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Whereas interior point methods provide polynomial-time linear programming algorithms, the running time bounds depend on bit-complexity or condition measures that can be unbounded in the problem dimension. This is in contrast with the simplex method that always admits an exponential bound. We introduce a new polynomial-time path-following interior point method where the number of iterations also admits a combinatorial upper bound $O(2^{n}n^{15}\log n)$ for an n-variable linear program in standard form. This complements previous work by Allamigeon, Benchimol, Gaubert, and Joswig (SIAGA 2018) that exhibited a family of instances where any path-following method must take exponentially many iterations. The number of iterations of our algorithm is at most $O(n^{15}\log n)$ times the number of segments of any piecewise linear curve in the wide neighborhood of the central path. In particular, it matches the number of iterations of any path following interior point method up to this polynomial factor. The overall exponential upper bound derives from studying the max central path’, a piecewise-linear curve with the number of pieces bounded by the total length of 2n shadow vertex simplex paths. From the existence of a line segment in the wide neighborhood we derive strong implications on the structure of the corresponding segment of the central path. Our algorithm is able to detect this structure from the local geometry at the current iterate, and constructs a step direction that descends along this segment. The bound $O(n^{15}\log n)$ that applies for arbitrarily long line segments is derived from a combinatorial progress measure. Our algorithm falls into the family of layered least squares interior point methods introduced by Vavasis and Ye (Math. Prog. 1996). In contrast to previous layered least squares methods that partition the kernel of the constraint matrix into coordinate subspaces, our method creates layers based on a general subspace providing more flexibility. Our result also implies the same bound on the number of iterations of the trust region interior point method by Lan, Monteiro, and Tsuchiya (SIOPT 2009).

Mass-scale red blood cell genotyping of donors and transfusion recipients increases the availability of extended antigen matched red blood cell units. Therefore, a new mathematical framework is developed that can be applied for general blood groups (i.e., beyond the ABO, RhD blood groups). It determines which red blood cell units should be issued from inventory, such that requests from hospitals can be satisfied with antigen compatible red blood cell units, shortages for future requests are avoided, and outdating is prevented. The optimization model consists of two steps: a binary vector representation for general blood groups and the formulation of the inventory allocation problem as a minimum cost flow problem. The potential practical performance of the optimization model is evaluated by iterative simulations, based on historical data on supply and demand of red blood cell units in the Netherlands. When including the fourteen clinically most relevant antigens, more than 90% of all requests can be satisfied with antigen identical red blood cell units. Shortages and outdating can be kept restricted and could even be reduced to virtually zero, when only the ABO, RhD blood groups are considered.

The COVID-19 outbreak and associated measures taken had an enormous impact on society as well as a disruptive, but not necessarily negative, impact on mobility. The Ministry of Infrastructure and Water Management received the most recent insights from the Dutch Mobility Panel (DMP) on a weekly basis. These insights were used to monitor the travel behaviour and to analyse changes in the behaviour of different groups and usage of modes of transport during COVID-19. The analysis shows an enormous decrease in travel at the beginning of the implementation of the so-called ‘intelligent’ lockdown and gradual increase again towards comparable levels as before this ‘intelligent lockdown, although the distribution over time, motives and used modes has changed. It becomes clear that not everyone needs to travel during peak hours and commuter travel is also not the main reason for the increase in car usage. Furthermore, cycling has shown to be an alternative option for travellers and public transport is hardly used anymore. If it is possible to sustain the lower level of car usage and integrate public transport as an important alternative for travel again, the COVID-19 impact on mobility could have a substantial remaining positive impact on mobility.

Recent developments in the mobile app industry have resulted in various types of mobile apps, each targeting a different need and a specific audience. Consequently, users access distinct apps to complete their information need tasks. This leads to the use of various apps not only separately, but also collaboratively in the same session to achieve a single goal. Recent work has argued the need for a unified mobile search system that would act as metasearch on users' mobile devices. The system would identify the target apps for the user's query, submit the query to the apps, and present the results to the user in a unified way. In this work, we aim to deepen our understanding of user behavior while accessing information on their mobile phones by conducting an extensive analysis of various aspects related to the search process. In particular, we study the effect of task type and user demographics on their behavior in interacting with mobile apps. Our findings reveal trends and patterns that can inform the design of a more effective mobile information access environment.

In recent years, fixed forms and computerized adaptive testing (CAT) forms have coexisted in many testing programs and often used interchangeably on the premise that both testing formats meet the same test specifications. In conventional CAT, however, items are selected through computer algorithms to meet primarily statistical criteria, whereas fixed forms are often created focusing heavily on content, non-statistical, and practical requirements. Founded on the optimal test design framework, the shadow-test approach to CAT and its generalization allows for constructing fixed and adaptive test forms to satisfy the same objective and (possibly complex) set of constraints. This approach can render a variety of testing formats with different levels of adaptation and relative efficiency. This paper provides an overview of the optimal test assembly approach and the development of the TestDesign package in R. Several illustrations from real-world testing scenarios are also presented.

