TY  - GEN
A1  - Kleinpeter, Erich
A1  - Shainyan, Bagrat A.
T1  - Very low-temperature dynamic Si-29 NMR study of the conformational equilibrium of (1,1-phenyl-1,1-silacyclohex-1-yl)disiloxane
T2  - Magnetic resonance in chemistry
Y1  - 2019
U6  - https://doi.org/10.1002/mrc.4870
SN  - 0749-1581
SN  - 1097-458X
VL  - 57
IS  - 6
SP  - 317
EP  - 319
PB  - Wiley
CY  - Hoboken
ER  - 
TY  - GEN
A1  - Sianipar, Johannes Harungguan
A1  - Willems, Christian
A1  - Meinel, Christoph
T1  - Virtual machine integrity verification in Crowd-Resourcing Virtual Laboratory
T2  - 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA)
N2  - In cloud computing, users are able to use their own operating system (OS) image to run a virtual machine (VM) on a remote host. The virtual machine OS is started by the user using some interfaces provided by a cloud provider in public or private cloud. In peer to peer cloud, the VM is started by the host admin. After the VM is running, the user could get a remote access to the VM to install, configure, and run services. For the security reasons, the user needs to verify the integrity of the running VM, because a malicious host admin could modify the image or even replace the image with a similar image, to be able to get sensitive data from the VM. We propose an approach to verify the integrity of a running VM on a remote host, without using any specific hardware such as Trusted Platform Module (TPM). Our approach is implemented on a Linux platform where the kernel files (vmlinuz and initrd) could be replaced with new files, while the VM is running. kexec is used to reboot the VM with the new kernel files. The new kernel has secret codes that will be used to verify whether the VM was started using the new kernel files. The new kernel is used to further measuring the integrity of the running VM.
KW  - Virtual Machine
KW  - Integrity Verification
KW  - Crowd-Resourcing
KW  - Cloud Computing
Y1  - 2019
SN  - 978-1-5386-9133-5
U6  - https://doi.org/10.1109/SOCA.2018.00032
SN  - 2163-2871
SP  - 169
EP  - 176
PB  - IEEE
CY  - New York
ER  - 
TY  - GEN
A1  - Salzwedel, Annett
A1  - Völler, Heinz
A1  - Reibis, Rona Katharina
T1  - Vocational reintegration in coronary heart disease patients - the holistic approach of the WHO biopsychosocial concept
T2  - European journal of preventive cardiology : the official ESC journal for primary & secondary cardiovascular prevention, rehabilitation and sports cardiology
Y1  - 2019
U6  - https://doi.org/10.1177/2047487319850699
SN  - 2047-4873
SN  - 2047-4881
VL  - 26
IS  - 13
SP  - 1383
EP  - 1385
PB  - Sage Publ.
CY  - London
ER  - 
TY  - GEN
A1  - Miklashevsky, Alex A.
T1  - Words as social tools
BT  - the old and the new. Bridging cognition and communication Comment on "Words as social tools: Language, sociality and inner grounding in abstract concepts" by Anna M. Borghi et al.
T2  - Physics of life reviews
Y1  - 2019
U6  - https://doi.org/10.1016/j.plrev.2019.04.002
SN  - 1571-0645
SN  - 1873-1457
VL  - 29
SP  - 164
EP  - 165
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - GEN
A1  - Halfpap, Stefan
A1  - Schlosser, Rainer
T1  - Workload-Driven Fragment Allocation for Partially Replicated Databases Using Linear Programming
T2  - 2019 IEEE 35th International Conference on Data Engineering (ICDE)
N2  - In replication schemes, replica nodes can process read-only queries on snapshots of the master node without violating transactional consistency. By analyzing the workload, we can identify query access patterns and replicate data depending to its access frequency. In this paper, we define a linear programming (LP) model to calculate the set of partial replicas with the lowest overall memory capacity while evenly balancing the query load. Furthermore, we propose a scalable decomposition heuristic to calculate solutions for larger problem sizes. While guaranteeing the same performance as state-of-the-art heuristics, our decomposition approach calculates allocations with up to 23% lower memory footprint for the TPC-H benchmark.
KW  - database replication
KW  - allocation problem
KW  - linear programming
Y1  - 2019
SN  - 978-1-5386-7474-1
SN  - 978-1-5386-7475-8
U6  - https://doi.org/10.1109/ICDE.2019.00188
SN  - 1084-4627
SN  - 2375-026X
SN  - 1063-6382
SP  - 1746
EP  - 1749
PB  - IEEE
CY  - New York
ER  -