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Embedded software is at the core of current and future telecommunication, automotive, multimedia, and industrial automation systems. The success of practically any industrial application depends on the embedded software system's dependability, and one method to verify the dependability of a system is testing its robustness. The motivation behind this paper is to provide a knowledge base of the state of the practice in robustness testing of embedded software systems and to compare this to the state of the art. We have gathered the information on the state of the practice in robustness testing from seven different industrial domains (telecommunication, automotive, multimedia, critical infrastructure, aerospace, consumer products, and banking) by conducting 13 semi-structured interviews. We investigate the different aspects of robustness testing, such as the general view of robustness, relation to requirements engineering and design, test execution, failures, and tools. We highlight knowledge from the state of the practice of robustness testing of embedded software systems. We found different robustness testing practices that have not been previously described. This paper shows that the state of the practice, when it comes to robustness testing, differs between organizations and is quite different from the state of the art described in the scientific literature. For example, methods commonly described in the literature (e.g., the fuzzy approach) are not used in the organizations we studied. Instead, the interviewees described several ad hoc approaches that take specific scenarios into account (e.g., power failure or overload). Other differences we found concern the classification of robustness failures, the hypothesized root causes of robustness failures, and the types of tools used for robustness testing. This paper is a first step in capturing the state of the practice of robustness testing of embedded software systems. The results can be used by both researchers and practitioners. Researchers can use our findings to understand the gap between the state of the art and the state of the practice and develop their studies to fill this gap. Practitioners can also learn from this knowledge base regarding how they can improve their practice and acquire other practices. TOCSYC

This paper discusses the digital automation workflows and co-design methods that made possible the comprehensive robotic prefabrication of the BUGA Wood Pavilion - a large-scale production case study of robotic timber construction. Latest research in architectural robotics often focuses on the advancement of singular aspects of integrated digital fabrication and computational design techniques. Few researchers discuss how a multitude of different robotic processes can come together into seamless, collaborative robotic fabrication workflows and how a high level of interaction within larger teams of computational design and robotic fabrication experts can be achieved. It will be increasingly important to discuss suitable methods for the management of robotics and computational design in construction for the successful implementation of robotic fabrication systems in the context of the industry. We present here how a co-design approach enabled the organization of computational design decisions in reciprocal feedback with the fabrication planning, simulation and robotic code generation. We demonstrate how this approach can implement direct and curated reciprocal feedback between all planning domains - paving the way for fast-paced integrative project development. Furthermore, we discuss how the modularization of computational routines simplify the management and computational control of complex robotic construction efforts on a per-project basis and open the door for the flexible reutilization of developed digital technologies across projects and building systems., European Regional Development Fund, Deutsche Forschungsgemeinschaft, Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, Projekt DEAL, Bundesgartenschau Heilbronn 2019 GmbH, GETTYLAB

Edge computing is projected to become the dominant form of cloud computing in the future because of the significant advantages it brings to both users (less latency, higher throughput) and telecom operators (less Internet traffic, more local management). However, to fully unlock its potential at scale, system designers and automated optimization systems alike will have to monitor closely the dynamics of both processing and communication facilities. Especially the latter is often neglected in current systems since network performance in cloud computing plays only a minor role. In this paper, we propose the architecture of MECPerf, which is a solution to collect network measurements in a live edge computing domain, to be collected for offline provisioning analysis and simulations, or to be provided in real-time for on-line system optimization. MECPerf has been validated in a realistic testbed funded by the European Commission (Fed4Fire+), and we describe here a summary of the results, which are fully available as open data and through a Python library to expedite their utilization. This is demonstrated via a use case involving the optimization of a system parameter for migrating clients in a federated edge computing system adopting the GSMA platform operator concept.

The wide availability of embedded sensor platforms and lowcost cameras—together with the developments in wireless communication—make it now possible the conception of pervasive intelligent systems based on vision. Such systems may be understood as distributed and collaborative sensor networks, able to produce, aggregate and process images in order to understand the observed scene and communicate the relevant information found about it. In this paper, we investigate the peculiarities of visual sensor networks with respect to standard vision systems and we identify possible strategies to accomplish image processing and analysis tasks over them. Although the rather strong constraints in computational and transmission power of embedded platforms that may prevent the use of state of the art computer vision and pattern recognition methods, we argue that multinode processing methods may be envisaged to decompose a complex task into a hierarchy of computationally simpler problems to be solved over the nodes of the network. These ideas are illustrated by describing an application of visual sensor network to infomobility. In particular, we consider an experimental setting in which several views of a parking lot are acquired by the sensor nodes in the network. By integrating the various views, the network is capable to provide a description of the scene in terms of the available spaces in the parking lot.

Short-range wireless technologies will enable vehicles to communicate and coordinate their actions, thus improving people's safety and traffic efficiency. Whereas IEEE 802.11p (and related standards) had been the only practical solution for years, in 2016 a new option was introduced with Release of 14 long-term evolution (LTE), which includes new features to enable direct vehicle-to-vehicle (V2V) communications. LTE-V2V promises a more efficient use of the channel compared to IEEE 802.11p, thanks to an improved PHY layer and the use of orthogonal resources at the MAC layer. In LTE-V2V, a key role is played by the resource allocation algorithm and increasing efforts are being made to design new solutions to optimize the spatial reuse. In this context, an important aspect still little studied, is therefore that of identifying references that allow, first, to have a perception of the space in which the resource allocation algorithms move and, second, to verify the performance of new proposals. In this paper, we focus on a highway scenario and identify two algorithms to be used as a minimum and maximum reference in terms of the packet reception probability (PRP). The PRP is derived as a function of various parameters that describe the scenario and settings, from the application to the physical layer. Results, obtained both in a simplified Poisson point process scenario and with realistic traffic traces, show that the PRP varies considerably with different algorithms and that there is room for the improvement of current solutions.

