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We show theoretically and experimentally how a flat-top second-order response can be achieved with a self-coupled single add-drop ring resonator based on two couplers with different splitting ratios. The resulting device is a 1x1 filter, reflecting light back in the input waveguide at resonating wavelengths in the passbands, and transmitting light in the output waveguide at all other non-resonating wavelengths. Different implementations of the filter have been designed and fabricated on a micron-scale silicon photonics platform. They are based on compact Euler bends - either U-bends or L-bends - and Multi-Mode Interferometers as splitters for the ring resonators. Different finesse values have been achieved by using either 50:50 MMIs in conjunction with 85:15 MMIs or 85:15 MMIs in conjunction with 95:05 double MMIs. Unlike ordinary lowest order directional couplers, the MMIs couple most of the power in the cross-port which make them particularly suitable for the topology of the self-coupled ring, which would otherwise require a waveguide crossing. Experimental results are presented, showing good agreement with simulations. The proposed devices can find applications as wavelength-selective reflectors for relatively broad-band lasers or used as 2x2 add-drop filters when two exact replicas of the device are placed on the arms of a Mach-Zehnder interferometer., 9 pages, 17 references, 12 figures

Distributed proofs are mechanisms enabling the nodes of a network to collectivity and efficiently check the correctness of Boolean predicates on the structure of the network, or on data-structures distributed over the nodes (e.g., spanning trees or routing tables). We consider mechanisms consisting of two components: a \emph{prover} assigning a \emph{certificate} to each node, and a distributed algorithm called \emph{verifier} that is in charge of verifying the distributed proof formed by the collection of all certificates. In this paper, we show that many network predicates have distributed proofs offering a high level of redundancy, explicitly or implicitly. We use this remarkable property of distributed proofs for establishing perfect tradeoffs between the \emph{size of the certificate} stored at every node, and the \emph{number of rounds} of the verification protocol. If we allow every node to communicate to distance at most $t$, one might expect that the certificate sizes can be reduced by a multiplicative factor of at least~$t$. In trees, cycles and grids, we show that such tradeoffs can be established for \emph{all} network predicates, i.e., it is always possible to linearly decrease the certificate size. In arbitrary graphs, we show that any part of the certificates common to all nodes can be evenly redistributed among these nodes, achieving even a better tradeoff: this common part of the certificate can be reduced by the size of a smallest ball of radius $t$ in the network. In addition to these general results, we establish several upper and lower bounds on the certificate sizes used for distributed proofs for spanning trees, minimum-weight spanning trees, diameter, additive and multiplicative spanners, and more, improving and generalizing previous results from the literature.

With the rapid development of the Internet, cloud storage has penetrated into every aspect of human society. However, cloud data disclosure happens more and more frequently, which makes cloud data security and privacy protection impact wide adoption of cloud storage. Control cloud data access based on reputation by introducing a Reputation Center (RC) was proposed and demonstrated to secure cloud data effectively in [9] . But the acceptance of such a system by cloud users and Cloud Service Providers (CSPs) is crucial for its practical deployment and final success. In this paper, we investigate the acceptance of a cloud data access control system based on reputation using Game Theory. Due to the existence of dishonest CSPs, there exists a social reputation dilemma among CSPs, which seriously impedes the popularity of cloud storage. To encourage users to use cloud storage and suppress collusion between CSPs and data requesters, a repeated public-goods game is built up by applying a compensation mechanism to improve the utilities of cloud users and a punishment mechanism based on reputation to incent honest behaviors. Theoretical analysis and simulation results show the effectiveness of the compensation and punishment mechanisms to increase cloud storage rate and restrain dishonest system entities.

This paper provides new insight into the theoretical principle and the practical applicability of the ranked nodes method (RNM) that is used to construct conditional probability tables (CPTs) for Bayesian networks (BNs) by expert elicitation. RNM is designed for specific types of discrete random variables called ranked nodes that are common in real-world applications of BNs. Despite its active use in recent years, there remains ambiguity about the exact theoretical basis of RNM which can hamper its effective employment. In addition, there are a lack of studies about the general ability of CPTs generated with RNM to represent probabilistic relationships in real-world applications. In this paper, it is shown how the generation of probabilities with RNM is underpinned by a regression model of continuous random variables. Then, it is experimentally determined that in typical applications of RNM, one can generate in a matter of seconds CPTs whose elements reflect well probabilities given by the underlying regression model. Another experiment discovers that CPTs generated with RNM provide a good average fit to a large portion of various real-world CPTs investigated. This confirms the usefulness of RNM in practical applications. The results of the experiment also indicate that choices made by the user of RNM can considerably impact the ability of a generated CPT to represent a given probabilistic relationship. This paper then provides practical advice on the efficient use of RNM with regard to the user-controlled features explored in the experiment.

We consider the problem of jumbled matching where the objective is to find all permuted occurrences of a pattern in a text. Besides exact matching we study approximate matching where each occurrence is allowed to contain at most k wrong or superfluous characters. We present online algorithms applying bit-parallelism to both types of jumbled matching. Two of our algorithms are filtration methods applying SIMD (Single Instruction Multiple Data) computation. Most of the other new algorithms are variations of earlier methods. We show by practical experiments that our algorithms are competitive with previous solutions.

