Search

In this paper the authors present how the ecosystem for innovation and ICT for logistics within Europe was shaped and played out in the recent one and a half decades. Better and more intelligent use of logistics' tangible and intangible resources, ranging from trucks to ICT back-office systems, will become necessary in the upcoming years. Various large-scale projects, programmes, and initiatives have been active and key trends in ICT – from IoT over smart services and the FI-PPP to ITS — were incorporated into the innovation ecosystem. Nevertheless, the final widespread integration and harvesting of its benefits is outstanding. Therefore, we present a single European Logistics Innovation Ecosystem which build upon a bottom-up, decentralised approach rather than the usual legacy system based top-down integration approach. The end of the paper presents possible options for innovation system benefits, deployment approaches, and application scenarios.

This work presents an experimental study of second mode positive streamers propagating along mineral oilpaper interfaces. A point-plane arrangement immersed in mineral oil with the paper inclined 60 degrees to the plane electrode is used to create the liquid-solid interface. Kraft paper and a kraft fibril paper, made from cellulosic micro and nano fibrils, with higher density and lower surface roughness are used as the solid materials. High speed shadowgraphy and charge recordings are used to compare the propagation of second mode positive streamers along the mineral oil-kraft paper and mineral oil-kraft fibril paper. Streamers creeping along the mineral oil-kraft paper interface propagate mainly into the liquid, with one or two main filaments. In comparison, the streamers propagating along the kraft fibril paper show a strong reduction of the branching; these streamers consist of a single filament that propagates exactly on the solid surface. Streamers along the kraft fibril paper also have longer propagation time than for the case with kraft paper. Mutual electrostatic shielding between filaments is observed for the streamers creeping on the kraft paper. An electrostatic analysis of the influence of permittivity, density and surface roughness of the solid in the electrical properties of the streamer filaments is also performed.

In this paper we exploit graphite's thermal absorption properties to drive the process of analyte sampling in paper-based biosensors. Graphite structures can be easily patterned or drawn on paper using a standard pencil and selective heating of the patterned layers can be remotely achieved using a light source. The resulting thermal gradient manifests itself as concentration gradients across the paper substrate which then triggers the flow of analyte to the selective areas. In this paper we have validated this hypothesis using a prototype made out of a low-cost filter paper substrate and a 300mW 808nm remote infrared laser source. Compared to a control paper substrate, we show an increase in temperature by more than 70°C (from 50°C to 120°C) in areas where the graphite is patterned. As a result the proposed prototype is also shown to demonstrate a higher sample-flow rate compared to the control. We anticipate that the proposed remote triggering of sample acquisition would be useful for different variants of paper-based biosensors that need to be integrated inside the food-package.

An investigation has been made on Paper-based substrate integrated waveguide (SIW) antenna operating around 21 GHz. The proposed antenna is an SIW square cavity backed radiating rectangular slot. It has the advantage of ultra low profile which makes it suitable for high integration microwave circuits. In addition, this antenna is well shielded in order to be fabricated for application in emerging wireless communication systems. Moreover, a wide bandwidth of almost 3.5 GHz is achieved and a high gain equal to 1.82dB is noted which is not commonly performed for paper-based antenna.

Biological information can easily be monitored by installing sensors in a lavatory bowl. Lavatories are usually shared by several people, so users need to be identified. Because of the need for privacy, using cameras, microphones, or scales is not appropriate. Though personal identification can be done using a touch panel, the user may forget to use it because the action is not necessary. In this paper, we focus on the differences in the way of pulling a toilet paper roll and propose a system that identifies individuals based on features of rotating of toilet paper rolls with a gyroscope. The evaluation results revealed that 83.9% accuracy was achieved for a five-person group in a laboratory environment, and 69.2% accuracy was achieved for a five-person group in a practical environment.