none 12 si In this paper we present D.A.V.I.D.E. (Development for an Added Value Infrastructure Designed in Europe), an innovative and energy efficient High Performance Computing cluster designed by E4 Computer Engineering for PRACE (Partnership for Advanced Computing in Europe). D.A.V.I.D.E. is built using best-in-class components (IBM’s POWER8-NVLink CPUs, NVIDIA TESLA P100 GPUs, Mellanox InfiniBand EDR 100 Gb/s networking) plus custom hardware and an innovative system middleware software. D.A.V.I.D.E. features (i) a dedicated power monitor interface, built around the BeagleBone Black Board that allows high frequency sampling directly from the power backplane and scalable integration with the internal node telemetry and system level power management software; (ii) a custom-built chassis, based on OpenRack form factor, and liquid cooling that allows the system to be used in modern, energy efficient, datacenter; (iii) software components designed for enabling fine grain power monitoring, power management (i.e. power capping and energy aware job scheduling) and application power profiling, based on dedicated machine learning components. Software APIs are offered to developers and users to tune the computing node performance and power consumption around on the application requirements. The first pilot system that we will deploy at the beginning of 2017, will demonstrate key HPC applications from different fields ported and optimized for this innovative platform. open Abu Ahmad, Wissam; Bartolini, Andrea; Beneventi, Francesco; Benini, Luca; Borghesi, Andrea; Cicala, Marco; Forestieri, Privato; Gianfreda, Cosimo; Gregori, Daniele; Libri, Antonio; Spiga, Filippo; Tinti, Simone Abu Ahmad, Wissam; Bartolini, Andrea; Beneventi, Francesco; Benini, Luca; Borghesi, Andrea; Cicala, Marco; Forestieri, Privato; Gianfreda, Cosimo; Gregori, Daniele; Libri, Antonio; Spiga, Filippo; Tinti, Simone

This paper presents the results of cement-based mortars characterizations that were taken from rendered layers of military bunkers in the Rupnik Line. The Rupnik Line was conceived as a part of the fortified defence system protecting the Rapallo Border between the Kingdom of Italy and the Kingdom of Yugoslavia, established in 1920. The bunkers were built between 1938 and 1941, and the renders of various compositions were applied as a camouflage layer that merged the bunkers with the environment. Results of petrographic examination and microstructural and chemical analysis of the samples confirmed that locally available crushed dolomite or limestone sands were used as an aggregate in the mortars. As binders, pure Portland cement or a mixture of cement and ground granulated blast furnace slag were used. The selection of the aggregate and binder type in a particular mortar was based on the colour of the artificial stone produced. All samples with the dolomite aggregate show the presence of alkali-aggregate reactions, dedolomitization of the grains and secondary calcite formation in the cement binder, along with the Mg-Al, Mg-Si and Mg-Al-Si phase formations. In mortar compositions with a high Portland cement content, the presence of delayed ettringite formation was also confirmed.

An alternative integration scheme for a half-bridge switch using 70 μm thin Si IGBTs and diodes is presented. This flat switch, which is designed for high-frequency application with high power density, exhibits high strength, high toughness, low parasitic inductance and high thermal conductivity. Such a novel assembly approach is suitable to optimize performance, reliability and availability of the power system in which it is used. The paper focuses on the thermal performance of this assembly at normal and extreme operating conditions, studied by means of FEM thermo-fluidynamic simulations of the module integrated with connectors and liquid cooler, and thermal measurement performed on an early prototype. Improved solutions are also investigated by the FE model.

A major drawback of nonlinear dimensionality reduction (DR) techniques is their inability to preserve some authentic information from the source domain, leading to projections that are often hard to interpret when it comes to observing anything other than the topological structure of the data. In this paper, we propose a nonlinear DR approach enforcing projection constraints resulting from an a priori knowledge about the structure of the data in multivariate periodical time series. We then propose several ways of exploiting this constrained projection to extract user-relevant information, such as the nominal behavior of a periodical dynamical system or the deviant behaviors which may occur at different time scales. The techniques are demonstrated on both a synthetic dataset composed of simulated multivariate data exhibiting a periodical behavior, and a real dataset corresponding to six months of sensor data acquisitions and recordings inside experimental buildings. 1 We would like to thank the Institut National de l'Energie Solaire (INES) and the CEA, LITEN, Laboratoire Energetique du Bâtiment for providing the data.

The distribution system is demanded to be more efficient, more flexible and more intelligent due to the continued growth of the electricity loads, the high efficiency of energy utilization and the environmental protection. The traditional distribution systems are facing the challenge of evolving from passive networks to the active distribution networks (ADN) with the integration of multiple controllable resources. This paper presents an optimal operation and schedule model of the ADN, considering a variety of controllable resources such as distributed generations, battery storages and interruptible loads. To solve the optimization problem, a modified fuzzy adaptive PSO assisted by Kriging model (KMA-MFAPSO) is developed in this paper. In KMA-MFPSO, a novel constraint handling technique (CHT) based on the PSO is proposed to handle the constraints effectively. In addition, under the premise of ensuring the accuracy of the calculation, the Kriging model is used in KMA-MFAPSO to calculate the power flow the ADN approximately, which greatly speeds up the solving process. Finally, the effectiveness of the proposed algorithm is tested on a modified IEEE-123 system. The optimal results obtained by the proposed method are compared with the results obtained by using other solving algorithms. The simulation results indicate that KMA-MFAPSO is very robust and fast to solve the optimization problem so that it can be used in practical systems.

