Reviewerly/exHarmony huggingface dataset:
Interactive Cyber-Physical System Hacking

Cyber Security as an education discipline covers a variety of topics that can be challenging and complex for students who are new to the subject domain. With this in mind, it is crucial that new students are motivated by understanding both the technical aspects of computing and networking, and the real-world implications of compromising these systems. In this paper we approach this task to create an engaging outreach experience, on the concept of cyber-physical systems, using a Scalextric slot-car racetrack. In the activity, students seek to compromise the underlying computer system that is linked to the track and updates the scoreboard system, in order to inflate their own score and to sabotage their opponent. Our investigation with this technique shows high levels of engagement whilst providing an excellent platform for teaching basic concepts of enumeration, brute forcing, and privilege escalation. It also provokes discussion on how this activity relates to real-world cases of cyber-physical systems security in the sports domain and beyond.
Image Aesthetic Score Prediction Using Image Captioning

Different kinds of images induce different kinds of stimuli in humans. Certain types of images tend to activate specific parts of our brain. Professional photographers use methods and techniques like rule of thirds, exposure, etc., to click an appealing photograph. Image aesthetic is a partially subjective topic as there are some aspects of the image that are more appealing to the person’s eye than the others, and the paper presents a novel technique to generate a typical score of the quality of an image by using the image captioning technique. The model for image captioning model has been trained using convolutional neural network, long short-term memory, recurrent neural networks, and attention layer. After the image caption generation we made, a textual analysis is done using RNN-LSTM, embedding layer, LSTM layer, and sigmoid function, and then the score of the image is predicted for its aesthetic quality.
Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies

Imposing hard constraints on deep reinforcement learning policies trained with model-free methods is a challenging task. In this paper we specifically focus on constraining the policy’s actions, by imposing state-dependent action bounds. Such bounds allow the designer to incorporate prior domain knowledge into the model-free learning framework and can be used to improve the stability or safety of the learned policies. The approach is applied to two benchmark environments and a more complicated autonomous driving problem. When correctly applied, the state-dependent action bounds can provide strong safety guarantees, as well as improve the convergence speed.
Management of an Automatic System to Generate Reports on the Attendance Control of Teachers in a Educational Center

Many countries were affected by the appearance of SARS-COV-2 that was spreading rapidly, causing damage to humanity and causing a global crisis, this generated a generalized quarantine to avoid the physical approaches recommended by the health system, affecting all students in the world, since it was wanted to avoid forming foci of contagion in educational centers, for this reason, some automated systems that are marketed in the markets were applied to combat the pandemic in educational centers, but they are inefficient when registering the work attendance of teachers, causing loss of time in the registration process and causing an agglomeration of people due to the failure in the registration process, in addition to not allowing to manage the reports of the teacher's work attendance. In view of this problem, in this article the management of an automatic system was carried out to generate reports on the attendance control of the teaching staff in the educational center and control the working hours of each teacher to be visualized through a user interface, being able to control the labor discipline of each teacher since all the records will be stored in a database. Through the development of the system, it was observed that the system works effectively since an efficiency of 98.87% was obtained in its operation to control the time of entry and exit of each teacher, being an accepted value since the process is conducted safely.
A Distributed Path Planning Algorithm via Reinforcement Learning

Recently, Q-Learning has gained increasing attention in path planning, due to its model-free feature and adaptability to dynamic environment. However, it requires vast storage space and suffers from slow convergence speed as the scale of planning space increases. In order to solve these problems, a kind of cooperation scheme is introduced. We divide the planning space into several sections and each section is equipped with a robot. Each robot is responsible for exploring the local space and sharing a small amount of local information with its neighboring robots. Asynchronous communication is considered in algorithm design, which facilitates the information interaction between robots. Additionally, a table is designed to record the most recent action taken in each state and the corresponding next state, which is used to update the Q-table. Finally, the efficacy of the proposed algorithm is validated in a maze.
The Impact of Hiring People with a Disability on Customers’ Perspectives: The Moderating Effect of Disability Type

