Reviewerly/exHarmony huggingface dataset:
Color watermarking algorithm combining the quantum discrete cosine transform with the sinusoidal–tent map

To overcome the drawbacks of the existing sinusoidal map and tent map, this paper proposes the design of a sinusoidal–tent (ST) map. The test results indicate that the new chaotic system exhibits more significant advantages in chaos control. Compared with the sinusoidal map and tent map, the proposed sinusoidal–tent map performs better in terms of bifurcation diagram and Lyapunov exponents. The trajectories of the sinusoidal–tent map can occupy all the phase planes over (0,4), while those of the two classic maps only occupy a small phase space, and the Lyapunov exponents of the ST map are all positive within the range of control parameters, higher than those of seed maps. Simultaneously, a novel quantum scrambling operation is devised based on the sinusoidal–tent map to avoid the periodicity of the quantum Arnold scrambling method. Initially, two chaotic sequences are generated to scramble the pixel positions of the watermark image, further enhancing the security of the watermarking algorithm. Subsequently, the host image is processed by the quantum discrete cosine transform, and finally, the scrambled watermark image is inserted into the medium-frequency band of the transformed host image, ensuring the invisibility of the watermarking. According to the simulation results, the quantum watermarking algorithm has excellent invisibility and robustness.
Blockchain-cloud privacy-enhanced distributed industrial data trading based on verifiable credentials

Abstract Industrial data trading can considerably enhance the economic and social value of abundant data resources. However, traditional data trading models are plagued by critical flaws in fairness, security, privacy and regulation. To tackle the above issues, we first proposed a distributed industrial data trading architecture based on blockchain and cloud for multiple data owners. Subsequently, we realized implemented distributed identity management by the distributed verifiable credentials scheme that possesses the desirable properties, i.e., selective disclosure, multi-show unlinkability, threshold traceability, and public verifiability. Finally, we presented a fair trading mechanism without trusted third parties based on smart contracts, and we employed blockchain and multi-signature to ensure data integrity during data storage and trading. The security and performance analysis shows that our proposal is feasible for sensitive data trading for multiple data owners and provides a useful exploration for future industrial data trading and management.
Super‐resolution reconstruction based on generative adversarial networks with dual branch half instance normalization

Abstract This paper proposes a super‐resolution reconstruction model, SRPGANto improve the visual quality of images based on generative adversarial networks (GANs) by improving the network structures of the generator and the discriminator. In the generator, a dual branch residual block is designed instead of the residual block, including a branch with an attention mechanism and a branch without an attention mechanism, to extract more differentiated features. Normalization methods are explored to avoid unstable training and bath normalization artifacts and use a half instance normalization layer that is more suitable for underlying visual problems compared with traditional batch normalization. In the discriminator, PatchGAN is applied instead of typical GAN to improve the generation of local texture by discriminating each patch rather than the global image. The experimental results on the public datasets demonstrate that the proposed SRPGAN can achieve excellent quantitative evaluation while improving the visual quality of reconstructed images.
Optimization on Manifolds via Graph Gaussian Processes

.This paper integrates manifold learning techniques within a Gaussian process upper confidence bound algorithm to optimize an objective function on a manifold. Our approach is motivated by applications where a full representation of the manifold is not available and querying the objective is expensive. We rely on a point cloud of manifold samples to define a graph Gaussian process surrogate model for the objective. Query points are sequentially chosen using the posterior distribution of the surrogate model given all previous queries. We establish regret bounds in terms of the number of queries and the size of the point cloud. Several numerical examples complement the theory and illustrate the performance of our method.KeywordsBayesian optimizationmanifold learninggraph Gaussian processesMSC codes60G1562F1505C8590C26
Research on path planning technology of mobile robot in the restaurant scene based on A* and D* algorithm

Intelligent robots have found widespread applications in various fields including medical treatment, industrial production, and social services. These robots are specifically designed to fulfill diverse tasks in different environments, the design and control of these robots play a crucial role in ensuring their efficiency and security. In response to the high demand for fast and convenient service, the catering field is a crucial and promising application field for intelligent robots. This article is primarily focused on the robot in the restaurant scene where the robot could automatically deliver food to the customers, thus reducing the burden on human service providers. In this special environment, the delivery robot must possess the capability to identify its surroundings within the restaurant and autonomously calculate an obstacle-free path from the starting point to the desired destination without colliding with either obstacles or individuals. In this article, the restaurant scene is built in the Gazebo, and map scanning and conversion methods are utilized to enable the robots to recognize the environment effectively. Furthermore, A* and D* algorithms are employed in Python to achieve global and local path planning, respectively, thus validating the feasibility of the methods. The combination of these two algorithms demonstrates a successful path-planning application within the context of a restaurant scene.
Mixed Reality: Present and future

