Indefensibility, Skepticism and Conceptual Truth

It is true of many truths that I do not believe them. It is\ud equally true that I cannot rationally assert of any such truth\ud that it is true and that I do not believe it. Such a claim is\ud indefensible, i.e. for internal reasons unable to convince. I\ud claim that such is the skeptic's predicament, trying to\ud convince us to bracket knowledge claims we have good\ud grounds to take ourselves to be entitled to. An analysis of\ud skepticism as an epidemic rather than epistemic challenge\ud will shed new light on what it is to doubt a proposition and\ud provide us with an analysis of conceptual truths as those\ud which cannot rationally be doubted

Machine learning regression on hyperspectral data to estimate multiple water parameters

In this paper, we present a regression framework involving several machine learning models to estimate water parameters based on hyperspectral data. Measurements from a multi-sensor field campaign, conducted on the River Elbe, Germany, represent the benchmark dataset. It contains hyperspectral data and the five water parameters chlorophyll a, green algae, diatoms, CDOM and turbidity. We apply a PCA for the high-dimensional data as a possible preprocessing step. Then, we evaluate the performance of the regression framework with and without this preprocessing step. The regression results of the framework clearly reveal the potential of estimating water parameters based on hyperspectral data with machine learning. The proposed framework provides the basis for further investigations, such as adapting the framework to estimate water parameters of different inland waters.Comment: This work has been accepted to the IEEE WHISPERS 2018 conference. (C) 2018 IEE

Overcommitment in Cloud Services -- Bin packing with Chance Constraints

This paper considers a traditional problem of resource allocation, scheduling jobs on machines. One such recent application is cloud computing, where jobs arrive in an online fashion with capacity requirements and need to be immediately scheduled on physical machines in data centers. It is often observed that the requested capacities are not fully utilized, hence offering an opportunity to employ an overcommitment policy, i.e., selling resources beyond capacity. Setting the right overcommitment level can induce a significant cost reduction for the cloud provider, while only inducing a very low risk of violating capacity constraints. We introduce and study a model that quantifies the value of overcommitment by modeling the problem as a bin packing with chance constraints. We then propose an alternative formulation that transforms each chance constraint into a submodular function. We show that our model captures the risk pooling effect and can guide scheduling and overcommitment decisions. We also develop a family of online algorithms that are intuitive, easy to implement and provide a constant factor guarantee from optimal. Finally, we calibrate our model using realistic workload data, and test our approach in a practical setting. Our analysis and experiments illustrate the benefit of overcommitment in cloud services, and suggest a cost reduction of 1.5% to 17% depending on the provider's risk tolerance

Estimating Chlorophyll a Concentrations of Several Inland Waters with Hyperspectral Data and Machine Learning Models

Water is a key component of life, the natural environment and human health. For monitoring the conditions of a water body, the chlorophyll a concentration can serve as a proxy for nutrients and oxygen supply. In situ measurements of water quality parameters are often time-consuming, expensive and limited in areal validity. Therefore, we apply remote sensing techniques. During field campaigns, we collected hyperspectral data with a spectrometer and in situ measured chlorophyll a concentrations of 13 inland water bodies with different spectral characteristics. One objective of this study is to estimate chlorophyll a concentrations of these inland waters by applying three machine learning regression models: Random Forest, Support Vector Machine and an Artificial Neural Network. Additionally, we simulate four different hyperspectral resolutions of the spectrometer data to investigate the effects on the estimation performance. Furthermore, the application of first order derivatives of the spectra is evaluated in turn to the regression performance. This study reveals the potential of combining machine learning approaches and remote sensing data for inland waters. Each machine learning model achieves an R2-score between 80 % to 90 % for the regression on chlorophyll a concentrations. The random forest model benefits clearly from the applied derivatives of the spectra. In further studies, we will focus on the application of machine learning models on spectral satellite data to enhance the area-wide estimation of chlorophyll a concentration for inland waters.Comment: Accepted at ISPRS Geospatial Week 2019 in Ensched