The present study investigates the effect of hiring people with a disability in the service staff on customers’ perspectives in Jordan, with the moderating role of disability type. The quantitative research method was adopted to obtain the necessary data for the research; the study sample was convenient and consisted of 765 participants who are social media users belonging to different age groups. The data were collected using an online questionnaire distributed through various social media platforms. Findings exhibited that the antecedent variable positively affects the consequent variables (quality, brand image, and loyalty), while there was no significant effect on purchase intention or stereotyping. Further, hiring people with a disability had the strongest impact on perceived brand image. The findings from this research could eventually furnish assistance to existing and future entrepreneurs to build sustainable enterprises while keeping pace with a nation becoming socially responsible.
Assessing the Relationship between Entrepreneurship and Economic Development in the MENA Region: An Empirical Investigation

Purpose: Entrepreneurship has come to be considered as one key to the economic development of countries. Our work assesses the connection between entrepreneurship and the economic development of nine MENA economies for 2006-2018. We aim to shed additional light on the role of entrepreneurship in affecting the economy of the Middle East and African countries, hence, relates some policy and managerial implications with the results. Design/methodology/approach: Two different indicators of entrepreneurship are used; self-employment rates and the number of new business formations. The study employed four control variables: human capital, the money supply, governance quality, and foreign direct investment. We use an econometric analysis based on panel data with fixed and random effects methodology (for every year) to test the feed-back effects between entrepreneurship, economic development, and other control variables in the MENA region. Findings: Our empirical analysis suggests that the effectiveness of entrepreneurship in generating economic development in the MENA region is not related to the number of self-employed but to the creative and imitative entry of existing businesses into new markets. On the other hand, the growth of new businesses is insufficient to spur the economy unless good governance quality manages entrepreneurship in the region. Originality/value: This research adds to the literature in two ways: first, we provide new insights into the effect of two types of entrepreneurship (self-employment rate/new business formation) on economic development in the MENA region. Secondly, our research innovates in the selection of control variables to investigate factors that relate entrepreneurship to economic development. Additionally, the novelty of this paper is to analyze the relationship between entrepreneurship and economic development in the MENA region for a new period and new combinations of intermediates variables.
Image moments-based visual servoing control of bagged agricultural materials handling robot

Manual handling is less efficient and sometimes even hazardous to humans in many areas, for example, agriculture. Using robots in those areas not only avoids human contact with such dangerous agricultural materials but also improves working efficiency. The motion of a robot is controlled using a technique called visual servoing that uses feedback information extracted from a vision sensor. In this study, a visual servoing method was proposed based on learning features and image moments for 3D targets to solve the problem of image moment-based visual servoing. A Gaussian process regression model was used to map the relationship between the image moment invariants and the rotational angles around the X- and Y-axes of the camera frame (denoted as γ and β). To obtain maximal decoupled structure and minimal nonlinearities of the image Jacobian matrix, it was assumed two image moment features, which are linearly proportional to γ and β. In addition to the other four standardized image moment features, a 6-DOF image moment-based visual servoing controller for the agricultural material handling robot was designed. Using this method, the problem of visual servoing task failure due to the singularity of the Jacobian matrix was solved, and it also had a better convergence effect for the part of the target image beyond the field of view and large displacement visual servoing system. The proposed algorithm was validated by carrying out experiments tracking bagged flour in a six-degree-of-freedom robotic system. The final displacement positioning accuracy was accurate to the millimeter level and the direction angle positioning accuracy was accurate to the 0.1 level. The method still has a certain convergence effect when the target image is beyond the field of view. The experimental results have been presented to show the adequate behavior of the presented approach in robot handling operations. It provides reference for the application of visual servoing technology in the field of agricultural robots and has important theoretical significance and practical value. Keywords: image moments, visual servoing, Jacobian matrix, agricultural material handling robot DOI: 10.25165/j.ijabe.20231601.7050 Citation: Li H, Zuo Z J, Chi R Q, Du Y F, Mao E R. Image moments-based visual servoing control of bagged agricultural materials handling robot. Int J Agric & Biol Eng, 2023; 16(1): 212–219.
Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images

