Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Method for Accurate Translation of Fuzzy Semantics in English Language and Literature

In order to address the drawbacks of semantic ambiguity, inaccurate quantifiers, and low translation accuracy in traditional grammar-based translation methods, this paper proposes an artificial intelligence translation method based on semantic analysis for English fuzzy semantics. Firstly, a comprehensive analysis of English language semantics was carried out from different semantic levels such as language, knowledge, and pragmatics, and the key points of fuzzy semantics were identified. Then, key feature quantities for accurate translation of fuzzy semantics in English vocabulary and literature were constructed, and artificial intelligence methods were used to optimize fuzzy semantics. The experimental results show that the proposed method can avoid semantic understanding ambiguity and improve the accuracy of English language translation.
Design of an Instant Data Analysis System for Sports Training Based on Data Mining Technology

Data mining (DM) is an in-depth approach to data analysis by mining useful information from large amounts of data, and this technique is now being used in an increasing number of fields. In this paper, the authors present the design of a real-time data analysis system for sports training based on DM technology and use the corresponding mining tools of DM technology to discover relevant patterns or laws hidden in the data. Therefore, using the real-time data analysis system for sports training based on DM technology, useful information and patterns for improving examination performance can be obtained, which can improve targeted teaching methods and help students overcome learning difficulties, providing rational teaching, synchronizing courses, establishing preparation, effectively guiding students in course selection, and improving course quality and educational effectiveness.
Prepare-and-measure based QKD protocol under free-space losses

In this study, we have theoretically presented a prepare-and-measure based SARG04 protocol over free-space. It has been shown that the highest secret key rate is achievable even under free-space losses with a maximum noise tolerance.
Parsimony and parameter estimation for mixtures of multivariate leptokurtic-normal distributions

Abstract Mixtures of multivariate leptokurtic-normal distributions have been recently introduced in the clustering literature based on mixtures of elliptical heavy-tailed distributions. They have the advantage of having parameters directly related to the moments of practical interest. We derive two estimation procedures for these mixtures. The first one is based on the majorization-minimization algorithm, while the second is based on a fixed point approximation. Moreover, we introduce parsimonious forms of the considered mixtures and we use the illustrated estimation procedures to fit them. We use simulated and real data sets to investigate various aspects of the proposed models and algorithms.
A multi-keyword searchable encryption sensitive data trusted sharing scheme in multi-user scenario

Searchable encryption technology provides a solution for the trusted sharing of sensitive data. A large amount of encrypted data stored in data sharing servers can support keyword search without exposing data privacy, which has attracted more and more scholars’ attentions and researches. However, some multi-keyword searchable encryption schemes that deal with multi-semantic data still have security problems or low efficiency. To improve the security and efficiency of trusted sharing of sensitive data, we propose a multi-keyword searchable encryption scheme. The scheme designs a sensitive data sharing architecture based on blockchain technology, and the tamper-proof of distributed ledgers ensures the authenticity of encrypted data and indexes, as well as the supervision of sharing behaviors. On this basis, we improved the inverted index structure to achieve efficient multi-keyword searchable encryption while avoiding keyword-pair result pattern leakage. Our proposed scheme has passed formal security analysis, and simulation results show that the multi-keyword searchable encryption scheme is efficient.
Deep transfer learning-based adaptive gesture recognition of a soft e-skin patch with reduced training data and time

Deep learning-based classification algorithms are promising in gesture recognition with soft e-skin patches. However, the reported algorithms usually require large amount of training data, resulting in the time-consuming data collection process. In this paper, we present a deep transfer learning-based adaptive strategy for accurate gesture recognition of a soft e-skin patch with reduced training data and time. To this end, we first train a base neural network as the general feature extraction network. Next, we transfer the front layers of the pre-trained base network to target networks of new gesture recognition tasks. Further, we apply the fine-tune technique to refine the copied parameters. Finally, with our custom-built soft e-skin patch, we experimentally verify the developed strategy on two typical transfer cases, termed as the user transfer case (Case I) and the gesture transfer case (Case II). The experimental results show that, to ensure the stable accuracy of 95 %, the training data with and without the adaptive strategy are 1,312 vs 10,912 for Case I, and 8,192 vs 12,032 for Case II, respectively. In this sense, the training time of target networks can be reduced by 62.96 % for Case I and 34.20 % for Case II, respectively. This work shows the potential to promote the widespread application of e-skins in human computer interaction.
Supervised Domain Adaptation by transferring both the parameter set and its gradient

