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Cloud security mechanisms
(2014)
Cloud computing has brought great benefits in cost and flexibility for provisioning services. The greatest challenge of cloud computing remains however the question of security. The current standard tools in access control mechanisms and cryptography can only partly solve the security challenges of cloud infrastructures. In the recent years of research in security and cryptography, novel mechanisms, protocols and algorithms have emerged that offer new ways to create secure services atop cloud infrastructures. This report provides introductions to a selection of security mechanisms that were part of the "Cloud Security Mechanisms" seminar in summer term 2013 at HPI.
claspfolio 2
(2014)
Building on the award-winning, portfolio-based ASP solver claspfolio, we present claspfolio 2, a modular and open solver architecture that integrates several different portfolio-based algorithm selection approaches and techniques. The claspfolio 2 solver framework supports various feature generators, solver selection approaches, solver portfolios, as well as solver-schedule-based pre-solving techniques. The default configuration of claspfolio 2 relies on a light-weight version of the ASP solver clasp to generate static and dynamic instance features. The flexible open design of claspfolio 2 is a distinguishing factor even beyond ASP. As such, it provides a unique framework for comparing and combining existing portfolio-based algorithm selection approaches and techniques in a single, unified framework. Taking advantage of this, we conducted an extensive experimental study to assess the impact of different feature sets, selection approaches and base solver portfolios. In addition to gaining substantial insights into the utility of the various approaches and techniques, we identified a default configuration of claspfolio 2 that achieves substantial performance gains not only over clasp's default configuration and the earlier version of claspfolio, but also over manually tuned configurations of clasp.
The adaptation of cell growth and proliferation to environmental changes is essential for the surviving of biological systems. The evolutionary conserved Ser/Thr protein kinase “Target of Rapamycin” (TOR) has emerged as a major signaling node that integrates the sensing of numerous growth signals to the coordinated regulation of cellular metabolism and growth. Although the TOR signaling pathway has been widely studied in heterotrophic organisms, the research on TOR in photosynthetic eukaryotes has been hampered by the reported land plant resistance to rapamycin. Thus, the finding that Chlamydomonas reinhardtii is sensitive to rapamycin, establish this unicellular green alga as a useful model system to investigate TOR signaling in photosynthetic eukaryotes.
The observation that rapamycin does not fully arrest Chlamydomonas growth, which is different from observations made in other organisms, prompted us to investigate the regulatory function of TOR in Chlamydomonas in context of the cell cycle. Therefore, a growth system that allowed synchronously growth under widely unperturbed cultivation in a fermenter system was set up and the synchronized cells were characterized in detail. In a highly resolved kinetic study, the synchronized cells were analyzed for their changes in cytological parameters as cell number and size distribution and their starch content. Furthermore, we applied mass spectrometric analysis for profiling of primary and lipid metabolism. This system was then used to analyze the response dynamics of the Chlamydomonas metabolome and lipidome to TOR-inhibition by rapamycin
The results show that TOR inhibition reduces cell growth, delays cell division and daughter cell release and results in a 50% reduced cell number at the end of the cell cycle. Consistent with the growth phenotype we observed strong changes in carbon and nitrogen partitioning in the direction of rapid conversion into carbon and nitrogen storage through an accumulation of starch, triacylglycerol and arginine. Interestingly, it seems that the conversion of carbon into triacylglycerol occurred faster than into starch after TOR inhibition, which may indicate a more dominant role of TOR in the regulation of TAG biosynthesis than in the regulation of starch.
This study clearly shows, for the first time, a complex picture of metabolic and lipidomic dynamically changes during the cell cycle of Chlamydomonas reinhardtii and furthermore reveals a complex regulation and adjustment of metabolite pools and lipid composition in response to TOR inhibition.
Recently, interest in collecting and mining large sets of educational data on student background and performance to conduct research on learning and instruction has developed as an area generally referred to as learning analytics. Higher education leaders are recognising the value of learning analytics for improving not only learning and teaching but also the entire educational arena. However, theoretical concepts and empirical evidence need to be generated within the fast evolving field of learning analytics. In this paper, we introduce a holistic learning analytics framework. Based on this framework, student, learning, and curriculum profiles have been developed which include relevant static and dynamic parameters for facilitating the learning analytics framework. Based on the theoretical model, an empirical study was conducted to empirically validate the parameters included in the student profile. The paper concludes with practical implications and issues for future research.
