Secure Authentication with Short Re-Usable Passwords

We present Knock Yourself Out (KYO), a password generator that enables secure authentication against a computationally unbounded adversary. Master passwords can be surprisingly short and may be re-used for multiple service accounts even in the event of client compromises and multiple server compromises. At the same time, KYO is transparent to service operators and backwards- compatible. Master passwords are fully client-manageable while secrets shared with service operators can be kept constant. Likewise, secrets can be changed without having to change one’s passwords. KYO does not rely on collision- resistant hash functions and can be implemented with fast non-cryptographic hash functions. We detail the design of KYO and we analyze its security mathematically in a random hash function model. In our empirical evaluation we find that KYO remains secure even if small sets of hash functions are used instead, in other words, KYO requires minimal storage and is highly practical

TESMA : Towards the Development of a Tool for Specification, Management and Assessment of Teaching Programs

Defining and managing teaching programs at university or other institutions is a complex task for which there is not much support in terms of methods and tools. This task becomes even more critical when the time comes to obtain certifications w.r.t. official standards. In this paper, we present an on-going project called TESMA whose objective is to provide an open-source tool dedicated to the specification and management (including certification) of teaching programs. This tool has been engineered using a development method called Messir for its requirements elicitations and introduces a domain-specific language dedicated to the teaching domain. This paper presents the current status of this project and the future activities planned

SEMKIS-DSL: A Domain-Specific Language to Support Requirements Engineering of Datasets and Neural Network Recognition

Neural network (NN) components are being increasingly incorporated into software systems. Neural network properties are determined by their architecture, as well as the training and testing datasets used. The engineering of datasets and neural networks is a challenging task that requires methods and tools to satisfy customers’ expectations. The lack of tools that support requirements specification languages makes it difficult for engineers to describe dataset and neural network recognition skill requirements. Existing approaches often rely on traditional ad hoc approaches, without precise requirement specifications for data selection criteria, to build these datasets. Moreover, these approaches do not focus on the requirements of the neural network’s expected recognition skills. We aim to overcome this issue by defining a domain-specific language that precisely specifies dataset requirements and expected recognition skills after training for an NN-based system. In this paper, we present a textual domain-specific language (DSL) called SEMKIS-DSL (Software Engineering Methodology for the Knowledge management of Intelligent Systems) that is designed to support software engineers in specifying the requirements and recognition skills of neural networks. This DSL is proposed in the context of our general SEMKIS development process for neural network engineering. We illustrate the DSL’s concepts using a running example that focuses on the recognition of handwritten digits. We show some requirements and recognition skills specifications and demonstrate how our DSL improves neural network recognition skills

TESMA: Requirements and Design of a Tool for Educational Programs

Defining and managing teaching programs at universities or other institutions is a complex task for which there is not much support in terms of methods and tools. This task becomes even more critical when the time comes to obtain certifications w.r.t. official standards. In this paper, we present an on-going project called TESMA, whose objective is to provide an open-source tool dedicated to the specification and management (including certification) of teaching programs. An in-depth market analysis regarding related tools and conceptual frameworks of the project is presented. This tool has been engineered using a development method called Messir for its requirements elicitation and introduces a domain-specific language dedicated to the teachi

RE-EDS Using GAFF Topologies: Application to Relative Hydration Free-Energy Calculations for Large Sets of Molecules

Free-energy differences between pairs of end-states can be estimated based on molecular dynamics (MD) simulations using standard pathway-dependent methods such as thermodynamic integration (TI), free-energy perturbation, or Bennett's acceptance ratio. Replica-exchange enveloping distribution sampling (RE-EDS), on the other hand, allows for the sampling of multiple end-states in a single simulation without the specification of any pathways. In this work, we use the RE-EDS method as implemented in GROMOS together with generalized AMBER force field (GAFF) topologies, converted to a GROMOS-compatible format with a newly developed GROMOS++ program amber2gromos, to compute relative hydration free energies for a series of benzene derivatives. The results obtained with RE-EDS are compared to the experimental data as well as calculated values from the literature. In addition, the estimated free-energy differences in water and in vacuum are compared to values from TI calculations carried out with GROMACS. The hydration free energies obtained using RE-EDS for multiple molecules are found to be in good agreement with both the experimental data and the results calculated using other free-energy methods. While all considered free-energy methods delivered accurate results, the RE-EDS calculations required the least amount of total simulation time. This work serves as a validation for the use of GAFF topologies with the GROMOS simulation package and the RE-EDS approach. Furthermore, the performance of RE-EDS for a large set of 28 end-states is assessed with promising results

