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The NIST selection process for standardizing Post-Quantum Cryptography Mechanisms is currently running. Many papers already studied their theoretical security, but the resistance in deployed device has not been much investigated so far. In particular, fault attack is a serious threat for algorithms implemented in embedded devices. One particularly powerful technique is to use safe-error attacks. Such attacks exploit the fact that a specific fault may or may not lead to a faulty output depending on a secret value. In this paper, we investigate the resistance of various Post-Quantum candidates algorithms against such attacks.

Most insulation in the power transformers consists of oil and cellulose (paper/pressboard). Mineral oil has been used as an insulating liquid for over a hundred years. However, mineral oil is a non-biodegradable oil and has a serious impact on the environment and the non- renewable energy resources. Therefore, it is important to replace this oil with another alternative insulating liquid, and PFAE was introduced in the year 2006 in Japan. In addition, the ability of an insulating system also depends on the thickness of the monomolecular cellulose fiber on the kraft paper. Hence, it is necessary to determine the appropriate thickness of kraft paper for use as an insulator on the power transformer for this type of oil. This project presents paper-oil (PFAE) charging and discharging current characteristics. The study was carried out through the implementation of experiments in the laboratory. Polarization and Depolarization Current (PDC) test has been carried out to test the conductivity and response function of the insulating materials. It was found that the thickness of kraft paper affecting the capacitance value and the PDC measurement of the tested model. In both dry and wet conditions, the sample of two layers of kraft insulation paper shows the best insulation status as it has the lowest conductivity compared to the other samples. It can be concluded that it is necessary to determine the ideal thickness of insulating paper to ensure that the power transformer has the best insulation.

In this work, Schottky diodes based on amorphous indium-gallium-zinc-oxide (IGZO) were fabricated on cellulose microfiber paper substrate. Silver lines used as the Schottky barrier were printed, in parallel to thermally evaporated Cr/Au ohmic contact, using a dispense printer. The morphological and electrical characteristics of the devices are presented. The fabricated diodes exhibited rectification ratios ranging from 3.4 to 34.9 at ±1V with ON voltages that range from 1.1V to 1.4 V, for device lengths (Ag to Au distance) from 145 µm to 894 µm. The diodes were characterized in a temperature range between 25°C and 80°C. They showed a decrease of the ON current when increasing temperature, which is mainly attributed to the change of the cellulose microstructure. Indeed, an opposite of the ON current behavior was registered when the diode was realized on a polyimide substrate. The realized flexible paper-based diodes offer a potential promising choice for printed environmental-friendly electronics.

The industry of disposables has undergone a huge revolution in this century owing to the busy lifestyles and technological advancements in its production and manufacturing process. Paper cups are one of the widely used products in disposable industry. It has multi utility options that is it can be used to drink water as well as beverages, soups, liquids etc. This paper entirely focuses on increasing the quality factor of production and manufacturing process of these paper cups by adding additional circuitry to the existing machinery to reduce waste of raw materials as well as the per unit cost involved in wastage of electricity. This research work is based on using proximity sensors to sense the materials and molded paper cups infront of machinery and reduce the electric wastage through a relay Mechanism assembly. This is a PLC based live project example of effectively increasing the overall efficiency of the manufacturing plant

For the detection of product color uniformity and seam texture defects in the production process of test paper, detection methods are proposed respectively. For the detection of the color uniformity, a uniformity detection method based on dynamic light correction is proposed. The illumination correction image is used for color dynamic compensation of the test paper image to solve the influence of uneven illumination on the test paper. For the detection of texture defects such as seams, a texture defect detection method based on gray gradient aggregation intensity is proposed. This method uses one-way Gaussian convolution to strengthen features, and then calculates the gradient, divides the region and calculates the gradient extremum aggregation strength to judge texture defects, which can quickly and efficiently find seam defects. The experimental results show that the above methods have obvious effects on the color uniformity and seam detection of the test paper. The color uniformity accuracy rate is 96%, the seam detection accuracy rate is 97%. The total detection time of a single test paper is about 90ms. The algorithm is real-time and accurate, and can be used in actual production detection.