In this paper, we propose a data augmentation framework for Optical Character Recognition (OCR). The proposed framework is able to synthesize new viewing angles and illumination scenarios, effectively enriching any available OCR dataset. Its modular structure allows to be modified to match individual user requirements. The framework enables to comfortably scale the enlargement factor of the available dataset. Furthermore, the proposed method is not restricted to single frame OCR but can also be applied to video OCR. We demonstrate the performance of our framework by augmenting a 15% subset of the common Brno Mobile OCR dataset. Our proposed framework is capable of leveraging the performance of OCR applications especially for small datasets. Applying the proposed method, improvements of up to 2.79 percentage points in terms of Character Error Rate (CER), and up to 7.88 percentage points in terms of Word Error Rate (WER) are achieved on the subset. Especially the recognition of challenging text lines can be improved. The CER may be decreased by up to 14.92 percentage points and the WER by up to 18.19 percentage points for this class. Moreover, we are able to achieve smaller error rates when training on the 15% subset augmented with the proposed method than on the original nonaugmented full dataset.

Laundry service is becoming more popular in all the time. Essentially, the new laundry shop offers self-service washing and drying, but customers must also enter the laundry service. The self-service laundry dilemma arises when there are too many customers and too few washing machines, requiring customers to wait longer for an appropriate machine. Nowadays, some housewives rely on using the laundry to help with the house choirs despite the fact that they are unable to drive. There is also no unique laundry service company that provides home-to-home laundry service. In this paper a review of the pickup and delivery laundry service applications and systems have been presented and its aim to show the existing applications that are currently used for this purpose and to suggest the best applications that can be used by the customers to save their time.

Lately, Deep Learning has indicated its capacity by adequately tackling complex learning issues which were not possible previously. Specifically, Convolutional Neural Networks are most generally utilized and have indicated their viability in image detection and recognition problems. Convolutional Neural Networks(CNN) can be used to perform different kinds of tasks. However, to recognize even a single image, billions of calculations are required. For example, ResNet 50 takes 8 billion calculations just to figure out what's in a single image. So we need to find out ways to be able to run that superfast and that's why we need hardware accelerators. In this paper the basic information about Convolutional Neural Networks is provided along with the key operations involved and the brief idea about Field Programmable Gate Arrays(FPGA) is given which enable them to be used for accelerating the inference process of Convolutional Neural Networks. Various techniques which were employed previously for accelerating the Convolutional Neural Networks are discussed. We are focussing on designing a energy efficient Field Programmable Gate Arrays accelerator for accelerating the inference process of Convolutional Neural Networks with reduced latency and increased throughput.

The emerging of information technology (IT)and operational technology (OT) convergence has driven the smart grid technology adoption in the European (EU) energy system for better visibility and automated controllability. On the other hand, the energy system infrastructures can be threaten by the cyber attacks due to the increasing of information and communication technology integration. The cyber vulnerabilities are caused by the increasing of internet connection and the application complexities. It is crucial to identify essential skills in the field of cyber security protection and defense for the students. This paper presents the outcome of a literature review and a workshop with stakeholders from industry and academia about the state of the art and trends in the education of cyber security in smart grids.

Massive Open Online Courses (MOOCs) have recently gained a lot of attention as a new e-learning paradigm. Despite its widespread use, the high percentage of students who do not use it raises concerns among institutions in developed countries who have spent considerable time and money developing it. As a result, it is critical to comprehend the variables that affect students' decisions to take MOOCs. This paper proposes a conceptual framework for assessing MOOC acceptance among students in Nigerian higher education institution, and it expands on the technology acceptance model (TAM). Five constructs and two moderators are identified and hypothesized and synthesis of related prior literature: perceived usefulness, perceived ease of use, reputation, subjective norm, facilitating condition, and technology awareness. The suggested conceptual framework in this research enriches existing literature on student’s intention to use MOOCs.

One great challenge of modern networks is how to select paths and react to highly variable and demanding traffic patterns. In recent years, emerging networking languages and architectures, such as P4/PISA, have created unprecedented opportunities for rapidly prototyping disruptive solutions in programmable data planes. In this direction, source routing (SR) is a prominent alternative to conventional table-based routing, since it reduces the convergence time of building distributed routing tables. In particular, PolKA explores the polynomial residue number system (RNS) for a fully stateless SR, i.e., no state update on packet headers needs to be conveyed along the route. This paper reports the deployment of PolKA in a continental testbed composed of P4 programmable switches. Results for end-to-end throughput and forwarding latency show that PolKA matches the data plane performance of traditional approaches, while paving the way for further innovative applications by exploring RNS properties in dynamic scenarios.

