Reviewerly/exHarmony huggingface dataset:
NASEI: Neural Architecture Search-Based Specific Emitter Identification Method

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, these methods highly rely on expert experience to design network structures. These hand-designed fixed network structures lack flexibility, which often leads to insufficient model generalization. Neural architecture search (NAS) can be seen as a subdomain of automatic machine learning (AutoML), which can automatically adjust network structure and parameters according to a specific task. In this paper, we propose a neural architecture search-based SEI method, which can achieve an efficient search of the architecture with the use of a gradient descent algorithm. Experimental results show that the proposed NASEI method both improves the accuracy and reduces the parameter quantity when compared with state-of-the-art methods. Code available at https://github.com/huangyuxuan11/NASEI.git.
URLLC Physical Layer Authentication based on non-linear Supervised Learning

In this work, non-linear supervised learning classifiers are proposed for Physical Layer Authentication (PLA). Their performance is evaluated using a Universal Software Radio Peripheral (USRP) based testbed under randomly distributed attacks and burst attacks within a mobile Ultra Reliable Low Latency Communication (URLLC) campus network scenario. It is shown, that in specific cases, non-linear classifiers can achieve promising results in terms of authentication accuracy and Receiver Operating Characteristics (ROCs) curve performance.
HQ-Sim: High-performance State Vector Simulation of Quantum Circuits on Heterogeneous HPC Systems

Quantum circuit simulations are applied in more and more circumstances as the quantum computing community becomes broader. It helps researchers to evaluate the quantum algorithms and relieve the burden of limited quantum computing resources. However, most of the state-of-the-art quantum simulators utilizes either CPU or GPU to store and calculate the state vector, which results in resources stravation. Morever, the mamximum number of qubits supported by simulator is bounded by the memory, since the memory utilization increases exponentially with the number of qubits. In this study, we leverage Heterogeneous computing to utilize both CPU and GPU to store and update state vectors. We also integrate lossy data compression to reduce memory requirements. Specifically, we develop a heterogeous framework that has a dynamic scheduler to fully utilize the computing resources. We apply lossy compression to chunked state vector to make the maximum number of qubits higher than the regular simulators, the compression also benifits the data movement between CPU and GPU.
Efficient QAOA Optimization using Directed Restarts and Graph Lookup

Variational Quantum Algorithms (VQA) aim to enhance the capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices. These algorithms utilize parameterized circuits and classical optimizers to iteratively execute circuits with varying parameters. However, VQA faces computational overheads due to repeated iterations and random restarts. Prior work suggests using basic sub-graphs to transfer parameters for the input graph, reducing optimizer overheads but limiting applicability to structured regular graphs. In real-world applications, random irregular graphs are common, and existing methods are not scalable or practical for such graphs. This paper presents a framework that aims to improve random irregular graphs in VQA. The framework uses graph similarity and important features like total edge counts, average edge counts, and variance. It follows an iterative process to choose basis sub-graphs from a small database and adjust parameters accordingly. Classical optimizers then utilize these parameters to determine when to restart and perform gradient descent. This approach increases the chances of reaching global maximum points.
Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput

Undoubtedly, quantum computing offers valuable acceleration for solving intricate problems. One of the primary hurdles lies in executing large-scale quantum applications on backend machines. Qubit noise, among other factors, dramatically influences the execution process. Implementing effective scheduling techniques for quantum circuits is crucial for practical quantum computing and preventing excessive waiting times. The quantum realm is distinct from classical computing in terms of optimization, performance, utilization, and waiting periods. Consequently, the parameters and components of quantum circuit scheduling diverge from those of classical computing. This paper presents Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput, a straightforward yet effective scheduling framework and policy that enhances noise resilience, throughput, and the fidelity of quantum circuits. Drawing inspiration from classical methods, our scheduling approach incorporates additional constraints tailored for quantum logic. The outcome demonstrates a substantial improvement in fidelity and resource management, which is vital for real-world quantum applications.
Robust and Efficient Quantum Communication