Life expectancy of power transformers is limited by the integrity of insulating cellulosic paper wound around transformer winding. The insulating paper is subjected to thermal, electrical and mechanical stresses. There are three main factors causing degradation in the paper, i.e. moisture, oxygen and temperature. In this work, the degradation rate due to temperature is only discussed. That means that transformer life could be shortened or extended by adjusting loads which results in higher or lower temperature conditions. The temperature, namely the hot-spot temperature is typically used to estimate the relative ageing rate and loss of insulation life. Being able to estimate the hot-spot temperature accurately given load and weather data helps operators to plan about transformer replacement properly. This could also reduce costs of early replacement due to a conservative estimation. In this work, thermal models have been developed using machine learning techniques to learn thermal behavior from historical data. The data are comprised of the hot-spot temperature estimated by winding temperature indicator (WTI), load profiles, ambient temperature, rainfall, wind speed and direction and solar radiation. The proposed method has been validated using data for five 400kV/275kV, four 400kV/132kV and two 275kV/66kV autotransformers and has been shown to work effectively.

Electromagnetic Fault Injection (EMFI) is a well known method of introducing faults for security analysis of digital devices. Such faults can be seen as analogous to the faults which are known to naturally occur in digital devices, a known problem with designing safety-critical systems.Numerous standards have been developed for safety-critical systems, including the development of standards for increasing the rate of naturally occurring faults using particle sources. In this work, we demonstrate that desktop EMFI tooling can be used to accomplish similar testing, but with more control, effectively speeding up the evaluation process.We demonstrate using EMFI tooling for safety evaluation to recreate a highly publicized safety issue present in an automotive ECU – one that could not easily be recreated with other techniques.

Although testing software is paramount to safeguard our digitizing society, students are reluctant to consider a career in the field. A reason could be that dedicated courses on software testing are rare. However, even when such a course exists, students perceive testing as a boring, unrewarding and even dogmatic chore. For more than 10 years, we have been teaching a software testing course at the Vrije Universiteit in Amsterdam. Driven by our belief that an abundant exposure to software bugs makes good testers, we experimented with many ideas to engage students and make them love the topic. The most unorthodox, yet effective interventions we are proud of, were: (1) to scare students by analyzing past, software-related accidents, such as Therac-25 or Boeing 737-MAX; (2) to thrill them using bug-hunting gamification, enabled by the in-house developed VU-BugZoo; (3) to trust them an end-To-end testing of safety-critical software-intensive systems, such as model trains, automatic insulin pumps and even radiotherapy facilities, and (4) to inspire their career, by opening a dialog with test professionals from industry. The result is a mature course, read yearly by 50 computer science graduates, where almost 80% of the participants find the topic interesting and challenging, and 40% consider a future carrier in testing. These positive results make us confident that we found a formula that works. In this position paper, we would like to share our innovative ideas and lessons learned. Also in the future, we will stay committed to educate enthusiastic and responsible software testers.

The purpose of this short position paper is to stimulate a community wide discussion on how situation awareness (SA) technologies may best support the decisions made during pandemics. Currently much of SA is directed towards the critical information needed to make epidemiological and public health decisions; as important as this is, an epidemic also requires doctors and front line medical professional to make numerous decisions regarding individualized patient care. The question is how can SA contribute to both the rapid collection, analysis, and sharing of critical patient information among medical personnel, and then across all levels of decision-making from individual patients to public health policy?

