Reviewerly/exHarmony huggingface dataset:
Embodying Multifunctional Mechano‐Intelligence in and Through Phononic Metastructures Harnessing Physical Reservoir Computing (Adv. Sci. 34/2023)

Mechano-Intelligence In article number 2305074, Yuning Zhang, Aditya Deshmukh, and Kon-Well Wang introduce an innovative method for embodying multifunctional mechano-intelligence into phononic metastructures, harnessing their wave dynamics as computational resources through a physical-body-based analog neural network. This breakthrough lays the foundation for the development of future intelligent structures that can perceive information, learn, make decisions, and execute actions within their mechanical domain.
Modular audio processes for vector graphic pattern design

This article outlines a methodology for algorithmic design of repeat patterns through synthesis and processing of audio signals. Vector graphic forms may be synthesized by mapping audio waveforms to coordinates in space. Using a modular architecture of synthesizers and synchronized symmetry operations, this work describes how audio tools can be used to arrange such forms into repeat patterns with emergent visual resonances. Audio effects are inserted between the devices that organize levels of pattern structure, such that the visual results may be explored. While there is a long history of artistic visualization of audio waveforms, they have seldom been used in pattern design. This work aims to highlight the underexplored potential of audio as a pattern generation and manipulation framework.
Effect of Teachers’ Transformational Leadership on Student Engagement: Self-Efficacy as a Mediator

This study aims to explore the effects of transformational leadership exhibited by teachers on student engagement as a mediator of student self-efficacy. The research sample comprised 1,034 students from five Chinese higher vocational education (HVE) colleges, including four public and one private institution. Furthermore, this study delves into the mediating role of self-efficacy in these relationships. We used a quantitative cross-sectional design to gather data through an online survey, which was subsequently analyzed using SPSS and AMOS software. This analysis involved descriptive statistics, correlation, regression, and structural equation modeling (SEM). A Bootstrapping technique further examined the mediation effect. The results reveal a notable link between teachers’ transformational leadership behaviors and student engagement, with self-efficacy as a partial mediator. These insights enhance the extant literature by presenting a more holistic understanding of how transformational leadership theory from a business lens is incorporated into the concept of teacher leadership in higher education and its influence on student engagement through the mediating role of self-efficacy. Moreover, the findings furnish crucial insights for educators, practitioners, and policymakers interested in tutor-teacher leadership training programs to amplify teaching quality in HVE colleges. Keywords: teachers, transformational leadership, students, engagement, self-efficacy. DOI: https://doi.org/10.55463/hkjss.issn.1021-3619.62.25
Offline Reinforcement Learning of Robotic Control Using Deep Kinematics and Dynamics

With the rapid development of deep learning, model-free reinforcement learning algorithms have achieved remarkable results in many fields. However, their high sample complexity and the potential for causing damage to environments and robots pose severe challenges for their application in real-world environments. Model-based reinforcement learning algorithms are often used to reduce the sample complexity. One limitation of these algorithms is the inevitable modeling errors. While the black-box model can fit complex state transition models, it ignores the existing knowledge of physics and robotics, especially studies of kinematic and dynamic models of the robotic manipulator. Compared with the black-box model, the physics-inspired deep models do not require specific knowledge of each system to obtain interpretable kinematic and dynamic models. In model-based reinforcement learning, these models can simulate the motion and be combined with classical controllers. This is due to their sharing the same form as traditional models, leading to higher precision tracking results. In this work, we utilize physics-inspired deep models to learn the kinematics and dynamics of a robotic manipulator. We propose a model-based offline reinforcement learning algorithm for controller parameter learning, combined with the traditional computed-torque controller. Experiments on trajectory tracking control of the Baxter manipulator, both in joint and operational space, are conducted in simulation and real environments. Experimental results demonstrate that our algorithm can significantly improve tracking accuracy and exhibits strong generalization and robustness.
On the non-existence of perfect codes in the Niederreiter-Rosenbloom-Tsfasman metric

