Reviewerly/exHarmony huggingface dataset:
Perkembangan Keaksaraan Awal melalui Alat Permainan Edukatif Drawing Board pada Anak

This study aimed to stimulate early literacy through a drawing board educational game tool in the A RA UMDI group in Kampung Baru, Parepare City. Based on the problems seen by the researchers, some children were lacking in knowing and knowing the letters of the alphabet and could not distinguish between vowels and consonants and the children were unable to know the letters of their names. Based on this problem, the researcher intends to apply an educational game tool, namely a drawing board which is expected to stimulate early literacy development in children in group A. The indicators for assessing the game are based on STPPA, namely 1) pronouncing letters, 2) bolding letters, 3) imitating letters 4) children can recognize the letters of their names. The results showed that before the educational game tool was implemented some children did not know about the letters of the alphabet. After the researchers applied the educational game tool, there was an increase in children who previously did not know about letters became able to distinguish between vowels and consonants. From the results of these activities, the researcher concluded that there was an increase in the development of early literacy through the drawing board game in which all the assessment indicators increased.
English sentence structures in Descriptive Writing: A case study of Business Management students at Indonesian vocational higher education

To meet the demand of current industries, the ability to write in English is highly essential in Indonesian vocational higher education. In today’s business world, students of business departments require a set of advance skills in writing business documents such as business letters and product descriptions. This research investigated the types of English sentence structures and errors in the descriptive paragraphs written by business management students of Politeknik Negeri Sriwijaya. The method used was quantitative case study research. A total of 44 descriptive paragraphs were analyzed by using syntactical structure analysis and surface taxonomy-based errors. It revealed that all types of sentence structures were used in the descriptive paragraphs. There were 34.77% simple sentence structures, 22.96% compound sentence structures, 26.36% complex sentence structures, and 15.91% compound-complex sentence structures. Thus, the most widely used was simple sentence structures. Regarding the errors, it was found that only a few errors were made by them; omission (3 errors), addition (7 errors), misformation (19 errors), and misordering (1 error). Hence, misformation was the dominant error. In summary, the students wrote their descriptive paragraphs in simple sentences mostly and made errors in misformation the most.
Artificial Intelligence and Copyright: Comments on a Notice requested by the U.S. Copyright Office

Comments on a Notice requested by the U.S. Copyright Office where we address the issues of human/author in the loop, copyright law, fair use and how copyright law is reimagined in the age of generative AI.
Dual-Supervised Contrastive Learning for Bundle Recommendation

Self-supervised contrastive learning has well advanced the development of bundle recommendation. However, self-supervised contrastive learning may misclassify some positive samples as negative samples, resulting in suboptimal models. Therefore, the industry has proposed supervised contrastive learning to alleviate this drawback. Inspired by this idea, we seek a more elegant contrastive learning paradigm in the field of recommendation, so we propose a dual-supervised contrastive learning for bundle recommendation (DSCBR), which integrates supervised and self-supervised contrastive learning to exploit the full potential of contrastive learning. Specifically, we first construct self-supervised contrastive learning between the two different views (bundle view and item view), which encourages the alignment of the two separately learned views and boosts the effectiveness of learned representations. Second, we use the interaction information to construct supervised contrastive learning and leverage the bundle–bundle cooccurrence graph for further enhancement. By introducing supervised contrastive learning, our model explicitly models user and bundle proximity in different views, improving the model’s robustness and generalization. Finally, we jointly perform self-supervised and supervised contrastive learning across multiple views. Extensive experiments on three public datasets demonstrate the effectiveness of our model.
Proxy-based Graph Convolutional Hashing for Cross-Modal Retrieval

Cross-modal hashing retrieval approaches have received extensive attention owing to their storage superiority and retrieval efficiency. To achieve better retrieval performances, hashing methods seek to embed more semantic information of multi-modal data into hash codes. Existing deep cross-modal hashing methods typically learn hash functions from the similarity of paired data to generate hash codes. However, such locally-oriented learning methods often suffer from low efficiency and incomplete acquisition of semantic information. To address these challenges, this paper presents a novel deep hashing approach, called Proxy-based Graph Convolutional Hashing (PGCH), for cross-modal retrieval. Specifically, we use global similarity to construct proxy hash codes for two different modalities. This strategy of these proxy hash codes ensures that they include data points with significant distribution differences. It helps to match data from different modalities to different proxy hash codes, which can capture the global similarity of multi-modal hash codes and improve the efficiency of hash code learning. Subsequently, we employ a multi-modal contrastive loss to learn the global similarity. Furthermore, by constructing a proxy hash matrix from the proxy hash codes, we apply graph convolution to efficiently narrow the gap between different modalities, leading to a substantial improvement in retrieval performance for cross-modal retrieval tasks. The comprehensive experiments on four benchmark multimedia datasets demonstrate that our PGCH approach achieves better retrieval performances than a bundle of state-of-the-art hashing approaches.
From Self Reliance to Spiritual Delusion: A Metaphorical Interpretation of Hawthorne’s Short Stories