International audience; Validation of mobile applications needs taking account of context (such network topology) and interactions between mobile nodes. Scenario-based approaches are well-suited to describe the behavior and interactions to observe in distributed systems. The difficulty to control accurately the execution context of such applications has led us to use passive testing. This paper presents a toolset which supports specification and verification of scenarios. A UML-based formal language, called TERMOS, has been implemented for specifying scenarios in mobile computing systems. These scenarios capture the key properties which are automatically checked on the traces, considering both the spatial configuration of nodes and their communication. We give an overview of the language design choices, its semantics and the implementation of the tool chain. The approach is demonstrated on a case study.

This paper presents a collaboratively adaptive vibration monitoring system that captures high-fidelity structural vibration signals induced by pedestrians. These signals can be used for various human activities’ monitoring by inferring information about the impact sources, such as pedestrian footsteps, door opening and closing, and dragging objects. Such applications often require high-fidelity (high resolution and low distortion) signals. Traditionally, expensive high resolution and high dynamic range sensors are adopted to ensure sufficient resolution. However, for sensing systems that use low-cost sensing devices, the resolution and dynamic range are often limited; hence this type of sensing methods is not well explored ubiquitously. We propose a low-cost sensing system that utilizes (1) a heuristic model of the investigating excitations and (2) shared information through networked devices to adapt hardware configurations and obtain high-fidelity structural vibration signals. To further explain the system, we use indoor pedestrian footstep sensing through ambient structural vibration as an example to demonstrate the system performance. We evaluate the application with three metrics that measure the signal quality from different aspects: the sufficient resolution rate to present signal resolution improvement without clipping, the clipping rate to measure the distortion of the footstep signal, and the signal magnitude to quantify the detailed resolution of the detected footstep signal. In experiments conducted in a school building, our system demonstrated up to 2× increase on the sufficient resolution rate and 2× less error rate when used to locate the pedestrians as they walk along the hallway, compared to a fixed sensing setting.

As the development of autonomous vehicles rapidly advances, the use of convoying/platooning becomes a more widely explored technology option for saving fuel and increasing the efficiency of traffic. In cooperative adaptive cruise control (CACC), the vehicles in a convoy follow each other under adaptive cruise control (ACC) that is augmented by the sharing of preceding vehicle acceleration through the vehicle to vehicle communication in a feedforward control path. In general, the desired velocity optimization for vehicles in the convoy is based on fuel economy optimization, rather than driveability. This paper is a preliminary study on the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles and the controller gain impact on the driveability. A simple low-level longitudinal model of the vehicle has been used along with a PD type cruise controller and a generic spacing policy for ACC/CACC. The acceleration of the previous vehicle is available to the next vehicle as input, and the simulations are performed as Cooperative Adaptive Cruise Control of a convoy of vehicles. Individual vehicle acceleration profiles have been analyzed for driveability for two different velocity profiles that are followed in a stretch of 720 m between stop signs. The controller gains have been re-tuned based on the parameter space robust control PID approach for driveability and compared with the original gains. The US06 SFTP drive cycle has also been used for the comparison of the two different controller gain sets.

In this paper, we propose to tackle egocentric action recognition by suppressing background distractors and enhancing action-relevant interactions. The existing approaches usually utilize two independent branches to recognize egocentric actions, i.e., a verb branch and a noun branch. However, the mechanism to suppress distracting objects and exploit local human-object correlations is missing. To this end, we introduce two extra sources of information, i.e., the candidate objects' spatial location and their discriminative features, to enable concentration on the occurring interactions. We design a Symbiotic Attention withObject-centric featureAlignmentframework (SAOA) to provide meticulous reasoning between the actor and the environment. First, we introduce an object-centric feature alignment method to inject the local object features to the verb branch and noun branch. Second, we propose a symbiotic attention mechanism to encourage the mutual interaction between the two branches and select the most action-relevant candidates for classification. The framework benefits from the communication among the verb branch, the noun branch, and the local object information. Experiments based on different backbones and modalities demonstrate the effectiveness of our method. Notably, our framework achieves the state-of-the-art on the largest egocentric video dataset.

Acute myocardial infarction (AMI) is the death of cardiomyocytes caused by a lack of energy due to ischemia. Nutrients supplied by the blood are the main source of cellular energy for cardiomyocytes. Sodium butyrate (NaB), a gastrointestinal nutrient, is a short-chain fatty acid (butyric acid) that may act as an energy source in AMI therapy. Poly(lactic-co-glycolic acid)-Poly (N-isopropylacrylamide) microspheres loaded with NaB (PP-N) are synthesized to prolong the release of NaB and are injected into ischemic zones in a Sprague-Dawley rat AMI model. Here, this study shows that PP-N can significantly ameliorate cardiac dysfunction in AMI, and NaB can specially bind to Sirt3 structure, activating its deacetylation ability and inhibiting the generation of reactive oxygen species, autophagy, and angiogenesis promotion. The results indicate that NaB, acting as a nutrient, can protect cardiomyocytes in AMI. These results suggest that the gastrointestinal nutrient NaB may be a new therapy for AMI treatment, and PP-N may be the ideal therapeutic regimen.