In this paper we consider codes in F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>s</i>×<i>r</i></sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>q</i></sub> with packing radius <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> regarding the NRT-metric (i.e. when the underlying poset is a disjoint union of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> chains with the same length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> ) and we establish necessary condition on the parameters <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> for the existence of perfect codes. More explicitly, for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ≥ 2 and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> ≥ 1 we prove that if there is a non-trivial perfect code then ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> + 1)( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> + 1) ≤ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rs</i> . We also establish a correspondence between perfect codes with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> > <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> and those with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> = <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> . Using this correspondence we prove the non-existence of non-trivial perfect codes in the cases <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ≥ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> + 2 and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> = 3 over non-binary alphabet.
BQM Hybrid Sorting Algorithm

In computer science, sorting is a fundamental operation and there are numerous sorting algorithms which have been developed with their own strengths and limitations. In this paper, I propose a hybrid sorting algorithm that combines the benefits of Merge Sort, Quick Sort, and Bubble Sort to overcome their respective drawbacks. The hybrid algorithm leverages Merge Sort's efficiency in merging sorted subarrays, Quick Sort's effectiveness in partitioning, and Bubble Sort's simplicity and efficiency for small data sets. By dynamically switching between these algorithms based on the input size, the hybrid approach offers improved performance and adaptability. I present a detailed description of the hybrid sorting algorithm, including the conditions for algorithm switching and the merging technique. Additionally, we conduct a comprehensive performance analysis, comparing the hybrid algorithm with standalone sorting techniques. Experimental results demonstrate the algorithm's efficiency and scalability across various data sets. Our findings show that the hybrid sorting algorithm achieves enhanced performance, particularly for large and partially sorted data sets. We discuss the practical applications of this algorithm, including scenarios where its adaptability, stability, and efficiency can be beneficial. Moreover, we outline potential directions for future research, such as optimizations and extensions of the hybrid algorithm. By combining the strengths of Merge sort, Quick Sort, and Bubble Sort, the proposed hybrid sorting algorithm offers a promising solution for efficient and adaptive sorting. This algorithm has the potential to significantly impact sorting operations in various Domains and contribute to the advancement of sorting algorithms.
Study of the K-Nearest Neighbors Method with Various Features for Text Classification in Machine Learning

The aim of this publication is to compare the accuracy of the K-Nearest Neighbors method using various distance calculation functions between two objects from the training sample in text classification within machine learning. The study involves classifying English language texts representing movie reviews. The compared functions are: L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</inf> metric (Minkowski), Manhattan metric (L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ), Euclidean distance (L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ), and cosine similarity. The results indicate that the highest accuracy in text classification using the K-Nearest Neighbors method was achieved when employing the cosine similarity function, with a difference of approximately 19 percent compared to the other functions, especially with larger data sets. When classifying opinions from user comments in English using the k-nearest neighbors method with the cosine similarity function, the examined parameters of accuracy, precision, and recall increased as the data volume expanded.
N-Queens Convergence: A Tactical Framework for Efficient Target-Directed Drone Formations and Attacks

This study introduces a novel methodology for enhancing the efficiency of Unmanned Aerial Vehicle (UAV) formations in order to achieve accurate target engagements. The concept draws inspiration from the N-Queens puzzle. By incorporating unmanned aerial vehicle (UAV) positioning into a simulated chessboard and expanding it to include a target in the (N + 1)th column, the algorithm produces arrangements that guarantee effective engagement with the target. Significantly, this particular technique ensures the absence of overlapping pathways among UAVs during strikes, hence augmenting operational safety. The methodology employed in this study involves the construction of formations through the generation of permutations for all possible queen positions. Subsequently, the eligible queen configurations are determined by filtering based on the diagonal lines of attack. The method has been tested on N values as high as 10. This study explores the intersection of combinatorial problem-solving with military applications, providing novel insights and tactics for deploying UAVs that minimize collisions.
ClassScan: Simplifying Classroom Attendance using Three-Dimensional Dense Face Alignment and Face Recognition