The industry of disposables has undergone an astronomically immense revolution in this century owing to the diligent lifestyles and technological advancements in its engenderment and manufacturing process. Paper cups are one of the widely used products in disposable industry. It has multi utility options that is it can be acclimated to imbibe water as well as beverages, soups, liquids etc. This paper entirely fixates on incrementing the quality factor of engenderment and manufacturing process of these paper cups by integrating adscititious circuitry to the subsisting machinery to truncate waste of raw materials as well as the per unit cost involved in wastage of electricity. There is a necessity to improvise the quality of overall production parameters using automation techniques. Replacing the whole machinery is a very high costing implementation hence economic feasibility of automation can be achieved by adding an additional circuitry and improving the overall perfoemance. This research work is predicated on utilizing proximity sensors to sense the materials and molded paper cups in front of machinery and minimize the electric wastage through a relay Mechanism assembly. This is a PLC predicated live project example of efficaciously incrementing the overall efficiency of the manufacturing plant

Facebook, Twitter, Instagram, and LinkedIn are all popular online social media sites these days. Everyone uses different social media platforms, from children to adults. The use of these social media applications is increasing, which leads to a rise in social media crime. Here is where the word “fake profiles” comes into play; these fake profiles are responsible for distributing misleading details about a particular user or attempting fraudulent activities with bad intentions. Many researchers have been carried in response to the issue of fake profiles that exist in any social media application. There is research available in different directions to get a real problem solution. This paper summarizes all of the research on fake profile identification in social media platforms. This paper gives survey of work, indicating whether the user profile is fake or real.

Paper dating analysis is an important research direction of document inspection, which is widely used in the inspection and identification of cultural relics and ancient books. This article focuses on the analysis of paper dating in ancient books, and proposes a non-destructive inspection and analysis method based on paper fiber texture images. Aiming at the global stacking morphology of paper fibers and the local features of specific types of fiber morphology, we propose a neural network-based hybrid texture feature extraction and representation method: On the one hand, we use convolutional networks to obtain global features of fiber texture; at the same time, we designed a method of extracting and representing local fiber morphological features based on the attention mechanism. By mixing the above two types of features, we realize the extraction and representation of paper fiber features. Furthermore, we use the GRU(Gate Recurrent Unit) model to establish a paper dating time series model and design a new loss function. In order to verify the method, this paper selects 36 domestic books published from 1950 to 2000, and uses the document checker VSC 6000 to collect paper texture images as a dataset, and verifies the effectiveness of the proposed method on this dataset. Experiments prove that the method proposed in this paper has achieved ideal results in paper dating analysis.

Utilizing the in-situ reducing method, a reduced graphene oxide (rGO) paper for 13~14 um was obtained and exhibits great Terahertz (THz) absorbing capability. The absorption of rGO paper exceeds 43% in the whole frequency band from 0.3~1.2 THz and maximum absorption reaches 59.33% at 1.13 THz. This makes it possible to realize ultra-thin broadband THz absorbing materials.

In order to realize smart factory which is appeared in the concepts of Society 5.0 and Industry 4.0, wireless Internet of Things (IoT) becomes a key technology. Inside of the factory, there are heavy DATA traffics exist between moving/movable machine tools/robots/automated guided vehicle (AGV) with wireless access point (AP) and high speed moving/rotating objects/materials with wireless sensor node (SN). Currently, wireless systems used for wireless IoT are limited such as wireless-LAN/Bluetooth/RFID. Since these system’s frequency bands are limited and narrow, the interference between these systems becomes severe. In this paper, we propose a simplified beam forming wireless IoT communication using novel Wi-Fi backscatter system to notify the existence of SN who wants to send its DATA to AP. For Wi-Fi AP with beamforming function, knowing the numbers of existing SNs in each beam area will be a help for efficient beam control. This paper describes the Wi-Fi backscatter system in which the SNs backscatter the Wi-Fi signal transmitted from the AP then the AP detects the backscatter signals from SNs and know the existing SN’s number in its beam area. Since this backscatter system uses Wi-Fi modulated signal, the separation of SNs’ backscatter signals from AP’s transmitting Wi-Fi signal becomes complicated. This paper also describes the transceiver architecture of AP which reduces the complexity of signal processing and improves the dynamic range of reception.