People re-identification is a fundamental stage in video surveillance systems. This stage is used for determining the labels of the people’s images usually based on their visual similarities. The appearance salience caused by wearing the clothes and carrying objects, with specific colors can be used for improving the performance of re-identification systems. To address the mentioned point, in this paper, we propose a color-based re-ranking process. In the proposed approach first, the input images are semantically segmented as: person’s body, possible carried objects, and background. Then the most frequent colors of person’s body and possible carried objects are used to re-order the ranked results obtained from two commonly used re-identification approaches i.e., Gaussian of Gaussian (GOG) and Hierarchical Gaussian Descriptors (HGDs). Experimental results on various people re-identification datasets show that our proposed re-ranking approach can improve the performance of re-identification systems.

Due to the increasingly growing demand for user identification on cell phones, PCs, laptops, and so on, face anti-spoofing has risen to significance and is an active research area in academia and industry. The detection of the real face then recognize it present an important challenge regarding the techniques that can be used to spoof any recognition system like masks, printed photos. This paper we present an anti-spoofing face method to solve the real-world scenario that learns the target domain classifier based on samples used for training in a particular source domain. Specifically, with the conventional regression CNN, the Spatial/Channel-wise Attention Modules were introduced. Two modules, namely the Spatial-wise Attention Module and the Channel-wise Attention Module, were used at spatial and channel levels to improve local features and ignore the irrelevant features. Extensive experiments on current collections with benchmarks datasets verifies that the recommended solution will significantly benefit from the two modules and better generalization capability by providing significantly improved results in anti-spoofing.

This study shows the agile management of a project using the Scrum methodology. The main objective is to analyze the use of Scrum for life, specifically in scientific production when writing a Paper. The results obtained recognize that the common practices of the agile approach such as revision and retrospective allow that the writing of the article can be improved throughout its development and that the collaboration between the participants is efficient and effective. Likewise, a consistent and effective alternative is provided to the scientific community for the administration in the development of scientific products.

In this work, capacitive touchpads are presented based on fabrication processes that are inexpensive, lab and solvent-free. Pencil and paper have been used to fabricate the sensors, and these materials are economical, biodegradable, and readily available. A touchpad comprising a combination of five sensors has been developed, and analyzed its working by interface controlling for LED's toggle operation. LCD and android app interface have been used to display the result. The sensor's response has been sensed for a gloved finger touch for sensors. The sensor's performance remained consistent, and the functionality was similar to common buttons and sensors. These sensors are recyclable and can be fabricated with ease. This can contribute to potential developments in smart packaging, medical and healthcare applications, and thin human-machine interfaces.

Drowsiness can be dangerous when performing tasks that require constant attention, such as driving a vehicle. Sleepiness is correlated with a variety of physiological variables, such as eye closing, head movements, pulse rate, eye twitch rate, etc. Also, the yawn can be considered as an accurate indicator of drowsiness and fatigue. Yawning detection is very important for the safety purpose of drivers as it will let the driver know if he/she is getting drowsy. Driving at that moment may not be safe. Several automatic yawning detection techniques have been developed for driver's drowsiness monitoring system. Nevertheless, correctly detecting the yawning of the driver and predicting exhaustion in real-time situations is still a crucial challenge. In this paper, we will be reviewing various existing machine learning approaches for driver's yawning detection. In previous approaches, various classical machine learning algorithms such as viola-Jones, contour activation algorithm and SVM have been used for yawning detection, but these approaches failed to predict yawning in realtime situations. Using Deep learning techniques, we can make a real-time yawn detection system with high accuracy. We find that some precious Deep learning algorithms like CNN, RNN, LSTM, Bi-LSTM can detect the patterns with high accuracy. After the comparison of various algorithms and techniques, we find that with the help of Deep learning algorithms the yawning can be detected in real time with high accuracy.

Human recogntion technologies are gaining significant research attention, where the model can be trained to be more precise to recognize the poses performed by objects. Activity identification is a kind of problem, which needs more research consideration and improvement. There is also an increasing need to detect different poses of objects. To tackle the issue, different sensors like Gyroscope and Accelerometer are required to classify the data in the form of images using machine learning algorithms like SVM and CNN. These approaches help us in implementing real-time applicaions such as health monitoring, tracking sports activity and security. The paper also discusses about its benefits, limitations and prominent approach for human activity recognition.

