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Functional papers are the subject of extensive research efforts and have already become an irreplaceable part of our modern society. Among other issues, they enable fast and inexpensive detection of a plethora of analytes and simplify laboratory work, for example in medical tests. This article focuses on the molecular and structural fundamentals of paper and the possibilities of functionalization, commercially available assays and their production, as well as on current and future challenges in research in this field. Graphic abstract

Technology is shaping our lives in a multitude of ways. This is fuelled by a technology infrastructure, both legacy and state of the art, composed of a heterogeneous group of hardware, software, services, and organisations. Such infrastructure faces a diverse range of challenges to its operations that include security, privacy, resilience, and quality of services. Among these, cybersecurity and privacy are taking the centre-stage, especially since the General Data Protection Regulation (GDPR) came into effect. Traditional security and privacy techniques are overstretched and adversarial actors have evolved to design exploitation techniques that circumvent protection. With the ever-increasing complexity of technology infrastructure, security and privacy-preservation specialists have started to look for adaptable and flexible protection methods that can evolve (potentially autonomously) as the adversarial actor changes its techniques. For this, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) were put forward as saviours. In this paper, we look at the promises of AI, ML, and DL stated in academic and industrial literature and evaluate how realistic they are. We also put forward potential challenges a DL based security and privacy protection system has to overcome. Finally, we conclude the paper with a discussion on what steps the DL and the security and privacy-preservation community have to take to ensure that DL is not just going to be hype, but an opportunity to build a secure, reliable, and trusted technology infrastructure on which we can rely on for so much in our lives.

At ASIACRYPT 2018, Castryck, Lange, Martindale, Panny and Renes proposed CSIDH, which is a key-exchange protocol based on isogenies between elliptic curves, and a candidate for post-quantum cryptography. However, the implementation by Castryck et al. is not constant-time. Specifically, a part of the secret key could be recovered by the side-channel attacks. Recently, Meyer, Campos, and Reith proposed a constant-time implementation of CSIDH by introducing dummy isogenies and taking secret exponents only from intervals of non-negative integers. Their non-negative intervals make the calculation cost of their implementation of CSIDH twice that of the worst case of the standard (variable-time) implementation of CSIDH. In this paper, we propose a more efficient constant-time algorithm that takes secret exponents from intervals symmetric with respect to the zero. For using these intervals, we need to keep two torsion points on an elliptic curve and calculation for these points. We implemented our algorithm by extending the implementation in C of Meyer et al. (originally from Castryck et al.). Then our implementation achieved 152.8 million clock cycles, which is about 29.03% faster than that of Meyer et al.

The clustering coefficient has been widely used for identifying the local structure of networks. In this paper, the weighted spectral distribution with 3-cycle (WSD3) that is similar (but not equal) to the clustering coefficient is studied on evolving social networks. It is demonstrated that the ratio of the WSD3 to the network size (i.e., the node number) provides a more sensitive discrimination for the size-independent local structure of social networks in contrast to the clustering coefficient. Moreover, the difference of the WSD3’s performances on social networks and communication networks is investigated, and it is found that the difference is induced by the different symmetrical features of the normalized Laplacian spectral densities on these networks.

We discuss a new approach to interactive exploration of high-dimensional data sets which is aimed at building human’s understanding of the data by iterative additions of recommended attributes and objects that can together represent a context in which it may be useful to analyze the data. We identify challenges and expected benefits that our methodology can bring to the users. We also show how our ideas got inspired by Formal Concept Analysis (FCA) and Rough Set Theory (RST). It is though worth emphasizing that this particular paper is not aimed at investigating relationships between FCA and RST. Instead, the goal is to discuss which algorithmic methods developed within FCA and RST could be reused for the purpose of our approach.

In Nigeria, poor waste management practices are evident in many urban centers. This study considered a University community as the model to explore the use of digital solutions in proper waste management. This study developed a web-based waste management system for the collection of plastics and paper in a University community. The framework developed utilized the use case and system block diagram. In this study, HTML, CSS, MySQL and Java programming language was used in the design of the web-based system. The result of this study was presented using screenshots. The web-based system had an interface to show profiles of the users, waste pickup information, incentive platform, and the admin dashboard to monitor and approve pickups and incentives. Past studies have indicated the success of using incentives to build sustainable behavioural change towards efficient waste disposal. Therefore, this study was able to introduce the incentive platform which converts weight of paper and plastics waste to points then to rewards for the users. The web-based waste management system for the collection of plastics and paper can be deployed for University communities to help reduce the number of paper and plastics that end up in landfills. Also, by picking the paper and plastics waste from source helps to prevent bin contamination.