Anstract- While wireless technologies are common solutions to the last mile problem for IoT systems, challenges exist with devices that are in remote locations, sparse deployments, have power and cost constraints, and low data applications as the cost of wireless and the supporting infrastructure presents barriers to entry. This work presents a last mile solution relying on smartphones to bridge the gap between the data generation device and the internet. A unique, low-power solution is presented based on QR codes and e-paper displays that allows a user's smartphone to carry the power and processing burden for data transmission. Utilizing the persistent nature of e-paper displays with low power requirements, QR codes can efficiently communicate data with minimal power impacts to a device's power budget and create large communication windows for user data pickup, leveraging the power of the smartphone without the need for extensive user training and costly infrastructure.

The increased demand for highly sensitive body sensors have created a range of smart wearables. Strain sensors have evolved from semi-rigid to highly, stretchable, and flexible devices that can conform to the human skin. In contrast to complex conventional fabrication methods, this paper proposes a simple and cost-effective technique that combines stencil printing with kirigami cutting to produce flexible, stretchable, linear, disposable sensors using temporary tattoo paper and conductive carbon paste. Here, two sensor designs (straight line and serpentine) were fabricated and analyzed. The number of carbon-ink coatings were also varied to study the effect of number of coating layers to the sensitivities of the sensor. A total of117 samples were fabricated and tested. When tested on skin, the maximum strain that could be measured was 20% for the straight-line patterned sensors and 35% for the Kirigami structured sensors. Both designs demonstrated good adhesion to skin, are stretchable, and flexible. The best performing designs with the most reliable data were straight-line type sensor with width 9 mm and three-layer carbon ink coatings, and Kirigami sensor with width 8 mm and two-layer coatings of carbon ink. Both sensors exhibited high linearity of 0.9956 and 0.9813, but low sensitivities of 0.25 and 0.28. These sensors have potential applications in the biomedical field.

In the last several years, we published several papers to address the problem of Face Identification. The techniques employed in those articles were implemented in transform domains. The Discrete Cosine (DCT) and the Discrete Wavelet (DWT) Transforms were utilized, either combined or individually, to extract features which form the final model for each participant in a given dataset. In this paper, we highlight significant parts of our previous works in order to give a fair comparison among all approaches. The results included here are for the following datasets: ORL, YALE, FERET, FEI, Georgia Tech, and Cropped AR. Features are DWT, DCT, energy-based selected DCT-DWT, and combined DCT-DWT coefficients while the classifier is Euclidean distance, either squared or with power of one.

In recent year, Graphene has witnessed an increasing demand in diversified domains including in realizing various electronic devices due to its unique electrical and mechanical properties. In place of the conventional cumbersome approaches, recently CO 2 laser induced graphene (LIG) on various substrates, like polymers, paper, cloth, have become a reliable alternative to fabricate such devices. Even though, development of LIG zones on diverse substrate and their optimization and implementation has been reported, a process to manifest single-step, cost-effective and scalabel LIG based flexible electronic devices remains a challenge. Herein, a novel process has been demonstrated for fabricating LIG based flexible electronic device on paper and cloth using a low-power blue laser. First, a porous filter paper and a cotton cloth were soaked in liquid polyimide and phenolic resin-polyvinyl alcohol (PR-PVA) respectively. Subsequently, a 3D printer, loaded with a 2.8 W blue laser diode, was appropriately ablated to form paper-based LIG (P-LIG) and cloth-based LIG (C-LIG), with desired designs and conductivity values. P-LIG and C-LIG zones provide optimum conductivity values of 198.54 S/m and 22.23 S/m respectively. Finally, P-LIG and C-LIG were integrated with the capacitive touch sensor and microcontroller modules, and display, to employ both the devices as flexible touch sensors.