All generations of renewable energy systems stepped into a huge evolution around the world. Countries with big potential of gaz and petroleum energies are finally concerned by the potential of this sector and are currently developing it. Algeria has a huge potential in fossil fuels, however, the government aim to exploit the renewable energy potential of the country. Focus is now on the solar energy above other energies sources such as wind energy. This paper will explore the potential of wind energy in Algeria and go through the challenge facing it development. Some recommendations aiming to develop and implement the wind energy are provided.

A waste carbon copies paper (WCCP) can be applied counter electrode, it made from carbon. In this work, The WCCP film counter electrode is particularly focused under the condition rate at 1 g of WCCP. A WCCP film counter electrode will be used to coat on the fluoride-doped tin oxide (FTO) glass and the anode of DSC use TiO 2 film coated on FTO glass. The both electrodes were coated on FTO glass by size 2x3 cm2. The DSC with WCCP film counter electrode is investigated by solar simulator at 250 W/m2. The morphological properties of WCCP film counter electrode were confirmed by X-ray diffraction (XRD) and scanning electron microscope (SEM). There has been found that open circuit voltage (V OC ) was 0.568 V. The short circuit current (I SC ) was 145.23 μA. The use WCCP film counter electrode is sufficient to be built and tested cathode electrode for DSCs application.

This paper is review on image fusion for different technique i.e. Non-subsampled Contourlet Transform (NSCT) and Neural Network (NN). NSCT is the pyramid structure and gives the multiscale feature of the image. NSCT used to directional filtering structure and used to filter bank. Neural network is a progression of calculations that tries to perceive fundamental connections in a bunch of information through an interaction that impersonates the manner in which the human mind works. The implemented algorithm will applied to MRI image and with the help of NN to calculated accuracy. The previous algorithm was work of different technique i.e. principal component analysis, discrete wavelet transorm and curvelet transform. The implemented algorithm will implement MATLAB software and calculate mean square error and peak signal to noise ratio.

New generation airborne embedded system has deployed Graphical Processing Units (GPUs) to raise processing capability to meet growing computational demands. Applications in the airborne embedded system have strict real-time constraints. Therefore, it is necessary to accurately predict timing behaviors of those applications. Many previous work propose GPU performance models to estimate the execution time of applications. However, most of those models do not consider the impact of co-execution on the GPU performance. In this paper, we propose a workload-aware GPU performance model to predict the execution time of applications executed concurrently on a single GPU. Experimental results illustrate that the proposed model can achieve a 5.1%-11.6% prediction error in a real airborne embedded hardware platform.

Wireless communication base stations have strong requirements in flexibility, performance and real-time responsiveness. Building an application-specific lightweight LibOS on a general OS is a good technique to make a balance among real-time responsiveness, performance and flexibility. Compared to traditional ext-kernel systems, the fast progress of virtualization technology brings a more efficient implementation of LibOS. In this paper, we propose a virtualization assisted real-time LibOS system named Tenma for wireless Baseband Unit (BBU). In Tenma, lightweight process virtualization, enhanced isolation between host OS and guest OS, interrupt direct-pass and application specific lightweight scheduling are used to guarantee the requirements of performance and real-time responsiveness while keeping system flexibility. The Tenma system is already in commercial deployment. Experiments in practice prove that it can meet the requirements of wireless base station applications.

This paper proposes an intelligent cockpit based on artificial intelligence technologies such as voice recognition ASR, TTS, NLP, and multi-round dialogue. The cockpit monitors the progress of the distribution network virtual dispatcher and analyzes the decision engine, and replays the virtual dispatcher online. The implementation process and thinking progress of the virtual dispatcher are overall monitored, and the online monitoring and safety monitoring of the operating personnel are supported.

In modern, real-time heterogeneous systems, ensuring the predictability of interconnects is becoming increasingly important. Existing interconnects are mainly designed to achieve high throughput, with their micro-architectures usually based on FIFO queues. This FIFO-based design prevents prioritization of transactions based on their importance, leading to difficulties in ensuring transaction predictability, especially in a system with a large number of system components. In this paper, we introduce AXI-InterconnectRT, a real-time AXI interconnect for heterogeneous SoCs, which redefines the micro-architecture of interconnects by enabling random accesses of buffered transactions and organizing transactions using dedicated hardware units. With the new micro-architecture, AXI-InterconnectRT can manage transactions based on their importance, guaranteeing their predictability.