Emerson's concept "Self-reliance" is an epitome of transcendental philosophy, in which he relates intellectual thought with mystic affluence. Hawthorne, too, in his short stories: "Young Goodman Brown", "The Minister's Black Veil", "The Man of Adamant," and "Ethan Brand," relates the intellectual struggle of his characters to a spiritual experience. These stories reflect the influence of Emerson's doctrine on the one hand, and indicate Hawthorne's anti-transcendental approach on the other hand. The objective of this research is to explore the metaphorical constructions of self-reliance in terms of spiritual delusion. Textual Analysis has been employed to explore the thematic pattern in the stories. Emerson's concept of self-reliance serves as a theoretical framework. The study explores that Hawthorne construes the philosophical inclinations of his age in metaphorical terms and brings home his own views about self-sufficiency and spirituality. The character’s spiritual quest based on independent thinking proves to be fake. Their intuitive comprehension is neither accepted as reality nor adopted as a belief. Their independent thinking turns to be delusion, and they become victims of their immediate cognition. This study also finds that Hawthorne makes his readers careful about their insight without supporting any particular creed. Thus, through the genre of short story, the philosophy of his age has been translated into exotically new terms.
Combined Techniques of Hill Cipher and Transposition Cipher

Encryption is a method of encoding data so that only authorized parties can read or access that data. With the fast improvement of digital data exchange in electronic way, Information Security is becoming much more important in data storage and transmission. In this paper, the authors work on improving the security of data using a combination of Hill cipher and a Transposition cipher (Gp) , the plaintext will be encrypted using ordinary Hill cipher technique and encrypted again using Gp cipher there by producing a more complex ciphertext that will be very difficult for cryptanalyst to intrude, and the decryption process is done in two phases, the first decryption will return the ciphertext of the ordinary Hill cipher method and the second decryption will return the original plaintext.
Replicability and reproducibility of predictive models for diagnosis of depression among young adults using Electronic Health Records

Abstract Background Recent advances in machine learning combined with the growing availability of digitized health records offer new opportunities for improving early diagnosis of depression. An emerging body of research shows that Electronic Health Records can be used to accurately predict cases of depression on the basis of individual’s primary care records. The successes of these studies are undeniable, but there is a growing concern that their results may not be replicable, which could cast doubt on their clinical usefulness. Methods To address this issue in the present paper, we set out to reproduce and replicate the work by Nichols et al. (2018), who trained predictive models of depression among young adults using Electronic Healthcare Records. Our contribution consists of three parts. First, we attempt to replicate the methodology used by the original authors, acquiring a more up-to-date set of primary health care records to the same specification and reproducing their data processing and analysis. Second, we test models presented in the original paper on our own data, thus providing out-of-sample prediction of the predictive models. Third, we extend past work by considering several novel machine-learning approaches in an attempt to improve the predictive accuracy achieved in the original work. Results In summary, our results demonstrate that the work of Nichols et al. is largely reproducible and replicable. This was the case both for the replication of the original model and the out-of-sample replication applying NRCBM coefficients to our new EHRs data. Although alternative predictive models did not improve model performance over standard logistic regression, our results indicate that stepwise variable selection is not stable even in the case of large data sets. Conclusion We discuss the challenges associated with the research on mental health and Electronic Health Records, including the need to produce interpretable and robust models. We demonstrated some potential issues associated with the reliance on EHRs, including changes in the regulations and guidelines (such as the QOF guidelines in the UK) and reliance on visits to GP as a predictor of specific disorders.
Framework and Change Path Analysis for the Improvement of Accountants’ Data Analysis Capability in the Era of “Digitization”

Abstract This paper first analyzes the importance of data analysis skills and uses information mining to discover valuable accounting knowledge. Secondly, in the process of calculating the weights of indicators, the Bayesian network classifier is used to enhance the data mining technology, filtering and processing the interfering information and redundant information to ensure the effectiveness of professional competence data acquisition. Finally, the framework of professional competence of accounting talents was constructed, the path of accountants’ change was analyzed, and the design of the numerical intelligence curriculum system was completed. The results show that the rating of data analysis ability is 4.63, and the average score of the others is roughly around 4, which proves the feasibility of the Numerical Intelligence Curriculum System.
Money Functions in Modern Economy

In advanced economies there is a tendency to move to digitization and cashless financial economic activities. In this sense, the number of cryptocurrencies that contributed more to this trend increased. In this article the authors aim to highlight the functions of money in the modern economy taking into account these trends and developments. In this sense, Bitcoin and other digital currencies can cause a change in the functions of money. In the article we sought to demonstrate the extent to which digital currencies can be considered money, using a complex methodology, logical and interpretive analyses, starting from the comparative study of the situation of coins and the existing situation in the monetary mass of the money that exists in circulation.
Hardware Implementation of a GMDH Controller for Mobile Robot Obstacle Following/Avoidance