Käsikirjoitus avataan, kun artikkeli julkaistu Virtual machine consolidation aims at reducing the number of active physical servers in a data center so as to decrease the total power consumption. In this context, most of the existing solutions rely on aggressive virtual machine migration, thus resulting in unnecessary overhead and energy wastage. Besides, virtual machine consolidation should take into account multiple resource types at the same time, since CPU is not the only critical resource in cloud data centers. In fact, also memory and network bandwidth can become a bottleneck, possibly causing violations in the service level agreement. This article presents a virtual machine consolidation algorithm with multiple usage prediction (VMCUP-M) to improve the energy efficiency of cloud data centers. In this context, multiple usage refers to both resource types and the horizon employed to predict future utilization. Our algorithm is executed during the virtual machine consolidation process to estimate the long-term utilization of multiple resource types based on the local history of the considered servers. The joint use of current and predicted resource utilization allows for a reliable characterization of overloaded and underloaded servers, thereby reducing both the load and the power consumption after consolidation. We evaluate our solution through simulations on both synthetic and real-world workloads. The obtained results show that consolidation with multiple usage prediction reduces the number of migrations and the power consumption of the servers while complying with the service level agreement.

Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an efficient and fast algorithm for binary classification. It combines the operating principles of Least Squares SVM (LS-SVM) and Twin SVM (T-SVM); it constructs two non-parallel hyperplanes (as in T-SVM) by solving two systems of linear equations (as in LS-SVM). Despite its efficiency, LST-SVM is still unable to cope with two features of real-world problems. First, in many real-world applications, labels of samples are not deterministic; they come naturally with their associated membership degrees. Second, samples in real-world applications may not be equally important and their importance degrees affect the classification. In this paper, we propose Fuzzy LST-SVM (FLST-SVM) to deal with these two characteristics of real-world data. Two models are introduced for FLST-SVM: the first model builds up crisp hyperplanes using training samples and their corresponding membership degrees. The second model, on the other hand, constructs fuzzy hyperplanes using training samples and their membership degrees. Numerical evaluation of the proposed method with synthetic and real datasets demonstrate significant improvement in the classification accuracy of FLST-SVM when compared to well-known existing versions of SVM.

Industrial cyber-physical systems require complex distributed software to orchestrate many heterogeneous mechatronic components and control multiple physical processes. Industrial automation software is typically developed in a model-driven fashion where abstractions of physical processes called plant models are co-developed and iteratively refined along with the control code. Testing such multi-dimensional systems is extremely difficult because often models might not be accurate, do not correspond accurately with subsequent refinements, and the software must eventually be tested on the real plant, especially in safety-critical systems like nuclear plants. This paper proposes a framework wherein high-level functional requirements are used to automatically generate test cases for designs at all abstraction levels in the model-driven engineering process. Requirements are initially specified in natural language and then analyzed and specified using a formalized ontology. The requirements ontology is then refined along with controller and plant models during design and development stages such that test cases can be generated automatically at any stage. A representative industrial water process system case study illustrates the strengths of the proposed formalism. The requirements meta-model proposed by the CESAR European project is used for requirements engineering while IEC 61131-3 and model-driven concepts are used in the design and development phases. A tool resulting from the proposed framework called REBATE (Requirements Based Automatic Testing Engine) is used to generate and execute test cases for increasingly concrete controller and plant models., Conference paper, 5 pages, 5 figures

The landscape of the distributed time complexity is nowadays well-understood for subpolynomial complexities. When we look at deterministic algorithms in the LOCAL model and locally checkable problems (LCLs) in bounded-degree graphs, the following picture emerges: - There are lots of problems with time complexities Theta(log^* n) or Theta(log n). - It is not possible to have a problem with complexity between omega(log^* n) and o(log n). - In general graphs, we can construct LCL problems with infinitely many complexities between omega(log n) and n^{o(1)}. - In trees, problems with such complexities do not exist. However, the high end of the complexity spectrum was left open by prior work. In general graphs there are problems with complexities of the form Theta(n^alpha) for any rational 0 < alpha, Leibniz International Proceedings in Informatics (LIPIcs), 121, ISSN:1868-8969, 32nd International Symposium on Distributed Computing (DISC 2018), ISBN:978-3-95977-092-7

The loading of resources from third-parties has evoked new security and privacy concerns about the current world wide web. Building on the concepts of forced and implicit trust, this paper examines cross-domain transmission control protocol (TCP) connections that are initiated to domains other than the domain queried with a web browser. The dataset covers nearly ten thousand domains and over three hundred thousand TCP connections initiated by querying popular Finnish websites and globally popular sites. According to the results, (i) cross-domain connections are extremely common in the current Web. (ii) Most of these transmit encrypted content, although mixed content delivery is relatively common; many of the cross-domain connections deliver unencrypted content at the same time. (iii) Many of the cross-domain connections are initiated to known web advertisement domains, but a much larger share traces to social media platforms and cloud infrastructures. Finally, (iv) the results differ slightly between the Finnish web sites sampled and the globally popular sites. With these results, the paper contributes to the ongoing work for better understanding cross-domain connections and dependencies in the world wide web., Proceedings of the 16th Annual Conference on Privacy, Security and Trust (PST 2018), Belfast, IEEE, pp. 1-5