Time synchronization is a critical task in robotic computing such as autonomous driving. In the past few years, as we developed advanced robotic applications, our synchronization system has evolved as well. In this paper, we first introduce the time synchronization problem and explain the challenges of time synchronization, especially in robotic workloads. Summarizing these challenges, we then present a general hardware synchronization system for robotic computing, which delivers high synchronization accuracy while maintaining low energy and resource consumption. The proposed hardware synchronization system is a key building block in our future robotic products.

Real time operating system (RTOS) is an operating system which is designed to execute tasks in a guaranteed time. In this paper, we introduce SylixOS - a powerful and open source RTOS. As an industrial RTOS, SylixOS has been used in aerospace, military and many more embedded devices. We introduce the development path and some advanced features of SylixOS. In addition, SylixOS also supports the multiprocess. We discuss how SMP scheduling algorithms can be applied in SylixOS. Performance evaluations of SylixOS are presented to assess the real-time efficiency of SylixOS.

Improper Input Validation (IIV) is a software vulnerability that occurs when a system does not safely handle input data. Even though IIV is easy to detect and fix, it still commonly happens in practice. In this paper, we study to what extent developers can detect IIV and investigate underlying reasons. This knowledge is essential to better understand how to support developers in creating secure software systems. We conduct an online experiment with 146 participants, of which 105 report at least three years of professional software development experience. Our results show that the existence of a visible attack scenario facilitates the detection of IIV vulnerabilities and that a significant portion of developers who did not find the vulnerability initially could identify it when warned about its existence. Yet, a total of 60 participants could not detect the vulnerability even after the warning. Other factors, such as the frequency with which the participants perform code reviews, influence the detection of IIV. Preprint: https://arxiv.org/abs/2102.06251. Data and materials: https://doi.org/10.5281/zenodo.3996696.

Internet-of-Things (IoT) devices can only afford very small batteries due to the size, weight, power and cost constraints. On the other hand, long battery life is expected for such devices, either for improving user experience or due to charging limitations. Therefore, it is critical to reduce the energy consumption of IoT devices. A good opportunity is to save energy on the system level, by letting the system to sleep occasionally. In this paper, we introduce Image Partitioning and Incremental Loading (IPIL), an operating-system-level sleep mechanism used in Huawei LiteOS to reduce the energy consumption of an IoT device. IPIL powers off both the CPU and the main memory during sleep to save as much energy as possible. Thus, system data have to be reloaded after the system wakes up, which may take a long time and thus reduce real-time responsiveness. To solve this problem, IPIL partitions the system image into clusters and incrementally loads the clusters on-demand, reducing the amount of data loading and time overhead in the wake-up step. Experimental results show that IPIL outperforms two widely adopted sleep approaches in terms of energy consumption. At the same time, IPIL offers satisfactory responsiveness with very short wake-up delay. We also present a case study on smart watch to demonstrate how battery life, as a core value of wearable devices, can be improved with IPIL provided by LiteOS.

Correctly specifying system requirement verification strategies plays an important role in Real-time embedded and safety-critical systems development. Traditionally, industries are using document-based/textual artefacts to capture system verification strategies in a way that is highly reliant on efficient communications between system requirement engineers and system verification engineers during systems development iterations. This paper describes a Model-Based System Verification Strategy specification framework and Tools used in Rolls-Royce. The contribution of this framework is 1. improving information reusability; 2. reduced risk of system verification and requirement inconsistency and redundancy 3. improved correlation between system requirements and verification artefacts by sharing the same system models 4. automated support for transformation from document-based system verification to model-based verification.

Constantly increasing botnets powered by vulnerable IoT devices perform record-breaking DDoS attacks to critical infrastructures. Therefore, it is imperative to find vulnerabilities in IoT devices ahead of attackers. In this paper, we first present a systematic analysis on various kinds of IoT malware to further explore the challenges of IoT Honeypot design. We then propose HoneyIoT, a scalable IoT Honeypot framework, which aims at attracting IoT attacks and recording malicious behaviors with configurable vulnerabilities and firmware support. During a 7day real-world industrial experiment, HoneyIoT observed over 12,500 malicious connections and 3,423 distinct login attempts.