Several network growth models have been proposed in the literature that attempt to incorporate properties of citation networks. Generally, these models aim at retaining the degree distribution observed in real-world networks. In this work, we explore whether existing network growth models can realize the diversity in citation growth exhibited by individual papers – a new node-centric property observed recently in citation networks across multiple domains of research. We theoretically and empirically show that the network growth models which are solely based on degree and/or intrinsic fitness cannot realize certain temporal growth behaviors that are observed in real-world citation networks. To this end, we propose two new growth models that localize the influence of papers through an appropriate attachment mechanism. Experimental results on the real-world citation networks of Computer Science and Physics domains show that our proposed models can better explain the temporal behavior of citation networks than existing models.

There are several frameworks for password-based authenticated key exchange (PAKE) protocols with common reference string following the work of Katz, Ostrovsky and Yung (Eurocrypt’01), and it seems that the IND-CCA secure encryption is inevitable when constructing PAKE in standard model.

Physically unclonable function (PUF) is a technology to generate a device-unique identifier using process variation. PUF enables a cryptographic key that appears only when the chip is active, providing an efficient countermeasure against reverse-engineering attacks. In this paper, we explore the data conversion that digitizes a physical quantity representing PUF’s uniqueness into a numerical value as a new attack surface. We focus on time-to-digital converter (TDC) that converts time duration into a numerical value. We show the first signal injection attack on a TDC by manipulating its clock, and verify it through experiments on an off-the-shelf TDC chip. Then, we show how to leverage the attack to reveal a secret key protected by a PUF that uses a TDC for digitization.

Paper-based microfluidic devices, which have emerged as a low-cost analytical platform technology, promise simple yet powerful solutions for point-of-care (POC) disease diagnostics. In this chapter, we present a microfluidic paper-based electrochemical biosensor array capable of multiplexed detection of physiologically related metabolic markers. Compared with existing paper-based electrochemical devices, our design consists of a multiplexing array of eight electrochemical biosensors and works together with a portable, custom-made potentiostat for signal readout. The integrated platform, including the paper-based biosensor array and the portable potentiostat, is able to detect multiple target markers from one sample and carry out multiple measurements for each marker. Based on this platform, we conduct the detection of three metabolic biomarkers (i.e., glucose, lactate, and uric acid) in artificial urine, and achieve analytical performance comparable to existing commercial and paper-based analytical devices. As an integrated electrochemical system, the proposed multiplexing biosensor array will permit rapid and inexpensive disease diagnosis at the POC and/or in resource-limited regions.

Many datasets change over time. As a consequence, long-running applications that cache and repeatedly use query results obtained from a SPARQL endpoint may resubmit the queries regularly to ensure up-to-dateness of the results. While this approach may be feasible if the number of such regular refresh queries is manageable, with an increasing number of applications adopting this approach, the SPARQL endpoint may become overloaded with such refresh queries. A more scalable approach would be to use a middle-ware component at which the applications register their queries and get notified with updated query results once the results have changed. Then, this middle-ware can schedule the repeated execution of the refresh queries without overloading the endpoint. In this paper, we study the problem of scheduling refresh queries for a large number of registered queries by assuming an overload-avoiding upper bound on the length of a regular time slot available for testing refresh queries. We investigate a variety of scheduling strategies and compare them experimentally in terms of time slots needed before they recognize changes and number of changes that they miss.

We overview a recent line of work [Rozhoň and Ghaffari at STOC 2020; Ghaffari, Harris, and Kuhn at FOCS 2018; and Ghaffari, Kuhn, and Maus at STOC 2017], which proved that any (locally-checkable) graph problem that admits an efficient randomized distributed algorithm also admits an efficient deterministic distributed algorithm, thereby resolving several central and decades-old open problems in distributed graph algorithms. We present a short and self-contained version of the proofs, and conclude by discussing several related questions that remain open. This article accompanies a keynote talk of the author at the International Colloquium on Structural Information and Communication Complexity (SIROCCO) 2020. The writing is based on [24, 28, 45] and primarily targets non-experts. ISSN:0302-9743 ISSN:1611-3349