Energy consumption by construction sector is estimated by multi- disciplinary energy auditing and results are represented in an energy performance building certificate. The building certification, which is a world trend today, is applied successfully for many buildings inAzerbaijantoo. The purpose of this paper to study the current methodology of energy building certification in terms of the technological aspects of energy auditing. Many documents of the European Union directives and Russian building codes and regulations in the field of ecology and energy saving in construction sector have been revised for implementing them as base for national energy performance certification system AZERI GREEN ZOOM. The assessment categories and main requirements have been considered. The results of this overview have shown the trends and problems in this field. There are given examples of certified buildings.

International audience; This paper describes the specifications of an interoperability platform based on the PPO (Product Process Organization) model developed by the French community IPPOP in the context of collaborative and innovative design. By using PPO model as a reference, this work aims to connect together heterogonous tools used by experts easing data and information exchanges. After underlining the growing needs of collaborative design process, this paper focuses on interoperability concept by describing current solutions and their limits. Then a solution based on the flexibility of the PPO model adapted to the philosophy of interoperability is proposed. To illustrate these concepts, several examples are more particularly described (robustness analysis, CAD and Product Lifecycle Management systems connections).

An investigation into an aerodynamic optimization benchmark case that exhibits multimodality is presented using a global optimization approach. The recently suggested case 6 of the AIAA Aerodynamic Design Optimization Discussion Group involves the drag minimization of a rectangular NACA0012 wing subject to lift and root bending moment, as well as other geometric constraints. In this paper, optimization of that case using a state-of-the-art constrained population-based global optimization framework is presented. Shape control is achieved via a hierarchical application of the radial basis function domainelement approach. Three different fidelity of optimization cases are performed to investigate the multimodality of various aspects of the full problem, with four independent runs of each case being performed. When considering chord optimization, the optimizer converges to two independently identifiable minima with very similar performance. When adding dihedral and sweep variation, further multimodality is introduced, though this multimodality is reasonably well defined, with minima including forward and rearward sweep, and an upward and downward winglet. Introducing thickness variables leads to a highly multimodal problem due to the coupling between the chord and thickness variables. Thetheoretical minimum drag for this case is 24.7 counts, which a number of the runs get close to achieving.

This paper reports the acoustic issues of open plan office environments. According to a detailed research based on the scientific literature of the most suitable acoustic descriptors recommended for the open plan offices analysis, the main typological-functional configurations of these environments have been analyzed in order to identify six spatial typologies. The variation of acoustic parameters of these typologies has been evaluated by using a sound pyramid tracing software. The analysis procedure was calibrated in a case study of an office environment, where a measurement campaign was carried out. Results point out that the acoustic improvement of open plan offices can usually be achieved by introducing a sound absorbing false ceiling and dividing panels between working positions, but there are different issues depending on spatial geometries of the office. Better results are referred to office typologies characterized by reduced height and equal plan dimensions.

This paper presents an unsupervised and semi-automatic image segmentation approach where we formulate the segmentation as an inference problem based on unary and pairwise assignment probabilities computed using low-level image cues. The inference is solved via a probabilistic graph matching scheme, which allows rigorous incorporation of low-level image cues and automatic tuning of parameters. The proposed scheme is experimentally shown to compare favorably with contemporary semi-supervised and unsupervised image segmentation schemes, when applied to contemporary state-of-the-art image sets.

In this paper, we investigated a buffer-aided decode-and-forward (DF) wireless relaying system over fading channels, where the source and relay harvest radio-frequency (RF) energy from a power station for data transmissions. We derived exact expressions for end-to-end throughput considering half-duplex (HD) and full-duplex (FD) relaying schemes. The numerical results illustrate the throughput and energy efficiencies of the relaying schemes under different self-interference (SI) cancellation levels and relay deployment locations. It was demonstrated that throughput-optimal relaying is not necessarily energy efficiency-optimal. The results provide guidance on optimal relaying network deployment and operation under different performance criteria.

Similar documents arise in software and business domains. Examples are user guides for different versions of a software product, contracts between vendors and clients, or legal documents. The usual practice is to capture common document formats and contents in templates that must be manually customized to a new context — often a slow, tedious, and error-prone process. We propose a method based on a proven approach developed for software reuse that simplifies and automates routine tasks involved in creating and updating families of similar documents. Our Document Management Environment (DME) provides functions to create templates capable of higher levels of document contents reuse than templates supported by word processors such as MS Word. DME allows users to designate any arbitrary document part as a template's variation point that can be customized to produce a specific document. DME automates document production by syncing inter-dependent customizations occurring at different variation points. The paper describes two “proof of concept” implementations of DME as Word add-in: The first one uses Content Control mechanism and is specific to MS Word. The second one is based on ART (Adaptive Reuse Technique), a general text manipulation method and tool, and can be used to manage similar documents in any editor that provides an access to the internal representation of documents.

The paper proposes a technique for Human Action Recognition (HAR) that uses a Convolutional Neural Network (CNN). Depth data sequences from the motion sensing devices are converted into images and fed into a CNN rather than using any conventional or statistical method. The initial data was obtained from 10 actions performed by six subjects captured by the Kinect v2 sensor as well as 20 actions performed by 7 subjects from the MSR 3D Action data set. A custom CNN architecture consisting of three convolutional and three max pooling layers followed by a fully connected layer was used. Training, validation, and testing was carried out on a total of 39715 images. An accuracy of 97.23% was achieved on the Kinect data set. On the MSR data set the accuracy was 87.1%.

Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years. Recent success of deep learning in computer vision have progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks. In this paper, we attempt solving these issues with transfer learning, where state-of-the-art architectures such as VGG and Inception are initialized with pre-trained weights from large benchmark datasets consisting of natural images, and the fully-connected layer is re-trained with only a small number of MRI images. We employ image entropy to select the most informative slices for training. Through experimentation on the OASIS MRI dataset, we show that with training size almost 10 times smaller than the state-of-the-art, we reach comparable or even better performance than current deep-learning based methods., Comment: Presented at IEEE BIBM 2017

This paper attempts at improving the accuracy of Human Action Recognition (HAR) by fusion of depth and inertial sensor data. Firstly, we transform the depth data into Sequential Front view Images(SFI) and fine-tune the pre-trained AlexNet on these images. Then, inertial data is converted into Signal Images (SI) and another convolutional neural network (CNN) is trained on these images. Finally, learned features are extracted from both CNN, fused together to make a shared feature layer, and these features are fed to the classifier. We experiment with two classifiers, namely Support Vector Machines (SVM) and softmax classifier and compare their performances. The recognition accuracies of each modality, depth data alone and sensor data alone are also calculated and compared with fusion based accuracies to highlight the fact that fusion of modalities yields better results than individual modalities. Experimental results on UTD-MHAD and Kinect 2D datasets show that proposed method achieves state of the art results when compared to other recently proposed visual-inertial action recognition methods.

Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning has been a subject of intense research in recent years. Recent success of deep learning in computer vision has progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks. In this paper, we attempt solving these issues with transfer learning, where the state-of-the-art VGG architecture is initialized with pre-trained weights from large benchmark datasets consisting of natural images. The network is then fine-tuned with layer-wise tuning, where only a pre-defined group of layers are trained on MRI images. To shrink the training data size, we employ image entropy to select the most informative slices. Through experimentation on the ADNI dataset, we show that with training size of 10 to 20 times smaller than the other contemporary methods, we reach state-of-the-art performance in AD vs. NC, AD vs. MCI, and MCI vs. NC classification problems, with a 4% and a 7% increase in accuracy over the state-of-the-art for AD vs. MCI and MCI vs. NC, respectively. We also provide detailed analysis of the effect of the intelligent training data selection method, changing the training size, and changing the number of layers to be fine-tuned. Finally, we provide Class Activation Maps (CAM) that demonstrate how the proposed model focuses on discriminative image regions that are neuropathologically relevant, and can help the healthcare practitioner in interpreting the model's decision making process.

International audience; The methods of automatic classification resulting from the artificial intelligence are generally the consequences of a formalism based on an artificial reasoning quasi similar to that of the human expert. All the approaches of automatic classification developed so far, whether in an exact or approximate context, are dissociated from each other by the membership concept of an object to a class. In this paper, we present a new approach hybrid of unsupervised automatic classification under the C-Means (means of C classes) family. This new approach, based on the fusion of fuzzy and possibility theory and initialized by a membership matrix, allows on the one hand to solve simultaneously the problem of overlapping and coincidence, to reduce the noise effect and on the other hand to accelerate the classification process. The model validation is carried out by the FCM (Fuzzy C-Means), the PCM (Possibilistic C-Means)and the FPCM (Fuzzy-Possibilistic C-Means) for two cases of initialization by using Iris, Textured image and Tight human data basis.

In the last decades, several studies have revealed how critical the urban heat island (UHI) effect can be in cities located in cold climates, such as the Canadian one. Meanwhile, many researchers have looked at the impact of the city design over the urban microclimate, and have raised concerns about the development of too dense cities. Under the effect of the “Places to Growth” plan, the city of Toronto is experiencing one of the highest rates of building development in North America. Over 48,000 and 33,000 new home permits were issued in 2012 and 2013 respectively, and at the beginning of 2015, almost 500 high-rise proposals across the Greater Toronto Area were released. In this context, it is important to investigate how new constructions will affect the urban microclimate, and to propose strategies to mitigate possible UHI effects. Using the software ENVI-met, microclimate simulations for the Church-Yonge corridor both in the current situation and with the new constructions are reported in this paper. The outdoor air temperature and the wind speed are the parameters used to assess the outdoor microclimate changes. The results show that the new constructions could increase the wind speed around the buildings. However, high-rise buildings will somewhat reduce the air temperature during day-time, as they will create large shadow areas, with lower average mean radiant temperature.

Instance reduction from class-balanced data has been investigated in much research. However, there is a lack of studies on class-imbalanced data. Learning from imbalanced data lately has attracted a lot of attention due to the practical applications. In the case of two-class imbalanced data, the instances from one class, majority class, are more numerous than the instances from the other class, which is a minority class. The present paper aims to introduce a new instance reduction method that preserves between-class distributions in the balanced data and handles minority class instance reduction in two-class imbalanced data, efficiently. The proposed method solves the instance reduction issue from an unconstrained multi-objective optimization problem aspect. Accordingly, a new combined weighted optimizer is designed. By employing the chaotic krill herd evolutionary algorithm, both the minority and majority class spaces with the accelerated convergence are explored. Through this method, the original data set is purged of those instances that decrease accuracy, and Gmean. The performance has been evaluated on both imbalanced and balanced data sets collected from the UCI repository by the 10-fold cross-validation method. Evaluations show that the proposed method outperforms state-of-the-art methods in terms of classification accuracy, Gmean, reduction rates, and computational time.