A well-known obstacle in the successful implementation of deep learning-based systems to real-world problems is the performance degradation that occurs when applying a network that was trained on data collected in one domain, to data from a different domain. In this study, we focus on the Supervised Domain Adaptation (SDA) setup where we assume the availability of a small amount of labeled data from the target domain. Our approach is based on transferring the gradient history of the pre-training phase to the fine-tuning phase in addition to the parameter set to improve the generalization achieved during pre-training while fine-tuning the model. We present two schemes to transfer the gradient's information. Mixed Minibatch Transfer Learning (MMTL) is based on using examples from both the source and target domains and Optimizer-Continuation Transfer Learning (OCTL) preserves the gradient history when shifting from training to fine-tuning. This approach is also applicable to the more general setup of transfer learning across different tasks. We show that our methods outperform the state-of-the-art at different levels of data scarcity from the target domain, on multiple datasets and tasks involving both scenery and medical images.
A general framework for preferences in answer set programming

We introduce a general, flexible, and extensible framework for quantitative and qualitative preferences among the stable models of logic programs. Since it is straightforward to capture propositional theories and constraint satisfaction problems with logic programs, our approach is also relevant to optimization in satisfiability testing and constraint processing. We show how complex preference relations can be specified through user-defined preference types and their arguments. We describe how preference specifications are handled internally by so-called preference programs, which are used for dominance testing. We also provide algorithms for computing one, or all, preferred stable models of a logic program, and study the complexity of these problems. We implemented our approach in the asprin system by means of multi-shot answer set solving technology. We demonstrate the generality and flexibility of our methodology by showing how easily existing preference languages can be implemented in asprin. Finally, we empirically evaluate our contributions and contrast them with dedicated implementations.
The quantum communication efficiency of the fractional anti-Jaynes–Cummings model

The efficiency of the fractional state that is generated between a single atom and field by using anti-Jaynes–Cummings model (AJCM) is discussed. The fractional degree and the interaction parameters may be used as controllers to increase the efficiency of the fractional state in the context of quantum communication. It has been shown that, the quantum correlation, capacity, and the ability of the fractional state, increase suddenly/gradually at small/large fractional degree, respectively. At small fractional orders, the constant behavior for all these phenomena is displayed at a short interaction time. The fractional quantum state that is generated by the anti-Jaynes–Cummings model is more efficient than that generated by the well-known Jaynes–Cummings model (JCM), where it can be used to teleport an unknown two-qubit state with larger fidelity.
Semiconductor-Quantum-Dot Modulator for Cryogenic Operation of Quantum Circuitry

Quantum computing devices have the potential to solve problems that are beyond even the most powerful modern supercomputers. Most quantum hardware operates at temperatures near absolute zero, but controlling a quantum computer at cryogenic temperatures using signals propagating from room temperature becomes infeasible for large-scale processors. The authors explore a device that allows control signals to be generated on-chip at cryogenic temperatures, potentially mitigating this cryogenic bottleneck. Their device is fabricated in silicon using established technologies for quantum processors, providing a pathway towards a hybrid quantum-classical integrated circuit.
Teaching Practicums During the Pandemic in an Initial English Teacher Education Program: The Preservice Teachers’ Perspective

In an initial English teacher education program at a Chilean institution, early and professional practicums were re-invented during the COVID-19 pandemic to ensure the continuity of this process. This study analyzed the context and conditions under which these practicums unfolded and how the process influenced participants’ pedagogical and professional knowledge development. Forty-two preservice English language teachers undergoing early and professional practicums online throughout 2020–2021 answered an online questionnaire which was analyzed through descriptive statistics and content analysis. Findings revealed preservice teachers’ problems, strengths, weaknesses, and challenges while interacting with pupils, cooperating teachers, and supervisors, as well as the development of some pedagogical and professional knowledge. The findings may serve to make adaptations to increasingly challenging teaching contexts.
Shaping Better Futures: Inside-Out Colombian English Language Teachers’ Gaps and Practices

This exploratory qualitative study reports gaps in the preservice teacher education and professional development of 15 English language teachers in a Colombian public university regarding their teaching knowledge and practice. Grounded theory was used to examine the data collected via focus groups and questionnaires, which were validated and triangulated. Findings reveal several teacher deficiencies, classified into three axes: language proficiency, teaching awareness, and teacher challenges. These tensions unveil imbalances between theory and practice, inefficient mentoring during the practicum stage, lack of classroom management skills for efficient teaching, and the need to revitalize and endorse information and communications technology education and use in the new classroom era. The article also offers a discussion on reconceptualizing teacher education.
On the rôle of singular functions in extending the probabilistic symbol to its most general class