Quantum communication between quantum processors offers new capabilities and applications in quantum computing. However, Noisy Intermediate-Scale Quantum (NISQ) devices face challenges such as decoherence, entanglement distillation latency, high communication-to-data qubit ratio, quantum error correction, and scalability. Inspired by distributed systems concepts, this paper presents two solutions for optimizing quantum communication: advanced quantum repeaters and machine learning for quantum network optimization. Advanced quantum repeaters will leverage topological quantum states to improve entanglement generation, swapping, and distillation efficiency. Concurrently, machine learning techniques using multi-armed bandit algorithms will dynamically allocate quantum processing resources across distributed quantum networks. This optimization enhances the efficiency of quantum teleportation protocols and reduces computational costs. By integrating advanced quantum repeaters with machine learning optimization, the proposed solutions aim to address the challenges in quantum communication.
Pulse-Level Variational Quantum Algorithms for Molecular Energy Calculations using Quanlse

At present, quantum computing is in the noisy intermediate-scale quantum (NISQ) era, marked by small qubit counts and high levels of noise and errors. Building a quantum computer with sufficient size and low error rates remains a challenge. In many promising quantum hardware architectures, the state of the physical qubits is controlled by pulse signals. In this paper, we will explore pulse-level control of quantum gates. Unlike the usual gate-level control, the pulse-level control provides increased flexibility and reduced latency. One direct application of pulse-level control is Variational Quantum Algorithms (VQA). The inherent properties of VQA allow us to disregard the gate-based evolution process and concentrate on the final target loss function. From the perspective of pulse-level control, we can generate a sequence of pulse-based gates to rotate the quantum state directly to the desired destination. In this study, we demonstrate an application of pulse-level VQA in estimating the ground state energy of molecular hydrogen. Our experiment is conducted using Quanlse which specializes in pulse-level control of quantum gates. The experimental results reveal a rapid convergence rate of optimization iterations, and the control pulses for each pulse-based gate is also displayed. These results highlight the considerable potential of pulse-level control techniques in practical applications.
Multi-channel feature fusion attention Dehazing network

Haze is a typical weather phenomena that has a significant negative impact on transportation safety, particularly in the port, highways, and airport runway areas. A multi-scale U-shaped dehazing network is proposed in this research, which is based on our multi-channel feature fusion attention structure. With the help of the feature fusion attention techniques, the model can focus on the intriguing locations with higher haze concentration area. In conjunction with UNet, it can achieve multi-scale feature reuse and residual learning, allowing it to fully utilize the feature information of each layer for image restoration. Experimental resulsts show that our technique performs well on a variety of test datasets. On highway data sets, the PSNR / SSIM / L∞ error performance over the novel technique is increased by 0.52% / 0.5% / 30.84%, 4.68% / 0.78% / 26.19% and 13.84% / 9.05% / 55.57% respectively, when compared to DehazeFormer, MIRNetv2, and FSDGN methods. The findings suggest that our proposed method performs better on image dehazing, especially in terms of L∞ error performance.
Sliding Surface Designs for Visual Servo Control of Quadrotors

Autonomy is the main task of a quadrotor, and visual servoing assists with this task while providing fault tolerance under GPS failure. The main approach to visual servoing is image-based visual servoing, which uses image features directly without the need for pose estimation. The classical sliding surface design of sliding mode control is used by the linear controller law of image-based visual servoing, and focuses only on minimizing the error in the image features as convergence. In addition to providing convergence, performance characteristics such as visual-feature-convergence time, error, and motion characteristics should be taken into consideration while controlling a quadrotor under velocity limitations and disturbance. In this study, an image-based visual servoing system for quadrotors with five different sliding surface designs is proposed using analytical techniques and fuzzy logic. The proposed visual servo system was simulated, utilizing the moment characteristics of a preset shape to demonstrate the effectiveness of these designs. The stated parameters, convergence time, errors, motion characteristics, and length of the path, followed by the quadrotor, were compared for each of these design approaches, and a convergence time that was 46.77% shorter and path length that was 6.15% shorter were obtained by these designs. In addition to demonstrating the superiority of the designs, this study can be considered as a reflection of the realization, as well as the velocity constraints and disturbance resilience in the simulations.
Gamification as a New Educational Technology in Modern Higher Education