The power density and total power from the package have been increased significantly. The rapid rate of power density increases from one generation to the next not only happens at the device level but also happens at the localized area on the devices. The objective of this study is to build more understanding of the localized high-power density in silicon blocks with respect to the impact in temperature gradient and reliability of the silicon. Conventionally, individual design block power usage numbers are often reported from circuit simulations without considering physical location of transistors and metal wires. However, in a silicon block design, certain transistors may consume significantly more power than others due to higher load capacitance, current draw, frequency, or toggle rate. Therefore, the power distribution is uneven within the design block even in the same use case. In order to gain a better understanding of power density peak spot inside of a single design block, in this study, the transistor activity is dynamically analyzed with circuit simulation vectors of an actual Versal product use case and the spatial power distribution of a 0.5mm x 0.5mm design block is reported in an granularity of 10um x 10um and turn x turn respectively. The analysis results show that the local heat flux can be several magnitudes higher when the design block is analyzed in more granularity. The local heat flux creates local hot spot that was overlooked. The max hotspot temperature would not be captured by the sensor because this specific hotspot sensor location would not be known at first hand due to uncertainty of the exact location created by the local hot spot. Understanding that there can be temperature sensors located some distance(700um) away from hotspot, reporting out the temperature, the device max temperature can be higher than the system monitor readout. Given this max temperature discrepancy, a correlation for max temperature based on heat flux density, source area, heat sink resistance, and senor location would be defined to estimate the max temperature. When the semiconductor chip has hot spots, it affects reliability of the chip. Most of reliability mechanisms result in shorter lifetime with higher temperature and temperature gradients. When a small portion of product has local temperature increase by whatever reason, long-term circuit performance should be altered due to enhanced or reduced aging effect. The device long term reliability is reassessed based on the new discovered localized hot spot.

Rate-Splitting Multiple Access (RSMA) is a flexible and robust multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers. In this work, we study the performance of RSMA in the scenarios related with the important core services of New Radio (NR) and 6G, namely, enhanced Ultra-Reliable and Low-Latency (URLLC) and enhanced Mobile Broadband Communications (eMBB). We present the optimal system designs employing RSMA that target short-packet and low-latency communications as well as robust communications with high-throughput under the practical and important setup of imperfect Channel State Information at Transmitter (CSIT) originating from user mobility and latency/delay (between CSI acquisition and data transmission) in the network. We demonstrate via numerical results that RSMA achieves significantly higher performance than Space Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA), and is capable of addressing the requirements for enhanced URLLC and eMBB in 6G efficiently.

We consider an edge computing framework for online distributed learning in UAV swarm, which allows swarm to collect data and train machine learning (ML) models over the air. Usually, UAVs are used as sensors to collect and transmit data to ground for further processing. However, the overhead for raw data transmission are becoming unaffordable as the task gets complex and the timeliness gets increasingly rigorous. Therefore, We have proposed a new distributed online learning scheme to allow each UAV collect data locally as well as perform ML model training onboard. We have theoretically proved the convergence of the ML model to be trained, and the performance gap between central training method and proposed scheme is analyzed. Furthermore, we have bridged the sensing and transmission via the definition of importance of each UAV based on its contribution in training, where we extend the widely-used timeliness metric Age of Information (AoI) to Age of Updates (AoU), and thus the data freshness and data value in training process are combined together. The performance of the proposed distributed learning scheme is validated, and the difference between AoI and AoU are compared via simulations.

We proposed a deep reinforcement learning (DRL) based approach to reconfigure the multicast sessions in an elastic optical network (EON) adaptively. Simulation results demonstrate that our proposal maintains the optimality of light-trees with less reconfigurations and reduces blocking probability.

This effort presents an analysis of a system intended for wireless crack sensing and based on zero-power harmonic transponders. The sensors, which are fabricated by using a paper substrate, are suitable for both structural health and supply-chain monitoring. In the text, different techniques to recover information from the proposed tags are firstly explored, with a particular focus on the potentialities and trade-offs of each approach. Then, the impact of the presence of multiple-nodes on the performance of the system is considered and, finally, some proposals for crack localization in presence of multiple tags are explored and experimentally evaluated.