Managing attendance in classrooms is a critical task that often relies on manual processes, leading to inefficiencies and potential inaccuracies. In this study, we address this challenge by proposing a web-based face recognition attendance system. Our solution encompasses key functionalities such as student registration, face extraction, and detection, student and teacher login, attendance tracking, and reporting. By using a face recognition method that generates a 3D face using TDDFA algorithm, our system showcased an accuracy rate of 95.0%. Furthermore, the average time required for attendance marking is significantly reduced to just 2.3 seconds/student, offering a rapid and streamlined process compared to traditional manual methods. User feedback from faculty members highlights a high level of satisfaction, with an average usability rating of 4.2 out of 5. The proposed face recognition attendance system provides an efficient solution and thus addresses the challenges associated with manual attendance management in classrooms.
Aguaviva

Aguaviva juxtaposes the spontaneous nature of biology with the predictable properties of digital technology.
AI Nüshu (Women's scripts) - An Exploration of Language Emergence in Sisterhood

This paper presents "AI Nüshu," an emerging language system inspired by Nüshu (women's scripts), the unique language created and used exclusively by ancient Chinese women who were illiterate under a patriarchy society. Through an interactive art installation, two artificial intelligent (AI) agents continuously observe their environment and communicate with each other, developing a writing system that encodes Chinese. In this system, two AI agents observe the environment through cameras, record the unconscious behaviors of the audience, and generate summaries of their observations through visual recognition. Subsequently, the agent associates the corresponding original Nüshu poetry lines and generates new poetry text through a Language Model (LLM), representing its reflection. To develop their language, they continuously switch roles between the speaker and listener, constantly communicating their reflections, and encrypting a word in the poetry line with their self-created AI Nüshu character, allowing the other to guess and learn. Gradually, they reach a consensus on AI Nüshu, forming a unique "AI Nüshu Dictionary" for machines. This language, algorithmically combined into corresponding characters, has components derived from Nüshu, similar to Chinese characters and traditional textile patterns. Thus, like ancient women, the two agents gradually developed their Chinese writing system, corresponding one-to-one with Chinese characters. In contrast, humans, as the authority of the language system, became an object observed, interpreted, and inspired by machines to stimulate non-human language. This is the first media art project to interpret Nüshu from a computational linguistics perspective, infusing AI and art research with non-English natural language processing, Chinese cultural heritage, and a feminist viewpoint. This encourages the creation of more non-English, linguistically-oriented artworks for diverse cultures. We simulate communication in sisterhood through a multi-agent learning system, which questioned knowledge authority between humans and machines through the lens of language development.
AI History 1890-2090

The aim of this artwork is to let the audience feel "brackish waters of fictionality and reality" by seeing AI-generated history. In the real world, humans train AI with human-generated data. But AI trains humans with AI-generated data in this artwork. AI becomes smarter by learning. Their intelligence might surpass that of humans. When AI acquires more advanced knowledge than humans, will people, in turn, learn from AI? And will we unilaterally accept the AI-generated information without verification of the truth? This artwork anticipates the possible future experience. As AI infiltrates our lives, the information generated by AI is increasing exponentially. In this work, a zoetrope is used to show how the history of AI is being generated as AI generates images, and we can do nothing when we see AI creating its own history with its own generated images. We can only accept the information as it is presented to us. This work shows how we are immersed in this "brackish zone of fictionality and reality brought about by AI".
Modeling and Simulation of Cooperative Exploration Strategies in Unknown Environments

Cooperative spatial exploration in initially unknown surroundings is a common embodied task in various applications and requires satisfactory coordination among the agents. Unlike many other research questions, there is a lack of simulation platforms for the cooperative exploration problem to perform and statistically evaluate different methods before they are deployed in practical scenarios. To this end, this paper designs a simulation framework to run different models, which features efficient event scheduling and data sharing. On top of such a framework, we propose and implement two different cooperative exploration strategies, i.e., the synchronous and asynchronous ones. While the coordination in the former approach is conducted after gathering the perceptive information from all agents in each round, the latter enables an ad-hoc coordination. Accordingly, they exploit different principles for assigning target points for the agents. Extensive experiments on different types of environments and settings not only validate the scheduling efficiency of our simulation engine, but also demonstrate the respective advantages of the two strategies on different metrics.
Stand with the Banned: Credibility bias and the Fetishization of the "Classic" Banned Books on Etsy