The relevance of the research is due to the need to improve the educational process in higher education. Edu-cational technologies should be aimed at increasing the motivation of students to acquire knowledge. The existing teaching methods are gradually losing their effectiveness due to changes in the moral, economic, and social compo-nents of the modern world. The trend of recent years is digitalization, affecting all spheres of society, so educational technologies must meet changing conditions. The purpose of the research is to identify the positive and negative features of gamification as an educational technique in modern higher education. The objectives of the research: to define the concept of "gamification" and to identify its differences from the concept of "game"; to consider the components of gamification as an educational technique; to identify the motiva-tional role of gaming technologies in the educational process; to consider possible ways to increase students' motiva-tion during educational activities; to analyze the shortcomings of the introduction of gaming technologies in the edu-cational process. Methodology . The methodological basis of the research is the method of dialectical scientific cognition; a sys-tematic approach to the problem under consideration, the historical method, the analytical method, etc. Results. In the course of the research, various approaches to the concept of "gamification" were identified and systematized. The authors identified the positive and negative features of the introduction of gaming technologies into the educational process, and also considered the motivational component of the modern student's education. Conclusion . Gaming technologies are rarely used in education, as many teachers underestimate their poten-tial. In addition, their introduction into the educational process requires the teacher to make efforts, including creative ones. Such technologies make it possible to involve more students in the educational process by increasing interest in something new and different from the classical teaching methods. Of course, the use of gaming technologies in the educational process has a number of disadvantages that can be offset by a successful combination of innovative and classical teaching methods.
Robust watermark based on Schur decomposition and dynamic weighting factors

Abstract Since multimedia data are now more widely distributed in digital form and easier to copy and change, copyright protection has become an essential need. One of the most common copyright protection techniques is watermarking. Invisibility and robustness are crucial aspects of watermarking; however, many of the currently used techniques simply focus more on invisibility than how robust they are. Moreover, the trade-off between invisibility and robustness is challenging. To this end, this paper proposes a novel watermark technique that efficiently overcomes the idea of a trade-off between robustness and invisibility, thereby increasing both under most attacks. Schur decomposition and a dynamic weighting factors matrix are added to the embedding process to improve the robustness of the proposed technique. Besides that, the embedding function is improved to simultaneously maximize imperceptibility and robustness. Another key contribution of the proposed approach is its use of a trajectory-based optimization algorithm rather than the more prevalent population-based algorithms to determine the optimal scaling factors. Consequently, the proposed technique rapidly identifies the best scaling factors for the embedding function. Statistical analysis is performed using the Friedman test. Experimental results show that the proposed technique outperforms other existing techniques for different sizes, shapes, and types of watermarks.
Research Activity of Schoolchildren as a Means of Increasing the Efficiency of Teaching Biology

At present, modern educational technologies include not only the use of information and communication technologies, as well as distance learning technologies, but also additionally teaching students the skills of research. When studying the issue of organizing the mass practice of teaching research activities to schoolchildren in biological education, it is useful to refer to the experience of higher educational institutions. A mandatory attribute of university education is field research practice. This practice is designed to train research skills and develop the competence of young scientists. Expeditionary activity is a unique tool for the development of the students personality. The author analyzed and critically reviewed modern educational technologies in the field of scientific research as a means of increasing the effectiveness of teaching biology.
Three-dimensional measurement of fringe projection based on the camera response function of the quantified exposure time

Abstract Fringe projection technology is usually used for three-dimensional (3D) measurement, but there will be large errors when the measurement is overexposed or the surface is too dark. Polarizing filters can be used to eliminate the highlights of the image, but they may also cause the image to be too dark and affect the measurement accuracy. Otherwise, to ensure measurement accuracy, the complexity of the operation of the polarization system will be increased. In order to ensure the measurement accuracy and improve the measurement efficiency, a 3D measurement method of quantified exposure time is proposed in this paper, and the intensity response function of the light intensity captured by the camera and the exposure time is established, and more meaningful variable value intervals can be selected by the method of this paper. It can avoid the complex polarization bidirectional reflectance distribution function model, and the required exposure time can be calculated quantitatively within the already screened intervals. Then it is combined with the image fusion algorithm to generate the optimal fringe pattern. Experimental results demonstrate that this method significantly eliminates the effects of highlights in the image. The fuzzy transition area between the black and white fringes is effectively reduced, and the edge information of the fringes is correctly restored.
An impact of artificial intelligence tools on technical students’ ESL oral communication skills: a study

