Themen wird anhand einer Anströmvorrichtung für Brenngase auf Motorzylinder durchgeführt. Dabei erreichen die Gase hohe Temperaturen und aufgrund von hohen Drücken und Spaltströmungen große Geschwindigkeiten.
Für die Simulation werden die Programme Ansys Fluent und Ansys Forte verwendet. Während ersteres primär für die Simulation von Strömungen verwendet wird, ist Forte speziell aufgebaut, um in Verbrennungsmotoren neben der Berechnung der Strömung auch die Einspritzung von Kraftstoff, die Verbrennung dessen und die resultierenden Schadstoffe zu berechnen. Da die Ergebnisse aus Forte eine große Gewichtung in der Beurteilung der Entwicklungsarbeit des Gasmotors hat, muss Forte selbst validiert werden. Dies wird durchgeführt anhand der angesprochenen Teilthemen und verglichen mit Messungen aus der Literatur und Simulationsergebnissen in Fluent.
Server Side Rendering (SSR), Single Page Application (SPA), and Static Site Generation (SSG) are the three most popular ways of making modern Web applications today. If we go deep into these processes, this can be helpful for the developers and clients. Developers benefit since they do not need to learn other programming languages and can instead utilize their own experience to build different kinds of Web applications; for example, a developer can use only JavaScript in the three approaches. On the other hand, clients can give their users a better experience.
This Master Thesis’s purpose was to compare these processes with a demo application for each and give users a solid understanding of which process they should follow. We discussed the step-by-step process of making three applications in the above mentioned categories. Then we compared those based on criteria such as performance, security, Search Engine Optimization, developer preference, learning curve, content and purpose of the Web, user interface, and user experience. It also talked about the technologies such as JavaScript, React, Node.js, and Next.js, and why and where to use them. The goals we specified before the program creation were fulfilled and can be validated by comparing the solutions we gave for user problems, which was the application’s primary purpose.
Editorial
(2022)
Editorial
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Editorial
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Editorial
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Editorial
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Editorial
(2022)
Decarbonisation Strategies in Energy Systems Modelling: Biochar as a Carbon Capture Technology
(2022)
The energy system is changing since some years in order to achieve the climate goals from the Paris Agreement which wants to prevent an increase of the global temperature above 2 °C. Decarbonisation of the energy system has become for governments a big challenge and different strategies are being stablished. Germany has set greenhouse gas reduction limits for different years and keeps track of the improvement made yearly. The expansion of renewable energy systems (RES) together with decarbonisation technologies are a key factor to accomplish this objective.
This research is done to analyse the effect of introducing biochar, a decarbonisation technology, and study how it will affect the energy system. Pyrolysis is the process from which biochar is obtained and it is modelled in an open-source energy system model. A sensibility analysis is made in order to assess the effect of changing the biomass potential and the costs for pyrolysis.
The role of pyrolysis is analysed in the form of different future scenarios to evaluate the impact. The CO2 emission limits from the years 2030 and 2045 are considered to create the scenarios, as well as the integration of flexibility technologies. Four scenarios in total are assessed and the result from the sensibility analysis considering pyrolysis are always compared to the reference scenario, where pyrolysis is not considered.
Results show that pyrolysis has a bigger impact in the energy system when the CO2 limit is low. Biochar can be used to compensate the emissions from other conventional power plant and achieve an energy transition with lower costs. Furthermore, it was also found that pyrolysis can also reduce the need of flexibility. This study also shows that the biomass potential and the pyrolysis costs can affect a lot the behaviour of pyrolysis in the energy system.
Durch die Fortschritte im Bereich der Quantencomputer rückt der Zeitpunkt näher, dass Quantencomputer die bestehenden mathematischen Probleme lösen können, welche in den aktuellen Public-Key-Verschlüsselungsverfahren verwendet werden. Als Reaktion darauf wurde ein Standardisierungsprozess für quantensichere Public-Key-Verschlüsselungsverfahren gestartet. Diese Arbeit analysiert diese und vergleicht sie untereinander, um Stärken und Schwächen der einzelnen Verfahren aufzuzeigen.
Die messbaren Elemente aus dem Film werden analysiert und bewertet. Dahingehend werden drei Dimensionen betrachtet: Die Textebene, die technische Ebene, sowie eine Metaebene. Diese Kategorien werden im Anschluss noch dem Marketingmaterial gegenübergestellt.Durch die modulare Art der Kriterien und die Vergleichbarkeit untereinander können Aussagen über den Erfolg eines Films getätigt werden.
Die Corona-Krise hat viele Bereiche getroffen und verändert. Unter anderem auch die Digitalisierung in verschiedenen Branchen. Nicht nur die Wirtschaft, sondern auch Strukturen und Prozesse innerhalb von Unternehmen wurden beeinflusst. Darunter auch die Lernstrukturen. Zwar gab es zu dieser Zeit bereits erste Ansätze vom digitalen Lernen, jedoch hauptsächlich in der Form Lerninhalte digital zur Verfügung zu stellen, ergänzend zur Präsenzlehre.
Durch die Kontaktbeschränkungen während der Pandemie konnten Arbeitsprozesse nicht mehr wie gewohnt ablaufen. Plötzlich waren Lösungen gefragt, welche die räumliche Distanz beim Lehren und Lernen überwinden können.
Lernprozesse wurden also durch eine digitale Hürde erschwert. Auch das gemeinsame Lernen über Präsenzseminare und Schulungen vor Ort wurden unterbunden. Die Leute sahen sich gezwungen sich digital zu vernetzen.
An dieser Stelle rückten sogenannte Kollaborationstools für Arbeits- und Lernprozesse stärker in den Fokus. Kollaborationstools sind Softwarelösungen, die digitale Zusammenarbeit an Projekten, Dokumenten und die gemeinsame Kommunikation, sowohl in der Arbeitswelt, als auch im Bereich Bildung fördern. Prozesse wurden nach und nach auf die Notwendigkeit der digitalen Gegebenheiten angepasst. Lerneinheiten zwischen Lernenden und Lehrenden fanden vermehrt über Videoanrufe statt, Inhalte wurden digital ausgetauscht und Ergebnisse wurden über digitale Wege geteilt, kontrolliert und korrigiert.
The tenth edition of the successful report "Project Management Software Systems" provided the complete guide to a successful project management software selection program. It includes an extensive overview of the leading products on the market. If you are seeking to purchase project (portfolio) management software for your organization, this report from BARC and GPM puts the facts at your fingertips to help you select the best tool to match your requirements.
Among the many highlights of this comprehensive report, you will discover
- the critical success factors in software selection processes,
- the phases of a systematic software selection process,
- basics on software architecture regarding modern PM software, and
- descriptions of all the functions you can except from today's PM software tools.
The second section contains a detailed analysis of market-leading products based on over 300 criteria. Each product reviewed in this report is assessed based on the same criteria so that product comparisons can be made easily.