000 Informatik, Informationswissenschaft, allgemeine Werke
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In den vergangenen Jahrzehnten hat es unübersehbar zahlreiche Fortschritte im Bereich der IT-Sicherheitsforschung gegeben, etwa in den Bereichen Systemsicherheit und Kryptographie. Es ist jedoch genauso unübersehbar, dass IT-Sicherheitsprobleme im Alltag der Menschen fortbestehen. Mutmaßlich liegt dies an der Komplexität von Alltagssituationen, in denen Sicherheitsmechanismen und Gerätefunktionalität sowie deren Heterogenität in schwer antizipierbarer Weise mit menschlichem Verständnis und Alltagsgebrauch interagieren. Um die wissenschaftliche Forschung besser auf Menschen und deren IT-Sicherheitsbedürfnisse auszurichten, müssen wir daher den Alltag der Menschen besser verstehen. Das Verständnis von Alltag ist in der Informatik jedoch noch unterentwickelt. Dieser Beitrag möchte das Forschungsfeld “Sicherheit in der Digitalisierung des Alltags” definieren, um Forschenden die Gelegenheit zu geben, ihre Anstrengungen in diesem Bereich zu bündeln. Wir machen dabei Vorschläge einerseits zur inhaltlichen Eingrenzung der informatischen Forschung. Andererseits möchten wir durch die Einbeziehung von Forschungsmethoden aus der Ethnografie, die Erkenntnisse aus der durchaus subjektiven Beobachtung des “Alltags” vieler einzelner Individuen zieht, zur methodischen Weiterentwicklung interdisziplinärer Forschung in diesem Feld beitragen. Die IT- Sicherheitsforschung kann dann Bestehendes gezielt für eine richtige Alltagstauglichkeit optimieren und neue grundlegende Sicherheitsfunktionalitäten für die konkreten Herausforderungen im Alltag entwickeln.
Understanding of financial data has always been a point of interest for market participants to make better informed decisions. Recently, different cutting edge technologies have been addressed in the Financial Technology (FinTech) domain, including numeracy understanding, opinion mining and financial ocument processing.
In this thesis, we are interested in analyzing the arguments of financial experts with the goal of supporting investment decisions. Although various business studies confirm the crucial role of argumentation in financial communications, no work has addressed this problem as a computational argumentation task. In other words, the automatic analysis of arguments. In this regard, this thesis presents contributions in the three essential axes of theory, data, and evaluation to fill the gap between argument mining and financial text.
First, we propose a method for determining the structure of the arguments stated by company representatives during the public announcement of their quarterly results and future estimations through earnings conference calls. The proposed scheme is derived from argumentation theory at the micro-structure level of discourse. We further conducted the corresponding annotation study and published the first financial dataset annotated with arguments: FinArg.
Moreover, we investigate the question of evaluating the quality of arguments in this financial genre of text. To tackle this challenge, we suggest using two levels of quality metrics, considering both the Natural Language Processing (NLP) literature of argument quality assessment and the financial era peculiarities.
Hence, we have also enriched the FinArg data with our quality dimensions to produce the FinArgQuality dataset.
In terms of evaluation, we validate the principle of ensemble learning on the argument identification and argument unit classification tasks. We show that combining a traditional machine learning model along with a deep learning one, via an integration model (stacking), improves the overall performance, especially in small dataset settings.
In addition, despite the fact that argument mining is mainly a domain dependent task, to this date, the number of studies that tackle the generalization of argument mining models is still relatively small. Therefore, using our stacking approach and in comparison to the transfer learning model of DistilBert, we address and analyze three real-world scenarios concerning the model robustness over completely unseen domains and unseen topics.
Furthermore, with the aim of the automatic assessment of argument strength, we have investigated and compared different (refined) versions of Bert-based models that incorporate external knowledge in the decision layer. Consequently, our method outperforms the baseline model by 13 ± 2% in terms of F1-score through integrating Bert with encoded categorical features.
Beyond our theoretical and methodological proposals, our model of argument quality assessment, annotated corpora, and evaluation approaches are publicly available, and can serve as strong baselines for future work in both FinNLP and computational argumentation domains.
Hence, directly exploiting this thesis, we proposed to the community, a new task/challenge related to the analysis of financial arguments: FinArg-1, within the framework of the NTCIR-17 conference.
We also used our proposals to react to the Touché challenge at the CLEF 2021 conference. Our contribution was selected among the «Best of Labs».
