Reviewerly/exHarmony huggingface dataset:
Calibration and cancellation of microwave crosstalk in superconducting circuits

The precise control and manipulation of the qubit state are vital for quantum simulation and quantum computation. In superconducting circuits, one notorious error comes from the crosstalk of microwave signals applied to different qubit control lines. In this work, we present a method for the calibration and cancellation of the microwave crosstalk and experimentally demonstrate its effectiveness in a superconducting 10-qubit chain. The method is convenient and efficient especially for calibrating the microwave crosstalk with large amplitudes and variations, which can be performed successively to reduce the microwave crosstalk by two to three orders. The qubit chain with microwave driving is governed by one-dimensional (1D) Bose–Hubbard model in transverse field, which is nonintegrable and shows thermalization behaviour during the time evolution from certain initial states. Such thermalization process is observed with excellent agreement between experiment and theory further confirming the effective global cancellation of the microwave crosstalk.
PTGNG: An Evolutionary Approach for Parameter Optimization in the Growing Neural Gas Algorithm

Growing Neural Gas (GNG) algorithm is an unsupervised learning algorithm which belongs to the competitive learning family. Since then, GNG has been a subject to vaious developments and implementations found in the literatures for two main reasons: first, the number of neurons (i.e., nodes) is adaptive. Meaning, it is periodically changed through adding new neurons and removing old neurons accordingly in order to find the best network which captures the topological structure of the given data, and to reduce the overall error in that representation. Second, GNG algorithm has no restrictions when compared to other competitive learning algorithms, as it is both free in the space and the number of the neurons. In this paper, we propose and implement an evolutionary based approach, namely PTGNG, to tune GNG algorithm parameters for dealing with data in multiple dimensional space, namely, 2D, 3D, and 4D. The idea basically relies on finding the optimum set of parameter values for any given problem to be solved using GNG algorithm. The evolutionary algorithm by its nature searches a vast space of applicable solutions and evaluates each solution individually. When we implemented our approach of parameters tuning, we can note that GNG captured datasets topological structure with a smaller number of neurons and with a better accuracy. It also showed that the same results appeared when working on datasets with three and four dimensions.
Path tracking control of solar-powered UAV based on energy management strategy

To coordinate the conflict between the high-altitude task and long-term energy efficiency of high-altitude long endurance solar UAV, it is one of the core issues of UAV control. A strategy combining the energy management with the route tracking is presented. Firstly, based on the three-dimensional particle motion model, energy storage battery model and solar energy acquisition model for the solar UAV, according to the aerodynamic parameters of the solar UAV and the typical horizontal flight, climb, descent and other motion processes of the solar UAV, the energy management strategy of the solar UAV is designed, and the allocation mechanism and optimal flight parameters of the energy acquisition, storage and consumption of the solar UAV in different flight stages are determined. The surplus solar energy is stored by using the gravitational potential energy, and then the energy management based on height adjustment is carried out to realize the longitudinal tracking control of the solar UAV under different lighting and energy conditions. Then based on the task requirements of the lateral movement of the solar UAV, a track tracking control method based on the feedback linearization method is established by decoupling the particle dynamics equation of the solar UAV, and the track tracking control of the solar UAV in the lateral direction is realized. Finally, a simulation throughout 24 hours is implemented and illustrated the effectiveness of the energy management strategy and route tracking control law.
Technical Perspective on 'R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys

Increased use of data to inform decision making has brought with it a rising awareness of the importance of privacy, and the need for appropriate mitigations to be put in place to protect the interests of individuals whose data is being processed. From the demographic statistics that are produced by national censuses, to the complex predictive models built by "big tech" companies, data is the fuel that powers these applications. A majority of such uses rely on data that is derived from the properties and actions of individual people. This data is therefore considered sensitive, and in need of protections to prevent inappropriate use or disclosure. Some protections come from enforcing policies, access control, and contractual agreements. But in addition, we also seek technical interventions: definitions and algorithms that can be applied by computer systems in order to protect the private information while still enabling the intended use.
A Bayesian network structure learning method for optimizing ordering search operator

Local search algorithm in ordering space is a good method which can effectively improve the efficiency of bayesian network structure learning. However, the existing algorithms usually have problems such as insufficient order optimization, low learning accuracy, and easy stop at a local optimal. In order to solve these problems, the local search algorithm in ordering space is studied, and a new method to improve the accuracy of bayesian network structure learning by optimizing order search operator is proposed. Combining the iterative local search algorithm with the window operator to search the neighborhood of a given order in the ordering space, the probability of the algorithm falling into the local optimal value is reduced, and the network structure with higher quality is obtained. Experimental results show that comparing with the bayesian network structure learning algorithm in network structure space, the learning efficiency of the present algorithm is improved by 54.12%. Comparing with the bayesian network structure learning algorithm in ordering space, the learning accuracy of the present algorithm is improved by 2.33%.
SENTIMENT ANALYSIS ON LGBT ISSUES IN INDONESIA WITH LEXICON-BASED AND SUPPORT VECTOR MACHINE ALGORITHMS

