Reviewerly/exHarmony huggingface dataset:
Research on Robot Vision Servo Based on image big data

Abstract With the integration and development of information technology and control theory, industrial robot has become an indispensable automation equipment in modern industrial production, improving product quality and work efficiency. However, there is still a need to improve robot technology in terms of intelligence and flexibility. Robot vision servo control technology uses image sensors to obtain the information of the external field of vision, which can improve its environmental perception and response ability to cope with different production work. The important control method of robot system is visual servo control. Because of its good performance, wide application and convenience, visual servo control technology has become one of the hot spots in the field of mechatronics. In this paper, the problem of visual feedback and noise processing in the vision system is described. The six degree of freedom pose correction of the three focus tensor is made use of the big image data, and some tensor elements are selected to define the visual feedback, which effectively avoids the display estimation of the camera pose. Based on the extracted tensor features, an adaptive controller is designed to drive the camera to the desired attitude. The visual servo control system improves the real-time performance of big data image processing and reduces the calibration error, achieving the expected purpose. Finally, the future research direction is prospected, which is expected to become an important support technology for robot application in dynamic and variable environment.
Hardware Model for Efficient Edge Detection in Images

Reconfigurable algorithm for digital image processing is now a growing area of research in real time embedded systems. The Edge Detection Algorithms are one of the primary algorithms for object detection through segmentation and extraction in digital image processing. These algorithms are usually used in software but can also be implemented for special purposes in hardware including high processing speed and good accuracy. The limitation of the processor speed affects the software implementation of image processing algorithms. The advances in VLSI technology have solved this. This project uses the Hardware Description Language (HDL) to implement the Sobel edge detection algorithm for Field Programmable Gate Array devices (FPGA) and focuses on the outstanding features of FPGA technology. The analysis shows that the hardware Sobel edge operator implements provide a higher speed than the simulation software. The proposed implementation uses a modified architecture that reduces hardware resources effectively. The images are transmitted through serial UART communication from PC to FPGA computer.
Research on Visual Navigation Manipulator for GIS Ultrasonic Partial Discharge Detection

Gas insulated switchgear(GIS) is an important electrical power equipment, which has been widely used in power systems. The GIS equipment will be broken due to some unpredictable reasons in use, so it needs regular maintenance and inspection. However, the traditional detection method is heavy in workload and high in risk. Therefore, it is necessary to design a GIS ultrasonic partial discharge detection robot. We study the visual navigation manipulator technology of the GIS ultrasonic partial discharge detection robot. According to the special environment of the substation, a new marker was designed, smeared on the position to be detected. And the corresponding detection algorithm for the marker is proposed. Then we used the binocular stereo camera to obtain the left and right images of the marker, solve the three-dimensional space position of the target position, and guide the manipulator to the target position to complete the ultrasonic signal acquisition. This research will effectively improve the efficiency and safety of GIS equipment inspection.
Accelerating the distributed Kaczmarz algorithm by strong over-relaxation

The distributed Kaczmarz algorithm is an adaptation of the standard Kaczmarz algorithm to the situation in which data is distributed throughout a network represented by a tree. We isolate substructures of the network and study convergence of the distributed Kaczmarz algorithm for relatively large relaxation parameters associated to these substructures. If the system is consistent, then the algorithm converges to the solution of minimal norm; however, if the system is inconsistent, then the algorithm converges to an approximated least-squares solution that is dependent on the parameters and the network topology. We show that the relaxation parameters may be larger than the standard upper-bound in literature in this context and provide numerical experiments to support our results.
Proposing cybersecurity regulations for smart contracts

ABSTRACT Smart contracts have the potential to improve many existing transactions and to enable entirely new business models. However, the technology supporting this new method of transacting is complex and the legal framework applying to it is somewhat unclear. Though theorised several decades ago, it was not until the advent of the distributed ledger technology known as blockchain that smart contracts were able to be practically implemented. This paper summarises the concept of smart contracts while providing the background and context of its development. It then distinguishes those smart contracts which are considered legally binding within the scope of US laws from those that may not have legal effect. Next, it provides an in-depth example of an exploitation of smart contracts and explores how the legal reaction to it is inadequate. To reduce the likelihood of future smart contract exploitations and to improve confidence for contracting parties, this article suggests adding explicit smart contract cybersecurity provisions to existing US legal frameworks. Specifically, I propose adapting several of the National Institute of Standards and Technology’s Federal Information Processing Standards to create minimum cybersecurity requirements for all legally binding smart contracts. I also examine the shortcomings of the Computer Fraud and Abuse Act while identifying it as a piece of legislation ripe for reform which, if done adequately, may provide a legal deterrent to would-be cyber hackers of smart contracts.
Process-Based Simulation with Stackless Coroutines