The fabrication of auricular prostheses is traditionally time consuming, and the definitive esthetic appearance is highly skill dependent. A method of creating the wax pattern for an auricular prosthesis by using optical scanning and 3D printing is described. A digital scan of the unaffected ear is used for computer-aided design and manufacturing of a mold for casting the wax pattern of the prosthesis. The process is efficient and increases the predictability of the esthetic outcome.

Teams operating in time-pressured, dynamic environments frequently need to cope with critical situations varying in complexity and hazard. To cope with critical situations, teams may have to adapt their communication processes. Adaptation of team communication processes has been studied mostly at short time frames (minutes). Literature on adapting communication at longer time frames is limited (hours, relative to minutes). We used the relational event model to compare team communication in critical and noncritical situations of pediatric cardiac surgeries and Apollo 13 flight director’s voice loops. Teams showed some flattening of communication structures in critical situations. Both teams maintained institutional roles and displayed closed-loop and information-seeking communication. Communication patterns may change further with increasing criticality. The exact way teams adapt to critical situations may differ depending on team, team size and situation. Findings may inform team training procedures or team structure development.

Learning to solve subtraction problems that require borrowing (e.g., 83–57=) is challenging, and these problems often cause ‘bridging’ errors, such as the smaller-from-larger error. This study explores how bridging errors in subtraction are related to students’ mathematical ability. The study involved 694 third-grade students and 35 teachers from 25 Dutch schools. Multilevel regression analyses showed that the number of bridging errors was positively related to the students’ mathematical ability, after controlling for the total number of errors in subtraction. Thus, the students who had a high proportion of bridging errors within the total number of errors had a relatively higher mathematical ability compared to the students who had a low proportion of bridging errors. This result implies that diagnosing bridging errors may help to identify where students’ stand within their mathematical development. The practical implications of this result for the design of diagnostic instruments are addressed in the discussion section.

One of the methods of controlling test security in adaptive testing is imposing random item-ineligibility constraints on the selection of the items with probabilities automatically updated to maintain a predetermined upper bound on the exposure rates. Three major improvements of the method are presented. First, a few modifications to improve the initialization of the method and accelerate the impact of its feedback mechanism on the observed item-exposure rates are introduced. Second, the case of conditional item-exposure control given the uncertainty of examinee's ability parameter is addressed. Third, although rare for a well-designed item pool, when applied in combination with the shadow-test approach to adaptive testing the method may meet occasional infeasibility of the shadow-test model. A big M method is proposed that resolves the issue. The practical advantages of the improvements are illustrated using simulated adaptive testing from a real-world item pool under a variety of conditions.

Recently, the usage of on-chip embedded instruments (EIs) to ensure dependable safety-critical systems is becoming inevitable. These EIs can help to provide self-awareness, and their feedback can be used in different applications, e.g. end-of-lifetime (EOL) predictions. However, inaccuracies present in data from these EIs, due to their resolution limitations, self-aging and quantization errors during digitization, can lead to an inaccurate EOL assessment. To address this challenge, a machine learning-based system-level approach for determining the EOL of a many-processor system-on-chip (MPSoC) is discussed. It is based on the synchronous data capture of different IJTAG compatible EIs. To this end, two different data fusion techniques have been used for enhancing the accuracy of lifetime prognostics of multiple EIs; use is made of Independent Component Analysis (ICA) and the auto-encoder (AE). Different combinations of fused EIs (based on ICA and AE) along with standalone EIs for four different critical paths (CPs) have been investigated. For lifetime prediction based on different EIs/fused EIs, a data-driven degradation model was derived, and nonlinear regression has been employed for parameter estimation. Results show that data fusion of different EIs helps in obtaining better estimation of the EOL as compared to using a standalone EI.

This paper was presented at the Invited Panel session “Technical Communication in China”. Findability is one of the most important qualitative factors of websites. With the rapid growth in navigation complexity and in number of technical documentations in help centers, whether users can easily locate the target document could directly determine the information retrieval task outcome. Providing users with a fine guide to target documents and then helping them find solutions to their problems is the most important function of a help center. Investigation on user search behavior data and perceived findability of documentation has to be done in order to further apply website log data to predicting user subjective assessment. In this paper we analyze the correlation between subjective document findability, subjective task complexity, and user search behavior. We found several search behavior metrics which significantly correlate with the two subjective measures above.

The Internet of Things (IoT) is widely expected to make our society safer, smarter, and more sustainable. However, a key challenge remains, which is how to protect users and Internet infrastructure operators from attacks on or launched through vast numbers of autonomously operating sensors and actuators. In this article, we discuss how the security extensions of the domain name system (DNS) offer an opportunity to help tackle that challenge, while also outlining the risks that the IoT poses to the DNS in terms of complex and quickly growing IoT-powered distributed denial of service (DDoS) attacks. We identify three challenges for the DNS and IoT industries to seize these opportunities and address the risks, for example, by making DNS security functions (e.g., response verification and encryption) available on popular IoT operating systems.