Mixed Reality (MR) is an emerging technology that can create a visual environment connecting the real world and the virtual environment. With this technology, the way information is presented has changed in a great number of domains. However, for common people, this technology still looks far away from life and seems unintelligible. Therefore, this essay reviews the development and recent research of MR, trying to help people without professional backgrounds understand this emerging technology and offer researchers another perspective on MR. By using the methods of text mining, this essay has reviewed more than twenty papers related to MR on different topics, including the definition, the principles, and the applications. The objective of this essay is to provide a comprehensive understanding of MR, which may promote wider applications and research.
Investigation of selection and application of Multi-Armed Bandit algorithms in recommendation system

The Multi-Armed Bandit (MAB) algorithm holds significant prominence as a recommendation system technique, effectively presenting user-centric content preferences based on the analysis of collected data. However, the application of the basic MAB algorithm in real-world recommendation systems is not without challenges, including issues related to data volume and data processing accuracy. Therefore, the optimization algorithm based on the MAB algorithm is more widely used in the recommendation system. This paper briefly introduces the multi-armed bandit algorithm, that is, the use of MAB in the recommendation system and the problems of the basic MAB algorithm. Aiming at the problems of the basic MAB algorithm, it introduces the MAB-based optimization algorithm used in the recommendation system. At the same time, this paper also analyzes and summarizes such algorithms. This paper introduces two different MAB-based optimization algorithms, namely The Details of Dynamic clustering based contextual combinatorial multi-armed bandit (DC3MAB) and Binary Upper Confidence Bound (BiUCB). In addition, this paper also introduces the application of algorithm in recommended system. Finally, this paper summarizes the introduced algorithms and proposes the future prospects for MAB optimization algorithms.
The Interaction Between Morphosyntactic Features and the Performance of Machine Translation Tools: The Case of Google Translate, Systran, and Microsoft Bing in English-Arabic Translation

The present study compares the performance of three machine translation tools in English-Arabic translation to answer the questions of (a) whether the three machine translation tools, Google Translate, Systran and Microsoft Bing can be ordered in a hierarchy of performance, and (b) whether they can handle lexically and structurally ambiguous sentences and garden path sentences. Using a number of constructed and selected English sentences, the morphosyntactic features of number, gender, case, definiteness, and humanness, agreement between cardinal numerals and their head nouns, lexically and structurally ambiguous sentences and garden path sentences are used to test the three machine translation tools for performance. The results show that (a) as far as handling the morphosyntactic features of subject-verb agreement in Standard Arabic, all three machine translation tools perform equally well, and no machine translation tool seems to perform significantly better than the other two (b) some marked features (e.g. dual number and humanness) of SA seem to pose some problems for machine translation tools, and (c) lexically and structurally ambiguous sentences and garden path sentences seem to be the most challenging sentences for the three machine translation tools.
Investigating the Effectiveness of Synchronous E-Learning Tools on Developing EFL Learners' Language Communication Skills Among the Hashemite University Students

After COVID-19, today’s language classes have become blended with e-learning tools that have become indispensable to the process of learning in many situations. Synchronous e-learning classrooms have been studied for their impact on EFL communication skills, but few studies have been conducted in the Jordanian institutions. The purpose of this study is to investigate the EFL students’ viewpoints on the efficiency of online classrooms in improving their verbal communication abilities. This study consisted of 284 participants who were EFL learners from the Hashemite University. It was conducted during the academic year 2022-2023, and it followed a mixed qualitative approach. Two means of data collection were used: a questionnaire and observations. The results indicated that students have a positive viewpoint toward online learning effectiveness in developing their communication. Nevertheless, students showed that low self-assurance, nervousness over making errors, and poor lexical knowledge are their main concerns when communicating in EFL classrooms.
Unravelling Yu Guangzhong’s Identity Through His Poetic Reflections: A Corpus-Based Investigation