The probabilistic symbol is the right-hand side derivative of the characteristic functions corresponding to the one-dimensional marginals of a stochastic process. This object, as long as the derivative exists, provides crucial information concerning the stochastic process. For a Lévy process, one obtains the characteristic exponent while the symbol of a (rich) Feller process coincides with the classical symbol which is well known from the theory of pseudodifferential operators. Leaving these classes behind, the most general class of processes for which the symbol still exists are Lévy-type processes. It has been an open question, whether further generalizations are possible within the framework of Markov processes. We answer this question in the present article: within the class of Hunt semimartingales, Lévy-type processes are exactly those for which the probabilistic symbol exists. Leaving Hunt behind, one can construct processes admitting a symbol. However, we show, that the applicability of the symbol might be lost for these processes. Surprisingly, in our proofs the upper and lower Dini derivatives corresponding to certain singular functions play an important rôle.
Approaching flexible production with planning methods

Abstract Flexible production systems are becoming more and more important for the industry. Higher requirements for flexibility in the production process demand further improvements in regards to flexible automation. In this perspective, the RoboCup Logistics League competition was designed to provide a testbed for dynamic production domains. The related challenges range from robust navigation over manipulation to efficient production scheduling. In this paper, we provide an update on how the team GRIPS approches the flexible production challenge using modern planning methods. This includes a new long-term planning and scheduling strategy as well as a hierarchical structure which further develops such plans into Behavior Trees.
Autonomous Optical-Only Spacecraft-to-Spacecraft Absolute Tracking and Maneuver Classification in Cislunar Space

Traditional concerns regarding spacecraft state estimation are exacerbated in cislunar space due to highly nonlinear dynamics, limited observational information, and increasingly overloaded Earth-based resources. These challenges motivate research into space-based navigation and observation capabilities that provide opportunities for autonomy. This paper demonstrates that relative optical measurements provide full inertial estimates of an agent and target spacecraft, while simultaneously enabling classification of unknown maneuvers in the cislunar environment. To achieve this objective two subtasks are demonstrated. First, the spacecraft-to-spacecraft absolute tracking method, which estimates the absolute state of two vehicles using relative measurements, is tested for observability and shown to produce consistent filter solutions given optical sensors. Second, maneuver classification is performed by labeling an estimated control profile generated from the optimal control based estimator. Collectively, this sequential methodology enables an agent craft to autonomously navigate itself while observing a target craft and classifying its maneuvers.
Machine-learning-inspired quantum optimal control of nonadiabatic geometric quantum computation via reverse engineering

Quantum control plays an irreplaceable role in practical use of quantum computers. However, some challenges have to be overcome to find more suitable and diverse control parameters. We propose a promising and generalizable average-fidelity-based machine-learning-inspired method to optimize the control parameters, in which a neural network with periodic feature enhancement is used as an ansatz. In the implementation of a single-qubit gate by cat-state nonadiabatic geometric quantum computation via reverse engineering, compared with the control parameters in the simple form of a trigonometric function, our approach can yield significantly higher-fidelity ($>99.99\%$) phase gates, such as the $\pi / 8$ gate (T gate). Single-qubit gates are robust against systematic noise, additive white Gaussian noise and decoherence. We numerically demonstrate that the neural network possesses the ability to expand the model space. With the help of our optimization, we provide a feasible way to implement cascaded multi-qubit gates with high quality in a bosonic system. Therefore, the machine-learning-inspired method may be feasible in quantum optimal control of nonadiabatic geometric quantum computation.
Semi-RainGAN: A Semisupervised Coarse-to-Fine Guided Generative Adversarial Network for Mixture of Rain Removal

Images taken in various real-world scenarios meet the symmetrical goal of simultaneously removing foreground rain-induced occlusions and restoring the background details. This inspires us to remember the principle of symmetry; real-world rain is a mixture of rain streaks and rainy haze and degrades the visual quality of the background. Current efforts formulate image rain streak removal and rainy haze removal as separate models, which disrupts the symmetrical characteristics of real-world rain and background, leading to significant performance degradation. To achieve this symmetrical balance, we propose a novel semisupervised coarse-to-fine guided generative adversarial network (Semi-RainGAN) for the mixture of rain removal. Beyond existing wisdom, Semi-RainGAN is a joint learning paradigm of the mixture of rain removal and attention and depth estimation. Additionally, it introduces a coarse-to-fine guidance mechanism that effectively fuses estimated image, attention, and depth features. This mechanism enables us to achieve symmetrically high-quality rain removal while preserving fine-grained details. To bridge the gap between synthetic and real-world rain, Semi-RainGAN makes full use of unpaired real-world rainy and clean images, enhancing its generalization to real-world scenarios. Extensive experiments on both synthetic and real-world rain datasets demonstrate clear visual and numerical improvements of Semi-RainGAN over sixteen state-of-the-art models.
Using Differential Evolution to avoid local minima in Variational Quantum Algorithms