Visions have an important role in guiding and legitimizing technical research, as well as contributing to expectations of the general public towards technologies. In this paper we analyze technical robotics papers published between 1998 and 2019 to identify themes, trends and issues with the visions and values promoted by robotics research. In particular, we identify the themes of robotics visions and implicitly normative visions; and we quantify the relative presence of a variety of values and applications within technical papers. We conclude with a discussion of the language of robotics visions, marginalized visions and values, and possible paths forward for the robotics community to better align practice with societal interest. We also discuss implications and future work suggestions for Responsible Robotics and HRI research.

Nowadays, the usage of smartphones keeps increasing day by day and it is also can be used effectively to increase the computational power and the security along with search and rescue. To maintain the attendance records, several organizations are depending on paper-based attendance. Manually recorded attendance for the employees has produced problems that related to information accuracy and workforce performance efficiency. In this paper, reviewed and discussed various methods, applications and systems that can be used by the employer to monitor their staff attendance using fingerprint, Iris, RFID and GPS. The outcome of this survey is getting knowledge on various techniques that used to record and monitor staff’s attendance.

As the demand for low-carbon and eco-friendly energy is increasing and the use of wearable devices is soaring, the triboelectric nanogenerator (TENG), which can economically harvest energy, is in the spotlight. Among many materials, paper is considered a promising substrate and frame material for self-powered TENG due to its high accessibility, flexibility, and biocompatibility. Here, we present a new structure of foldable paper based TENG (FP-TENG) composed of paper and PTFE/Si-rubber. The double-folded FP-TENG generates voltages and currents of 153 V and 2.2 µA, respectively, and shows excellent durability without signal degradation under 5,000 cycles of external force. A watch strap was manufactured with FP-TENG and integrated with an electronic watch pannel to supply power, and several LEDs were turned on. Therefore, FP-TENG is expected to be utilized as a promising eco-friendly energy source for small electronic devices.

Even current OCR technology has made significant progress with the help of deep learning, rapid identification algorithms for specific information in documents still need elaborate design. In this paper, a lightweight operating framework based on deep neural network is established to deal with the extraction of date and time information from paper worksheets used in transformer station. In the framework, YOLOv4-Tiny is employed to detect the locations of the dates and the time in document images which are mixed with printing and handwritten fonts. Since the handwritten dates in pictures are difficult to segment, a novel lightweight CNN named MobileDateNet is proposed that can recognize the date serially without segmentation. The experimental results show that the identification accuracy of the whole framework reaches 92.1% at the average running speed of 36.5 FPS on an NVIDIA 3090 GPU, ensuring the recognition accuracy and the operating efficiency.

Cloud computing is a new paradigm. In recent years, cloud computing has been a popular model for hosting and providing services, as it has gained great popularity in providing multiple services such as storage and cloud hosting. Moreover, the cloud provides a powerful computing platform that enables many individuals and organizations to perform many tasks such as: use of online storage space, approval on business applications, developing custom computer software, and creating a virtual network environment. In previous years, the number of people using cloud services increased dramatically and a lot of data was stored in cloud computing environments. Meanwhile, data breaches of cloud services are also increasing every year due to hackers who are always trying to exploit security holes in the cloud architecture, so the security problems in these systems are horrific because they store a lot of private and confidential data. In this paper, the authors review security and privacy issues, bottlenecks associated with cloud computing, and the specific security issues in cloud computing layers and vulnerabilities, and how to avoid and control them. This paper helps the reader understand the problems related to cloud computing.