This study examines Yu Guangzhong’s identity presented in his self-translated poetry collection, The Night Watchman. It employs corpus linguistic methodologies and utilizes the corpus linguistic tool AntConc for analysis. This study highlights Yu Guangzhong’s identity, which is presented in different categories and topics of poems. It is found that Yu Guangzhong’s identity is ever-changing, varying at different times, and occasionally conflicting and opposing. This study may have practical implications for Chinese literature and culture looking to enhance their international influence, and it may also provide academic insights for investigating other literary giants.
Research and application analysis of typical recommendation methods

Recommendation systems are vital tools in the modern digital landscape for handling vast amounts of online data and are now employed across a variety of sectors. This manuscript underlines the importance of video recommendation algorithms and delves into the three main types: content-based, collaborative filtering, and hybrid algorithms. Content-based algorithms offer suggestions by assessing and matching the descriptive attributes of videos. These attributes could range from the genre, the actors involved, the director, or any other related metadata. On the other hand, collaborative filtering algorithms provide recommendations by comparing the user's historical data with that of others. They function on the principle that individuals who have agreed in the past are likely to agree again in the future. Hybrid recommendation algorithms, as the name suggests, are a combination of both content-based and collaborative filtering algorithms. They aim to harness the strengths of the two while mitigating their individual weaknesses, providing more balanced and accurate recommendations. This detailed exploration of these algorithms not only underscores their significance but also paves the way for the future, heralding the potential advancements and adaptations these systems could undergo. Continued innovation in this realm has the potential to revolutionize how information is processed and shared, ultimately enriching the user experience across the digital platform landscape.
Single‐Transistor Optoelectronic Spiking Neuron with Optogenetics‐Inspired Spatiotemporal Dynamics

Abstract The visual system is crucial for human perception and learning, and the retina is responsible for pre‐processing visual information. Mimicking the neurobiological functions of the retina provides the basis for neuromorphic vision hardware. Here, an optogenetics‐inspired prototype capable of modifying neuron‐spiking behavior in a retina‐like manner is demonstrated. In addition to basic information processing, neuronal signal transmission in the retina is bio‐faithfully emulated using optical modulation, that is, neuronal firing is inhibited in the dark and activated upon illumination. The proposed single threshold‐switch field‐effect transistor optoelectronic neuron features an ultra‐compact design by integrating a gate‐modulated 2D MoS 2 channel with a volatile threshold switch, and optoelectronic performance is achieved through a multi‐physics resistance‐matching mechanism. This study provides a promising pathway toward highly scalable and hierarchically designed spiking electronics, expanding the spectrum of neuromorphic hardware applications in the emerging field of biomimetic vision.
DECISION SUPPORT SYSTEM IN IMPROVING THE QUALITY OF BANJARMASIN TOURISM, GET TOUR APPLICATION USING THE SAW METHOD

Tourism is an activity that is liked by many people, even tourism is one of the important needs, especially regarding socio-economic activities which are seen as having good prospects in the future. In South Kalimantan, especially the city of Banjarmasin, there are many good tourist attractions such as the historical mosque of Sultan Suriansyah, Siring Park, Banjarmaisn City, and culinary tours of Arab villages. Of the tourist attractions that have been mentioned, tourists are still confused in determining tourist attractions because there are many places and a lack of information about tourist attractions in South Kalimantan. From this description, a Decision Support System (DSS) or Decision Support System (DSS) was created to determine tourist attractions in South Kalimantan called the Get Tour application. In addition to displaying information about tourist attractions, this application also displays information on tourist attractions in the form of a map. The results of calculations in the application are in accordance with the formula and expected results based on several criteria, namely distance, parking area, the first special criteria, the second special criteria and the third special criteria using the Simple Additive Weighting (SAW) method.
Social Media English in Nigeria and the New Crime Wave

This study explores the social media English in Nigeria and the new crime wave. Utilizing the theoretical frameworks of systemic functional linguistics and forensic linguistics, the study adopts a qualitative data analysis approach to examine the linguistic devices used in the portrayal of criminal activities. The research delves into the language of crime, reviewing related literature and analyzing selected posts for their use of specific linguistic devices. These devices include slang and code words, metaphors and euphemisms, sensational headlines, and biased language. The study reveals that these linguistic devices play a crucial role in the construction and perception of criminal activities on social media platforms. Slang and code words, for instance, often serve as a form of coded communication among criminals, while metaphors and euphemisms are used to downplay the severity of criminal acts or to disguise illegal activities. Sensational headlines and biased language, on the other hand, can influence public opinion and perpetuate stereotypes about crime and criminals. This study contributes to the understanding of how language is used in the context of crime on social media platforms, offering valuable insights for law enforcement agencies, policy makers, and researchers in the fields of linguistics and criminology. It underscores the need for further research in this area, particularly in the era of digital communication where language plays a pivotal role in shaping societal narratives about crime.
Graph neural network based method for robot path planning problem