Herein, we report the chain-growth tin-free room temperature polymerization method to synthesize n-type perylene diimide-dithiophene-based conjugated polymers (PPDIT2s) suitable for solar cell and transistor applications. The palladium/electron-rich tri-tert-butylphosphine catalyst is effective to enable the chain-growth polymerization of anion-radical monomer Br-TPDIT-Br/Zn to PPDIT2 with a molecular weight up to Mw ≈ 50 kg mol−1 and moderate polydispersity. This is the second example of the polymerization of unusual anion-radical aromatic complexes formed in a reaction of active Zn and electron-deficient diimide-based aryl halides. As such, the discovered polymerization method is not a specific reactivity feature of the naphthalene-diimide derivatives but is rather a general polymerization tool. This is an important finding, given the significantly higher maximum external quantum efficiency that can be reached with PDI-based copolymers (32–45%) in all-polymer solar cells compared to NDI-based materials (15–30%). Our studies revealed that PPDIT2 synthesized by the new method and the previously published polymer prepared by step-growth Stille polycondensation show similar electron mobility and all-polymer solar cell performance. At the same time, the polymerization reported herein has several technological advantages as it proceeds relatively fast at room temperature and does not involve toxic tin-based compounds. Because several chain-growth polymerization reactions are well-suited for the preparation of well-defined multi-functional polymer architectures, the next target is to explore the utility of the discovered polymerization in the synthesis of end-functionalized polymers and block copolymers. Such materials would be helpful to improve the nanoscale morphology of polymer blends in all-polymer solar cells.
Catalytic bio–chemo and bio–bio tandem oxidation reactions for amide and carboxylic acid synthesis
(2014)
A catalytic toolbox for three different water-based one-pot cascades to convert aryl alcohols to amides and acids and cyclic amines to lactams, involving combination of oxidative enzymes (monoamine oxidase, xanthine dehydrogenase, galactose oxidase and laccase) and chemical oxidants (TBHP or CuI(cat)/H2O2) at mild temperatures, is presented. Mutually compatible conditions were found to afford products in good to excellent yields.
Recently, C K-edge Near Edge X-ray Absorption Fine Structure (NEXAFS) spectra of graphite (HOPG) surfaces have been measured for the pristine material, and for HOPG treated with either bromine or krypton plasmas (Lippitz et al., Surf. Sci., 2013, 611, L1). Changes of the NEXAFS spectra characteristic for physical (krypton) and/or chemical/physical modifications of the surface (bromine) upon plasma treatment were observed. Their molecular origin, however, remained elusive. In this work we study by density functional theory, the effects of selected point and line defects as well as chemical modifications on NEXAFS carbon K-edge spectra of single graphene layers. For Br-treated surfaces, also Br 3d X-ray Photoelectron Spectra (XPS) are simulated by a cluster approach, to identify possible chemical modifications. We observe that some of the defects related to plasma treatment lead to characteristic changes of NEXAFS spectra, similar to those in experiment. Theory provides possible microscopic origins for these changes.
Entrepreneurship is known to be a main driver of economic growth. Hence, governments have an interest in supporting and promoting entrepreneurial activities. Start-up subsidies, which have been analyzed extensively, only aim at mitigating the lack of financial capital. However, some entrepreneurs also lack in human, social, and managerial capital. One way to address these shortcomings is by subsidizing coaching programs for entrepreneurs. However, theoretical and empirical evidence about business coaching and programs subsidizing coaching is scarce. This dissertation gives an extensive overview of coaching and is the first empirical study for Germany analyzing the effects of coaching programs on its participants. In the theoretical part of the dissertation the process of a business start-up is described and it is discussed how and in which stage of the company’s evolvement coaching can influence entrepreneurial success. The concept of coaching is compared to other non-monetary types of support as training, mentoring, consulting, and counseling. Furthermore, national and international support programs are described. Most programs have either no or small positive effects. However, there is little quantitative evidence in the international literature. In the empirical part of the dissertation the effectiveness of coaching is shown by evaluating two German coaching programs, which support entrepreneurs via publicly subsidized coaching sessions. One of the programs aims at entrepreneurs who have been employed before becoming self-employed, whereas the other program is targeted at former unemployed entrepreneurs. The analysis is based on the evaluation of a quantitative and a qualitative dataset. The qualitative data are gathered by intensive one-on-one interviews with coaches and entrepreneurs. These data give a detailed insight about the coaching topics, duration, process, effectiveness, and the thoughts of coaches and entrepreneurs. The quantitative data include information about 2,936 German-based entrepreneurs. Using propensity score matching, the success of participants of the two coaching programs is compared with adequate groups of non-participants. In contrast to many other studies also personality traits are observed and controlled for in the matching process. The results show that only the program for former unemployed entrepreneurs has small positive effects. Participants have a larger survival probability in self-employment and a larger probability to hire employees than matched non-participants. In contrast, the program for former employed individuals has negative effects. Compared to individuals who did not participate in the coaching program, participants have a lower probability to stay in self-employment, lower earned net income, lower number of employees and lower life satisfaction. There are several reasons for these differing results of the two programs. First, former unemployed individuals have more basic coaching needs than former employed individuals. Coaches can satisfy these basic coaching needs, whereas former employed individuals have more complex business problems, which are not very easy to be solved by a coaching intervention. Second, the analysis reveals that former employed individuals are very successful in general. It is easier to increase the success of former unemployed individuals as they have a lower base level of success than former employed individuals. An effect heterogeneity analysis shows that coaching effectiveness differs by region. Coaching for previously unemployed entrepreneurs is especially useful in regions with bad labor market conditions. In summary, in line with previous literature, it is found that coaching has little effects on the success of entrepreneurs. The previous employment status, the characteristics of the entrepreneur and the regional labor market conditions play a crucial role in the effectiveness of coaching. In conclusion, coaching needs to be well tailored to the individual and applied thoroughly. Therefore, governments should design and provide coaching programs only after due consideration.