We consider the Green Vehicle Routing Problem (G-VRP) which is an extension of the classical vehicle routing problem for alternative fuel vehicles. In the G-VRP, vehicles' driving autonomy and possible refueling stops en-route are explicitly modeled. We propose a multi-start local search algorithm that consists of three phases. The first two phases iteratively construct new solutions, improve them by local search, and store all vehicle routes forming these solutions in a route pool. Phase three optimally combines vehicle routes in the route pool by solving a set partitioning problem and improves the final solution by local search. The algorithm is based on a multigraph reformulation of the G-VRP in which nodes correspond to customers and a depot, and arcs correspond to possible sequences of refueling stops for vehicles traveling between two nodes. All local search operators used by our algorithm are tailored to exploit this reformulation and do not explicitly deal with refueling stations. We report computational results on benchmark instances with up to ~ 470 customers, showing that the algorithm is competitive with state-of-the-art heuristics.

We prove upper bounds for the cardinality of constant dimension codes (CDC) which contain a lifted maximum rank distance (LMRD) code as a subset. Thereby we cover all parameters fulfilling $k\lt 3d/2$ , where $k$ is the codeword dimension and $d$ is the minimum subspace distance. The proofs of the bounds additionally show that an LMRD code $L$ can be unioned with a CDC $C$ (of fitting parameters) without violating the subspace distance condition iff each codeword of $C$ intersects the special subspace of $L$ in at least dimension $d/2$ . This connection is used to find the new largest and sometimes bound achieving CDCs for small parameters.

High Mach number shocks are ubiquitous in interstellar turbulence. The Pencil Code is particularly well suited to the study of magnetohydrodynamics in weakly compressible turbulence and the numerical investigation of dynamos because of its high-order advection and time evolution algorithms. However, the high-order algorithms and lack of Riemann solver to follow shocks make it less well suited to handling high Mach number shocks, such as those produced by supernovae (SNe). Here, we outline methods required to enable the code to efficiently and accurately model SNe, using parameters that allow stable simulation of SN-driven turbulence, in order to construct a physically realistic galactic dynamo model. These include the resolution of shocks with artificial viscosity, thermal conductivity, and mass diffusion; the correction of the mass diffusion terms; and a novel generalization of the Courant condition to include all source terms in the momentum and energy equations. We test our methods with the numerical solution of the one-dimensional (1D) Riemann shock tube (Sod, J. Comput. Phys. 1978, 27), also extended to a 1D adiabatic shock with parameters and Mach number relevant to SN shock evolution, including shocks with radiative losses. We extend our test with the three-dimensional (3D) numerical simulation of individual SN remnant evolution for a range of ambient gas densities typical of the interstellar medium and compare these to the analytical solutions of Sedov-Taylor (adiabatic) and the snowplough and Cioffi, McKee and Bertschinger (Astrophys. J. 1988, 334) results incorporating cooling and heating processes. We show that our new timestep algorithm leads to linear rather than quadratic resolution dependence as the strength of the artificial viscosity varies, because of the corresponding change in the strength of interzone gradients., 25 pages, 20 figures, Pencil Code special edition

The network Lasso (nLasso) has been proposed recently as an efficient learning algorithm for massive networked data sets (big data over networks). It extends the well-known least absolute shrinkage and selection operator (Lasso) from learning sparse (generalized) linear models to network models. Efficient implementations of the nLasso have been obtained using convex optimization methods lending to scalable message passing protocols. In this letter, we analyze the statistical properties of nLasso when applied to localized linear regression problems involving networked data. Our main result is a sufficient condition on the network structure and available label information such that nLasso accurately learns a localized linear regression model from a few labeled data points. We also provide an implementation of nLasso for localized linear regression by specializing a primal-dual method for solving the convex (non-smooth) nLasso problem.

This paper proposes a new dysarthric speech classification method from coded telephone speech using glottal features. The proposed method utilizes glottal features, which are efficiently estimated from coded telephone speech using a recently proposed deep neural net-based glottal inverse filtering method. Two sets of glottal features were considered: (1) time- and frequency-domain parameters and (2) parameters based on principal component analysis (PCA). In addition, acoustic features are extracted from coded telephone speech using the openSMILE toolkit. The proposed method utilizes both acoustic and glottal features extracted from coded speech utterances and their corresponding dysarthric/healthy labels to train support vector machine classifiers. Separate classifiers are trained using both individual, and the combination of glottal and acoustic features. The coded telephone speech used in the experiments is generated using the adaptive multi-rate codec, which operates in two transmission bandwidths: narrowband (300 Hz - 3.4 kHz) and wideband (50 Hz - 7 kHz). The experiments were conducted using dysarthric and healthy speech utterances of the TORGO and universal access speech (UA-Speech) databases. Classification accuracy results indicated the effectiveness of glottal features in the identification of dysarthria from coded telephone speech. The results also showed that the glottal features in combination with the openSMILE-based acoustic features resulted in improved classification accuracies, which validate the complementary nature of glottal features. The proposed dysarthric speech classification method can potentially be employed in telemonitoring application for identifying the presence of dysarthria from coded telephone speech.