Fallstudie Vontobel : «Digitalisiertes Service Management schafft Kundennutzen»

Die Fallstudie aus den Operation Services des Schweizer Private Wealth und Asset Managers Vontobel zeigt, dass auch die digitale Transformation interner Bereitstellungsprozesse ein hohes Nutzenpotenzial birgt und dazu beiträgt, dass sich Servicefunktionen in der Organisation neu positionieren und ihre Rolle in der Organisation verändern können. Kundenzufriedenheit, mehr Transparenz und Effizienz in der Leistungserbringung und eine bessere Verfügbarkeit von Informationen in kritischen Supportprozessen standen im Mittelpunkt der in dieser Fallstudie beschriebenen Transformation des IT-gestützten Service Managements bei Vontobel

Pathways to Developing Digital Capabilities within Entrepreneurial Initiatives in Pre-Digital Organizations

To enable new digital business models, pre-digital organizations launch entrepreneurial initiatives. However, in developing the required digital capabilities, pre-digital organizations often face challenges as they are marked by the ways they have historically established their organizational identity. Research on how pre-digital organizations can develop digital capabilities remains scarce. This study draws on a single case study to illustrate potential pathways for the development of digital capabilities. Two key characteristics are identified: the source of digital capability development and the set-up of the actors involved. The authors synthesize four possible pathway manifestations, discuss the dynamic nature of pathway combinations, and suggest that managing a portfolio of pathways may be crucial for pre-digital organizations. Therefore, the study contributes to a better understanding of digital transformation in pre-digital organizations. Furthermore, it provides guidance for practitioners to reflect on when deciding which pathways to follow

ir_metadata: An Extensible Metadata Schema for IR Experiments

The information retrieval (IR) community has a strong tradition of making the computational artifacts and resources available for future reuse, allowing the validation of experimental results. Besides the actual test collections, the underlying run files are often hosted in data archives as part of conferences like TREC, CLEF, or NTCIR. Unfortunately, the run data itself does not provide much information about the underlying experiment. For instance, the single run file is not of much use without the context of the shared task's website or the run data archive. In other domains, like the social sciences, it is good practice to annotate research data with metadata. In this work, we introduce ir_metadata - an extensible metadata schema for TREC run files based on the PRIMAD model. We propose to align the metadata annotations to PRIMAD, which considers components of computational experiments that can affect reproducibility. Furthermore, we outline important components and information that should be reported in the metadata and give evidence from the literature. To demonstrate the usefulness of these metadata annotations, we implement new features in repro_eval that support the outlined metadata schema for the use case of reproducibility studies. Additionally, we curate a dataset with run files derived from experiments with different instantiations of PRIMAD components and annotate these with the corresponding metadata. In the experiments, we cover reproducibility experiments that are identified by the metadata and classified by PRIMAD. With this work, we enable IR researchers to annotate TREC run files and improve the reuse value of experimental artifacts even further.Comment: Resource pape

Evaluating Temporal Persistence Using Replicability Measures

In real-world Information Retrieval (IR) experiments, the Evaluation Environment (EE) is exposed to constant change. Documents are added, removed, or updated, and the information need and the search behavior of users is evolving. Simultaneously, IR systems are expected to retain a consistent quality. The LongEval Lab seeks to investigate the longitudinal persistence of IR systems, and in this work, we describe our participation. We submitted runs of five advanced retrieval systems, namely a Reciprocal Rank Fusion (RRF) approach, ColBERT, monoT5, Doc2Query, and E5, to both sub-tasks. Further, we cast the longitudinal evaluation as a replicability study to better understand the temporal change observed. As a result, we quantify the persistence of the submitted runs and see great potential in this evaluation method.Comment: To be published in Proceedings of the Working Notes of CLEF 2023 - Conference and Labs of the Evaluation Forum, Thessaloniki, Greece 18 - 21, 202