Software is a critical component of brain–computer interfaces (BCIs). While BCI hardware enables the retrieval of brain signals, BCI software is required to analyze these signals, produce output, and provide feedback. Users from multiple research areas have adopted BCI software platforms to investigate various concepts. Recently, interest in web-based BCI software has also emerged. The system design and control signal techniques of state-of-the-art BCI software platforms have been previously investigated. However, there is limited literature discussing user adoption of BCI software platforms. Additionally, there is a lack of work discussing the recent emergence of web tools relevant to BCI applications. This article aims to address these gaps by presenting a bibliometric review of the state-of-the-art BCI software. Furthermore, we discuss web-based BCIs and present tools that may be used to develop future web-based BCI applications.

The Bayesian way of accounting for the effects of error in the ability and item parameters in adaptive testing is through the joint posterior distribution of all parameters. An optimized Markov chain Monte Carlo algorithm for adaptive testing is presented, which samples this distribution in real time to score the examinee’s ability and optimally select the items. Thanks to extremely rapid convergence of the Markov chain and simple posterior calculations, the algorithm is ready for use in real-world adaptive testing with running times fully comparable with algorithms that fix all parameters at point estimates during testing.

Frequency offset transmit reference (FoTR)—a noncoherent spread spectrum technique—is considered in dense multipath fading environments, in the context of wideband communication. The interplay between system parameters and propagation effects is investigated. Analytical relations between key design parameters and the channel delay spread are derived, which provide a framework for determining reasonable system parameters that optimize the performance. An approximated closed-form expression for the outage probability is also obtained, which is shown to be fairly accurate for low outage probabilities. It is shown that FoTR suffers significantly from noise-enhancement, but, is nonetheless, robust against frequency-selective fading. A large value of frequency offset can significantly deteriorate the performance, particularly in environments with large channel delay spreads. This restricts the maximum data rate as well as the multiple-access capability of the scheme. Despite the limitations, FoTR can be a useful communication scheme for low data rate sensor networks deployed in dense multipath environments, particularly where the design demands a simple receiver and a low outage probability.

While analyzing multiple data sequences, the following questions typically arise: how does a single sequence change over time, how do multiple sequences compare within a period, and how does such comparison change over time. This paper presents a visual technique named STBins to answer these questions. STBins is designed for visual tracking of individual data sequences and also for comparison of sequences. The latter is done by showing the similarity of sequences within temporal windows. A perception study is conducted to examine the readability of alternative visual designs based on sequence tracking and comparison tasks. Also, two case studies based on real-world datasets are presented in detail to demonstrate usage of our technique.

Human Activity Recognition is a machine learning task for the classification of human physical activities. Applications for that task have been extensively researched in recent literature, specially due to the benefits of improving quality of life. Since wearable technologies and smartphones have become more ubiquitous, a large amount of information about a person’s life has become available. However, since each person has a unique way of performing physical activities, a Human Activity Recognition system needs to be adapted to the characteristics of a person in order to maintain or improve accuracy. Additionally, when smartphones devices are used to collect data, it is necessary to manage its limited resources, so the system can efficiently work for long periods of time. In this paper, we present a semi-supervised ensemble algorithm and an extensive study of the influence of hyperparameter configuration in classification accuracy. We also investigate how the classification accuracy is affected by the person and the activities performed. Experimental results show that it is possible to maintain classification accuracy by adjusting hyperparameters, like window size and window overlap, depending on the person and activity performed. These results motivate the development of a system able to automatically adapt hyperparameter settings for the activity performed by each person.

Given a process model and an event log, conformance checking aims to relate the two together, e.g. to detect discrepancies between them. For the synchronous product net of the process and a log trace, we can assign different costs to a synchronous move, and a move in the log or model. By computing a path through this (synchronous) product net, whilst minimizing the total cost, we create a so-called optimal alignment — which is considered to be the primary target result for conformance checking. Traditional alignment-based approaches (1) have performance problems for larger logs and models, and (2) do not provide reliable diagnostics for non-conforming behaviour ( e.g. bottleneck analysis is based on events that did not happen). This is the reason to explore an alternative approach that maximizes the use of observed events. We also introduce the notion of milestone activities, i.e. unskippable activities, and show how the different approaches relate to each other. We propose a data structure, that can be computed from the process model, which can be used for (1) computing alignments of many log traces that maximize synchronous moves, and (2) as a means for analysing non-conforming behaviour. In our experiments we show the differences of various alignment cost functions. We also show how the performance of constructing alignments with our data structure relates to that of the state-of-the-art techniques.

The existing methods of building change detection (CD) using remote sensing (RS) images are still deficient in handling scale variation and class imbalance problems, indicating a decrease in the robustness of small-object detection and pseudo-change information. Thus, a novel building CD framework called the multiscale context aggregation network (MSCANet) is proposed. The high-resolution network is integrated into the feature extracting stage to maintain high-resolution representations throughout the whole process. Then, multiscale context information is aggregated using a scale-aware feature pyramid module (FPM). Recognition performance can be improved from discriminant feature representation learning by using a channel–spatial attention module. Furthermore, a class-balanced loss is proposed to reduce the impact of class imbalance in long-tail datasets. Experimental results from using the LEVIR-CD and SZTAKI AirChange benchmark datasets prove the superiority of the MSCANet over the other baseline methods, with improved maximum F1 scores of 5.28 and 8.47, respectively.