This work introduces concepts and corresponding tool support to enable a complementary approach in dealing with recovery. Programmers need to recover a development state, or a part thereof, when previously made changes reveal undesired implications. However, when the need arises suddenly and unexpectedly, recovery often involves expensive and tedious work. To avoid tedious work, literature recommends keeping away from unexpected recovery demands by following a structured and disciplined approach, which consists of the application of various best practices including working only on one thing at a time, performing small steps, as well as making proper use of versioning and testing tools. However, the attempt to avoid unexpected recovery is both time-consuming and error-prone. On the one hand, it requires disproportionate effort to minimize the risk of unexpected situations. On the other hand, applying recommended practices selectively, which saves time, can hardly avoid recovery. In addition, the constant need for foresight and self-control has unfavorable implications. It is exhaustive and impedes creative problem solving. This work proposes to make recovery fast and easy and introduces corresponding support called CoExist. Such dedicated support turns situations of unanticipated recovery from tedious experiences into pleasant ones. It makes recovery fast and easy to accomplish, even if explicit commits are unavailable or tests have been ignored for some time. When mistakes and unexpected insights are no longer associated with tedious corrective actions, programmers are encouraged to change source code as a means to reason about it, as opposed to making changes only after structuring and evaluating them mentally. This work further reports on an implementation of the proposed tool support in the Squeak/Smalltalk development environment. The development of the tools has been accompanied by regular performance and usability tests. In addition, this work investigates whether the proposed tools affect programmers’ performance. In a controlled lab study, 22 participants improved the design of two different applications. Using a repeated measurement setup, the study examined the effect of providing CoExist on programming performance. The result of analyzing 88 hours of programming suggests that built-in recovery support as provided with CoExist positively has a positive effect on programming performance in explorative programming tasks.
In the field of disk-based parallel database management systems exists a great variety of solutions based on a shared-storage or a shared-nothing architecture. In contrast, main memory-based parallel database management systems are dominated solely by the shared-nothing approach as it preserves the in-memory performance advantage by processing data locally on each server. We argue that this unilateral development is going to cease due to the combination of the following three trends: a) Nowadays network technology features remote direct memory access (RDMA) and narrows the performance gap between accessing main memory inside a server and of a remote server to and even below a single order of magnitude. b) Modern storage systems scale gracefully, are elastic, and provide high-availability. c) A modern storage system such as Stanford's RAMCloud even keeps all data resident in main memory. Exploiting these characteristics in the context of a main-memory parallel database management system is desirable. The advent of RDMA-enabled network technology makes the creation of a parallel main memory DBMS based on a shared-storage approach feasible.
This thesis describes building a columnar database on shared main memory-based storage. The thesis discusses the resulting architecture (Part I), the implications on query processing (Part II), and presents an evaluation of the resulting solution in terms of performance, high-availability, and elasticity (Part III).
In our architecture, we use Stanford's RAMCloud as shared-storage, and the self-designed and developed in-memory AnalyticsDB as relational query processor on top. AnalyticsDB encapsulates data access and operator execution via an interface which allows seamless switching between local and remote main memory, while RAMCloud provides not only storage capacity, but also processing power. Combining both aspects allows pushing-down the execution of database operators into the storage system. We describe how the columnar data processed by AnalyticsDB is mapped to RAMCloud's key-value data model and how the performance advantages of columnar data storage can be preserved.
The combination of fast network technology and the possibility to execute database operators in the storage system opens the discussion for site selection. We construct a system model that allows the estimation of operator execution costs in terms of network transfer, data processed in memory, and wall time. This can be used for database operators that work on one relation at a time - such as a scan or materialize operation - to discuss the site selection problem (data pull vs. operator push). Since a database query translates to the execution of several database operators, it is possible that the optimal site selection varies per operator. For the execution of a database operator that works on two (or more) relations at a time, such as a join, the system model is enriched by additional factors such as the chosen algorithm (e.g. Grace- vs. Distributed Block Nested Loop Join vs. Cyclo-Join), the data partitioning of the respective relations, and their overlapping as well as the allowed resource allocation.
We present an evaluation on a cluster with 60 nodes where all nodes are connected via RDMA-enabled network equipment. We show that query processing performance is about 2.4x slower if everything is done via the data pull operator execution strategy (i.e. RAMCloud is being used only for data access) and about 27% slower if operator execution is also supported inside RAMCloud (in comparison to operating only on main memory inside a server without any network communication at all). The fast-crash recovery feature of RAMCloud can be leveraged to provide high-availability, e.g. a server crash during query execution only delays the query response for about one second. Our solution is elastic in a way that it can adapt to changing workloads a) within seconds, b) without interruption of the ongoing query processing, and c) without manual intervention.