The present study focused on exploring the impact of Artificial Intelligence (AI) tools on Technical students’ ESL (English as Second language) Oral Communication skills. The researcher used preposttest results and speaking rubrics as research methodology. The samples of the study were 60 technical engineering students at VFSTR University, India. The study was designed with pre-posttest and a self-reflection questionnaire for selected samples as quasi experimental study. The aim of the study is enhancing the students’ accuracy and Fluency levels in Oral Communication skills through mobile applications based on AI (Artificial Intelligence). The analysis of the study revealed the significant difference of mean score of the speaking skills pre-post test of the selected group in favour of the post-test results. The study results showed that Artificial Intelligence based mobile applications can develop the students’ Speaking skills in the area of Accuracy and Fluency. The present study concluded with further recommendations related to mobile applications based on AI (Artificial Intelligence) for enhancing Writing skills of the Professional students.
Functional Perception of Artificial Intelligence and Robot Technology

Today, beyond being just technological objects, artificial intelligence and robots create a multidimensional relationship network within the social structure. This multidimensional network of relationships includes human actors such as mathematicians, engineers, bankers, doctors, soldiers, students, and teachers and non-human smart actors such as chatbots, virtual assistants, autonomous vehicles, translation programs, CCTV systems, drones, humanoid robots, and smart home robots. This study is aimed to determine the perception of function towards artificial intelligence and robots of individuals who use the said technology and follow the developments and whether this perception changes according to some variables. Some data on the perception of function towards artificial intelligence and robots are handled in line with Merton's functionality perspective. Qualitative and quantitative methods obtained the data, and it was observed that the perception of function towards the technology in question differs according to the people's expectations, needs, and positions. It is thought that the data obtained will be useful to the literature and the experts on the subject.
Comparing software product lines and Clone and Own for game software engineering under two paradigms: Model-driven development and code-driven development

Game developers often face more challenges when reusing code compared to non-game developers, and Software Product Lines (SPLs) have been successful in addressing this issue. In this study, we compare different approaches to code reuse in Classic Software Engineering (CSE) and Game Software Engineering (GSE) through a commercial video game called Kromaia. We specifically focus on two development paradigms: Model-Driven Development (MDD) and Code-Driven Development (CDD). We conduct an empirical evaluation where subjects develop game elements using two approaches: Clone and Own (CaO) and SPLs. The results show that game elements developed using SPLs are more correct (over 23%) than those developed with CaO in both MDD and CDD paradigms. In CDD, there are significant improvements in efficiency (51%) and satisfaction (13%) when using SPLs compared to CaO. However, no improvements are observed when working under MDD. The impact of using SPLs or CaO is greater in CDD than in MDD for game developers. Our findings suggest that SPLs in GSE may have a different role compared to their traditional role in CSE. Specifically, SPLs can be valuable in balancing game difficulty or generating new video game content, such as the one present in the bosses of the game.
A new context correctness measure CMoC and corresponding context inconsistency elimination algorithm

Context-aware systems (CASs) have become increasingly prevalent in modern-day settings such as digital home and smart healthcare, primarily owing to the expansion of the Internet of Things (IoT). High-quality contexts play a vital role in the effectiveness of CASs, especially in applications involving multiple sensors. When multiple sensors provide identical information, utilizing various sources of context can enhance the dependability of said contexts through the provision of mutual support. However, there is a high possibility that multiple contexts conflict, which can lead to a problem of context inconsistency. Various methods that include quality of context (QoC) parameters have been employed to solve the issue of context inconsistency. One of the limitations of these methodologies is their inadequate consideration of the context correctness. Moreover, when attempting to address context inconsistency through the utilization of QoC parameters, the conventional Dempster-Shafer (D-S) evidence theory may result in significant conflicts. To tackle the aforementioned concerns, a comprehensive measure of correctness (CMoC) is introduced to offer a more efficient assessment of the correctness of contexts. In light of the limited flexibility in assigning probabilities to contexts using D-S evidence theory, the two-dimensional mass function (TDMF) is introduced. This method allows for assigning probabilities to contexts from various sources. On this basis, a context inconsistency elimination algorithm based on CMoC and TDMF is proposed. The effectiveness of the proposed algorithm has been verified through experimental analyses conducted across various dimensions. Based on the experimental findings, it has been demonstrated that the proposed algorithm’s context-judge rate can achieve 97.08%, provided that the high precision configuration of sensors, as outlined in this paper, is utilized. This achievement surpasses that of other inconsistency elimination algorithms by at least 2.05%.