Sampling-based path planning is widely used in robotics, particularly in high-dimensional state spaces. In the path planning process, collision detection is the most time-consuming operation. Therefore, we propose a learning-based path planning method that reduces the number of collision checks. We develop an efficient neural network model based on graph neural networks. The model outputs weights for each neighbor based on the obstacle, searched path, and random geometric graph, which are used to guide the planner in avoiding obstacles. We evaluate the efficiency of the proposed path planning method through simulated random worlds and real-world experiments. The results demonstrate that the proposed method significantly reduces the number of collision checks and improves the path planning speed in high-dimensional environments.
Deep Learning for Daily 2‐m Temperature Downscaling

Abstract This study proposes a novel method, which is a U‐shaped convolutional neural network that combines non‐local attention mechanisms, Res2net residual modules, and terrain information (UNR‐Net). The original U‐Net method and the linear regression (LR) method are conducted as benchmarks. Generally, the UNR‐Net has demonstrated promise in performing a 10× downscaling for daily 2‐m temperature over North China with lead times of 1–7 days and shows superiority to the U‐Net and LR methods. To be specific, U‐Net and UNR‐Net demonstrate higher Nash‐Sutcliffe Efficiency coefficient values compared to LR by 0.052 and 0.077, respectively. The corresponding improvements in pattern correlation coefficient are 0.013 and 0.016, while the root mean square error values are higher by 0.22 and 0.338, respectively. Additionally, the structural similarity index metric is higher by 0.033 and lower by 0.015. Furthermore, regions with significant errors are primarily distributed in complex terrain areas such as the Taihang Mountains, where UNR‐Net exhibits noticeable improvements. In addition, the 12 components‐based error decomposition method is proposed to analyze the error source of different models. Generally, the smallest errors are observed during the summer season and the sequence error component is proven to be the main source error of 2‐m temperature forecasts. Furthermore, UNR‐Net consistently demonstrates the lowest errors among all 12 error components. Therefore, combining the numerical weather prediction model and deep learning method is very promising in downscaling temperature forecasts and can be applied to routine forecasting of other atmospheric variables in the future.
Controlling chaotic maps using next-generation reservoir computing

In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dynamical systems. We demonstrate the performance of the controller in a series of control tasks for the chaotic Hénon map, including controlling the system between unstable fixed points, stabilizing the system to higher order periodic orbits, and to an arbitrary desired state. We show that our controller succeeds in these tasks, requires only ten data points for training, can control the system to a desired trajectory in a single iteration, and is robust to noise and modeling error.
Assembling an illustrated family‐level tree of life for exploration in mobile devices

Abstract Since the concept of the tree of life was introduced about 150 years ago, a considerable fraction of the scientific community has focused its efforts on its reconstruction, with remarkable progress during the last two decades with the advent of DNA sequences. However, the assemblage of a comprehensive and explorable tree of life has been a difficult task to achieve due to two main obstacles: (i) information is scattered into several individual sources and (ii) practical visualization tools for exploring large trees are needed. To overcome both challenges, we aimed to synthesize a family‐level tree of life by compiling over 1400 published phylogenetic studies, choosing the source trees that represent the best phylogenetic hypotheses to date based on a set of objective criteria. Moreover, we dated the tree by employing over 550 secondary calibrations using publicly available sequences for more than 5000 taxa and by incorporating age ranges from the fossil record for over 2800 taxa. Additionally, we developed a mobile app for smartphones to facilitate the visualization and exploration of the resulting tree. Interactive features include exploration by the zooming and panning gestures of touch screens, collapsing branches, visualizing specific clades as subtrees, a search engine, and a timescale to determine extinction and divergence dates, among others. Small illustrations of organisms are displayed at the terminals to better visualize the morphological diversity of life. Our assembled tree currently includes over 7000 families, and its content will be expanded through regular updates to cover all life on earth at the family level.