China’s packaging industry has formed a packaging big data system from three aspects: implementing information engineering, building a collaborative symbiosis network platform of packaging information resources, and building an industrial chain knowledge map centered on product chain. Packaging big data visualization design uses big data information acquisition, hierarchical division and management, problem and audience, visual transformation and other thinking modes, and adopts text information visualization, multi-dimensional information visualization, hierarchical relationship visualization and other technologies to realize the visual presentation of packaging industry data information. This paper analyzes the network data, extracts the keywords related to the key elements, and then analyzes the construction method of product paper packaging container automation system based on the keywords extraction algorithm. On the one hand, it emphasizes the importance of big data in online shopping commodity packaging design; on the other hand, it explores new ideas and methods of online shopping commodity packaging design through the application of big data in online shopping commodity packaging design.

As a material, cellulose/paper has attracted significant attention for its fibrous and bendable properties. Paper is a renewable resource with the most common forms coming from trees. This brief article describes efforts to create electronics "on" and "in" paper. Examples of electronics on paper include touch sensors and cold plasma-based sanitizers. The skin-like touch sensors use capacitance and patterned resistive networks for passive, scalable sensing with a reduced number of interconnects. When touched or wetted with water, the interdigitated electrodes in the resistive networks detect significant changes in electrical impedance. The plasma generators with layered and patterned sheets of paper use a simple and flexible format for dielectric barrier discharge to create atmospheric plasma without an applied vacuum. Examples of electronics in paper include sheets with tunable electrical conductivity and piezoresistive force sensors, along with functionalized paper-based electrodes capable of detecting small proteins and clinically relevant biomarkers. These papertronic devices have the potential to supply multi-functionality to new forms of smart bandages, skin-like sensing, food packaging, and robotic sensing and actuation.

We report an eco-friendly and easy-going way of fabricating a vast range of passive electronic components and low pass filters. This is achieved by directly writing graphite on paper and cutting and pasting aluminum foil and copper tape on different substrates. Resistance was achieved from 4 kΩ to MΩ(s) by penciling several sustainable layers of different graphite to clay content pencils. Capacitors of capacitance from 2 pF to 10 μF are fabricated using graphite, aluminum foil, and copper tape as conductive materials on printing A4, baking, and butter paper as substrate and dielectric materials as for parallel plate capacitors. Pencil on paper-based interdigitated capacitors is reported to achieve lower capacitance and higher cut-off frequency values of low pass filters. Resistors and capacitors of different values are fabricated in a plane to design low pass filters with 100 Hz – 2 MHz cut-off frequency range. All the components and circuits are fabricated from foldable, eco-friendly, and readily available paper materials as substrates. These paper-based components are helpful for foldable electronics, foldable printed circuit boards, and soft robotics.

Industrial control systems have strict requirements for time-sensitive applications and clock synchronization services. Performance of such applications is adversely impacted by packet delays and jitters. The impact is especially critical in process industries due to harsh environmental conditions. In this paper, we analyze delays and jitters to assess the performance of time-sensitive applications. To this end, we captured and analyzed round trip delay data retrieved from a paper factory. Analysis shows that a sub-millisecond level average delays and the jitters derived from the observed data are sufficient to meet the minimum 10ms update frequency required for most critical control applications. Moreover, the filtered delay variations at the end devices are less than the recommended 150us, which guarantees an adequate time synchronization accuracy in the factory network. Besides, this analysis can provide significant insights into performance bottlenecks for factory applications.

This contribution describes the feasibility of a wireless sensor that uses Micro-Electro-Mechanical Systems (MEMS) on cellulose materials. In particular, the focus is on a paper-aluminum (paper-Al) bimorph cantilever formed by an Al foil glued on top of a paper layer. It is well known that paper relaxes its fibers in the presence of humidity. This means that the length of the paper layer increases (respect to the Al layer) when humidity is absorbed, thus determining the cantilever deflection. Such a mechanism is used to control a capacitor formed by the cantilever itself (Al layer) and by a metal plate printed on a PCB. The capacitor is then applied to a planar notch filter whose resonant frequency is hence determined by the humidity. The preliminary experiments show that the developed circuit features a 2.45 GHz center frequency and a sensitivity of about 3.7 MHz ner each nercent of relative humidity variation.