Gesture recognition is one of the most popular tasks in computer vision, where convolutional neural networks (CNNs) based method has obtained the state-of-the-art performance. However, it is generally acknowledged that CNNs need a large amount of data to achieve such performance. Event Camera is a kind of biologically inspired event-based camera, which can keep the information of moving objects and remove the redundant background data. In this paper, we set up a rock-paper-scissor hand gesture recognition system based on FlowNet and Event Camera. Event camera is used to acquire event data. Then we propose an algorithm for the proposed gesture recognition. Specifically, FlowNet2.0 is employed to extract the motion representation of the pre-processed visual data, and a CNN classification network is applied to recognize the symbols extracted according to the motion representation. As a comparison, we also apply the rock-paper-scissor gesture recognition algorithm on traditional camera. The experimental results show that the proposed system based on Event Camera gets better performance, and to some extent, weaken the dependence on the training data. The whole system achieves 94.0\(\%\) out-of-sample accuracy and allows computation at up to 30 fps.

Near infrared (NIR) spectroscopy is used in many applications to gather information about chemical composition of materials. For paper waste sorting, a small number of scores computed from NIR-spectra and assuming more or less unimodal clustered data, a pixel classifier can still be crafted by hand using knowledge about chemical properties and a reasonable amount of intuition. Additional information can be gained by visual data (VIS). However, it is not obvious what features, e.g. based on color, saturation, textured areas, are finally important for successfully separating the paper classes in feature space. Hence, a rigorous feature analysis becomes inevitable. We have chosen a generic machine-learning approach to successfully fuse NIR and VIS information. By exploiting a classification tree and a variety of additional visual features, we could increase the recognition rate to 78% for 11 classes, compared to 63% only using NIR scores. A modified feature ranking measure, which takes redundancies of features into account, allows us to analyze the importance of features and reduce them effectively. While some visual features like color saturation and hue showed to be important, some NIR scores could even be dropped. Finally, we generalize this approach to analyze raw NIR-spectra instead of score values and apply it to plastic waste sorting.

Software Defined Networking (SDN) was introduced with a philosophy of decoupling the control plane from the data plane which facilitates network management while ensuring programmability in order to improve performance and monitoring. OpenFlow which enabled SDN was first introduced to match twelve header fields whilst at current it matches forty one which is expected to grow exponentially. Therefore future networks must have the ability to flexibly parse packets through a common interface. Programming Protocol independent Packet Processing (P4) was introduced to achieve the aforementioned by programming the underlying switch, providing instructions and utilizing APIs to populate the forwarding tables. A P4 programmed switch will forward packets through a parser into multiple stages of match+action tables to find the destination node which is considered the most efficient mechanism for routing. This paper takes into the account the latest platform developed for service providers, Open Networking Operating System (ONOS) to deploy two environments configured in the aforementioned technologies in order to test their performance. Four case studies were drawn which were simulated in Mininet which incorporated SDN + P4 switches. A significant increase of performances were recorded when compared with the performance of cases using SDN only.

Based on the ZYNQ platform, this paper proposes a hardware/software co-design approach, and implements a binocular stereo vision system with high real-time performance and good human-computer interaction, which can be used to assist advanced driver assistance systems to improve driving safety. Combining the application characteristics of binocular stereo vision, the approach firstly modularizes the system’s functions to perform hardware/software partitioning, accelerates the data processing on FPGA, and performs the data control on ARM cores; then uses the ARM instruction set to configure the registers within FPGA to design relevant interfaces to complete the data interaction between hardware and software; finally, combines the implementation of specific algorithms and logical control to complete the binocular stereo vision system. The test results show that the frame rate with an image resolution of 640 * 480 can reach 121.43 frames per second when the FPGA frequency is 100M, and the frame rate is also high for large resolution images. At the same time, the system can achieve real-time display and human-computer interaction with the control of the graphical user interface.