Non-heterosexual sexual orientation (LGBT) behavior today is one of the most pervasive issues in Indonesian culture. Because of its domino effect on social stability and physical and mental health, the phenomenon known as lesbian, gay, bisexual, and transgender (LGBT) has always been under scrutiny. The development of LGBT people in Indonesia reflects cultural changes that concern many people. Freedom of speech for LGBT people on social media has many public implications. Observation of this phenomenon gives rise to views of anomalies and discrepancies that have drawn criticism. Various attempts have been made to prevent the movement of LGBT people. However, until now, many still debate the pros and cons of this LGBT movement. The lexicon-based method uses a support vector machine to classify public opinion in TikTok video comments about LGBT issues. The lexicon-based method is used as a weighting method, and the support vector machine method is used as a classification method. The results show that the highest gain in sentiment is neutral, with percentage values of 61%, 56%, 68%, 69%, and 63%. The second is positive sentiment, with percentage values of 27%, 27%, 20%, 20%, and 29%. The rest have negative sentiments. With a relatively high accuracy of the five data sets sequentially at 93%, 89%, 95%, 97%, and 91%. This shows that the majority of Indonesians prefer to ignore the issue.
A preliminary investigation into the efficacy of training soccer heading in immersive virtual reality

Abstract Recent research has suggested a link between repetitive soccer heading and the increased incidence of neurodegenerative disease in retired players. In response, restrictions have been introduced to limit the amount of soccer heading in training and competitive matches. Therefore, while heading remains an integral part of the game, players are restricted in the amount of training that they can gain on this important skill without potentially harming their long-term wellbeing. The aim of this study was to provide a preliminary investigation into the efficacy of training soccer heading in immersive virtual reality (VR) which allows the practice of the skill without the risk of repetitive head impacts. Thirty-six recreational soccer players were divided into a VR group ( n = 18) who trained soccer heading on three occasions over a 7–10-day period in VR and a control group ( n = 18) who received no training in soccer heading. Measures of real-world heading performance (i.e. the number of goals scored and shot accuracy), perceived confidence and perceived self-efficacy were assessed pre- and post-training. The results showed that the VR group experienced significant improvements in the number of goals scored and increased their perceptions of confidence and self-efficacy. These results show preliminary support for the inclusion of VR-based training in soccer heading where players can hone their heading skills without exposure to repeated head impacts. Implications and practical applications are discussed.
Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain

For copyright protection of diffusion-weighted imaging (DWI) images, traditional robust watermarking techniques result in irreversible distortions, while reversible watermarking methods exhibit poor robustness. We propose a two-stage lossless watermarking algorithm based on a Transformer network to solve this problem. The first stage of the algorithm is to train the robust watermarking network, embed the watermark into the cover image in the wavelet domain, and design the frequency information enhancement module to improve the reconstruction quality. In the second stage, based on the pre-trained robust watermarking network, the difference image between the watermarked image and the cover image is reversibly embedded into the watermarked image as the compensation information to losslessly recover the cover image. The difference image is compressed using DCT and Huffman coding to reduce the compensation information. Finally, the watermark extraction network is trained on the second embedding result to avoid weakening the robustness of the first stage caused by the reversible embedding. The experimental results demonstrate that the PSNR of the watermarked image reaches 60.18 dB. Under various types of image attacks, the watermark extraction BER is below 0.003, indicating excellent robustness. The cover image can be recovered losslessly under no attack.
An improved Henry gas optimization algorithm for joint mining decision and resource allocation in a MEC-enabled blockchain networks

Abstract This paper investigates a wireless blockchain network with mobile edge computing in which Internet of Things (IoT) devices can behave as blockchain users (BUs). This blockchain network’s ultimate goal is to increase the overall profits of all BUs. Because not all BUs join in the mining process, using traditional swarm and evolution algorithms to solve this problem results in a high level of redundancy in the search space. To solve this problem, a modified chaotic Henry single gas solubility optimization algorithm, called CHSGSO, has been proposed. In CHSGSO, the allocation of resources to BUs who decide to engage in mining as an individual is encoded. This results in a different size for each individual in the entire population, which leads to the elimination of unnecessary search space regions. Because the individual size equals the number of participating BUs, we devise an adaptive strategy to fine-tune each individual size. In addition, a chaotic map was incorporated into the original Henry gas solubility optimization to improve resource allocation and accelerate the convergence rate. Extensive experiments on a set of instances were carried out to validate the superiority of the proposed CHSGSO. Its efficiency is demonstrated by comparing it to four well-known meta-heuristic algorithms.
Polygonized Silhouettes and Polygon Coding Based Feature Representation for Human Action Recognition