Variational Quantum Algorithms (VQAs) are among the most promising NISQ-era algorithms for harnessing quantum computing in diverse fields. However, the underlying optimization processes within these algorithms usually deal with local minima and barren plateau problems, preventing them from scaling efficiently. Our goal in this paper is to study alternative optimization methods that can avoid or reduce the effect of these problems. To this end, we propose to apply the Differential Evolution (DE) algorithm to VQAs optimizations. Our hypothesis is that DE is resilient to vanishing gradients and local minima for two main reasons: (1) it does not depend on gradients, and (2) its mutation and recombination schemes allow DE to continue evolving even in these cases. To demonstrate the performance of our approach, first, we use a robust local minima problem to compare state-of-the-art local optimizers (SLSQP, COBYLA, L-BFGS-B and SPSA) against DE using the Variational Quantum Eigensolver algorithm. Our results show that DE always outperforms local optimizers. In particular, in exact simulations of a 1D Ising chain with 14 qubits, DE achieves the ground state with a 100% success rate, while local optimizers only exhibit around 40%. We also show that combining DE with local optimizers increases the accuracy of the energy estimation once avoiding local minima. Finally, we demonstrate how our results can be extended to more complex problems by studying DE performance in a 1D Hubbard model.
A discourse analysis of critical commenting online: A study of comments on a self-mockery event

This paper examines critical comments hidden behind the humorous topic of self-mockery. Based on a discourse analysis of 51 critical comments identified by GooSeeker of a self-mockery event from Weibo, this paper aims to unpack how the commenters actively exploit the relevancy of a topic to fulfill socio-political functions. Three strategies are found to be key in enabling them to accomplish socio-political functions: immoralizing the peripheral party, deauthorizing privilege and irrationalizing competitiveness, the meanings of which are discursively constructed across the critical comments. In this process, the self-mockery event serves as a weapon of social power to formulate critique and articulate discontent without breaking a consistent performance. The creative (re)appropriation in use is believed to be triggered by the policy of the platform and user’s self-motivated interactional practice. These findings are expected to have implications for understanding comments as a social behavior at the nexus of language and social power.
Digital Forensic Framework for Protecting Data Privacy during Investigation

Rapid technological breakthroughs, a surge in the use of digital devices, and the enormous amount of data that these devices can store continuously put the state of digital forensic investigation to the test. The prevention of privacy breaches during a digital forensic investigation is a significant challenge even though data privacy protection is not a performance metric. This research offered solutions to the problems listed above that centre on the efficiency of the investigative process and the protection of data privacy. However, it’s still an open problem to find a way to shield data privacy without compromising the investigator's talents or the investigation's overall efficiency. This system proposes an efficient digital forensic investigation process which enhances validation, resulting in more transparency in the inquiry process. Additionally, this suggested approach uses machine learning techniques to find the most pertinent sources of evidence while protecting the privacy of non-evidential private files.
Mathematical Foundation of a Functional Implementation of the CNF Algorithm

The conjunctive normal form (CNF) algorithm is one of the best known and most widely used algorithms in classical logic and its applications. In its algebraic approach, it makes use in a loop of a certain well-defined operation related to the “distributivity” of logical disjunction versus conjunction. For those types of implementations, the loop iteration runs a comparison between formulas to decide when to stop. In this article, we explain how to pre-calculate the exact number of loop iterations, thus avoiding the work involved in the above-mentioned comparison. After that, it is possible to concatenate another loop focused now on the “associativity” of conjunction and disjunction. Also for that loop, we explain how to calculate the optimal number of rounds, so that the decisional comparison phase for stopping can be also avoided.
Fixed-time controller of visual quadrotor for tracking a moving target

In order to eliminate the dependence of finite-time control on the initial state of the system in the image-based visual servoing (IBVS) of a quadrotor UAV (QUAV) for the moving target, we propose a control scheme for the moving target of a QUAV based on fixed-time stability. We select the image moments of the virtual camera plane as features and establish an image moment dynamics model that includes target motion parameters based on them. Then, we use the backstepping method to design the fixed-time controller for the system. The design steps of the controller mainly consist of two parts. Firstly, we designed a fixed-time stable linear velocity observer to address the unmeasurable linear velocity and unmeasurable monocular camera depth issues of QUAV. Then, using a high-order tracking differentiator to estimate the target linear velocity as a feedforward, combined with the fixed-time linear velocity observer estimated QUAV linear velocity, we designed a fixed-time controller for the system using the backstepping method. We demonstrated the fixed-time stability of the controller using the Lyapunov theory. The effectiveness and robustness of the proposed method are demonstrated by numerical simulation. At the same time, the comparative simulation shows that the method can guarantee the rate of convergence of the system and has high control accuracy.