Recent efforts at book banning in the United States’ schools and libraries have produced a number of material iterations of anti-banning sentiment in online retail spaces like Etsy. Most scholarship on banned books comes from an education or library science perspective, with little book or media studies scholarship focused on how banned books are represented in online spaces. In this paper, we examine the top 50 results from searching “banned books” on Etsy to understand how merchandise that engages with the topic visually represents banned books. We find that banned book imagery often ignores more recent banned books, especially those featuring LGBTQ+ characters, in favor of older or more “classic” banned books. We also find that the banned book merchandise under examination here, like other social media reading spaces such as Instagram and BookTube, participates in glorifying the physical book as an object of credibility, despite the role digital reading devices play as both objects of banning and as a means of resistance. The results of our examination show ongoing disconnect between the perceived threat and the realities of book banning, as well as a desire to maintain an aesthetic of the “classic” as under attack.
A method of knowledge graph construction for the field of mechanical manufacturing processes

Mechanical manufacturing process knowledge exhibits complexity, detail-oriented nature, experiential characteristics, high learning cost, and continuous evolution, posing challenges in its management, utilization, and transmission. Enterprises currently face low knowledge reuse rates and high knowledge management costs. To address these issues, we propose a novel approach utilizing deep learning models to extract entities and relationships from unstructured knowledge, constructing a process knowledge graph. With deep learning's powerful comprehension abilities, we automatically extract critical information, organizing it into a structured knowledge graph consisting of 12 entities and 12 relationships. The process knowledge graph enables convenient knowledge transmission and sharing, increasing reuse rates and reducing redundant learning. It serves as an effective tool for process formulation, enhancing efficiency and enterprise benefits. Additionally, it facilitates prompt identification of suitable process parameters, accurate prediction of manufacturing issues, and timely adjustments. Moreover, the process knowledge graph fosters innovation by identifying optimization solutions and improving product quality and cost reduction. This approach offers significant competitive advantages and commercial value to the manufacturing industry.
Anomaly detection by using multimodal deep learning

In the field of power systems, the detection of multidimensional fault data is essential. Introducing the concept of IT Operations Artificial Intelligence (AIOps), combined with big data and machine learning techniques, aims to take place extensive IT operational tasks, including service availability and performance monitoring. Multiple models are utilized to process monitoring data patterns, such as text attributes and real-value response times extracted from logs and traces, enabling the detection of faults and potential anomalies in cloud services. To detect anomalies in the execution of system components, a dual-mode distributed tracing data from large-scale cloud infrastructure is employed, and a novel method for anomaly detection is proposed. The application of LSTM (Long Short-Term Memory) multimodal neural networks is demonstrated to learn the sequential characteristics of the two modes of data in traces. The capability to detect dependencies and concurrent events is showcased through a method that reconstructs execution paths using the proposed models. In experimental evaluations using large-scale production cloud data, the new approach outperforms traditional architectures and other deep learning methods.
Flatness loss for image dehazing

In recent years, significant progress has been made in image dehazing, but most dehazing convolutional neural networks only learn from hazy images to the corresponding feature maps of clean images, ignoring the details of the images. In this paper, a new flatness loss function is proposed for single image dehazing, thereby improving the overall effect of dehazing results. This loss function allows the model to focus on the texture features of hazy images and clean images and supervises edge information by reducing the flatness difference between clean pixels and blurred pixels. The experiments on the benchmark dataset using the flatness loss function on the single image dehazing model show that this method can effectively improve the quantitative performance of the model.
Research on big data and artificial intelligence technology in geological disaster risk monitoring and early warning

With the research on the application of big data and artificial intelligence technology to geological disaster risk monitoring and early warning, the relevant dimensions are disassembled more and more carefully, and the results are more accurate. Based on the hydrogeological conditions of the river basin, combined with major national, regional, and local water resource needs and natural resource management requirements, coordinate the groundwater level measurement and groundwater resource evaluation work in the Yangtze River Basin, integrate the water resource achievements of the Yangtze River Basin through data translation and summary, establish a conceptual water resource model for the Yangtze River Basin, conduct water resource carrying capacity evaluation, and propose water resources, water ecology, and Earth science solutions to water environment issues, supporting the work of poverty alleviation and water resource management centers.