The discontinuous Galerkin (DG) method is a class of finite element methods using completely discontinuous piecewise smooth functions (typically polynomials) as basis and test functions. Since its inception in 1973 [10], it has seen a sustained development, both in the computational mathematics community and in many scientific and engineering application communities. The DG methods have several advantages, such as its extreme flexibility in dealing with complex geometry and adaptive computation (both h- and p-adaptivities are easy to implement), extremely high parallel efficiency, good stability properties (energy and entropy stability has been established for DG methods in many situations), nice convergence and superconvergence properties, and capability to solve hyperbolic and convection- dominated problems effectively.

Over 20 years ago, Information Retrieval (IR) researchers began their quest for sound IR systems for children. The path was not straightforward. Challenges posed by interface design, relevance determination, diverse contexts, ethics, and many more, were taken up and explored from different perspectives. Large projects such as Puppy-IR and the International Children's Digital Library gave this field a certain boost; still, there is neither a sound solution for children in the search area in 2021 nor a roadmap to get there. What is the reason for this? Does the field cry out for specific IR solutions developed on a small scale for very small sub-fields and specific target groups? Are there some significant unforeseen barriers that hinder researchers? What about obstacles natural to areas of study such as this one that require a multidisciplinary approach or involve protected populations? With this workshop, we want to bring together as many key experts as possible from research and industry who focus on IR for children to understand why, unlike other IR areas, this one has not flourished and look for the biggest challenges for the next 10 years. We are not only thinking of traditional researchers and designers but also of those who develop and use IR systems for fields, such as in music, film, and education, as a way to push past this immobility and look at the problem from new, and perhaps more stimulating, perspectives.

We report on the exploration we conducted to understand better children’s needs for the design of Search Engine Result Pages (SERP) that can help them notice relevant resources when performing online inquiry tasks in a classroom context. We analyse children’s interactions with traditional and emoji-enriched SERP and look for trends linking children’s engagement with SERP and search success (based on experts’ assessments). We also identify areas remaining to be interpreted and considered in future studies. On this mixed ground, we discuss the complexity of this design space and the need to bypass the one-size-fits-all approach in favor of adaptive SERP to cater to children’s different and ever-evolving skills in searching and recognising useful results if we aim to support learning.

This work presents BACGRAPH, a tool that extracts relationships among configuration parameters of Building Automation and Control Systems (BACSs) implemented using the BACnet protocol (ISO 16484-5). BACnet models these configuration parameters as object data structures comprised of multiple properties, some of which contain references to other objects. Given the regular exchange of objects among devices, we leverage these explicit references to build a graph of BACnet objects exclusively from network traffic. We tested BACGRAPH using traffic collected from a real building located at the University of Twente. After analyzing 66.8 hours of traffic, the resulting graph is comprised of 13,733 nodes and 3,169 edges. Such a graph improves the system visibility that BACS administrators have over their infrastructure, which is crucial for troubleshooting and security.

We investigated how different graphical representations convey the underlying uncertainty distribution in ensemble predictions. In ensemble predictions, a set of forecasts is produced, indicating the range of possible future states. Adopting a use case from life sciences, we asked non-expert participants to compare ensemble predictions of the growth distribution of individual children to that of the normal population. For each individual child, the historical growth data of a set of 20 of its best matching peers was adopted as the ensemble prediction of the child’s growth curve. The ensemble growth predictions were plotted in seven different graphical formats (an ensemble plot, depicting all 20 forecasts and six summary representations, depicting the peer group mean and standard deviation). These graphs were plotted on a population chart with a given mean and variance. For comparison, we included a representation showing only the initial part of the growth curve without any future predictions. For 3 months old children that were measured at four occasions since birth, participants predicted their length at the age of 2 years. They compared their prediction to either (1) the population mean or to (2) a “normal” population range (the mean ± 2(standard deviation)). Our results show that the interpretation of a given uncertainty visualization depends on its visual characteristics, on the type of estimate required and on the user’s numeracy. Numeracy correlates negatively with bias (mean response error) magnitude (i.e. people with lower numeracy show larger response bias). Compared to the summary plots that yield a substantial overestimation of probabilities, and the No-prediction representation that results in quite variable predictions, the Ensemble representation consistently shows a lower probability estimation, resulting in the smallest overall response bias. The current results suggest that an Ensemble or “spaghetti plot” representation may be the best choice for communicating the uncertainty in ensemble predictions to non-expert users.