Recent research demonstrated that the classical guitar can be advantageously augmented using a pickup to drive an actuator mounted on the guitar’s back plate. This allows enrichment of the instrument’s timbral palette with audio effect processors in the loop. The feedback problem that results from such a setup is similar to what occurs in live music performance setups where the sound of a guitar is amplified using a loudspeaker. In the present case, measurements of the augmented guitar’s open-loop response demonstrate that instabilities are susceptible to occurring from the string’s modes and not from the guitar’s sound-box. In particular, the shape of the magnitude response suggests frequency shifting as a viable solution to string instability. Introduction of an upward frequency shift in the forward path is proposed as a means for stabilizing the closed-loop system. Experimental results demonstrate that the proposed solution leads to improved stability even for a modest frequency shift of 3 Hz. The achievedgain margin improvement, which is shown to be of at least 3 dB, then comes at the cost of a clearly perceptible amplitude modulation, which may be acceptable in conjunction with other audio effects chosen by the performer.

An assessment of filters for classic oversampled audio waveshaping schemes is carried out, pursuing aliasing reduction. For this purpose, the quality measure of the A-weighted noise-to-mask ratio is computed for test tones covering the frequency range from 27.5 Hz to 4.186 kHz, sampled at 44.1 kHz, and processed at eight-times oversampling. All filters are designed to have their passband contained within a ±1 dB range and to exhibit a minimum stopband attenuation value of 40 dB. Waveshaping of sinusoids via hard clipping is investigated—spectral enrichment due to the discontinuities introduced by its nonlinear transfer function maximizes aliasing distortion. The obtained results suggest that linear interpolation equalized with a high shelving filter is a sufficiently good method for upsampling. Concerning decimation, the interpolated FIR, elliptic, and cascaded integrator-comb filters all improve the results with respect to the trivial case. The cascaded integrator-comb filter is the only tested decimation filter that achieves perceptually sufficient aliasing suppression for the entire frequency range when combined with the linear interpolator.

The radical changes in today's competitive environment created a “big bang” effect: companies and industries are being disrupted in an unprecedented short time. The challenge has shifted from predicting the unknown or conventional method of venture creation to agile actions, which is to act rapidly before the competition catches up and eliminate any advantage one may have, especially for entrepreneurs and small businesses with significant resource constraints. The objective of this research is to investigate alternative approaches under a time-constrained setting. A recent national open innovation initiative of Finland challenged practitioners and researchers to transform technologies into commercializable innovations in under six months. A high-tech case within the challenge piloted the agile effectuation of a business model with three parallel approaches: effectuation, causation and lean startup method, thus providing antecedent on how effectuation theory can be integrated with agile development and business model theory.

This paper proposes a novel algorithm for target tracking with direction-of-arrival measurements, modeled by von Mises–Fisher distributions. The algorithm makes use of the assumed density framework with Gaussian distributions, in which the posterior probability density of the target state is approximated by a Gaussian density. A key component of this algorithm is that the proposed Bayesian model of the measurements takes into account the specific characteristics of angular measurements by using a von Mises–Fisher distribution. We propose two implementations of the algorithm, one based on first-order Taylor series expansion and another one based on sigma points. Simulation results show the benefits of the proposed algorithms in relation to other Gaussian filters in the literature.

Smartphone-based indoor navigation services are desperately needed in indoor environments. However, the adoption of them has been relatively slow, due to the lack of fine-grained and up-to-date indoor maps, or the potentially high deployment and maintenance cost of infrastructure-based indoor localization solutions. This work proposes ViNav, a scalable and cost-efficient system that implements indoor mapping, localization, and navigation based on visual and inertial sensor data collected from smartphones. ViNav applies structure-from-motion (SfM) techniques to reconstruct 3D models of indoor environments from crowdsourced images, locates points of interest (POI) in 3D models, and compiles navigation meshes for path finding. ViNav implements image-based localization that identifies users’ positions and facing directions, and leverages this feature to calibrate dead-reckoning-based user trajectories and sensor fingerprints collected along the trajectories. The calibrated information is utilized for building more informative and accurate indoor maps, and lowering the response delay of localization requests. According to our experimental results in a university building and a supermarket, the system works properly and our indoor localization achieves competitive performance compared with traditional approaches: in a supermarket, ViNav locates users within 2 seconds, with a distance error less than 1 meter and a facing direction error less than 6 degrees.

The rise of the Internet of Things (IoT) implies new technical challenges such as managing a universally vast number of IoT devices. Despite the fact that there are already a variety of secure management frameworks for IoT, they are based on centralized models, which limits their applicability in scenarios with a large number of IoT devices. In order to overcome those limitations, we have developed a distributed IoT management system based on blockchain. In this paper, we compare the performance of our solution with the existing access management solutions in IoT. We study the delays and the throughput rate associated with the systems and analyze different configurations of our solution to maximize its scalability. The objective of the paper is to find out whether our solution can scale as well as the existing management systems in IoT.