Computer simulations are useful tools to predict and test accumulated knowledge about the behavior of complex systems. The use of process-based simulators has historically been established in combination with object-oriented languages and requires stackful coroutines for their execution. This approach delivers excellent performance if the compiler has a built-in coroutine-concept that is translated into machine code directly. Languages without this feature move their implementations of coroutines into libraries which typically use OS-dependent calls or non-portable assembly to implement them and perform worse than the direct approach.
WITHDRAWN: Design of chinese language learning platform for foreign students based on fpga system

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Hameem Shanavas, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Deformable Mixed Domain Attention Network for Scene Text Recognition

As a hot research area in computer vision in recent years, scene text recognition is still challenging due to the large variance in irregular text. The current methods treat the recognition process as a sequence-to-sequence task and solve it by an encoder-decoder framework. In this work, we propose a DMDAN for robust scene text recognition. First, we utilize deformable convolution to strengthen the ability to adapt to irregular text. Then, mix domain visual attention and self-attention are respectively employed in the encoder and decoder, which can effectively alleviate the problem of “attention drifting”. Finally, we integrate the center loss to reduce the intra-class distances and make each class easier to distinguish. Extensive experimental results show that our model outperforms the baseline CRNN a lot and achieves a comparable performance against existing attention-based methods on both regular and irregular datasets.
The lattice of cycles of an undirected graph

We study bases of the lattice generated by the cycles of an undirected graph, defined as the integer linear combinations of the 0/1-incidence vectors of cycles. We prove structural results for this lattice, including explicit formulas for its dimension and determinant, and we present efficient algorithms to construct lattice bases, using only cycles as generators, in quadratic time. By algebraic considerations, we relate these results to the more general setting with coefficients from an arbitrary Abelian group. Our results generalize classical results for the vector space of cycles of a graph over the binary field to the case of an arbitrary field.
AMMDAS: Multi-Modular Generative Masks Processing Architecture With Adaptive Wide Field-of-View Modeling Strategy

The usage of transportation systems is inevitable; any assistance module which can catalyze the flow involved in transportation systems, parallelly improving the reliability of processes involved is a boon for day-to-day human lives. This paper introduces a novel, cost-effective, and highly responsive Post-active Driving Assistance System, which is "Adaptive-Mask-Modelling Driving Assistance System" with intuitive wide field-of-view modeling architecture. The proposed system is a vision-based approach, which processes a panoramic-front view (stitched from temporal synchronous left, right stereo camera feed) & simple monocular-rear view to generate robust & reliable proximity triggers along with co-relative navigation suggestions. The proposed system generates robust objects, adaptive field-of-view masks using FRCNN+Resnet-101_FPN, DSED neural-networks, and are later processed and mutually analyzed at respective stages to trigger proximity alerts and frame reliable navigation suggestions. The proposed DSED network is an Encoder-Decoder-Convolutional-Neural-Network to estimate lane-offset parameters which are responsible for adaptive modeling of field-of-view range (157 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> -210 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> ) during live inference. Proposed stages, deep-neural-networks, and implemented algorithms, modules are state-of-the-art and achieved outstanding performance with minimal loss(L{p, t}, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δ</sub> , L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Total</sub> ) values during benchmarking analysis on our custombuilt, KITTI, MS-COCO, Pascal-VOC, Make-3D datasets. The proposed assistance-system is tested on our custom-built, multiple public datasets to generalize its reliability and robustness under multiple wild conditions, input traffic scenarios & locations.
Two-dimensional picometer comb for three-dimensional reconstruction