Essentially, all motion in living organisms emerges from the collective action of biological molecular machines transforming chemical energy, originally harvested from light, into ordered activity. As a man-made counterpart to nature’s biomolecular machines, chemists have created artificial molecular machines that display controlled and even directional motion in response to light. However, to be of practical value, the motion of these light-fuelled molecular machines will have to be coupled to the rest of the world. Inspired by the complex functional movement seen in the plant and animal world, chemists have undertaken the challenge to harness molecular motion and, so, they have set artificial molecular motors and switches to work and perform useful mechanical action at the macroscopic level. Here, we review these recent developments. We show how modern research has embraced the full complexity of the molecular world by aiming at the design of autonomous, and sometimes adaptive, molecular systems that work continuously under the effect of illumination. We report evidence that molecular motion can be engineered into highly sophisticated movements and that, from a fundamental point of view, continuous movement can only emerge when man-made molecules cooperate, in space and time. Eventually, unravelling the rules of molecular motion will support the creation of molecular materials that produce work continuously under a constant input of energy. The natural world has long provided inspiration for the production of artificial, adaptive materials. This Review discusses how unravelling the rules of molecular motion has enabled integration of the cooperative, and sometimes synchronized, operation of light-responsive molecular machines, across length scales, into responsive and autonomous matter.

Purpose This contribution to the special issue integrates findings addressed by the other papers. In order to structure the insights delivered by the studies and to address the perspectives with the objectives of the special issue, the purpose of this paper is to identify two major components: system characteristics and culture. Design/methodology/approach These are discussed and subsequently the studies are positioned according to this framework. Findings The authors further discuss elements involved in increasing the power of clients of the educational system, needs for capacity building, and the need for horizontal and vertical accountability. Originality/value Discussion paper to a special issue on data-based decision making.

We consider the integrated planning of spare parts and service engineers that are needed for serving a group of systems. These systems are subject to different failure types, and for each failure, a service engineer with the necessary spare part has to be assigned to repair the system. The service provider follows a backlogging policy with part reservations. That is, a repair request is backlogged if one of the required resources is not immediately available upon demand. Moreover, a spare part is reserved if the requested spare part is in stock but no service engineer is immediately available. The spare parts are typically slow-movers and are managed according to a base-stock policy. The objective is to jointly determine the stock levels and the number of service engineers to minimize the total service costs subject to a constraint on the expected total waiting times of the repair calls. For the evaluation of a given setting, we present an exact method (computationally feasible for small problems) and an accurate approximation. For the joint optimization, we present a greedy heuristic that efficiently produces close-to-optimal results. We test how the heuristic performs compared to the optimal solution and the separate optimization of spare parts and service engineers in an extensive numerical study. In a case study with 93 types of spare parts, we show that the solution of the greedy algorithm is always within 2% of the optimal solution and is up to 20% better than a separated optimization approach encountered in practice.

Highlights Amount of transistors on chip oubles every 1.5–2 years accordig to Moore’s law. Multiple patterning is required to obtain ∼10–16 nm half-pitch using immersion lithography. EUV scanners will extend Moore’s Law for the foreseeable future. View of further extension of EUV in the future will be discussed in this article.

Purpose Three-dimensional (3D) printing is one of the most promising technologies that can significantly improve the efficiency of various radiotherapy (RT) procedures. In order to 3D print any structure from a DICOM file, it first has to be converted to one of the typical formats recognized by printing software, such as STL files. Due to the specificity of the data from RT planning systems and a lack of standardized methods, the geometry processing might be tricky and complex. However, all the articles presented in the literature so far either describe this step very briefly, or do not mention it at all. Methods We present a detailed description of an algorithm and a piece of open-source software, that allow conversion of contour-sequence data included within DICOM files into ready-to-print 3D structures saved as STL files. The algorithm allows automatic handling of the data from RT planning systems and takes into account and corrects for all the typical issues, such as different orientation of curves or artifacts. The implementing software, included as a supplementary material, is distributed as open source under GNU GPL license, which means that it can be freely used, modified, and redistributed by anyone. Results The algorithm and implementing software were used to convert numerous structures contained within DICOM files created with RT planning systems into ready-to-print 3D structures. The structures were converted with absolute fidelity, without any errors. Conclusions The presented tools can be used for research and development of patient-specific accessories for RT. The described algorithm and software can make 3D printing in RT more accessible to medical professionals, contributing to its further development.

This paper presents an energy-/area-efficient digitally controlled oscillator (DCO)-based phase-tracking Receiver for Internet-of-Things (IoT) applications. The RX leverages the constant-envelope nature of frequency shift keying modulation adopted in many IoT protocols, e.g., IEEE802.15.4 and Bluetooth low energy (BLE), to enhance the energy efficiency and to reduce the chip area. The proposed RX, with the DCO acting as a local oscillator (LO) and binary frequency feedback, promotes low-power and low-voltage circuit design, while a data-aided carrier-frequency tracking ensures the received carrier stability without a power-/area-hungry phase-locked loop (PLL). The RX avoids a compromise between a power-hungry I/Q LO generation and the image rejection in traditional RXs. An equivalent mathematical model is further presented, which helps to analyze and optimize the frequency response of the proposed RX. Fabricated in 40-nm CMOS, the RX consumes 1.55 mW from a 0.85-V supply and has a −87-dBm sensitivity at 2 Mb/s, which leads to a 0.77-nJ/b energy efficiency and 178-dB RX sensitivity FoM.