Made available in DSpace on 2019-10-06T16:12:39Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-06-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) This paper proposes a multistage convex distribution system planning model to find the best reinforcement plan over a specified horizon. This strategy determines planning actions such as reinforcement of existing substations, conductor replacement of overloaded feeders, and siting and sizing of renewable and dispatchable distributed generation units. Besides, the proposed approach aims at mitigating the greenhouse gas emissions of electric power distribution systems via a monetary form. Inherently, this problem is a non-convex optimization model that can be an obstacle to finding the optimal global solution. To remedy this issue, convex envelopes are used to recast the original problem into a mixed integer conic programming (MICP) model. The MICP model guarantees convergence to optimal global solution by using existing commercial solvers. Moreover, to address the prediction errors in wind output power and electricity demands, a two-stage stochastic MICP model is developed. To validate the proposed model, detail analysis is carried out over various case studies of a 34-node distribution system under different conditions, while to show its potential and effectiveness a 135-node system with two substations is used. Numerical results confirm the effectiveness of the proposed planning scheme in obtaining an economic investment plan at the presence of several planning alternatives and to promote an environmentally committed electric power distribution network. Electrical Engineering Department São Paulo State University (UNESP), Ilha Solteira Department of Electrical Engineering and Automation Aalto University, Maarintie 8 Electrical Engineering Department São Paulo State University (UNESP), Ilha Solteira FAPESP: 2015/21972-6 CNPq: 305318/2016-0

The extreme learning machine (ELM) and the minimal learning machine (MLM) are nonlinear and scalable machine learning techniques with a randomly generated basis. Both techniques start with a step in which a matrix of weights for the linear combination of the basis is recovered. In the MLM, the feature mapping in this step corresponds to distance calculations between the training data and a set of reference points, whereas in the ELM, a transformation using a radial or sigmoidal activation function is commonly used. Computation of the model output, for prediction or classification purposes, is straightforward with the ELM after the first step. In the original MLM, one needs to solve an additional multilateration problem for the estimation of the distance-regression based output. A natural combination of these two techniques is proposed and experimented here: to use the distance-based basis characteristic in the MLM in the learning framework of the regularized ELM. In other words, we conduct ridge regression using a distance-based basis. The experimental results characterize the basic features of the proposed technique and surprisingly, indicate that overlearning with the distance-based basis is in practice avoided in classification problems. This makes the model selection for the proposed method trivial, at the expense of computational costs. peerReviewed

Mobile Edge Computing (MEC) is an important and effective platform to offload the computational services of modern mobile applications, and has gained tremendous attention from various research communities. For delay and resource constrained mobile devices, the important issues include: 1) minimization of the service latency; 2) optimal revenue maximization; 3) high quality-of-service (QoS) requirement to offload the computational service offloading. To address the above issues, an adaptive service offloading scheme is designed to provide the maximum revenue and service utilization to MEC. Unlike most of the existing works, we consider both the delay-tolerant and delay-constraint services in order to achieve the optimized service latency and revenue. Furthermore, we consider the different priorities to prioritize the edge services for optimal service offloading. We formulate the proposed scheme mathematically. Simulation results are presented to demonstrate the effectiveness of the proposed adaptive service offloading scheme over other existing state-of-the-art solutions, in terms of service latency, utility value, revenue and utilization. peerReviewed

Finite-difference time-domain method has gained increasing interest for room acoustic prediction use. A well-known limitation of the method is a frequency- and direction-dependent dispersion error. In this study, the audibility of dispersion error in the presence of a single surface reflection is measured. The threshold is measured for three different distance conditions with a fixed reflection arrival azimuth angle of 54.7 ° . The error is placed either in the direct path, or in the reflection path. Additionally a qualitative follow-up experiment to evaluate how the measured thresholds reflect the audibility of error in short room responses is carried out. The results indicate that the threshold varies depending whether the error is in the direct path or in the reflection path. For transient signals the threshold is higher when the error is located in the direct path, whereas for speech signal, the threshold is higher when it is located in the reflection path. Evidence is found that the error is detectable in rendered room responses at the measured threshold levels.Finite-difference time-domain method has gained increasing interest for room acoustic prediction use. A well-known limitation of the method is a frequency- and direction-dependent dispersion error. In this study, the audibility of dispersion error in the presence of a single surface reflection is measured. The threshold is measured for three different distance conditions with a fixed reflection arrival azimuth angle of 54.7 ° . The error is placed either in the direct path, or in the reflection path. Additionally a qualitative follow-up experiment to evaluate how the measured thresholds reflect the audibility of error in short room responses is carried out. The results indicate that the threshold varies depending whether the error is in the direct path or in the reflection path. For transient signals the threshold is higher when the error is located in the direct path, whereas for speech signal, the threshold is higher when it is located in the reflection path. Evidence is found that the error is dete...

Private information retrieval (PIR) schemes for coded storage with colluding servers are presented, which are not restricted to maximum distance separable (MDS) codes. PIR schemes for general linear codes are constructed, and the resulting PIR rate is calculated explicitly. It is shown that codes with transitive automorphism groups yield the highest possible rates obtainable with the proposed scheme. In the special case of no server collusion, this rate coincides with the known asymptotic PIR capacity for MDS-coded storage systems. While many PIR schemes in the literature require field sizes that grow with the number of servers and files in the system, we focus especially on the case of a binary base field, for which Reed–Muller codes serve as an important and explicit class of examples.