Spatio-temporal data is one of the most important assets in the context of smart cities. Spatio-temporal big data comes from a variety of sensor devices, implies the state of urban operation, insight into the development trend. Due to the multidimensional characteristics and diverse analysis needs of spatial-temporal data, data analysis based on spatial-temporal data must take into account the large capacity, diversity and frequent changes of data. This makes spatial and temporal data analysis more difficult. In order to simplify the analysis of spatio-temporal data, a service-oriented intelligent framework is proposed. Firstly, the concept of spatio-temporal data service is introduced into the framework, and several common spatio-temporal data service models are defined. Then, a configurable scripting language was proposed to define the analytic application. We also developed a prototype tool to implement spatio-temporal data services on Hadoop. In order to prove the applicability of our method, we demonstrate the effectiveness of our work through a practical application-based study.

In this chapter, we describe the application of paper microfluidics to develop a high-performance malaria test appropriate for use in low-resource settings. Malaria ranks in the top three for infectious disease burden worldwide, resulting in about two million deaths per year. Gold standard diagnostic tests for malaria use multistep protocols that require laboratory facilities and are often not accessible to patients in the developing world. Lateral flow tests are simple bioassays that have been used in low-resource settings for decades but can lack the sensitivity needed for clinical utility. Using paper microfluidic tools to automate the multistep sample processing that is characteristic of laboratory tests, we have developed a signal-amplified immunoassay for malaria detection with a higher sensitivity and a lower limit of detection than conventional lateral flow tests. Alternative fluid control tools are described within the same malaria system, and their potential for use in other systems is discussed.

In the last decade, several permissionless proof-of-work blockchain protocols have focused on scalability. Since these protocols are very difficult to change once deployed, their robustness and security are of paramount importance. This paper summarizes the desired end properties of blockchain consensus protocols and sheds light on the critical role of theoretical analyses of their design. We summarize the major paradigms in prior constructions and discuss open issues in this space.

Automated techniques for analysis and optimization of finite-precision computations have recently garnered significant interest. Most of these were, however, developed independently. As a consequence, reuse and combination of the techniques is challenging and much of the underlying building blocks have been re-implemented several times, including in our own tools. This paper presents a new framework, called Daisy, which provides in a single tool the main building blocks for accuracy analysis of floating-point and fixed-point computations which have emerged from recent related work. Together with its modular structure and optimization methods, Daisy allows developers to easily recombine, explore and develop new techniques. Daisy’s input language, a subset of Scala, and its limited dependencies make it furthermore user-friendly and portable.

Fog-based radio access networks (F-RAN) are posed to play a pivotal role in the much-anticipated 5th Generation (5G) cellular networks. The philosophy of F-RAN is to harness the distributed resources of collaborative edge devices to deliver localized RAN services to the end users. The current F-RAN is implemented mainly utilizing dedicated hardware and do not leverage on the available large number of distributed edge devices. This paper introduces the idea of opportunistic fog RAN (OF-RAN) which comprises of virtual fog access points (v-FAPs). The v-FAPs are formed opportunistically by one or more local edge devices also referred to as service nodes, such as WiFi access points, femtocell base stations and more resource rich end user devices under the coverage and management of the physical FAP, which can be dedicated fog server, fog-enabled remote radio heads (RRHs) or macrocell base stations. The proposed OF-RAN can be a low latency and high scalable solution for 5G cellular networks.

New academic papers appear rapidly in the literature nowadays. This poses a challenge for researchers who are trying to keep up with a given field, especially those who are new to a field and may not know where to start from. To address this kind of problems, we have developed a topic browsing system for research papers where the papers have been automatically categorized by a probabilistic topic model. Rather than using Latent Dirichlet Allocation (LDA) for topic modeling, we use a recently proposed method called hierarchical latent tree analysis, which has been shown to perform better than some state-of-the-art LDA-based methods. The resulting topic model contains a hierarchy of topics so that users can browse topics at different levels. The topic model contains a manageable number of general topics at the top level and allows thousands of fine-grained topics at the bottom level.

Modern cryptographic schemes have been focusing on protecting attacks from computational bounded adversaries. The various cryptographic primitives are designed concretely following some randomization design strategies, so that one of the goals is to make it hard for the attacker to distinguish between the real ciphers and the randomly distributed ones. Recently, Google Brain team proposed the idea to build cryptographic scheme automatically based on the neural network, and they claim that the scheme can defeat neural network adversaries. While it is a whole new direction, the security of the underlined scheme is remained unknown. In this paper, we investigate their basic statistical behavior from traditional cryptography’s point of view and extend their original scheme to discuss how the encryption protocol behave under a much more stronger adversary.