In the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) are designed to provide low energy consumption while maintaining a long communications’ range for End Devices (EDs). LoRa is a communication protocol that can cover a wide range with low energy consumption. To evaluate the efficiency of the LoRa Wide-Area Network (LoRaWAN), three criteria can be considered, namely, the Packet Delivery Rate (PDR), Energy Consumption (EC), and coverage area. A set of transmission parameters have to be configured to establish a communication link. These parameters can affect the data rate, noise resistance, receiver sensitivity, and EC. The Adaptive Data Rate (ADR) algorithm is a mechanism to configure the transmission parameters of EDs aiming to improve the PDR. Therefore, we introduce a new algorithm using the Multi-Armed Bandit (MAB) technique, to configure the EDs’ transmission parameters in a centralized manner on the Network Server (NS) side, while improving the EC, too. The performance of the proposed algorithm, the Low-Power Multi-Armed Bandit (LP-MAB), is evaluated through simulation results and is compared with other approaches in different scenarios. The simulation results indicate that the LP-MAB’s EC outperforms other algorithms while maintaining a relatively high PDR in various circumstances.
After the enactment of the GDPR in 2018, many companies were forced to rethink their privacy management in order to comply with the new legal framework. These changes mostly affect the Controller to achieve GDPR-compliant privacy policies and management.However, measures to give users a better understanding of privacy, which is essential to generate legitimate interest in the Controller, are often skipped. We recommend addressing this issue by the usage of privacy preference languages, whereas users define rules regarding their preferences for privacy handling. In the literature, preference languages only work with their corresponding privacy language, which limits their applicability. In this paper, we propose the ConTra preference language, which we envision to support users during privacy policy negotiation while meeting current technical and legal requirements. Therefore, ConTra preferences are defined showing its expressiveness, extensibility, and applicability in resource-limited IoT scenarios. In addition, we introduce a generic approach which provides privacy language compatibility for unified preference matching.
Network communication has become a part of everyday life, and the interconnection among devices and people will increase even more in the future. A new area where this development is on the rise is the field of connected vehicles. It is especially useful for automated vehicles in order to connect the vehicles with other road users or cloud services. In particular for the latter it is beneficial to establish a mobile network connection, as it is already widely used and no additional infrastructure is needed. With the use of network communication, certain requirements come along.
One of them is the reliability of the connection. Certain Quality of Service (QoS) parameters need to be met. In case of degraded QoS, according to the SAE level specification, a downgrade of the automated system can be required, which may lead to a takeover maneuver, in which control is returned back to the driver. Since such a handover takes time, prediction is necessary to forecast the network quality for the next few seconds. Prediction of QoS parameters, especially in terms of Throughput (TP) and Latency (LA), is still a challenging task, as the wireless transmission properties of a moving mobile network connection are undergoing fluctuation. In this thesis, a new approach for prediction Network Quality Parameters (NQPs) on Transmission Control Protocol (TCP) level is presented. It combines the knowledge of the environment with the low level parameters of the mobile network. The aim of this work is to perform a comprehensive study of various models including both Location Smoothing (LS) grid maps and Learning Based (LB) regression ones. Moreover, the possibility of using the location independence of a model as well as suitability for automated driving is evaluated.
The autonomic composition of Virtual Networks (VNs) and Service Function Chains (SFCs)based on application requirements is significant for complex environments. In this paper, we use graph transformation in order to compose an Extended Virtual Network (EVN) that is based on
different requirements, such as locations, low latency, redundancy, and security functions. The EVN can represent physical environment devices and virtual application and network functions. We build
a generic Virtual Network Embedding (VNE) framework for transforming an Application Request (AR) to an EVN. Subsequently, we define a set of transformations that reflect preliminary topological, performance, reliability, and security policies. These transformations update the entities and demands of the VN and add SFCs that include the required Virtual Network Functions (VNFs). Additionally, we propose a greedy proactive heuristic for path-independent embedding of the composed SFCs. This heuristic is appropriate for real complex environments, such as industrial networks. Furthermore, we present an Industrail Internet of Things (IIoT) use case that was inspired by Industry 4.0 concepts,in which EVNs for remote asset management are deployed over three levels; manufacturing halls and edge and cloud computing. We also implement the developed methods in Alevin and show exemplary mapping results from our use case. Finally, we evaluate the chain embedding heuristic while using a random topology that is typical for such a use case, and show that it can improve the admission ratio and resource utilization with minimal overhead.