Both disciplinary and interdisciplinary knowledge is needed in order for a student to succeed as a professional after graduation. Interdisciplinary knowledge, such as project management skills are important in working life regardless the competence area. In order for a student to gain competences relevant for working life, both traditional and agile project management frameworks should be a part of their studies – in theory and in practice. In this article, a case study is presented on the integration of an agile project management framework into university-industry collaboration projects. First, the methodologies used in theFIRMA are introduced. Thereafter, the activities and the roles of theFIRMA are described and an externally funded R&D project is presented. Finally, the experiences of past and current activities are discussed, and future development thoughts are presented.

Blood glucose plays an important role in maintaining body’s activities. For example, brain only uses glucose as its energy source. However, when blood glucose level is abnormal, it causes some serious consequences. For instance, low-blood glucose phenomenon referred to as hypoglycemia can cause heart repolarization and induce cardiac arrhythmia causing sudden cardiac deaths. Diabetes, which can be viewed as a high-blood glucose level for a long period of time, is a dangerous disease as it can directly or indirectly cause heart attack, stroke, heart failure, and other vicious diseases. A solution for reducing the serious consequences caused by diabetes and hypoglycemia is to continuously monitor blood glucose level for real-time responses such as adjusting insulin levels from the insulin pump. Nonetheless, it is a misstep when merely monitoring blood glucose without considering other signals or data such as Electrocardiography (ECG) and activity status since they have close relationships. When hypoglycemia occurs, a fall can easily occur especially in case of people over 65 years old. Fall’s consequences are more hazardous when a fall is not detected. Therefore, we present a Fog-based system for remote health monitoring and fall detection. Through the system, both e-health signals such as glucose, ECG, body temperature and contextual data such as room temperature, humidity, and air quality can be monitored remotely in real-time. By leveraging Fog computing at the edge of the network, the system offers many advanced services such as ECG feature extraction, security, and local distributed storage. Results show that the system works accurately and the wearable sensor node is energy efficient. Even though the node is equipped with many types of sensors, it can operate in a secure way for up to 157 h per a single charge when applying a 1000 mAh Lithium battery.

Currently, geographic information system (GIS) models are popular for studying location-allocation-related questions concerning bioenergy plants. The aim of this study was to develop a model to investigate optimal locations for two different types of bioenergy plants, for farm and centralized biogas plants, and for wood terminals in rural areas based on minimizing transportation distances. The optimal locations of biogas plants were determined using location optimization tools in R software, and the optimal locations of wood terminals were determined using kernel density tools in ArcGIS. The present case study showed that the utilized GIS tools are useful for bioenergy-related decision-making to identify potential bioenergy areas and to optimize biomass transportation, and help to plan power plant sizing when candidate bioenergy plant locations have not been defined in advance. In the study area, it was possible to find logistically viable locations for 13 farm biogas plants (>100 kW) and for 8 centralized biogas plants (>300 kW) using a 10-km threshold for feedstock supply. In the case of wood terminals, the results identified the most intensive wood reserves near the highest road classes, and two potential locations were determined. acceptedVersion

A novel microtiter plate array for the quantification and identification of metal ions in drinking water was compared to human taste panel analysis. The array is based on nonspecific interactions between analyte metal ions and lanthanide chelates with non-antenna and antenna ligands, leading to a luminescence signal profile unique to sample components. The performance of the method was demonstrated in the detection of Cu2+ and Fe2+ ions in active coal filtered and nonfiltered household water. The sensitivity and objectiveness was compared to human taste panel analysis. Detection limits of

On-the-job medical training is known to be challenging due to the fast-paced environment and strong vocational profile. It relies on on-site supervisors, mainly doctors and nurses with long practical experience, who coach and teach their less experienced colleagues, such as residents and healthcare students. These supervisors receive pedagogical training to ensure that their guidance and teaching skills are constantly improved. The aim of such training is to develop participants’ patient, collegiate and student guidance skills in a multiprofessional environment, and to expand their understanding of guidance as part of their work as supervisors of healthcare professionals. In this paper, we investigate open-ended answers on guidance experience of 281 healthcare supervisors that participated in these training courses. To automate the analysis of the contents of the answers, we apply clustering to the natural language processed textual data. The results summarize the most common guidance experiences and allow an automatic grouping of the healthcare supervisors’ reflection. Such an evidence-based knowledge can be used to further improve the organization of the training courses.

Objective The study aims to develop an automatic sleep scoring method by fusing different polysomnography (PSG) signals and further to investigate PSG signals’ contribution to the scoring result. Methods Eight combinations of four modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) were considered to find the optimal fusion of PSG signals. A total of 232 features, covering statistical characters, frequency characters, time-frequency characters, fractal characters, entropy characters and nonlinear characters, were derived from these PSG signals. To select the optimal features for each signal fusion, four widely used feature selection methods were compared. At the classification stage, five different classifiers were employed to evaluate the validity of the features and to classify sleep stages. Results For the database in the present study, the best classifier, random forest, realized the optimal consistency of 86.24% with the sleep macrostructures scored by the technologists trained at the Sleep Center. The optimal accuracy was achieved by fusing four modalities of PSG signals. Specifically, the top twelve features in the optimal feature set were respectively EEG features named zero-crossings, spectral edge, relative power spectral of theta, Petrosian fractal dimension, approximate entropy, permutation entropy and spectral entropy, and EOG features named spectral edge, approximate entropy, permutation entropy and spectral entropy, and the mutual information between EEG and submental EMG. In addition, ECG features (e.g. Petrosian fractal dimension, zero-crossings, mean value of R amplitude and permutation entropy) were useful for the discrimination among W, S1 and R. Conclusions Through exploring the different fusions of multi-modality signals, the present study concluded that the multi-modality of PSG signals’ fusion contributed to higher accuracy, and the optimal feature set was a fusion of multiple types of features. Besides, compared with manual scoring, the proposed automatic scoring methods were cost-effective, which would alleviate the burden of the physicians, speed up sleep scoring, and expedite sleep research.