Abstract Solar eruptions and the solar wind are sources of space weather disturbances, and extreme-ultraviolet (EUV) observations are widely used to research solar activity and space weather forecasts. Fengyun-3E is equipped with the Solar X-ray and Extreme Ultraviolet Imager, which can observe EUV imaging data. Limited by the lower resolution, however, we research super-resolution techniques to improve the data quality. Traditional image interpolation methods have limited expressive ability, while deep-learning methods can learn to reconstruct high-quality images through training on paired data sets. There is a wide variety of super-resolution models. We try these three representative models: Real-ESRGAN combined with generative adversarial networks, residual channel-attention networks (RCAN) based on channel attention, and SwinIR, based on self-attention. Instruments on different satellites differ in observation time, angle, and resolution, so we selected Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) 193 Å images with similar wavelengths as a reference and used a feature-based method for image registration to eliminate slight deformations to build training data sets. Finally, we compare the above methods in their evaluation metrics and visual quality. RCAN has the highest peak signal-to-noise ratio and structural similarity evaluation. Real-ESRGAN model is the best in the Learned Perceptual Image Patch Similarity index, and its results visually show that it has more highly detailed textures. The corrected super-resolution results can complement the SDO/AIA data to provide solar EUV images with a higher temporal resolution for space weather forecasting and solar physics research.
Noise-specific beating in the higher-level Ramsey curves of a transmon qubit

In the higher levels of superconducting transmon devices and more generally charge sensitive devices, T2* measurements made in the presence of low-frequency time-correlated 1/f charge noise and quasiparticle-induced parity flips can give an underestimation of the total dephasing time. The charge variations manifest as beating patterns observed in the overlay of several Ramsey fringe curves and are reproduced with a phenomenological Ramsey curve model, which accounts for the charge variations. T2* dephasing times, which more accurately represent the total dephasing time, are obtained. The phenomenological model is compared with a Lindblad master equation model. Both models are found to be in agreement with one another and the experimental data. Finally, the phenomenological formulation enables a simple method in which the power spectral density for the low-frequency noise can be inferred from the overlay of several Ramsey curves.
Online and offline blended teaching for a database principles course

The advantages of traditional offline teaching and emerging online teaching in a database principles course were examined to assess the efficacies of online and offline blended teaching. A three-pronged design strategy—theoretical teaching, practical teaching, and diverse assessment methods—was introduced. Further, to improve the theoretical and practical teaching effects, a combination of online resources, such as Massive Open Online Courses (MOOC), Chaoxing platform, QQ group were utilized with student-centered offline teaching. A total of 130 students from the 2020 Software Engineering major Classes 1–4 in Yanshan University participated in the study. The diverse assessment results showed that compared with the traditional offline teaching control class, the teaching effects of the blended teaching were significantly better.
Revisiting deep neural network test coverage from the test effectiveness perspective

Many test coverage metrics have been proposed to measure the Deep Neural Network (DNN) testing effectiveness, including structural coverage and non-structural coverage. These test coverage metrics are proposed based on the fundamental assumption: they are correlated with test effectiveness. However, the fundamental assumption is still not validated sufficiently and reasonably, which brings question on the usefulness of DNN test coverage. This paper conducted a revisiting study on the existing DNN test coverage from the test effectiveness perspective, to effectively validate the fundamental assumption. Here, we carefully considered the diversity of subjects, three test effectiveness criteria, and both typical and state-of-the-art test coverage metrics. Different from all the existing studies that deliver negative conclusions on the usefulness of existing DNN test coverage, we identified some positive conclusions on their usefulness from the test effectiveness perspective. In particular, we found the complementary relationship between structural and non-structural coverage and identified the practical usage scenarios and promising research directions for these existing test coverage metrics.
Privacy Preserving Based Personal Health Records Sharing Using Rail Fence Data Encryption (RFDE) for Secure Cloud Environment

The development of cloud technology is a fundamental idea for offering unfettered access to many different sources in the planning of the networking, memory, infrastructure, and software. Computers are becoming more and more widespread across a wide range of industries due to their numerous advantages, notably in the healthcare industry. Typically, it is essential to the interchange of health information. In light of the ongoing issues with password security, sending private medical information via the internet still raises serious privacy concerns. Whether or whether they have complete permission, patients are not forced to divulge any of their private or personal information. This article examines several noteworthy recent studies that address the problems of password security and data privacy for cloud-based health services. These compare the benefits and drawbacks of different physical access preservation techniques. The paper also proposes a combined authentication procedure based on RFDE models. Cloud security is usually greatly hampered by the necessity for information privacy in an effort to protect sensitive and non-sensitive data for decision-making and to solve the problem of information leakage. One of the most challenging parts of the transfer of personal health records (PHRs) to the cloud is the reuse and exchange of accurate, complete medical evidence. When PHRs are outsourced to third-party businesses, such as cloud services, they are often used as patient-centered, private ways of exchanging health information. Data about a particular PHR doctor is coded for protection before being sent to the cloud. However, there are still substantial barriers due to issues with security, things that can be improved, lawful consumer privacy portfolio management, efficiency, and regulation over sensitive and non-sensitive data kept in the cloud. The PHR file may be encrypted using the Rail Fence Data Encryption (RFDE) technique to provide strong confidentiality rules and enable PHR and modular connectivity control to perform at their very best. Unauthorized users are managed to stop from accessing information with the aid of the transposition cypher, also used by RFDE and known as “zigzag encryption.” The recommended technique generates the secret key while encrypting the PHR information. The recipient decrypts the PHR data using the private key. The algorithm works brilliantly in comparison to the prior strategy.
Monaco Support for an Improved Exception Specification in C++

