A logic of argumentation for specification and verification of abstract argumentation frameworks

In this paper, we propose a logic of argumentation for the specification and verification (LA4SV) of requirements on Dung??s abstract argumentation frameworks. We distinguish three kinds of decision problems for argumentation verification, called extension verification, framework verification, and specification verification respectively. For example, given a political requirement like ??if the argument to increase taxes is accepted, then the argument to increase services must be accepted too,?? we can either verify an extension of acceptable arguments, or all extensions of an argumentation framework, or all extensions of all argumentation frameworks satisfying a framework specification. We introduce the logic of argumentation verification to specify such requirements, and we represent the three verification problems of argumentation as model checking and theorem proving properties of the logic. Moreover, we recast the logic of argumentation verification in a modal framework, in order to express multiple extensions, and properties like transitivity and reflexivity of the attack relation. Finally, we introduce a logic of meta-argumentation where abstract argumentation is used to reason about abstract argumentation itself. We define the logic of meta-argumentation using the fibring methodology in such a way to represent attack relations not only among arguments but also among attacks. We show how to use this logic to verify the requirements of argumentation frameworks where higher-order attacks are allowed [A preliminary version of the logic of argumentation compliance was called the logic of abstract argumentation?(2005).]     

Effects of Berry Anthocyanins on Cognitive Performance,Vascular Function and Cardiometabolic Risk Markers: A Systematic Review of Randomized Placebo-Controlled Intervention Studies in Humans

Supplementation with anthocyanins, which are a type of flavonoids mainly found in various berries, is hypothesized to be a promising approach to lower the risk of developing cognitive decline. The aim of this systematic review was to provide a comprehensive overview of dietary intervention trials describing effects of berry anthocyanins on cognitive performance in humans, while also addressing potential underlying mechanisms. A total of 1197 articles were identified through a systematic search, and 49 studies reporting effects on cognitive performance (n = 18), vascular function (n = 22), or cardiometabolic risk markers (n = 32) were included. Significant improvements were observed on memory, while some of the studies also reported effects on attention and psychomotor speed or executive function. Vascular function markers such as brachial artery flow-mediated vasodilation were also affected and consistent evidence was provided for the beneficial effects of berry anthocyanins on endothelial function. Finally, studies reported improvements in blood pressure, but effects on metabolic risk markers (e.g. carbohydrate and lipid metabolism) were less consistent. In conclusion, this review provides evidence for the beneficial effects of berry anthocyanins on cognitive performance as memory improved. Whether observed anthocyanin-induced improvements in vascular function and blood pressure underlie beneficial effects on cognitive performance warrants further study.     

Improving robustness of industrial object detection by automatic generation of synthetic images from CAD models

Object detection (OD) is used for visual quality control in factories. Images that compose training datasets are often collected directly from the production line and labeled with bounding boxes manually. Such data represent well the inference context but might lack diversity, implying a risk of overfitting. To address this issue, we propose a dataset construction method based on an automated pipeline, which receives a CAD model of an object and returns a set of realistic synthetic labeled images (code publicly available). Our approach can be easily used by non-expert users and is relevant for industrial applications, where CAD models are widely available. We performed experiments to compare the use of datasets obtained by the two different ways—collecting and labeling real images or applying the proposed automated pipeline—in the classification of five different industrial parts. To ensure that both approaches can be used without deep learning expertise, all training parameters were kept fixed during these experiments. In our results, both methods were successful for some objects but failed for others. However, we have shown that the combined use of real and synthetic images led to better results. This finding has the potential to make industrial OD models more robust to poor data collection and labeling errors, without increasing the difficulty of the training process.     

Healing Effect of Controlled Anti‐Electromigration on Conventional and High‐Tc Superconducting Nanowires

The electromigration process has the potential capability to move atoms one by one when properly controlled. It is therefore an appealing tool to tune the cross section of monoatomic compounds with ultimate resolution or, in the case of polyatomic compounds, to change the stoichiometry with the same atomic precision. As demonstrated here, a combination of electromigration and anti‐electromigration can be used to reversibly displace atoms with a high degree of control. This enables a fine adjustment of the superconducting properties of Al weak links, whereas in Nb the diffusion of atoms leads to a more irreversible process. In a superconductor with a complex unit cell (La_2?x Ce_x CuO₄), the electromigration process acts selectively on the oxygen atoms with no apparent modification of the structure. This allows to adjust the doping of this compound and switch from a superconducting to an insulating state in a nearly reversible fashion. In addition, the conditions needed to replace feedback controlled electromigration by a simpler technique of electropulsing are discussed. These findings have a direct practical application as a method to explore the dependence of the characteristic parameters on the exact oxygen content and pave the way for a reversible control of local properties of nanowires.     