The characteristics of human silhouette shape can be used for action recognition and classification. In this paper, a novel feature extraction method for the silhouette-based classification of human actions in videos is proposed. The proposed method is based on polygonization of silhouette images and coding. Since conventional silhouette generation methods do not satisfy the integrity of silhouettes, Yolact++ is modified as a silhouette generator. Our innovative approach Yolact++ masks are used as silhouettes to overcome this problem. For this purpose, a new image form called Poly Silhouette (PoS), a new Polygonization (PoG) algorithm and a new Polygon Coding (PoC) algorithm have been developed. The polygonization step is based on, but is not similar to curve and image polygonization. It is fast, adaptable, and accurate on the contour coordinates of the PoS images. PoCs were generated by projecting each edge vector generated from the corner coordinates of the PoS onto the angular areas and codes for the PoS were formed. These codes are grouped into k-mers similar to genetic algorithms and are used as features. The proposed innovative feature extraction method guarantees that feature vectors of equal length are generated from any action video. Thus, no additional action is required to overcome the dimensionality problem. By using different k-mer lengths, the classification accuracy of the method versus computation time was analyzed and depicted in figures. The method developed was tested on HMDB51 & UCF101 datasets: for SVM 20.98%, 1.63% for k-NN 4.96%, 6.83%, respectively, and significant improvements were achieved.
A Case Study on the Impact of Forensic-Ready Information Systems on the Security Posture

Abstract While approaches aimed at developing forensic-ready systems are starting to emerge, it is still primarily a theoretical concept. This paper presents a case study of integrating forensic readiness capabilities into SensitiveCloud, an information system for storing and processing sensitive data. A risk-based approach to forensic readiness design is followed to achieve it. Consequently, weaknesses in both processes and systems are identified, and forensic readiness requirements are formulated. This case study reports on lessons learned in a practical implementation of a forensic-ready system, its impact on security, and its support towards ISO/IEC 27k.
Toward Green and Efficient Blockchain for Energy Trading: A Noncooperative Game Approach

Blockchain has gained significant adoption in energy trading, offering benefits for both economy and environment. Consensus, in particular, is a decisive factor for blockchain-based energy trading systems to operate efficiently and securely. However, the consensuses currently applied have been criticized for being too energy intensive or not sufficiently decentralized, which counteracts the positive effect of energy trading. Besides, consensus and energy trading are treated separately in many energy trading blockchain-based studies. In this article, we propose a green and efficient consortium blockchain-enabled transaction system for energy trading, meeting the requirement of low energy consumption under security. We then design a two-stage consensus mechanism called proof-of-energy that is coupled to trading through “energy” and naturally uses the monetary rewards to stimulate prosumer participation. Specifically, it retains a strong degree of decentralization, which selects a dynamic delegation with high historical energy generation and motivates delegates to compete for new blocks by solving a meaningful puzzle. Furthermore, a variable block reward is investigated as the incentive to regulate trading and consensus behavior within a reasonable range of energy consumption. Finally, we design a two-layer iterative algorithm to obtain the optimal consensus strategy and block rewards, taking the noncooperative game approach with the consideration of the strategy effect on the pricing model. Our simulation results show that the proposed blockchain-enabled system has a high energy efficiency ratio that improves the social welfare and reduces the consensus overhead.
Deterministic remote preparation of multi-qubit equatorial states through dissipative channels

We investigate the influence of a noisy environment on the remote preparation of the multi-qubit equatorial state, and specifically deduce the final states and fidelities of the remote preparation of the three-qubit and four-qubit equatorial states under diverse types of noisy environments, namely, amplitude damping, bit flip, phase damping, phase flip, bit-phase flip, depolarization, and non-Markov environments. The results show that when the decoherence factors of the front six noises are equal, the influence degrees of phase damped noise, bit flip noise, phase flip noise, and bit-phase flip noise are similar, while the information loss caused by the amplitude damped noise and depolarizing noise is less. In particular, the bit flip noise and depolarizing noise may have more complex effects on the remote state preparation (RSP) schemes depending on the phase information of the target states, even for the ideal cases where the fidelity values are always 1 for specific phase relations. In the non-Markov environment, owing to the back and forth of information between the environment and systems, fidelities exhibit oscillating behavior and the minimum value may stay greater than zero for a long evolutionary time. These results are expected to have potential applications for understanding and avoiding the influence of noise on remote quantum communication and quantum networks.
EXPLORATION ON FINANCING STRATEGIES OF SMALL AND MEDIUM-SIZED ENTERPRISES APPLYING THE BLOCKCHAIN TECHNOLOGY