Nowadays a handful applications are designed to consume dynamic real-time continuous stream data from IoT, Social network, Smart sensors and more. Several RDF Stream Processing (RSP) engines are available to query those data streams. Application designers have the freedom to select the best available RSP engine based on their application requirements. However, this selection needs to be done at design time resulting in early bound rigid solutions that are unable to adapt to changing application requirements. In this paper, we have evaluated two most popular RSP engines to proof that adaptivity is required to bridge the gap between RSP engines and applications requirement. Then we propose an adaptive middleware to adapt to dynamic application requirements during run-time. Moreover, adaptive middleware includes input and output control, monitoring the status of the underlying RSP engines, as a result the adaptive middleware is essential when single or multiple instances of same type engines are available.

Cloud platforms often take advantage of virtualization technology and make their actual hosts virtualized. As network attack events occur frequently, providing system security in a virtualized environment is the focus of this study. We have designed and implemented a lightweight network-based intrusion prevention system (IPS) named VMM-IPS for the virtual machine (VM) execution environment. To ensure the system safety of VMs and the host system at the same time, VMM-IPS is operated in the Linux kernel of the host system and co-located with the Kernel-based Virtual Machine that turns Linux kernel into a hypervisor. As packets enter the system, no matter destined to VMs or passing through the host, they are detected by VMM-IPS. Unlike user-level IPS that needs switching protection domain and copying packets to user buffer for inspection, VMM-IPS is more efficient because of the capability to perform in-place packet inspection. It adopts signature-based detection and is implemented with the multiple-pattern search algorithm AC-BM for efficient string matching. Besides, VMM-IPS can protect the system against attacks using packet splitting and reassembly to evade introduction detection system (IDS). The experimental results demonstrate VMM-IPS can achieve system safety effectively and efficiently.

Runtime verification is an approach growing in popularity to verify the correctness of complex and distributed systems by monitoring their executions. Domain Specific Modeling Languages are a technique used for specifying such systems in an abstract way, but still close to the solution domain. This paper aims at integrating runtime verification and domain specific modeling into the development process of complex systems. Such integration is achieved by linking the elements of the system model with the atomic propositions of the temporal correctness properties used to specify monitors. We provide a unified approach used for both the code generation and the simulation of the system through instance model transformations. This unification allows to check correctness properties on different abstraction levels of the modeled system.

Okumura proposed a candidate of post-quantum cryptosystem based on Diophantine equations of degree increasing type (DEC). Sizes of public keys in DEC are small, e.g., 1,200 bits for 128 bit security, and it is a strongly desired property in post-quantum erea.

Reliability, availability, maintenance and safety (RAMS) analysis is essential in the evaluation of safety critical systems like nuclear power plants and the railway infrastructure. A widely used methodology within RAMS analysis are fault trees, representing failure propagations throughout a system. We present DFTCalc, a tool-set to conduct quantitative analysis on dynamic fault trees including the effect of a maintenance strategy on the system dependability.

Part 3: Short Paper; International audience; We present our preliminary work towards a comprehensive solution for the hybrid (static + dynamic) verification of open distributed systems, using session types. We automate a solution for binary sessions where one endpoint is statically checked, and the other endpoint is dynamically checked by a monitor acting as an intermediary between typed and untyped components. We outline our theory, and illustrate a tool that automatically synthesises type-checked session monitors, based on the Scala language and its session programming library (lchannels).

This paper is a position paper based on an invited talk at WISA’16 in Korea. We argue that Quantum-Safe Cryptography (QSC) will likely have a deep impact on the practice of IT professionals. We detail also in the second part a classical candidate for quantum-safe cryptography: multivariate cryptography. Finally, we conclude by presenting HFEBoost a real-life deployment of multivariate cryptography.

The advent of crowdsensing technology has provided a promising possibility for monitoring noise pollution in large-scale areas. Constructing noise map by using mobile smart phones in a cost-effective manner is being widely used in the city and industrial plants. In this short paper, the state-of-the-art crowdsensing-based noise map applications are first summarized. Furthermore, open research challenges associated with building up noise map are highlighted.