Although physical buttons provide tactile sensations that allow them to be identified and pressed without visual focus, their static nature limits their use for dynamic interfaces. Conversely, touchscreens offer highly flexible, task-specific interfaces, but they do not provide the tactile qualities needed for vision-free interaction. Here, we present a stretchable display that can change shape from a flat sheet into a dome when pressurized. The vanishing interface we designed uses hyperelastic light-emitting capacitors (HLECs) that actively emit light, sense strain, and detect finger presses. We characterize the stretch and luminance of the device as the thin sheet is pressurized. Interestingly, but not unexpectedly, these HLEC panels show a pressure-dependent luminance, which we use to highlight where they are being pressed, a visual display of haptic information. We further demonstrate the co-located touch sensing and light-emitting capabilities by developing an interactive memory game.

Percutaneous needle procedures are among the most frequently performed minimally invasive surgical procedure. For tracking the needle tip in the tissue, 2D ultrasound (US) imaging is commonly used; however, the low resolution of the images creates a challenge for tracking. This paper describes a robotic system that can perform US image guided biopsies by tracking the needle and the target simultaneously. It uses a template-based visual tracking method for small and deformable targets. During the experiments, a needle was inserted into realistic phantoms using a 5-DOF robot. The 2D US probe was held by a robotic arm that was servoed along the needle path. The 3D shape of the needle was estimated using the 2D transverse US images, which was used to align the needle axis with the 2D imaging plane. The accuracy of the visual needle tip tracking was evaluated using an optical tracking system, and a computed tomography scanner was used to determine the accuracy of the 3D needle shape estimation method. Target reaching accuracies were measured using an electromagnetic tracking system. The results of the experiments showed that the proposed system can track the needle tip in 2D US guided needle procedures in real-time with a sub-millimeter positional error.

UHMWPE is a biomaterial that is widely used in the orthopedic field. UHMWPE is used as a liner and is paired with a femoral head to replace the damaged hip joint. Currently, there is no research on parameters of salat biomechanics such as range of motion, joint reaction force. The results of hip joint reaction force during salat are used for simulation load parameter on UNDIP bipolar artificial hip joint simulation with those loads using finite element method (FEM). From the simulation results, impingement occurs during last sitting. Patient who using an artificial hip joint should avoid last sitting.

Recent technological advances are rekindling educational interest in hypervideo (HV). HV provides a good opportunity for enhanced teaching and learning thanks to its affordances for interactivity (advanced navigation control, hyperlinking with supplementary materials, and communication options). This study investigated how these functionalities can be employed in a vocational education context by examining the design of three prototypical HV lesson scenarios (expository, individual learning, and collaborative learning) and comparing their effectiveness with that of a traditional lesson. Effects on motivation and knowledge acquisition were assessed. Results indicated that the HV lessons supported learning more effectively than traditional lessons. On a delayed posttest, all experimental conditions scored significantly higher than the control condition. The special benefits of HV use for expository instruction are singled out for discussion. This study provides an encouraging starting point for scholars who would like to investigate the functional integration of HV in educational practices.

Our minds tend to frequently drift away from present technology-related situations and tasks. Against this background, we seek to provide a better understanding of mind-wandering episodes while using information technology and its link to decisive variables of Information Systems research, such as performance, creativity and flow. Since the academic literature still lacks reliable and validated measurements that can fully account for all facets of mind-wandering episodes while using information technology, our work addresses this gap by presenting a way to triangulate data in the context of a digital insight problem-solving task. This new approach enables researchers to further investigate the effects of spontaneous thought in technology-related settings and is a promising building block for the development of neuroadaptive systems.

In the last few decades, the use of power converters has become essential in the smart grid environment. Consequently, this leads to the presence of a high-level of conducted electromagnetic interference between the smart grid elements. This paper studies the effect of two power converter modulation techniques: conventional fixed-frequency modulation and spread-spectrum modulation, on the performance of the Power Line Communication (PLC) signal. The paper presents a practical implementation of the system and discusses the results for different operating scenarios.

Our future urban environments are smart. Sensors and actuators are embedded in these environments and their inhabitants. We have an Internet of Things, where the ‘Things’ include objects, cars, tools, buildings, street furniture, and whatever can be equipped with sensors and actuators, including human and non-human animals. Augmented humans and augmented animals have their senses extended with digital technology. Their smart wearables connected with the smart environment make humans and animals smarter. Rather than on living animals, in this survey paper we focus on non-living virtual and augmented reality non-human animals that will inhabit our smart and playable urban environments. They will co-exist with robotic animals and (digitally augmented) humans and non-human animals. We include observations on augmented humans interacting with virtual and augmented reality animals. The paper is meant to raise awareness for the possibilities of augmented reality to introduce virtual animals for social, entertainment, and educational reasons.