For a long time, due to the limitations of traditional financial institutions and small and medium-sized enterprises, most SMEs have not been able to obtain sufficient development funds. The main reason is the information asymmetry between financial institutions and enterprises, as well as limited fundraising channels for SMEs. In recent years, blockchain technology has become a global hotspot due to its characteristics of decentralization, transparency, anonymization, and traceability mechanisms. It is considered to be the core digital technology of the next-generation technological revolution and has great development potential. A literature review is employed in this article to list the technical architecture of the blockchain technology, introduces the meaning and characteristics of the blockchain technology, analyses how the blockchain technology fundamentally eliminates the problem of information asymmetry from the technical level, checks the existing proposals for SMEs to apply the blockchain technology for fundraising. Based on the evaluation of existing approaches, with a combination of exploratory and descriptive research methods, this article proposes of building a comprehensive solution to find the path for SMEs to conveniently raise funds applying the blockchain technology.
Influences of COVID-19 Knowledge, eHealth Literacy, COVID-19 Literacy, and Social Support on COVID-19 Preventive Behaviors among Foreign Students in Korea

Purpose: This study was conducted to identify the factors affecting coronavirus disease 2019 (COVID-19) preventive behaviors among foreign students in Korea.Methods: The study administered a descriptive cross-sectional survey to 201 foreign students. Data were collected from June 25 to July 6, 2022 using self-report structured questionnaires and analyzed using descriptive statistics and hierarchical regression analysis with SPSS for Windows version 27.0.Results: The participants were from China (n=58) or Vietnam (n=143), with a mean age of 22.28 years (SD=2.25 years) and length of stay in Korea of 13.94 months (SD=9.12 months). COVID-19 preventive behaviors were significantly different based on participants’ nationality (t=-3.31, &lt;i&gt;p&lt;/i&gt;=.001), gender (t=2.05, &lt;i&gt;p&lt;/i&gt;=.042), education level (t=-2.41, &lt;i&gt;p&lt;/i&gt;=.017), and perceived health status (t=-4.46, &lt;i&gt;p&lt;/i&gt;&lt;.001), and were significantly correlated with COVID-19 knowledge (r=.17, &lt;i&gt;p&lt;/i&gt;=.016), eHealth literacy (r=.33, &lt;i&gt;p&lt;/i&gt;&lt;.001), COVID-19 literacy (r=.22, &lt;i&gt;p&lt;/i&gt;=.001), and social support (r=.26, &lt;i&gt;p&lt;/i&gt;&lt;.001). Hierarchical multiple regression analysis with those aforementioned variables explained approximately 16% of the variance in preventive behaviors.Conclusion: Based on the results of this study, it is necessary to find ways to improve eHealth literacy in order to create a safe environment for foreign students in Korea during outbreaks of infectious diseases such as COVID-19.
Optimal High-Dimensional Entanglement Concentration for Pure Bipartite Systems

Considering pure quantum states, entanglement concentration is the procedure where, from N copies of a partially entangled state, a single state with higher entanglement can be obtained. Obtaining a maximally entangled state is possible for N=1. However, the associated success probability can be extremely low when increasing the system's dimensionality. In this work, we study two methods to achieve a probabilistic entanglement concentration for bipartite quantum systems with a large dimensionality for N=1, regarding a reasonably good probability of success at the expense of having a non-maximal entanglement. Firstly, we define an efficiency function Q considering a tradeoff between the amount of entanglement (quantified by the I-Concurrence) of the final state after the concentration procedure and its success probability, which leads to solving a quadratic optimization problem. We found an analytical solution, ensuring that an optimal scheme for entanglement concentration can always be found in terms of Q. Finally, a second method was explored, which is based on fixing the success probability and searching for the maximum amount of entanglement attainable. Both ways resemble the Procrustean method applied to a subset of the most significant Schmidt coefficients but obtaining non-maximally entangled states.
Pseudo random number generator based on vertical-cavity surface-emitting lasers under current modulation embedded in microcontroller

Abstract This paper explores the dynamics, microcontroller validation of a vertical-cavity surface-emitting laser (VCSEL) under current modulation (CM) and employs it to model a process of pseudo-random number generator (PRNG). Solitary VCSEL has three equilibrium points whose stabilities depend on the direct current density. VCSEL under CM (VCSELCM) experiences antimonotonicity, chaotic bubble attractor as well as chaotic attractor. A microcontroller implementation of VCSELCM is used to establish the dynamical behaviors spotted in VCSELCM during the numerical simulations. Lastly, the chaotic characteristics depicted by the VCSELCM are used to design a process of PRNG. The generated random bits are validated successfully by a standard statistical tool set by the National Institute of Standards and Technology (NIST-800–22).