The high charge, as well as the complicity of the effluents, make that the assimilative capacity of water is ineffective in the majority of the cases. Wastewaters treatment plants are a suitable alternative to solve the problem of this inefficiency of the natural assimilative capacity of water. The control and the determination of the optimal values of parameters that influence the function and the effectiveness of the treatment is a necessity. A classical study can be done to determine the good conditions of the treatment, this way is effective but it requires more time and reactive products that increase the cost invested in the study and design of the treatment. In our case, we will try to apply predictive analytics based on a statistic approach to optimize the efficiency of manganese dioxide extracted from a mining waste to treat synthesized textile effluent. In this study, we will use the response surface method using the JMP11 software to visualize the effect of the presence of salts in textile effluents on the removal of dyes using manganese dioxide. According to the p-Value of each studied parameter, both manganese dioxide dose and contact time have a significant effect on dye elimination. Also, the best retention rate (70.5%) is observed with a value of manganese dioxide dose equal to 6 mg/L and contact time of 180 min with an initial charge of 1.5 g/L for dye and 20 g/L for NaCl.

In this position paper, we argue for what we believe is a correct pathway to achieving scalable symbolic verification of JavaScript based on separation logic. We highlight the difficulties imposed by the language, the current state-of-the-art in the literature, and the sequence of steps that needs to be taken. We briefly describe Open image in new window , our semi-automatic toolchain for JavaScript verification.

Given a really low resolution input image of a face (say \(16\,{\times }\,16\) or \(8\,{\times }\,8\) pixels), the goal of this paper is to reconstruct a high-resolution version thereof. This, by itself, is an ill-posed problem, as the high-frequency information is missing in the low-resolution input and needs to be hallucinated, based on prior knowledge about the image content. Rather than relying on a generic face prior, in this paper we explore the use of a set of exemplars, i.e. other high-resolution images of the same person. These guide the neural network as we condition the output on them. Multiple exemplars work better than a single one. To combine the information from multiple exemplars effectively, we introduce a pixel-wise weight generation module. Besides standard face super-resolution, our method allows to perform subtle face editing simply by replacing the exemplars with another set with different facial features. A user study is conducted and shows the super-resolved images can hardly be distinguished from real images on the CelebA dataset. A qualitative comparison indicates our model outperforms methods proposed in the literature on the CelebA and WebFace datasets.

Users’ opinions can be greatly beneficial in developing and providing products and services and improving marketing techniques for customer recommendation and retention. For this reason, sentiment analysis algorithms that automatically extract sentiment information from customers’ reviews are receiving growing attention from the computer science community. Aspect-based sentiment analysis (ABSA) allows for a more detailed understanding of customer opinions because it enables extracting sentiment polarities along with the sentiment target from sentences. ABSA consists of two steps: Aspect Extraction (AE) that allows recognizing the target sentiment; Aspect Sentiment Classification (ASC) that enables to classify the sentiment polarity. Currently, most diffused sentiment analysis algorithms are based on deep learning. Such algorithms require large labeled datasets that are extremely expensive and time consuming to build. In this paper, we present two approaches based on transfer learning and weak supervision, respectively. Both have the goal of reducing the manual effort needed to build annotated datasets for the ASC problem. In the paper, we describe the two approaches and experimentally compare them.

We address the problem of security of cyber-physical systems where some sensors may be malicious. We consider a multiple-input, multiple-output stochastic linear dynamical system controlled over a network of communication and computational nodes which contains (i) a controller that computes the inputs to be applied to the physical plant, (ii) actuators that apply these inputs to the plant, and (iii) sensors which measure the outputs of the plant. Some of these sensors, however, may be malicious. The malicious sensors do not report the true measurements to the controller. Rather, they report false measurements that they fabricate, possibly strategically, so as to achieve any objective that they may have, such as destabilizing the closed-loop system or increasing its running cost. Recently, it was shown that under certain conditions, an approach of “dynamic watermarking” can secure such a stochastic linear dynamical system in the sense that either the presence of malicious sensors in the system is detected, or the malicious sensors are constrained to adding a distortion that can only be of zero power to the noise already entering the system. The first contribution of this paper is to generalize this result to partially observed MIMO systems with both process and observation noises, a model which encompasses some of the previous models for which dynamic watermarking was established to guarantee security. This result, similar to the prior ones, is shown to hold when the controller subjects the reported sequence of measurements to two particular tests of veracity. The second contribution of this paper is in showing, via counterexamples, that both of these tests are needed in order to secure the control system in the sense that if any one of these two tests of sensor veracity is dropped, then the above guarantee does not hold. Finally, a survey of recent results in Dynamic Watermarking is presented, along with a laboratory demonstration in securing a prototypical intelligent transportation system. The proposed approach has several potential applications, including in smart grids, automated transportation, and process control.