In vivo T1ρ and T2 mapping of articular cartilage in osteoarthritis of the knee using 3T MRI

SummaryObjectiveEvaluation and treatment of patients with early stages of osteoarthritis (OA) is dependent upon an accurate assessment of the cartilage lesions. However, standard cartilage dedicated magnetic resonance (MR) techniques are inconclusive in quantifying early degenerative changes. The objective of this study was to determine the ability of MR T1rho (T1ρ) and T2 mapping to detect cartilage matrix degeneration between normal and early OA patients.MethodSixteen healthy volunteers (mean age 41.3) without clinical or radiological evidence of OA and 10 patients (mean age 55.9) with OA were scanned using a 3Tesla (3T) MR scanner. Cartilage volume and thickness, and T1ρ and T2 values were compared between normal and OA patients. The relationship between T1ρ and T2 values, and Kellgren–Lawrence scores based on plain radiographs and the cartilage lesion grading based on MR images were studied.ResultsThe average T1ρ and T2 values were significantly increased in OA patients compared with controls (52.04±2.97ms vs 45.53±3.28ms with P=0.0002 for T1ρ, and 39.63±2.69ms vs 34.74±2.48ms with P=0.001 for T2). Increased T1ρ and T2 values were correlated with increased severity in radiographic and MR grading of OA. T1ρ has a larger range and higher effect size than T2, 3.7 vs 3.0.ConclusionOur results suggest that both in vivo T1ρ and T2 relaxation times increase with the degree of cartilage degeneration. T1ρ relaxation time may be a more sensitive indicator for early cartilage degeneration than T2. The ability to detect early cartilage degeneration prior to morphologic changes may allow us to critically monitor the course of OA and injury progression, and to evaluate the success of treatment to patients with early stages of OA

Correlative analysis on InGaN/GaN nanowires: structural and optical properties of self-assembled short-period superlattices

: The influence of self-assembled short-period superlattices (SPSLs) on the structural and optical properties of InGaN/GaN nanowires (NWs) grown by PAMBE on Si (111) was investigated by STEM, EDXS, µ-PL analysis and k·p simulations. STEM analysis on single NWs indicates that in most of the studied nanostructures, SPSLs self-assemble during growth. The SPSLs display short-range ordering of In-rich and In-poor InxGa1-xN regions with a period of 2-3 nm that are covered by a GaN shell and that transition to a more homogenous InxGa1-xN core. Polarization- and temperature-resolved PL analysis performed on the same NWs shows that they exhibit a strong parallel polarized red-yellow emission and a predominantly perpendicular polarized blue emission, which are ascribed to different In-rich regions in the nanostructures. The correlation between STEM, µ-PL and k·p simulations provides better understanding of the rich optical emission of complex III-N nanostructures and how they are impacted by structural properties, yielding the significant impact of strain on self-assembly and spectral emission

VIPP2 interacts with VIPP1 and HSP22E/F at chloroplast membranes and modulates a retrograde signal for HSP22E/F gene expression

VIPP proteins aid thylakoid biogenesis and membrane maintenance in cyanobacteria, algae, and plants. Some members of the Chlorophyceae contain two VIPP paralogs termed VIPP1 and VIPP2, which originate from an early gene duplication event during the evolution of green algae. VIPP2 is barely expressed under nonstress conditions but accumulates in cells exposed to high light intensities or H2O2, during recovery from heat stress, and in mutants with defective integration (alb3.1) or translocation (secA) of thylakoid membrane proteins. Recombinant VIPP2 forms rod-like structures in vitro and shows a strong affinity for phosphatidylinositol phosphate. Under stress conditions, >70% of VIPP2 is present in membrane fractions and localizes to chloroplast membranes. A vipp2 knock-out mutant displays no growth phenotypes and no defects in the biogenesis or repair of photosystem II. However, after exposure to high light intensities, the vipp2 mutant accumulates less HSP22E/F and more LHCSR3 protein and transcript. This suggests that VIPP2 modulates a retrograde signal for the expression of nuclear genes HSP22E/F and LHCSR3. Immunoprecipitation of VIPP2 from solubilized cells and membrane-enriched fractions revealed major interactions with VIPP1 and minor interactions with HSP22E/F. Our data support a distinct role of VIPP2 in sensing and coping with chloroplast membrane stress