The power demand (kW) and energy consumption (kWh) of data centers were augmenteddrastically due to the increased communication and computation needs of IT services. Leveragingdemand and energy management within data centers is a necessity. Thanks to the automated ICTinfrastructure empowered by the IoT technology, such types of management are becoming more feasiblethan ever. In this paper, we look at management from two different perspectives: (1) minimization of theoverall energy consumption and (2) reduction of peak power demand during demand-response periods.Both perspectives have a positive impact on total cost of ownership for data centers. We exhaustivelyreviewed the potential mechanisms in data centers that provided flexibilities together with flexiblecontracts such as green service level and supply-demand agreements. We extended state-of-the-artby introducing the methodological building blocks and foundations of management systems for theabove mentioned two perspectives. We validated our results by conducting experiments on a lab-gradescale cloud computing data center at the premises of HPE in Milano. The obtained results support thetheoretical model, by highlighting the excellent potential of flexible service level agreements in Green IT:33% of overall energy savings and 50% of power demand reduction during demand-response periods inthe case of data center federation.
Natural Language Processing has an important role in Artificial Intelligence for easing human-machine interaction. Processing human language, though, poses many challenges, among which is the semantics-related phenomenon known as language variability, the fact that the same thing can be said in several ways. NLP applications' inputs and outputs can be expressed in different forms, whose equivalence can be verified through inference. The textual entailment paradigm was established to enable the creation of a unifying framework for applied inference, providing a means of delivering other NLP task from handling inference issues in an ad-hoc manner, using instead the outputs of an inference-dedicated mechanism.
Text entailment, the task of determining whether a piece of text logically follows from another piece of text, involves different scenarios, which can range from a simple syntactic variation to more complex semantic relationships between sentences. However, most approaches try a one-size-fits-all solution that usually favors some scenario to the detriment of another. The commonsense world knowledge necessary to support more complex inferences is also usually employed in a limited way, with most approaches sticking to shallow semantic information, leaving more elaborate semantic relationships aside. Furthermore, most systems still work as a "black box", providing a yes/no answer that does not explain the underlying reasoning process.
This thesis aims at addressing these issues by proposing a composite interpretable approach for recognizing text entailment where the entailment pair is analyzed so the most relevant phenomenon is detected and the suitable method can be used to solve it. Syntactic variations are dealt with through the analysis of the sentences' syntactic structures, and semantic relationships are detected with the aid of a knowledge graph built from natural language dictionary definitions. Also, if a semantic matching is involved, the answer is made interpretable through the generation of natural language justifications that explain the semantic relationship between the pieces of text. The result is the XTE - Explainable Text Entailment - a system that outperforms well-established tools based on single-technique entailment algorithms, and that also gives an important step towards Explainable AI, allowing the inference model interpretation, making the semantic reasoning process explicit and understandable.
Programming is a key skill in a world where businesses are driven by digital transformations. Although many of the programming demand can be addressed by a simple set of instructions composing libraries and services available in the web, non-technical professionals, such as domain experts and analysts, are still unable to construct their own programs due to the intrinsic complexity of coding. Among other types of end-user development, natural language programming has emerged to allow users to program without the formalism of traditional programming languages, where a tailored semantic parser can translate a natural language utterance to a formal command representation able to be processed by a computational machine. Currently, semantic parsers are typically built on the top of a learning method that defines its behaviours based on the patterns behind a large training data, whose production frequently are costly and time-consuming. Our research is devoted to study and propose a semantic parser for natural language commands targeting a scenario with low availability of training data. Our proposed semantic parser follows a multi-component architecture, composed of a specialised shallow parser that associates natural language commands to predicate-argument structures, integrated to a distributional ranking model that matches the command to a function signature available from an API knowledge base. Systems developed with statistical learning models and complex linguistics resources, as the proposed semantic parser, do not provide natively an easy way to associate a single feature from the input data to the impact in system behaviour. In this scenario, end-user explanations for intelligent systems has become a strong requirement to increase user confidence and system literacy. Thus, our research designed an explanation model for the proposed semantic parser that fits the heterogeneity of its multi-component architecture. The explanation model explores a hierarchical representation in an increasing degree of technical depth, providing higher-level explanations in the initial layers, going gradually to those that demand technical knowledge, applying different explanation strategies to better express the approach behind each component. With the support of a user-centred experiment, we compared the utility of different types of explanations and the impact of background knowledge in their preferences.
In this thesis we consider real analytic functions, i.e. functions which can be described locally as convergent power series and ask the following: Which real analytic functions definable in R_{an,exp} have a holomorphic extension which is again definable in R_{an,exp}? Finding a holomorphic extension is of course not difficult simply by power series expansion. The difficulty is to construct it in a definably way.
We will not answer the question above completely, but introduce a large non trivial class of definable functions in R_{an,exp} where for example functions which are iterated compositions from either side of globally subanalytic functions and the global logarithm are contained. We call them restricted log-exp-analytic. After giving some preliminary results like preparation theorems and Tamm's Theorem for this class of functions we are able to show that real analytic restricted log-exp-analytic functions have a holomorphic extension which is again restricted log-exp-analytic.