Malware classification based on call graph clustering

Each day, anti-virus companies receive tens of thousands samples of potentially harmful executables. Many of the malicious samples are variations of previously encountered malware, created by their authors to evade pattern-based detection. Dealing with these large amounts of data requires robust, automatic detection approaches. This paper studies malware classification based on call graph clustering. By representing malware samples as call graphs, it is possible to abstract certain variations away, enabling the detection of structural similarities between samples. The ability to cluster similar samples together will make more generic detection techniques possible, thereby targeting the commonalities of the samples within a cluster. To compare call graphs mutually, we compute pairwise graph similarity scores via graph matchings which approximately minimize the graph edit distance. Next, to facilitate the discovery of similar malware samples, we employ several clustering algorithms, including k-medoids and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Clustering experiments are conducted on a collection of real malware samples, and the results are evaluated against manual classifications provided by human malware analysts. Experiments show that it is indeed possible to accurately detect malware families via call graph clustering. We anticipate that in the future, call graphs can be used to analyse the emergence of new malware families, and ultimately to automate implementation of generic detection schemes.     

A coupling algorithm for partitioned solvers applied to bubble and droplet dynamics

Bubbles and droplets both consist of a liquid in contact with a gas. In this paper, we consider the interface between the incompressible liquid and the gas as a zero thickness structure. The position of the interface is determined by the equilibrium between surface tension effects and the fluid pressure difference across the interface. So, the structure interacts with the fluids on either side. The behaviour of a limited number of bubbles and droplets can therefore be simulated as a Fluid-Structure Interaction (FSI) problem.Most existing techniques frequently used for studying bubble and droplet dynamics, such as Level Set or Volume Of Fluid, use monolithic schemes. The flow on both sides of the interface and the position of the interface are calculated in a single code. In this contribution, a partitioned approach is presented. The position of the interface is calculated with a structural solver. Given a displacement of the interface, a separate flow solver calculates the flow on the liquid side of the interface with the Arbitrary Lagrangian-Eulerian (ALE) technique. The structural solver uses a reduced order model of the flow solver to obtain implicit coupling between both solvers. This reduced order model is built up during the coupling iterations of a time step. Grid and time converged solutions of two axisymmetric problems are calculated: an oscillating water droplet in air and the growth and detachment of an air bubble from the outlet of a vertical needle, submerged in quiescent water.     

Multi-contact vertical ladder climbing with an HRP-2 humanoid

We describe the research and the integration methods we developed to make the HRP-2 humanoid robot climb vertical industrial-norm ladders. We use our multi-contact planner and multi-objective closed-loop control formulated as a QP (quadratic program). First, a set of contacts to climb the ladder is planned off-line (automatically or by the user). These contacts are provided as an input for a finite state machine. The latter builds supplementary tasks that account for geometric uncertainties and specific grasps procedures to be added to the QP controller. The latter provides instant desired states in terms of joint accelerations and contact forces to be tracked by the embedded low-level motor controllers. Our trials revealed that hardware changes are necessary, and parts of software must be made more robust. Yet, we confirmed that HRP-2 has the kinematic and power capabilities to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturing factories (e.g. aircraft, shipyard) and construction sites.     

Fracture Assessment of Carbon Fibre/Epoxy Reinforcing Rings through a Combination of Full-Scale Testing, Small-Scale Testing and Stress Modeling

Carbon fibre/epoxy rings are used as radial reinforcement for polymer bearing elements with nominal diameter 250 mm functioning under 150 MPa. Full-scale static and dynamic testing revealed no catastrophic failure for loading to 400 MPa, although there was circumferential splitting of carbon fibres at the machined top edge causing counterface wear under sliding. A combined numerical–experimental analysis was applied for design improvement with a representative small-scale qualification test on the real ring geometry, inducing additional stress concentrations compared to ASTM standards. Full-scale modelling revealed high radial–axial shear stresses (33 MPa) in non-hydrostatically loaded zones, while it increased towards 104 MPa under hydrostatic load conditions. The former is the most critical and should be simulated either on a small-scale unidirectional compression test or on a representative short beam shear test, respectively, measuring the radial–axial or radial–tangential shear strength. A relation between both small-scale states of stress was experimentally and numerically studied, experiencing that the composite ring has lower radial–tangential shear stress compared to radial–axial shear stress as a different hydrostatic stress state is observed in the bulk of the composite ring. As a compressive test is however more difficult to perform than a short-beam-shear test, a representative design criterion for shear fracture is determined from failure at 27 kN normal load in a short-beam-shear test. Finally, fracture is avoided by optimising the cross-sectional geometry of the composite reinforcing ring and close control of the processing parameters.     

Physical Metallurgy of Multi‐Phase Steel for Improved Passenger Car Crash‐Worthiness

The dynamic testing of high strength automotive steel grades is of great practical importance if their crash‐worthiness is to be evaluated. During forming operations, steels are processed in a controlled dynamic manner. In collisions, the deformation is different in the sense that the deformation is not controlled, i.e. both strain and strain rate are not pre‐determined. No clear standard testing procedures are currently available to test high strength steels dynamically, in order to evaluate their performance during car crashes. High tensile strength TRIP‐aided steels have been developed by the steel industry because of their promising high strain rate performance. The present contribution focuses on the effect of the strain rate and temperature on the mechanical behaviour of the low alloy high strength TRIP steel. The tests were carried out on the separated phases in order to determine their specific high strain rate deformation response. The temperature‐dependence of the transformation rate of the retained austenite is presented. It is argued that the adiabatic conditions present during high strain rate deformations have a beneficial effect on the behaviour of TRIP steel.