Forgery of paper invoices and certificates negatively causes huge economic loss every year. In the molding process of paper, plant fibres inside will distribute randomly and form the unique microscopic surface. Accordingly, forensic researchers have presented many techniques to build its feature descriptor for paper anti-counterfeiting. Among those methods, normal vector field and reconstructed surface based on multi-view photos captured by mobile camera have indicated incredibly effectiveness and practicality. However, the inefficiency of existing models using mobile camera is the neck in real time detection scenarios. In this paper, we focus on the efficient microstructure orientation estimation of paper surface for authentication. To address this problem, we investigate the reflection characteristics of rough surface and quantify the light attenuation accurately. Then, we model the entire propagation path of light emitted by flash, and exploit single photo captured by mobile camera to reconstruct microscopic inclination based on beam energy loss. Reduction of required pictures significantly enhances forensics efficiency. Through comparison experiments, we demonstrate that our proposed method can be used for paper discrimination effectively with speedy feature extraction.

In this paper we propose to implements blockchain technology for Internet of Things (IoT) networks that use Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. To eliminate the reliance on a single leader and improve performance, we propose to allow multiple leaders to propose request batches independently and, possibly, concurrently, by using different overlay networks. Nodes participate in parallel consensus rounds without contention, and they only contend with others to reserve the next available spot for the atomic insertion of a new transaction batch or block into the replicated blockchain ledger. We develop an analytical model for the multiple leader PBFT ordering service by using a Discrete-time Markov chain. Our evaluations show that our model outperforms the original multiple entry point PBFT protocol in a wide range of parameter values, and that it scales well with the number of orderer nodes in the PBFT committee and block arrival rate.

Long-term evolution in unlicensed spectrum (LTE-U) can make use of centralized scheduling, interference coordination and other technologies to achieve better spectrum efficiency (SE). As the evolution of cloud computing, mobile edge computing (MEC) sinks the computing and storage capacity from the centralized data center to the edge of the network, which is the key technology to achieve low delay and high speed. However, the traditional centralized scheme in small cell networks (SCNs) structure will bring huge signal overheads. In order to adapt to the complex and changeable network system, this paper proposes a distributed resource and power allocation scheme under MEC environment, which enables small base stations (SBSs) to work autonomously. The SBSs need only little information exchange through the information cloud (IC) and finally obtain the global optimal SE. Simulation results confirm the correctness and effectiveness of the proposed scheme, and demonstrate the superiority of the distributed scheme over the centralized scheme.

The pervasive use of software systems and current threat environment demand that software systems not only survive cyberattacks, but also bounce back better, stronger, and faster. However, what constitutes a modern software system? Where should the security and resilience mechanisms be-in the application software or in the cloud environment where it runs? In this concept paper, we set up a context to pose these questions and present a roadmap to answer them. We describe challenges to achieving resilience and beyond, and outline potential research directions to stimulate discussion in the workshop.

The experiment of quality assurance of polymeric outdoor insulators service life is necessary to be checked the class of wettability by using the digital image processing tools. The image processing can improve its pictorial information for human interpretation, render it more suitable for autonomous machine perception. This paper provides a survey of the recent technology and theoretical concept to explain the development of the Hydrophobicity Class (HC) of porcelain insulator. The hydrophobic properties of the housing material were found degraded to a different extent between field-aged insulators due to differences in material structure and pollution conditions. This paper contributes to the latest development of reviews relating to image processing for the implementation of porcelain insulator hydrophobic systems testing., and their related studies. We categorized the computer vision mainstream into 2 group such as Machine Learning Methods Applied to Digital Image Processing and Deep Learning Methods Applied to Digital Image Processing. Then compare pros/cons in each type of technique. We also provide brief explanation on the up-to-date information about the techniques and their performance.