openaire: EC/H2020/611570/EU//MindSee The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first-of-its-kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology-based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).

The degree splitting problem requires coloring the edges of a graph red or blue such that each node has almost the same number of edges in each color, up to a small additive discrepancy. The directed variant of the problem requires orienting the edges such that each node has almost the same number of incoming and outgoing edges, again up to a small additive discrepancy. We present deterministic distributed algorithms for both variants, which improve on their counterparts presented by Ghaffari and Su [SODA'17]: our algorithms are significantly simpler and faster, and have a much smaller discrepancy. This also leads to a faster and simpler deterministic algorithm for (2+o(1))Delta-edge-coloring, improving on that of Ghaffari and Su., Leibniz International Proceedings in Informatics (LIPIcs), 91, ISSN:1868-8969, 31st International Symposium on Distributed Computing (DISC 2017), ISBN:978-3-95977-053-8

openaire: EC/FP7/600825/EU//RECONFIG In a multirobot system, using shared symbols for objects in the environment is a prerequisite for collaboration. Sharing symbols requires that each agent has anchored a symbol with an internal, sensor level representation, as well as that these symbols match between the agents. The problem can be solved easily when the internal representations can be communicated between the agents. However, with heterogeneous embodiments the available sensors are likely to differ, making it impossible to share the internal representations directly. We propose the use of pointing gestures to align symbols between a heterogeneous group of robots. We describe a planning framework that minimizes the required effort for anchoring representations across robots. The framework allows planning for both the gesturing and observing agents in a decentralized fashion. It considers both implicit sources of failure, such as ambiguous pointing, as well as costs required by actions. Simulation experiments demonstrate that the resulting planning problem has a complex solution structure with multiple local minima. Demonstration with a heterogeneous two-robot system shows the practical viability of this approach.

A wide range of Internet of Things devices, platforms and applications have been implemented in the past decade. The variation in platforms, communication protocols and data formats of these system ...

The purpose of this study was to demonstrate how the inclusion of a tactical task goal awareness measure complemented mental workload and performance measures in a simulated air combat mission. It was hypothesized that the evaluation of the tactical task goal awareness could provide additional information concerning the cognitive demands a pilot is exposed to during a complex air combat task. A test setting was developed to test this hypothesis in a virtual flight training device. To highlight the impact of task complexity, high performance aircraft pilots’ heart rate (HR), interbeat-interval (IBI), and performance in two simple flying tasks were first compared. Then, a similar comparison, complemented with the tactical task goal awareness measure, was made with two complex flying tasks. It was found that when the pilot's awareness of the tactical goals was low, a combination of low performance and low mental workload occurred. It was concluded that when the pilots’ performance is evaluated on a complex air combat task, the awareness of the tactical goals, performance, and mental workload should be studied together as the pilot's awareness can explain some of HR/IBI responses that could be otherwise misinterpreted. More generally, mental workload, performance, and task goal awareness should all be considered when the operator's performance in any complex human–machine system is assessed.

In this work, a mixed-integer binary non-linear two-echelon inventory problem is formulated for a vendor-buyer supply chain network in which lead times are constant and the demands of buyers follow a normal distribution. In this formulation, the problem is a combination of an (r, Q) and periodic review policies based on which an order of size Q is placed by a buyer in each fixed period once his/her on hand inventory reaches the reorder point r in that period. The constraints are the vendors’ warehouse spaces, production restrictions, and total budget. The aim is to find the optimal order quantities of the buyers placed for each vendor in each period alongside the optimal placement of the vendors among the buyers such that the total supply chain cost is minimized. Due to the complexity of the problem, a Modified Genetic Algorithm (MGA) and a Particle Swarm Optimization (PSO) are used to find optimal and near-optimum solutions. In order to assess the quality of the solutions obtained by the algorithms, a mixed integer nonlinear program (MINLP) of the problem is coded in GAMS. A design of experiment approach named Taguchi is utilized to adjust the parameters of the algorithms. Finally, a wide range of numerical illustrations is generated and solved to evaluate the performances of the algorithms. The results show that the MGA outperforms the PSO in terms of the fitness function in most of the problems and also is faster than the PSO in terms of CPU time in all the numerical examples. peerReviewed

Information-Centric Networking (ICN) has emerged as a paradigm to cope with the increasing demand for content delivery on the Internet. In contrast to the Internet Protocol (IP), the underlying architecture of ICN enables users to request contents based on their name rather than their hosting location (IP address). On the one hand, this preserves users’ anonymity since packet routing does not require source and destination addresses of the communication parties. On the other hand, semantically-rich names reveal information about users’ interests, which poses serious threats to their privacy. A curious ICN node can monitor the traffic to profile users’ or censor specific contents for instance. In this paper, we present PrivICN: a system that enhances users privacy in ICN by protecting the confidentiality of content names and content data. PrivICN relies on a proxy encryption scheme and has several features that distinguish it from existing solutions: it preserves full in-network caching benefits, it does not require end-to-end communication between consumers and providers and it provides flexible user management (addition/removal of users). We evaluate PrivICN in a real ICN network (CCNx implementation) showing that it introduces an acceptable overhead and little delay. PrivICN is publicly available as an open-source library.