This position paper proposes CsPI (Cybersecurity Performance Index) as a new way to evaluate the cyber security investment decisions and measure the progresses and effects of the investments and projects. In a conventional way, cyber security budgets are usually evaluated by the return on investment (ROI), which has some undesirable side effects. Since we don't ask for return on investment for other similar expenses (like IT or capital), why should we use ROI for cybersecurity? Here, we propose to adopt a well-established technique in the project management field called "earned value" to the field of cyber security. Specifically, what we propose is to measure the progress of cybersecurity expenses against a plan. The plan should include protection, detect, respond, recover goals that cybersecurity expenses (people, process, technology) attempt to meet – and this has to take into account business objectives and threat/vulnerabilities. Similar to the "Cost Performance Index" or "Schedule Performance Index" in Earned Value, we can define "Cybersecurity Performance Index" (CsPI). The main idea is to show how well current cybersecurity expenses are performing against a plan. At the end, future research directions are presented.

The large software applications of today provide abstractions of the real-life systems that they support. A digital model of the system, and of the changes that occur within, are being maintained and updated, as triggered by real-life events. Morphologically, such applications contain several distinct architectural entities: databases holding the state, central components describing how the system reacts to external events and mechanisms through which the user can view the current state and issue new commands. Each of these entities may use distinct paradigms and employ different technologies. A production-ready software application ends up assembling a relatively high technology stack and provides the final abstractions for both the problem and its solution. In this paper we propose a short-circuit for the long chain of technologies that are usually employed in large, production-ready software applications. The resulting architecture is a distributed, message-based system which behaves as a hybrid between a database and a runtime environment. The system operates with persistent and live entities, encapsulating both state and operations and therefore easily assimilated with OOP classes.

The reduction of production waste impact started to be an important subject in the context of industrial development. In the last years the authorities became to be interested in the reduction of impacts generated by the large amount of waste, by recycling or reuse in processes. In this context, the present study is developed to help decision makers to take the best decision regarding the management of production waste from paper manufacturing so as to mitigate its impacts. According to the results obtained after an analysis of the production waste conducted in the Life Cycle Analysis (LCA) framework, the recycling of this type of waste can reduce considerable the impact on the environment and human health by reducing tree cutting and increasing the absorption capacity of CO 2 from the environment.

This paper takes a view on the Quality-of-Experience (QoE) perspective of Cyber-Physical System (CpS) applications involving users. We provide an overview of characteristics of CPS applications, their relation to QoE and implications for assessing QoE of such systems. Furthermore, we incorporate eudaimonic aspects such as meaningfulness and personal growth that need to be considered in order to grasp the strongly multi-dimensional aspects of QoE in this context.

Flexible pressure sensors are gaining considerable attention as wearable devices to be potentially utilized in human-machine interfaces. However, the preparation of pressure sensors through a simple, environmentally friendly, and cost-efficient fabrication process, remains as a challenge. In this study, we developed a flexible and wearable piezoresistive pressure sensor which consists of a polyethylene terephthalate (PET) as a flexible substrate for printing silver interdigitated electrodes and a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) coated tissue paper as an active material. The PEDOT-Paper pressure sensor showed a sensitivity of 1.7/kPa in the range of 0 to 0.46 kPa, a response time of 240 ms and a recovery time of 306 ms. The potential application of this sensor for human voice detection was also demonstrated by mounting the sensor on the throat of a volunteer and pronouncing the words, “Hello” and “Inha University”. The good repeatability and characteristic curves were achieved for each word, confirming the promising application of the PEDOT-Paper pressure sensor in the field of voice recognition.

Since its first discovery in 2014, laser-induced graphene (LIG) has gained increasingly attention as a simple and low-cost alternative to printing techniques, which instead require additional materials such as functional ink or adhesive layers. While there are several works on LIG supercapacitors, gas sensors, and triboelectric generators, this method is still very unexplored for antennas, especially if realized on eco-friendly paper substrates. In this paper, we realized a microstrip patch antenna using LIG on a food-derived cellulose-based paper. The antenna was first designed and simulated using a High-Frequency Structure Simulator (HFSS) software, using different values of relative permittivity ϵ r (from 2 to 3.6), to understand the impact of laser-induced graphitization of the substrate on the resonance frequency. The subsequently fabricated antenna showed a strong resonance peak of −25dB at 2.4 GHz, corresponding to an ϵ r of 2.2, in agreement with experimental results.