The paper presents an original solution for the nonlinear Gaussian control of the 18O isotope concentration at the output of a separation column. The design of the nonlinear controller is based on the proposed separation column mathematical model which is an accurate one. In order to highlight the advantages of using a nonlinear controller, the comparison with using a linear Proportional Integral Derivative controller is made. Both the architecture and the tuning procedure of the nonlinear controller represent original elements introduced in the paper. It is proved, through simulation, that the nonlinear controller improves significantly the control system performances, especially the settling time, which is a dominant parameter in analyzing the separation processes work. Also, a solution to determine the instantaneous value of the separation column length constant is proposed, solution which opens the possibility to implement new control strategies.

Gait analysis has potential use in various applications, such as health care, clinical rehabilitation, sport training, and pedestrian navigation. This paper addresses the problem of detecting gait events based on inertial sensors and body sensor networks (BSNs). Different methods have been presented for gait detection in the literature. Generally, straightforward rule-based methods involve a set of detection rules and associated thresholds, which are empirically predetermined and relatively brittle; whereas adaptive machine learning-based methods require a time-consuming training process and an amount of well-labeled data. This paper aims to investigate the effect of type, number and location of inertial sensors on gait detection, so as to offer some suggestions for optimal sensor configuration. Target gait events are detected using a hybrid adaptive method that combines a hidden Markov model (HMM) and a neural network (NN). Detection performance is evaluated with multi-subject gait data that are collected using foot-mounted inertial sensors. Experimental results show that angular rate hold the most reliable information for gait recognition during forward walking on level ground.

Most public domain images are exposed to copyright infringement due to different Rights Management Information (RMI) expression system and/or out-of-date RMI. To solve this problem, this paper proposes a reliable method of integrating the RMI representation system and updating the RMI with the up-to-date information based on the most reliable data among information collected from each site through a comparative search technique for public domain images based on deep learning. Through the RMI updating method proposed in this paper, public domain image sites will be able to minimize copyright infringement, since they can provide the most up-to-date RMI and may transform different RMI representation systems into a unified system.

The paper provides a practical guide on initialization of the recursive mixture-based clustering of non-negative data. For modeling the non-negative data, mixtures of uniform, exponential, gamma and other distributions can be used. Initialization is known to be an important task for a start of the mixture estimation algorithm. Within the considered recursive approach, the key point of initialization is a choice of initial statistics of the involved prior distributions. The paper describes several initialization techniques for the mentioned types of components that can be beneficial primarily from a practical point of view.

Paper-based devices are a portable and low-cost technology alternative among conventional analytical tools for point-of-care testing that have been growing in popularity due to their versatility and ease of use. Since their introduction in 2007, different methods to fabricate such devices have been proposed in literature, including photolithography, plotting, inkjet printing, wax printing, and stamping, among other methods, each with its unique advantages and drawbacks. Here, we present the most common current fabrication methods of microfluidic paper-based analytical devices (μPADs) in two and three dimensions, comparing their processes of fabrication, resolution of the features, and costs associated with their manufacture presenting insights towards the most adequate choice of fabrication method.

With the advent of manycore systems, shared memory parallelisation has gained importance in high performance computing. Once a code is decomposed into tasks or parallel regions, it becomes crucial to identify reasonable grain sizes, i.e. minimum problem sizes per task that make the algorithm expose a high concurrency at low overhead. Many papers do not detail what reasonable task sizes are, and consider their findings craftsmanship not worth discussion. We have implemented an autotuning algorithm, a machine learning approach, for a project developing a hyperbolic equation system solver. Autotuning here is important as the grid and task workload are multifaceted and change frequently during runtime. In this paper, we summarise our lessons learned. We infer tweaks and idioms for general autotuning algorithms and we clarify that such a approach does not free users completely from grain size awareness.