A two-dimensional (2D) picometer comb, a novel optical element made by picometer-differential four times exposed in two perpendicular directions, is proposed to generate the dot array projection pattern for three-dimensional (3D) shape reconstruction and other applications. Not only does a 2D picometer comb generate a stable light field distribution with extremely long depth of field and small divergence angle as a one-dimensional picometer comb, it also has new properties, such as periodicity of diffraction field in two perpendicular directions and high concentration of energy of points, which is particularly suitable for providing dot array structured light. We demonstrate that the diffraction field of a 2D picometer comb provides a solution for non-defocusing 3D reconstruction with a dot array. In fabrication of a 2D picometer comb, we can modulate the holography by changing the angle of two beams slightly, so its period can be measured at picometer accuracy. A 2D picometer comb can be made to any scale, then it can be integrated to mobile devices, such as a mobile phone, for 3D shape reconstruction. Furthermore, the concept of a 2D picometer comb would be applied to generate a picometer light field for opening the door of pico-optics in the future.
Image Enhancement Using Non-Linear Root-Signal and Guided Filtering Approach

In this paper, a new method of image enhancement using image fusion algorithm is proposed which is based on deriving multiple root-signals from the given input image and enhancing each of them individually. In order to obtain a single enhanced image, these multiple enhanced images are fused. The overall system for the proposed methodology turns out to be a nonlinear system. The various enhanced images using root-signal approach are further passed through the guided filter for the improvement of the quality of the enhancement results. The proposed approach has been evaluated on number of images and results are compared with existing enhancement methods such as histogram equalization (HE) and adaptive histogram equalization (AHE). The efficacy of the proposed approach is evaluated using several quality metrics and it is observed that, compared to some of the existing techniques, it improves the quality of the enhanced images.
An Effective Filtering Process for the Noise Suppression in Eye Movement Signals

All eye movement measurement contains some level of noise; denoising requires the use of digital filters and subsequent measurement processing. The elimination of noise in the eye movement signals commonly uses some linear time-invariant filter, such as the Butterworth, Gauss, or Savitzky-Golay filters. However, in high noise signals, the low-pass part of the filters can significantly distort and alter speeds saccades. This can be remedied by adding a high pass component, but this introduces its artifacts, especially false oscillations. Some of the problems can be improved using the mean filter, the Kalman filter, or the Bilateral filter. Although they are efficient, they still share the main feature: the resulting signal contains the remaining noise. Our proposed method is using the Wiener filter to denoise signal, and we will consider the estimation of the noise power spectrum calculated using an artificial eye. The results indicate that the method eliminates noise efficiently.
Combining Software Engineering and Design Thinking Practices in the Ideation Process of Augmented Digital Experiences

Abstract Designing augmented and tangible experiences that intertwine human practices and expectations, interaction spaces and complex digital artifacts is a complex and multifaceted task that relies upon iterative and multidisciplinary ideation processes. Design thinking techniques have been traditionally used in ideation of such digital artifacts. In this paper, we posit that integrating some software engineering practices can improve ideation by providing a structure to the process and helping to build a shared and permanently documented design rationale. It is not a matter of software engineering versus design thinking but a question of developing a holistic understanding of technological development where discipline and creativity, rationality and emotions and quality centered and people centered coexist. Based on this assumption, we conceived a software tool called CoDICE that offers a virtual co-design space where augmented digital experiences are documented and analyzed in a shared and distributed way. The paper discusses how CoDICE contributes to alleviate some problems of co-design events including the need to support multiple co-design spaces, make explicit the co-design process and its goals, support documentation, justify design decisions, explore multiple ideas and generate a shared representation of the outcomes. Two scenarios are used to illustrate the tool utility: short-term co-design workshops in which the tool enabled multidisciplinary teams of novice designers to explore and structure their ideas and a long-term co-design project where the tool facilitated traceability, documentation, the reuse of design components and the shared elaboration of the design rationale and evolution of the deployed technologies.
Image Enhancement in Automatic Mode by Recursive Mean-Separate Contrast Stretching

In this paper, the problem of improving the efficiency of transforming the intensity of complex images by piecewise linear contrast stretching in an automatic mode was considered. A new technique of intensity transformation by recursive mean-separate contrast stretching (RMSCS) was proposed. Our proposed technique of RMSCS allows us to improve the image by piecewise-linear contrast stretching in automatic mode using an arbitrary number of mean-separate intervals. The proposed approach to defining the gain factors allows more evenly distributed the average brightness of objects in the image based on the analysis of the number of pixels and their cumulative sum in chosen intervals. The proposed technique provides an effective enhance the images without the appearance of unwanted artifacts through a more evenly distributes the average brightness of objects in an image. The results of the research confirm the effectiveness of the proposed approach to enhance images in automatic mode.
Internet of Things (IoT)-Based Advanced Voting Machine System Enhanced Using Low-Cost IoT Embedded Device and Cloud Platform