This paper studies the coordinated use of smart inverters with legacy voltage regulating devices to help increase the energy savings from conservation voltage reduction (CVR) in distribution systems with high photovoltaic penetration. Two voltage optimization algorithms are developed to evaluate the CVR benefit by considering two types of smart inverter functions: aggregated reactive power control and autonomous volt/VAR control. In each algorithm, smart inverters are coordinately controlled with the operation of legacy voltage regulating devices including load tap changers and capacitor banks. Both algorithms are tested on representative utility distribution system models. Simulation results demonstrate that the proposed algorithms can achieve around 1.8-3.6% energy savings when only using legacy voltage regulating devices and an additional 0.3-0.9% when adding smart inverters.

VLSI mask optimization is one of the most critical stages in manufacturability aware design, which is costly due to the complicated mask optimization and lithography simulation. Recent researches have shown prominent advantages of machine learning techniques dealing with complicated and big data problems, which bring potential of dedicated machine learning solution for DFM problems and facilitate the VLSI design cycle. In this paper, we focus on a heterogeneous OPC framework that assists mask layout optimization. Preliminary results show the efficiency and effectiveness of proposed frameworks that have the potential to be alternatives to existing EDA solutions., 6 pages; accepted by 25th Asia and South Pacific Design Automation Conference (ASP-DAC 2020)

In this paper, an original trade-off function is proposed to deal with robust design in engineering. Robust design in engineering aims at sizing systems with a low variation in all performance measures under uncertainties. It is mainly related to the generation and evaluation of candidate solutions. However, it is also suitable to consider the robustness in the decision-making process to ensure the selection of the most preferred design in the early stages of the design process. The purpose of our research work consists in tackling the robust design problem as a trade-off between two design objectives: (i) improve the global level of performance of the system and (ii) reduce the sensitivity of the performance with regard to uncertainty sources. The robustness of the candidate solutions is evaluated by a trade-off function modelling the designer's intention with an a priori articulation of preferences. Design objective formulation is based on the concept of desirability function and indices. Candidates sol...

Earthquake-induced pounding between structures has been widely studied by using different impact models. In this paper, we first briefly introduced the existing impact analysis models and discussed their shortcomings. For the Hertz-damp impact analysis model, the damping constants do not accord with the physical facts; then, based on the Hertz theory, the expression of the damping constant in the Hertz-damp impact analysis model is improved. The approximate formula of the damping constant is theoretically derived, and the accuracy and effectiveness of the calculation formula of the damping constant are verified by the selected collision analysis problems.

An increasing number of companies aim to enable their development teams to work in an agile manner. When introducing agile teams, companies face several challenges. This paper explores the kind of leadership needed to support teams to work in an agile way. One theoretical agile leadership concept describes a Scrum Master who is supposed to empower the team to lead itself. Empirical findings on such a leadership role are controversial. We still have not understood how leadership unfolds in a team that is by definition self-organizing. Further exploration is needed to better understand leadership in agile teams. Our goal is to explore how leadership changes while the team matures using the example of the Scrum Master. Through a grounded theory study containing 75 practitioners from 11 divisions at the Robert Bosch GmbH we identified a set of nine leadership roles that are transferred from the Scrum Master to the Development Team while it matures. We uncovered that a leadership gap and a supportive internal team climate are enablers of the role transfer process, whereas role conflicts may diminish the role transfer. To make the Scrum Master change in a mature team, team members need to receive trust and freedom to take on a leadership role which was previously filled by the Scrum Master. We conclude with practical implications for managers, Product Owners, Development Teams and Scrum Masters which they can apply in real settings., Robert Bosch Automotive Steering GmbH, Projekt DEAL

Solar energy is widely available in nature and electricity can be easily extracted using solar PV cells. A fuel cell being reliable and environment friendly becomes a good choice for the backup so as to compensate for continuously varying solar irradiation. This paper presents simple control schemes for power management of the DC microgrid consisting of PV modules and fuel cell as energy sources and a hydrogen electrolyzer system for storing the excess power generated. The supercapacitor bank is used as a short term energy storage device for providing the energy buffer whenever sudden fluctuations occur in the input power and the load demand. A new power control strategy is developed for a hydrogen storage system. The performance of the system is assessed with and without the supercapacitor bank and the results are compared. A comparative study of the voltage regulation of the microgrid is presented with the controller of the supercapacitor bank, realized using a traditional PI controller and an intelligent fuzzy logic controller.

In this paper, we propose a method to analyze the amount of operation reserves procured in a system based on two stages. The first stage is a detailed hourly unit commitment and the second stage is a simulation model with a shorter time period. The method is applied to the Spanish hydrothermal system and several cases are analyzed to determine the impact of the time period in the use of the operation reserves.

A domestic water heating system that combines photovoltaic thermal (PVT) solar collectors with two heat pumps, in a novel cascade arrangement, is presented and analyzed in this paper. The goal of this analysis is to determine and compare the annual thermal energy output of the PVT cascade heat pump system with an evacuated tube and simultaneous consumption PVT single heat pump water heating system. The computational technique that was developed to analyze the PVT cascade heat pump system, which determines solar panel energy outputs, and heat pump operating characteristics, is described in detail. Case study simulation results are also presented, which include transient power output profiles, temperature profiles, and yearly energy output results. Hourly weather data, including air dry-bulb temperatures and solar flux values, from locations with a range of climates was used in these simulations. The results of this study show that the PVT cascade heat pump system has an annual thermal energy output improvement over the evacuated tube heating system ranging from 37% to 68%, depending on the selected simulation location.