Food consumption decisions require consumers to evaluate the characteristics of products. However, the literature has given limited attention to how consumers determine the impact of food on health (e.g., kilocalories) and on the environment (e.g., carbon footprint). In this exercise, 1511 consumers categorised 43 food products as healthy/unhealthy and good/bad for the environment, and estimated their kilocalories and carbon footprint, which were known to the investigator. The task was performed either with no stimuli (a control group), under time pressure only, with traffic-light labels only, or both. Results show that traffic-light labels: 1) operate through improvements in knowledge, rather than facilitating information processing under pressure; 2) improve the ability to rank products by both kilocalories and carbon footprint, rather than the ability to use the metric; 3) reduce the threshold used to categorise products as unhealthy/bad for the environment, whilst raising the threshold used to classify products as good for the environment (but not healthy). Notably, traffic-light increase accuracy by reducing the response compression of the metric scale. The benefits of labels are particularly evident for carbon footprint. Overall, these results indicate that consumers struggle to estimate numerical information, and labels are crucial to ensure consumers make sustainable decisions, particularly for unfamiliar metrics like carbon footprint. QC 20200929 project SFLAB

In upstream oil and gas production, flow measurement errors are common at multiple locations in the production system. These measurement errors lead to imbalances, which are reconciled by using allocation methods. The allocation method should redistribute the imbalance in a fair manner to all involved stakeholders. In this paper, data validation and reconciliation (DVR) is proposed as an alternative allocation method in multiphase production systems. DVR is a model-based optimization method that exploits redundancies in process data to minimize random measurement errors, and simultaneously provides a basis for the detection of gross errors. To study the applicability of DVR compared to conventional allocation methods, a model for generic multiphase production systems is developed, and the corresponding DVR problem is formulated. In order to deal with nonlinear flow effects (e.g. interphase mass transfer) the model is linearized around the operating conditions and the DVR problem is solved iteratively. From the simulation studies, it is concluded that DVR reduces measurement errors by up to 56% for gas allocation systems and 33% for multiphase systems. Gross errors in interior locations of the network can be located precisely, while allocating gross errors to exterior locations is generally not possible, due to limited network redundancy. Compared to conventional methods, DVR provides a better means for the detection of gross errors, and results in more accurate attribution of oil/gas ownership and more fair division of revenues between stakeholders.

The usage of embedded instruments (EIs) in a processor core to address dependability challenges of modern-day Multi-Processor System-on-Chip (MP-SoC) has been studied in literature extensively. Data from these EIs can be used in applications like end-of-lifetime (EOL) predictions. However, inaccuracies present in the data from these EIs, due to their selfaging and resolution limitations during digitization, can lead to an inaccurate EOL assessment. In this paper, it is presented that in the presence of such inaccuracies from EIs as well as correlation between EIs, principal component analysis (PCA) based datafusion approach for determining the EOL of selected critical paths provided overall better EOL predictions as compared to EOL predictions based on standalone EIs. Verification was performed with a commercial software-based EOL predictor tool ARULE running on a personal computer. Moreover, the presented results on the computational requirements for the presented data-fusion approach showed little overhead in terms of memory, execution time and energy requirements.

Eye gaze is used to convey crucial information during interactions with humans and robots. Modern video conferencing systems, an aspect of mobile robotic telepresence (MRP) systems, have limited eye gaze functionality due to the Mona Lisa effect. This paper compares the effects of eye gaze during brief interactions with MRP operators. In an online between-subjects study (N = 79), participants viewed a video of a hallway encounter between a passer-by and an MRP system; the operator either used or did not use mutual gaze. We used an observer ‘trick’ to study the effect cheaply. Results showed that participants’ social impressions of the MRP operator were higher when the operator used versus did not use mutual gaze, showing that eye gaze can lead to an improvement in social perception even in a seemingly insignificant encounter such as a hallway passing. We argue for the importance of social acknowledgment cues to improve impressions of telepresence operators and to make mingling smoother and social roles more balanced. We describe potential opportunities for technological innovation in MRP systems and propose research directions to extend this work.

Concern has been mounting about Internet centralization over the few last years -- consolidation of traffic/users/infrastructure into the hands of a few market players. We measure DNS and computing centralization by analyzing DNS traffic collected at a DNS root server and two country-code top-level domains (ccTLDs) -- one in Europe and the other in Oceania -- and show evidence of concentration. More than 30% of all queries to both ccTLDs are sent from 5 large cloud providers. We compare the clouds resolver infrastructure and highlight a discrepancy in behavior: some cloud providers heavily employ IPv6, DNSSEC, and DNS over TCP, while others simply use unsecured DNS over UDP over IPv4. We show one positive side to centralization: once a cloud provider deploys a security feature -- such as QNAME minimization -- it quickly benefits a large number of users.