Nanjing has been listed as the one of the worst performers across China with respect to the high level of haze-fog, which impacts people’s health greatly. For the severe condition of haze-fog, PM2.5 is the main cause element of haze-fog pollution in China. So it’s necessary to forecast PM2.5 concentration accurately. In this paper, an artificial intelligence method is employed to forecast PM2.5 in Nanjing. At the data pre-processing stage, the main factors among the air pollutants (O3, NO2, SO2, CO, etc.) as well as meteorological parameters (pressure, wind direction, temperature, etc.) that affect PM2.5 are selected, and these factors of previous hours are as input data to predict PM2.5 concentration of next hours. Considering the air pollutants and meteorological data are typical time series data, a special recurrent neural network, which is called long short term memory (LSTM) network, is applied in this paper. To determine the amount of nodes in the hidden layer, a self-organizing method is used to automatically adjust the hidden nodes during the training phase. Finally, the PM2.5 concentrations of the next 1 h, 4 h, 8 h, and 12 h are predicted separately by using the self-organizing LSTM network based approach. The experimental result has been validated and compared to other algorithms, which reflects the proposed method performs best.

Operations Research Proceedings 2017 : Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Freie Universiät Berlin, Germany, September 6-8, 2017 International Conference on Operations Research, GOR 2017, Berlin, Germany, 6 Sep 2017 - 8 Sep 2017; Cham : Springer International Publishing, Operations Research Proceedings 729-735 (2018). doi:10.1007/978-3-319-89920-6_97, Published by Springer International Publishing, Cham

Generalized communicating P systems are particular variants of networks of cells where each rule moves only two objects. In this paper we show that GCPSs with three cells and with only join, or only split, or only chain rules are computationally complete computing devices. These bounds are improvements of the previous results.

Vehicles with centered orientable standard wheels are known to be omnidirectional and precise in their motion. The common decentralized strategy to control the driving velocities and wheel orientations leads to unnecessary high torques, an increased energy consumption as well as additional slippage due to the redundant actuation. This paper introduces a novel model-based control concept that overcomes these. It uses a minimal set of control variables within a PD control law to avoid counteractions. An additional inverse dynamics solution complements the control and further reduces slippage. The resulting generalized torque demand is optimal distributed among the drives. The paper additionally shows how the control performance can be further improved by an additional experiment which identify the inertia parameters of the vehicle. The overall approach is at the end validated by some experiments.

We consider a restless multi-armed bandit (RMAB) in which each arm can be in one of two states, say 0 or 1. Playing an arm generates a unit reward with a probability that depends on the state of the arm. The belief about the state of the arm can be calculated using a Bayesian update after every play. This RMAB has been designed for use in recommendation systems where the user’s preferences depend on the history of recommendations. In this paper we analyse the RMAB by first studying single armed bandit. We show that it is Whittle-indexable and obtain a closed form expression for the Whittle index. For a RMAB to be useful in practice, we need to be able to learn the parameters of the arms. We present Thompson sampling scheme, that learns the parameters of the arms and also illustrate its performance numerically.

Ad hoc teamwork has been introduced as a general challenge for AI and especially multiagent systems [16]. The goal is to enable autonomous agents to band together with previously unknown teammates towards a common goal: collaboration without pre-coordination. A long-term vision for ad hoc teamwork is to enable robots or other autonomous agents to exhibit the sort of flexibility and adaptability on complex tasks that people do, for example when they play games of “pick-up” basketball or soccer. As a testbed for ad hoc teamwork, autonomous robots have played in pick-up soccer games, called “drop-in player challenges”, at the international RoboCup competition. An open question is how best to evaluate ad hoc teamwork performance—how well agents are able to coordinate and collaborate with unknown teammates—of agents with different skill levels and abilities competing in drop-in player challenges. This paper presents new metrics for assessing ad hoc teamwork performance, specifically attempting to isolate an agent’s coordination and teamwork from its skill level, during drop-in player challenges. Additionally, the paper considers how to account for only a relatively small number of pick-up games being played when evaluating drop-in player challenge participants.

International audience; Stream GSOS is a specification format for operations and calculi on infinite sequences. The notion of bisimilarity provides a canonical proof technique for equivalence of closed terms in such specifications. In this paper, we focus on open terms, which may contain variables, and which are equivalent whenever they denote the same stream for every possible instantiation of the variables. Our main contribution is to capture equivalence of open terms as bisimilarity on certain Mealy machines, providing a concrete proof technique. Moreover, we introduce an enhancement of this technique, called bisimulation up-to substitutions, and show how to combine it with other up-to techniques to obtain a powerful method for proving equivalence of open terms.