In this paper, we consider Rao-Blackwellization of linear substructures in sigma-point-based Gaussian assumed density smoothers. We derive marginalized prediction, smoothing, and update steps for the mixed linear/nonlinear Gaussian state-space model as well as for a hierarchical model for both conventional and iterated posterior linearization Gaussian smoothers. The proposed method is evaluated in a numerical example and it is shown that the computational complexity is reduced considerably compared to non-Rao--Blackwellized Gaussian smoothers for systems with high-dimensional linear subspaces.

Neurofeedback requires a direct translation of neuronal brain activity to sensory input given to the user or subject. However, decoding certain states, e.g., mindfulness or wandering thoughts, from ongoing brain activity remains an unresolved problem. In this study, we used magnetoencephalography (MEG) to acquire brain activity during mindfulness meditation and thought-inducing tasks mimicking wandering thoughts. We used a novel real-time feature extraction to decode the mindfulness, i.e., to discriminate it from the thought-inducing tasks. The key methodological novelty of our approach is usage of MEG power spectra and functional connectivity of independent components as features underlying mindfulness states. Performance was measured as the classification accuracy on a separate session but within the same subject. We found that the spectral- and connectivity-based classification approaches allowed discriminating mindfulness and thought-inducing tasks with an accuracy around 60% compared to the 50% chance-level. Both classification approaches showed similar accuracy, although the connectivity approach slightly outperformed the spectral one in a few cases. Detailed analysis showed that the classification coefficients and the associated independent components were highly individual among subjects and a straightforward transfer of the coefficients over subjects provided near chance-level classification accuracy. Thus, discriminating between mindfulness and wandering thoughts seems to be possible, although with limited accuracy, by machine learning, especially on the subject-level. Our hope is that the developed spectral- and connectivity-based decoding methods can be utilized in real-time neurofeedback to decode mindfulness states from ongoing neuronal activity, and hence, provide a basis for improved, individualized mindfulness training.

Industry 4.0 architecture has been studied in a large number of publications in the fields of Industrial Internet of Things, Cyber Physical Production Systems, Enterprise Architectures, Enterprise Integration and Cloud Manufacturing. A large number of architectures have been proposed, but none of them has been adopted by a large number of research groups. Two major Industry 4.0 reference architectures have been developed by industry-driven initiatives, namely the German Industry 4.0 and the US-led Industrial Internet Consortium. These are the Reference Architecture Model Industry 4.0 and Industrial Internet Reference Architecture, which are being standardized by the International Electrotechnical Commission and the Object Management Group, respectively. The first research goal of this article is to survey the literature on Industry 4.0 architectures in a factory context and assess awareness and compatibility with Reference Architecture Model Industry 4.0 and Industrial Internet Reference Architecture. The second research goal is to adapt a previously proposed advanced manufacturing concept to Reference Architecture Model Industry 4.0. With respect to the first research goal, it was discovered that only a minority of researchers were aware of the said reference architectures and that in general authors offered no discussion about the compatibility of their proposals with any internationally standardized reference architecture for Industry 4.0. With respect to the second research goal, it was discovered that Reference Architecture Model Industry 4.0 was mature with respect to communication and information sharing in the scope of the connected world, that further standardization enabling interoperability of different vendors’ technology is still under development and that technology standardization enabling executable business processes between networked enterprises was lacking.

Optimal power flow (OPF) is one of the most important tools in power system operation and control, which determines the minimum operating cost and retains the control variables in their secure boundaries. This paper takes into account several unbridled practical constraints in the OPF problem, three of which – that is – valve-point effect, multi-fuel option, and, above all, prohibited operating zone are the most conspicuous ones. Further, the flexible alternating current transmission systems (FACTS) devices are considered, as well, which have several merits such as decreasing the active power transmission loss, controlling the power flow, and improving the voltage stability/profile, to name but a few. Accordingly, thyristor controlled series capacitor (TCSC) – the most popular and common component of the FACTS equipment’s category – is utilized in this study. As a result, the OPF problem integrated with such practical constraints referred to above as well as FACTS devices becomes a highly nonlinear-nonconvex optimization problem and to solve it, a reliable and efficient evolutionary algorithm such as fuzzy-based improved comprehensive-learning particle swarm optimization (FBICLPSO) algorithm is introduced. The proposed approach is scrutinized on IEEE 30-bus test system, which is a commonly used test system for solving the non-smooth and non-convex versions of the OPF problem. Comparing the obtained results by the proposed algorithm with the available alternatives in the literature corroborate the potential and effectiveness of the proposed approach.