Here, we present the outcome of the 4th workshop on Multisensory Approaches to Human-Food Interaction (MHFI), developed in collaboration with ICMI 2020 in Utrecht, The Netherlands. Capitalizing on the increasing interest on multisensory aspects of human-food interaction and the unique contribution that our community offers, we developed a space to discuss ideas ranging from mechanisms of multisensory food perception, through multisensory technologies, to new applications of systems in the context of MHFI. All in all, the workshop involved 11 contributions, which will hopefully further help shape the basis of a field of inquiry that grows as we see progress in our understanding of the senses and the development of new technologies in the context of food.

International audience; Organised to celebrate the 25th anniversary of the FMICS international conference, the present survey addresses 30 questions on the past, present, and future of formal methods in research, industry, and education. Not less than 130 high-profile experts in formal methods (among whom three Turing award winners and many recipients of other prizes and distinctions) accepted to participate in this survey. We analyse their answers and comments, and present a collection of 111 position statements provided by these experts. The survey is both an exercise in collective thinking and a family picture of key actors in formal methods.

International audience; This conceptual research presents a new stochastic model of the dynamics of state-to-state transitions in social systems, the Zhukov–Khvatova model. Employing a mathematical approach based on percolation theory the model caters for random changes, system memory and self-organisation. Curves representing the approach of the system to the percolation threshold differ significantly from the smooth S-shaped curves predicted by existing models, showing oscillations, steps and abrupt steep gradients. The modelling approach is new, working with system level parameters, avoiding reference to node-level changes and modelling a non-Markov process by including self-organisation and the effects (memory) of previous system states over a configurable number of time intervals. Computational modelling is used to demonstrate how the percolation threshold (i.e. the share of nodes which allows information to spread freely within the network) is reached. Possible applications of the model discussed include modelling the dynamics of viewpoints in society during social unrest and elections, changing attitudes in social networks and forecasting the outcome of promotions or uptake of campaigns. The easy availability of system level data (network connectivity, evolving system penetration) makes the model a particularly valuable addition to the toolkit for social sciences, politics, and potentially marketing.

Wireless sensor networks (WSN) are the basis for wildlife monitoring systems (WMS) because of their flexibility and energy-efficiency. However, their utilization often involves an inherently frequent data advertising mechanism that can result in a significantly higher energy consumption for monitoring mobile animals. To overcome this issue, we propose an mobility event-driven beacon advertising control scheme based on an unsupervised algorithm. We transmit more frequently in a panic state and less frequently during passive state. We compared our approach with a conventional periodic advertising and K-Mean clustering based schemes by analyzing the network energy consumption. The results show that the proposed mechanism could indeed reduce the energy consumption substantially by adaptively adjusting the node wake-up time to specific animal movement states.

Children turn to online search tools to complete classroom-related assignments. Unfortunately, these tools are rarely explicitly tailored to meet children's needs and skills. We seek to design a Search Agent (SA) for children in the context of education; one that can foster natural, effective and rewarding interactions in the educational context. To do so, we build on previous explorations of children's interactions with search tools and study how they imagine a SA that can help with assignments required online quests: What are the pragmatic and hedonistic qualities of such SA? Also, do previous experience and familiarity with search technology affect children's preferences? We involved in our study children ages 10-11 and inquired on factors that define their overall experience with a SA. We outline the steps of our research and the emerging lessons learned so that they can guide the design of a SA.

In the classroom, children mainly use general search systems such as Google, Baidu or Bing. For many years and from different perspectives, a call has been made that it is necessary to provide children in an educational context with child-friendly search systems. Research responding to this call often focuses on the relevance, readability and reliability of the retrieved documents. Instead, inspired by a recent study based on adult users on the role emotions play in web search, we explore whether and how children searching in a school context react to the emotional content often part of Search Engine Result Pages. We do so by examining emotions inferred from queries and corresponding retrieved resources in query logs produced by children ages 9 to 11 in a classroom setting in 3 different countries. We also consider teachers' observations that contextualize this analysis.

This paper was presented at the Invited Panel session “Technical Communication in China”. There has been various research on the reading time and legibility of online texts with people's tendency to online materials. Text-related attributes like font size or letterspacing are commonly used variables in this field. The objective of this study is to investigate the influential factors on the reading time of Chinese technical documentation, and to build a Decision Tree model to predict its reading time. In the experiment, log data including information of over a million user visits from a cloud service provider's website are collected. User's visit time, stay time, visit step, visit device and many other data fields are recorded in a user session. In addition to user behavioral data from log files, data metrics concerning technical documentation itself are also collected. For all documents used in the experiment, their word counts, image counts, link counts and section counts are scraped using web crawlers. The linear correlation analysis is applied in order to explore the correlations between variables for predictions. The results show that a 75 percent accuracy is achieved using the Decision Tree model.

Data about people are constantly collected and stored to help understand patterns which can then be leveraged by information retrieval systems (IRS), such as search engines or recommender systems, to identify and rank resources that respond to diverse users' needs. As a significant group of technology users, children's data are also collected for IRS. In the 4th edition of our proposed workshop, we seek to continue to build professional community connected to children's IRS and expand on the framework identified in the 3rd KidRec that outlines how to evaluate good IRS. This time, we are particularly interested in exploring how design, research, and practice perspectives can cohesively define policy in this area.