The traditional voting system is basically of two major types. One is voting through ballot paper and another one is voting through electronic voting machine (EVM). Voting system through ballot paper requires so much resources as well as security. There will be maximum possibility of malfunctioning in case of ballot paper-based voting machine. Electronic voting machine-based voting system is better than ballot paper-based system, but it is not authenticated. This paper describes the design of smart and secure electronic voting machine based on the IoT platform. The suggested system is more efficient than both traditional systems, as both traditional systems are time consuming and also not authenticated. The proposed system functions into two specific phases. One is user authentication, and another is user voting. Authentication process can be done using fingerprint authentication. Fingerprint database of all the voters is stored in the system initially as prerequisites. If any person wants to vote to any party, the authentication of respective person is to be done by fingerprint matching process. Once the fingerprint matches successfully, the person can vote to any specific party. Data analysis can be done in form statistics of the percentage voting of individual party and is to be uploaded on the web server as well as Google spreadsheet. Due to the fingerprint authentication method, malfunctioning like fake voting and repeat vote can be avoided. As the system is based on the fingerprint authentication, in future it can be linked with the Aadhaar Card of the respective person.
Moving ships target detection algorithms for GAOFEN-4 sequence images

GAOFEN-4 is a high-resolution optical remote sensing satellite on geosynchronous orbit, which has been found the potential to monitor the moving ships on the sea. Some classic moving ships target detection algorithms are studied. A new algorithm is proposed for the sequence images of GAOFEN-4 satellite, which can effectively suppress the interference of noise on moving ships target detection by performing the defogging of dark channel prior and a moving detection with visual background extractor. Some experiments are executed with real sequence images. It shows that the new algorithm is better than classic target detection algorithms for the sequence images.
Transfer Learning on Natural YES/NO Questions

Although neural network approaches achieve remarkable success on QA, many of them struggle to answer questions that require retrieving relevant factual information from the given paragraph. Inferring from a given paragraph and answering the question to be true or false is an essential part of natural language understanding. To improve the model's inferring ability on Natural YES/NO Question, we provide a simple and effective method. First, we find the tasks related to main task Natural YES/NO Question to fine-tune the model by multi-task learning, then we fine-tune the model on the main task. Results on dataset BoolQ show this method is competitive with other recently published methods, which means transferring from the related datasets through multi-task learning in first stage can save more beneficial information about main task Natural YES/NO. Further analysis show that this method can not only have benefit in this task, it also can be used to other tasks.
Appendable-block Blockchain Evaluation over Geographically-Distributed IoT Networks

In the last few years, different researchers presented proposals for using blockchain in the Internet of Things (IoT) environments. These proposals consider that IoT environments can be benefited from different blockchain characteristics, such as: resilience, distributed processing, integrity and non-repudiation of produced information. However, researchers faced some challenges to use blockchain in IoT, e.g., latency, hardware and energy constraints, and performance requirements. One of the prominent solutions is the appendable-block blockchain, which uses a hierarchical peer-to-peer (p2p) gateway-based architecture. Additionally, current proposals present simplified evaluation scenarios, usually performed in controlled environments, which do not include important network features, for example, latency. Consequently, a model to evaluate a geographically distributed environment, for example, in a situation in which health data have to be collected from different countries in a pandemic situation, can help to understand the behavior and possible flaws of blockchains. In order to evaluate appendable-block blockchains in a realistic scenario, this paper presents an analysis of different consensus algorithms in geographically distributed hosts, in which latency can impact the performance of main operations in a blockchain, such as block and transaction insertion.
A concern-oriented software engineering methodology for micro-service architectures

Component-Based Systems (CBS) allow for the construction of modular, highly scalable software. Decomposing a system into individually maintainable and deployable components enables a targeted replication of performance bottlenecks, and promotes code modularity. Over the last years, the Micro-Service Architecture (MSA) style has become a popular approach to maximize the benefits of CBS. However, MSA introduces new challenges, by imposing a conceptual and technological stack on adherent projects, which require new critical design choices. Throughout my PhD I want to investigate to which extent a systematic reuse of MSA solutions of various granularity can streamline MSA application development by guiding design decisions.