The paper presents the results of a study of parameters used for determining the amount of organic pollutants in wastewater flowing into a collective wastewater treatment plant with a population equivalent of about 120 000 PE. The plant constituted part of a sewage system. Assays were performed for biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), permanganete index (CODMn) and total organic carbon (TOC). In addition, the pH of wastewater and its alkalinity were determined. Sampling of incoming wastewater and measurement of physicochemical parameters were performed once a week, in the spring. A total of 44 samples of wastewater were collected. The correlations between the individual parameters and the correlations between the ratios of these parameters were determined. It was found that it was possible to estimate, with suffi cient accuracy, the values of selected parameters for determining the concentrations of organic compounds in municipal wastewater, knowing the values of other parameters in this group. Moreover, it was shown that, knowing the relationship between the analyzed parameters, one can find multiple regression equations for a given type of wastewater, which allow one to calculate the remaining relationships with a good fit, i.e. a determination factor R2greater than 0.6

The energy consumption of a data center and hence the carbon footprint from it largely depends on the energy consumption by its active Physical Machines (PMs). Researchers have taken many attempts to minimize the data center energy consumption through the Virtual Machines (VMs) allocation into a minimal number of PMs of homogeneous types. However, the current VM placement strategies do not consider useful information that can be extracted from data logs of data center. This paper presents profile-based VM placement approach to improve energy efficiency of data centers. The approach formulates the energy consumption problem as a profile-based optimization problem. Then, the problem decomposed into multiple smaller ones in a number of intervals. For each intervals, a number VMs and PMs are sorted in terms of resource requirements and energy efficiency respectively. Then, the First Fit-Decreasing(FFD) is adopted to place the sorted VMs to the sorted PMs. Experiments conducted to demonstrate the presented approach with comparisons with the original FFD algorithm. The experimental results have shown that the presented approach can reduce more energy consumption than the original FFD algorithm and is scalable for larger test problems.

In this paper, a direct method was proposed for determining the traction–separation laws (TSL) of cohesive zone model in modes I, II and mixed-mode using a mixed mode bending (MMB) specimen tested in different mixed-mode ratios and a compliance based beam method. The method was applied on adhesively bonded joints. TSLs obtained using the proposed method were in good agreement with TSLs obtained independently from double cantilever beam (DCB) and end notch flexure (ENF) tests representing pure mode stress states. Moreover, the mechanical behaviors of the MMB, DCB and ENF specimens were reasonably well predicted using the obtained TSLs.

Online recognition of gestures is critical for intuitive human-robot interaction (HRI) and further push collaborative robotics into the market, making robots accessible to more people. The problem is that it is difficult to achieve accurate gesture recognition in real unstructured environments, often using distorted and incomplete multisensory data. This paper introduces an HRI framework to classify large vocabularies of interwoven static gestures (SGs) and dynamic gestures (DGs) captured with wearable sensors. DG features are obtained by applying data dimensionality reduction to raw data from sensors (resampling with cubic interpolation and principal component analysis). Experimental tests were conducted using the UC2017 hand gesture dataset with samples from eight different subjects. The classification models show an accuracy of 95.6% for a library of 24 SGs with a random forest and 99.3% for 10 DGs using artificial neural networks. These results compare equally or favorably with different commonly used classifiers. Long short-term memory deep networks achieved similar performance in online frame-by-frame classification using raw incomplete data, performing better in terms of accuracy than static models with specially crafted features, but worse in training and inference time. The recognized gestures are used to teleoperate a robot in a collaborative process that consists in preparing a breakfast meal.

Bayesian Networks, BNs, are suitable for mixed-initiative dialog modeling allowing a more flexible and natural spoken interaction. This solution can be applied to identify the intention of the user considering the concepts extracted from the last utterance and the dialog context. Subsequently, in order to make a correct decision regarding how the dialog should continue, unnecessary, missing, wrong, optional and required concepts have to be detected according to the inferred goals. This information is useful to properly drive the dialog prompting for missing concepts, clarifying for wrong concepts, ignoring unnecessary concepts and retrieving those required and optional. This paper presents a novel BNs approach where a single BN is obtained from N goal-specific BNs through a fusion process. The new fusion BN enables a single concept analysis which is more consistent with the whole dialog context.

Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on off-policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.

Recent works have found a negative correlation between the amount of food waste generated in foodservice outlets and the extent to which managers perceive it as a relevant problem. However, it is hard to believe that food flows would be impacted by wishful thinking alone. In this paper, we try to offer an explanation to these findings by testing the associations between importance recognition, food waste measurement and compliance to waste reduction strategies in three different stages (pre-kitchen, in-kitchen and post-kitchen). We do so by fitting a generalized structural equation model in the results of a survey ran in Italy with almost 500 Horeca (Hotels, restaurants and cafeterias) managers. Our findings reveal that importance perception triggers strategies’ adoption in only one of the three stages (in-kitchen). Furthermore, this association is essentially dependent on the mediation performed by measurement, suggesting that importance recognition covers a complex pathway to arrive in reduced amounts of waste. Additional results indicate that larger and higher-end outlets tend to place more importance on food waste. Moreover, although 77% of the sample recognizes the relevance of food waste to their businesses, the incorporation of some of the main reduction strategies is still low, as 85% of the establishments report the adoption of no more than 3 out of the 9 strategies presented.