In this paper, the problem of intelligent test paper generation is discussed, and the advantages and disadvantages of current commonly used intelligent test paper generation algorithms are analyzed. By comparing genetic algorithm with ant colony algorithm, it is found that the two algorithms have complementary advantages and they can be integrated organically. On this basis, an intelligent test paper generating strategy based on hybrid genetic algorithm and ant colony algorithm is proposed. The experiments show that the convergence speed of hybrid test paper generating strategy is faster than that of single genetic algorithm and ant colony algorithm. Compared with single genetic algorithm and ant colony algorithm, the efficiency and success of hybrid test paper generating strategy are greatly improved, which has better practicability.

In this invited paper, applications for Machine Learning (ML) in several areas of semiconductor manufacturing and test are reviewed and potential opportunities are discussed.

Over the last few years, web data extraction has gained popularity. Product information on the Ecommerce website floods the internet with big data. Web-based business sites these days have gotten one of the most significant hotspots for getting a large amount of relevant data. Wide range of software application designs to extract relevant data from web pages in order to draw in more business. The extracted data can be used for retailer business and data analysis purposes. The web pages on such sites are based on different technologies, and the generated web documents are in structured or unstructured formats. Manually extract such relevant product data and multimedia type Information from the websites is complex and time-consuming. After extraction of data needs to be classified because web content contains unwanted data e.g. design information, advertising content. This paper describes different Procedures for web document classification and extraction.

The rapid growth of Internet technologies and further dependence on online services, increase the demand for keeping these networks and data secure. The protection of online information is becoming even more vital to the national security and economic stability. Recently, network security has become one of the most concerning subjects in the current research and industry fields. Intrusion Detection Systems (IDSs) are considered as the backbone for network and data protection. Throughout time, different IDS approaches have been implemented to attain maximum detection accuracy. Machine learning IDS is one of the promising IDS techniques that have been created to detect known as well as unknown attacks. This paper investigates various machine learning techniques used to deploy Network-based Intrusion Detection System (NIDS). This survey could provide a more robust understanding of the existing techniques and assists intrigued researchers to identify research opportunities and investigate more in this direction.

Nowadays, in this digital word text is one the vital knowledge carrier. Text can be perceived as structure of symbols which is accustomed to convey, transfer, or inherit civilization and way of life. It is among the major influential novelties of man kind; text performs crucial role in existence of human being. Text can be seen in all places in our day-to-day life. Thus, obtaining data from text which is present in scenarios of our day-to-day life is important. Processing the text that is present in natural scenario has two primary tasks namely Text identification and Text acknowledgment. The method of taking out text body from the natural scene is called as Text detection whereas the procedure of categorizing the characters from scene image which form expressive words is called as Text recognition. There are so many applications of scene text detection in different fields, e.g., intelligent inspection, blind navigation, industrial automation, automotive assistance, robot navigation, instant translation, automatic driving and product search, etc. Thus, street view text detection and recognition have allured rising research consideration in past few years. This paper as first step focuses on Text detection methods for natural scene images.

International audience; This paper presents a parallel-plate waveguide lens for wide-angle mechanical beam steering. A previously developed ray-tracing procedure is used for designing the quasi-optical system. The feed system is based on gap waveguide technology enabling a simple and rapid mechanical actuation. Validation of the proposed concept is given by numerical results for a Ka-band lens design. A high scanning performance over an angular range of ± 50° with a worst-case scan loss of about 3 dB is achieved. The simulated return loss is greater than 15dB in the 27.5-31 GHz band. The proposed lens beamformer is an attractive ground-segment solution for future Satcom applications. © 2021 EurAAP.

Information technologies have a significant impact on digital development and the advancement of all spheres of life and business. Mobile applications have become a significant resource for raising awareness and using different areas of services. Many institutions have digitized their services, and cultural institutions are increasingly digitally present. This paper presents an application developed for iOS and Android devices that helps tourists in Belgrade, Serbia to explore the city ’s museums.