Exception handling is a beneficial language construct in modern programming languages. However, C++’s type system does not really conform to these elements. As a consequence, developers have to pay attention to avoid mistakes because of the missing compiler support. Moreover, C++11 provides an approach in which exceptions appear in the function’s signature in an inverse manner compared to the earlier standards. Static analysis is an approach in which we reason about a program based on the source with no execution of the analyzed code. It can be used for many purposes, for instance, finding bugs, refactoring the code, or measuring code complexity. In this paper, we analyze how the older style exception specifications can be rejuvenated for modern idioms. Explicitly marking the functions as having a guaranteed exception-free execution is the primary way since C++11. We develop a static analyzer tool for providing hints for these specifications. We evaluate our method with the analysis of open-source projects. Based on the previous evaluation, not using the strictest possible exception-specification is a problem that occurred in every analyzed project. We would like to assist developers in identifying instances of this problem by providing an integrated comprehension tool, that enables them to make use of the exception analysis results in an interactive way in their IDEs.
Blockchain papers depository system based on web 3.0

Web 3.0 is considered to be the next generation of the Internet. It is a decentralized Internet based on blockchain. Research on web 3.0 is essential for developing future Internet infrastructure and can also provide sustainable support for developing future business models. Web 3.0 applications are user-centered and can give convenience to data sharing while enhancing data privacy protection. However, Current web applications are less compatible with web 3.0. And there are relatively few application examples. In this paper, we have implemented a blockchain papers depository system based on web3.0. It uses smart contracts and web3.js to implement user interaction with the blockchain. In addition, we verify the feasibility of the system.
Human-in-control and quality assurance aspects for a benchmarking framework for DeepFake detection models

Human-in-control is a principle that has long been established in forensics as a strict requirement and is nowadays also receiving more and more attention in many other fields of application where artificial intelligence (AI) is used. This renewed interest is due to the fact that many regulations (among others the the EU Artificial Intelligence Act (AIA)) emphasize it as a necessity for any critical AI application scenario. In this paper, human-in-control and quality assurance aspects for a benchmarking framework to be used in media forensics are discussed and their usage is illustrated in the context of the media forensics sub-discipline of DeepFake detection.
Deep Learning

NN models with more hidden layers than the traditional NN are referred to as deep neural network (DNN) or deep learning (DL) models, which are now widely used in environmental science. For image data, the convolutional neural network (CNN) has been developed, where in convolutional layers, a neuron is only connected to a small patch of neurons in the preceding layer, thereby greatly reducing the number of model weights. Popular architectures of DNN include the encoder-decoder and U-net models. For time series modelling, the long short-term memory (LSTM) network and temporal convolutional network have been developed. Generative adversarial network (GAN) produces highly realistic fake data.
Orientation-Aware Pedestrian Attribute Recognition based on Graph Convolution Network

Pedestrian attribute recognition (PAR) aims to generate a structured description of pedestrians and plays an important role in surveillance. Current work focusing on 2D images can achieve decent performance when there is no variation in the captured pedestrian orientation. However, the performance of these works cannot be maintained in scenarios when the orientation of pedestrians is ignored. To mitigate this problem, this paper proposes orientation-aware pedestrian attribute recognition based on graph convolution network (GCN), which is composed of an orientation-aware spatial attention (OSA) module and an orientation-guided attribute-relation learning (OAL) module. Since some attributes can be invisible for certain orientations, OSA is proposed for orientation-aware feature extraction to enhance the learned representation of the visual attributes. Moreover, since different orientations result in different relations among attributes, OAL is proposed to achieve distinguishable and impactful attribute relations by eliminating the confusion of attribute relations in different orientations. Experiments on three challenging datasets (PETA, RAP, and PA100K) demonstrate that the proposed PAR outperforms the state-of-the-art methods by considerable margins.