Part 2: Security Engineering; International audience; Visualization is the essential part of Security Information and Event Management (SIEM) systems. The paper suggests a common framework for SIEM visualization which allows incorporating different visualization technologies and extending easily the application functionality. To illustrate the framework, we developed a SIEM visualization component VisSecAnalyzer. The paper demonstrates its possibilities for the tasks of attack modeling and security assessment. To increase the efficiency of the visualization techniques we applied the principles of the human information perception and interaction.

Deep latent generative models have attracted increasing attention due to the capacity of combining the strengths of deep learning and probabilistic models in an elegant way. The data representations learned with the models are often continuous and dense. However in many applications, sparse representations are expected, such as learning sparse high dimensional embedding of data in an unsupervised setting, and learning multi-labels from thousands of candidate tags in a supervised setting. In some scenarios, there could be further restriction on degree of sparsity: the number of non-zero features of a representation cannot be larger than a pre-defined threshold $L_{0}$ . In this paper we propose a sparse deep latent generative model SDLGM to explicitly model degree of sparsity and thus enable to learn the sparse structure of the data with the quantified sparsity constraint. The resulting sparsity of a representation is not fixed, but fits to the observation itself under the pre-defined restriction. In particular, we introduce to each observation $i$ an auxiliary random variable $L_{i}$ , which models the sparsity of its representation. The sparse representations are then generated with a two-step sampling process via two Gumbel-Softmax distributions. For inference and learning, we develop an amortized variational method based on MC gradient estimator. The resulting sparse representations are differentiable with backpropagation. The experimental evaluation on multiple datasets for unsupervised and supervised learning problems shows the benefits of the proposed method.

This paper presents the technique of flex-and-flip manipulation. It is suitable for grasping thin, flexible linear objects lying on a flat surface. During the manipulation process, the object is first flexed by a robotic gripper whose fingers are placed on top of it, and later the increased internal energy of the object helps the gripper obtain a stable pinch grasp while the object flips into the space between the fingers. The dynamic interaction between the flexible object and the gripper is elaborated by analyzing how energy is exchanged. We also discuss the condition on friction to prevent loss of contact. Our flex-and-flip manipulation technique can be implemented with open-loop control and lends itself to underactuated, compliant finger mechanism. A set of experiments in robotic page turning performed with our customized hardware and software system demonstrates the effectiveness and robustness of the manipulation technique., 6 pages,8 figures, 2 tables

The proliferation of enterprise social media generates an ever-growing record of digital traces that provides ample opportunities to study the social making of organisations. Subsequently, we present the social fabric framework, which comprises a structured five-step approach for eliciting, interpreting, and representing the situated social idiosyncrasies and underlying patterns of the social making of organisations. The paper focuses on the application of the social fabric framework as a research method. However, the framework also lends itself to practice as a diagnostic tool that can detect emergent changes in the social fabric of an organisation as well as support organisational development and change. Moreover, by providing a vocabulary for articulating the social making of organisations, the framework can help organisation members reify their dispositions, make sense of the social dynamics, and enable a constructive discussion at the grassroots level about any controversy or aspiration.

Microsoft VBA (Visual Basic for Applications) is a programming language widely used by end-user programmers, often alongside the popular spreadsheet software Excel. Together they form the popular Excel-VBA application ecosystem. Despite being popular, spreadsheets are known to be fault-prone, and to minimize risk of faults in the overall Excel-VBA ecosystem, it is important to support end-user programmers in improving the code quality of their VBA programs also, in addition to improving spreadsheet technology and practices. In traditional software development, automatic code inspection using static analysis tools has been found effective in improving code quality, but the practical relevance of this technique in an end-user development context remains unexplored. With the aim of popularizing it in the end-user community, in this paper we examine the relevance of automatic code inspection in terms of how inspection rules are perceived by VBA programmers. We conduct a qualitative study consisting of interviews with 14 VBA programmers, who share their perceptions about 20 inspection rules that most frequently detected code quality issues in an industrial dataset of 25 VBA applications, obtained from a financial services company. Results show that the 20 studied inspection rules can be grouped into three categories of user perceptions based on the type of issues they warn about: i) 11 rules that warn about serious problems which need fixing, ii) 7 rules that warn about bad practices which do not mandate fixing, and iii) 2 rules that warn about purposeful code elements rather than issues. Based on these perceptions, we conclude that automatic code inspection is considerably relevant in an end-user development context such as VBA. The perceptions also indicate which inspection rules deserve the most attention from interested researchers and tool developers. Lastly, our results also reveal 3 additional issue types that are not covered by the existing inspection rules, and are therefore impetus for creating new rules.

Rural social enterprises are increasingly recognized as organisations that contribute to local development by providing goods and/or services to meet community needs and by fostering inclusive social and governance relations. The purpose of this paper is to explore how rural social enterprises engage in a plurality of socio-economic relations with different dimensions of their ‘place’ when contributing to the development of their localities. Based on three in-depth case studies of social enterprises operating in rural Ireland, our findings illustrate how rural social enterprises engage with locational, institutional, material and identity aspects of their ‘place’, which indicates their ‘placial embeddedness’. Moreover, our findings also demonstrate how these organisations engage in, and combine market, redistribution and reciprocity relations, which indicates their ‘substantive hybridity’. Based on the interrelated nature of these findings, we argue that it is through a process of placial substantive hybridity that rural social enterprises foster social innovation in order to contribute to an integrated development of their localities. They harness and (re)valorise (untapped) local resources while complementing these with other resources from extra-local sources and accommodate and/or respond to structural-exogenous forces based on the needs of their local population in line with neo-endogenous rural development.