The accurate knowledge of dielectric substrate characteristics is essential in designing RF and Microwave printed circuit boards. In this work, a straightforward method based on microstrip transmission lines for characterization of paper substrates is implemented. By using this technique, the frequency-dependent behavior of dielectric constant ϵ r and loss tangent tanδ of the paper are extracted from 10MHz to 10GHz. To validate this technique, the ADS simulation results and the measurement results of a miniature circuit based on an interdigital capacitor on paper are compared and they show a good agreement.

In modern academic social network, it is very difficult for scholars to find academic papers consistent with their research direction. Time is a critical factor in paper recommendation. As time goes on, the impact of an academic paper would gradually fade. Likewise, the research interests of users may also change. Therefore, we propose a temporal perceptual neural collaborative filtering model that integrates the multi-dimensional features of papers. We conducted our experiments on the dataset from CiteULike, comparing the recommended results by using four time-decay functions and evaluating our model with multiple evaluation indicators. The satisfactory results show that our model is effective in filtering out the expired papers by considering the characteristics of papers and the changes of scholars' interests.

The space-based integrated information network is composed of space-based backbone network, space-based access network and ground-based node network. It is interconnected with the ground Internet and mobile communication network to build a “ global coverage, on-demand access, on-demand service, safe and reliable ” space-based integrated information network system. As an important part of the system, the space-ground laser communication equipment is faced with many problems, such as the integration of multiple modulation systems and the location of different places. In this paper, the related problems are analyzed and verified by experiments. The results show that the system works at 1550nm wavelength, adopts coherent communication system and IM/DD communication system, coherent communication system supports BPSK, QPSK and DPSK modulation format, IM/DD communication system adopts OOK modulation format, the communication rate reaches 5Gbps under the condition of 1E-9 bit error rate.

The traditional space telemetry, tracking, command and communication system works in the radio frequency band. With the improvement on measurement accuracy and communication capacity in space missions, the traditional means have a certain bottleneck in dealing with the development of future space missions. The inherent lightweight, high parallel, high capacity and fine processing capabilities of Optics and microwave photonics show the potential to solve the above problems. This paper summarizes the innovative work of the Southwest China Institute of Electronic Technology in the application of laser measurement and communication integration, microwave photonic RF front-end technology in space telemetry, tracking, command and communication system. The experimental platform is established for verification, the accuracy of ranging and velocity measurement reaches millimeter level at 10Gbps single channel transmission rate with 1E-9 bit error rate under a simulation satellite-ground link. The microwave photonic RF front-end can be compatible with S to Ka band, with a sampling rate of 20Gsps and a signal bandwidth of 5GHz. It is expected to be applied to next generation of space telemetry, tracking, command and communication system or navigation and relay system.

As opposed to multiple-antenna solutions, in-band full-duplex (IBFD) systems can utilize a shared antenna for signal transmission and reception to reduce the device form factor. These single-antenna designs, however, must additionally consider the self-interference (SI) that is generated by antenna impedance-mismatch reflections. This paper investigates the effect of different antenna loads and transmission line lengths for commercially-available magnetic circulator and omnidirectional antenna components operating between 2 and 6 GHz. The results indicate that both the load impedance and the distance between the antenna and interface device can drastically impact the composite SI frequency-selectivity, which influences the overall IBFD system bandwidth.

This paper discusses recent advancements in smart connected biophysical signal sensing technology focusing on the development of wearable biosensors and the use of Electro-Quasistatic Human Body Communication and Edge-Analytics in the human state measurement systems for lower power consumption and physical layer security.

Critical flaws continue to exist at the level of domain, requirement, and/or design specification, and specification verification (i.e. to check whether a specification has desirable properties) is still one of the most important challenges in software/system engineering. CafeOBJ is an executable algebraic specification language system and domain/requirement/design engineers can write proof scores in CafeOBJ for improving quality of specifications by the specification verification. This paper describes advances of the proof scores for the specification verification in CafeOBJ from the author’s point of view.

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.