Proportional, integral, and derivative (PID) controllers have been widely adopted for industrial applications. However, these controllers are not very efficient for non-linear systems. Artificial neural networks (ANN) based on the Group Method of Data Handling (GMDH) have great potential to replace the PID controllers due to their polynomial structure, allowing complex non-linear systems to be controlled. This work presents a hardware architecture of a GMDH network applied to speed control of a mobile robot platform. The proposed GMDH controller was implemented using a 16-bit floating-point arithmetic representation and was mapped on a Zynq7020 device. A hardware-in-the-loop based on the Universal Direct Memory Access methodology was developed to validate the proposed circuits, allowing for performance comparisons between a classical PID and the GMDH controllers for different simulated scenarios.
Optimal Risk Sensitive Controller Design Applied to a Stochastic Dynamics of a Simple Pendulum Robot

In this paper the Risk-Sensitive (RS) Optimal Controller Equations for Polynomial Stochastic Systems of first degree with tuning in the real states are applied to the of simple pendulum model. This RS controller contains the RS optimal control equations and the RS filtering equations connecting the control into the estimate RS equations. The dynamic of the state contains a parameter ϵ, as attenuation of the noise and parameter γ which permits adjustment to the gain control values to obtain solution in R. Values of the exponential quadratic cost criterion function are obtained for some values of parameter ϵ, which are selected according to the real tolerance of the motor of the pendulum. Results (errors values, exponential quadratic cost criterion values) presented in a table show the feasibility of this proposal.
Damping Injection Learning for Robots with Actor-Critic Driven by Integral Sliding Manifolds

Reinforcement Learning (RL) is characterized by enforcing a reward collection mechanism that qualifies task performance aimed at optimizing a value function that tunes the action (control). In contrast to control theory, RL satisfies two metrics simultaneously: the task metric commonly, defined as the task error, and the temporal difference (TD) error. Thus, the RL has been empowered to learn how to fulfill control tasks based on a learning stage that collects rewards that evaluate a second metric of the robot’s performance. In this way, including RL in control systems is an advantageous scheme for some critical tasks nowadays. Consequently, the stability can be guaranteed, largely missing in the RL’s literature, to safe system operation.. In this paper, we propose an Actor-Critic (AC) scheme using the Critic Neural Network (C-NN) to approximate a value function using the TD error reduction, whereas the adaptive tuning of damping injection gain is provided by the Actor NN (A-NN) to enforce stability in closed-loop finally; achieving asymptotic stability with an underlying integral sliding mode loop. In this way, our proposal contributes to a novel model-free RL scheme that yields local exponential convergence with a smooth control action. Numeric simulations for a 2-DoF robot manipulator are presented and discussed under different conditions..
Transforming Pixels into a Masterpiece: AI-Powered Art Restoration using a Novel Distributed Denoising CNN (DDCNN)

Art restoration plays a pivotal role in preserving and revitalising cultural heritage. However, conventional art restoration methods are often fraught with limitations, including the challenge of faithfully reproducing the original artwork's essence while addressing issues such as fading, staining, and physical damage. In this context, we present a pioneering approach in this paper that harnesses the potential of deep learning, particularly Convolutional Neural Networks (CNNs), coupled with Computer Vision techniques, to revolutionize the art restoration process. Our method begins by generating artificially induced deteriorated art images, creating a comprehensive dataset encompassing various forms of distortions and multiple levels of degradation. This dataset is the foundation for training a Distributed Denoising CNN (DDCNN), capable of effectively removing distortions while preserving the intricate details inherent to artworks. This integration of Computer Vision and CNN-based denoising enables the restoration of artworks with high accuracy, ensuring that the original artistic essence is faithfully preserved. One of the key strengths of our approach lies in its adaptability to different kinds of distortions at varying levels of degradation. Utilizing a versatile training dataset, our Distributed Denoising CNN can address a wide spectrum of distortion types, ranging from subtle colour variations to more severe structural damage. This adaptability empowers our method to cater to a diverse array of deteriorated artworks, including paintings, sketches and photographs. Through extensive experimentation on a diverse dataset, our results consistently demonstrate the efficiency and effectiveness of our proposed approach by comparing it against other De-noising CNN models. We showcase the substantial reduction of distortion in the art image, transforming deteriorated artworks into masterpieces. Quantitative evaluations further underscore the superiority of our method compared to traditional restoration techniques, reaffirming its potential to reshape the landscape of art restoration and contribute significantly to the preservation of our cultural heritage. In summary, our paper introduces a groundbreaking AI-powered solution that leverages the synergy of Computer Vision and deep learning, exemplified by Distributed Denoising CNN, to restore artworks with unprecedented accuracy and fidelity. This transformative approach not only overcomes the limitations of existing methods but also paves the way for future advancements in the field of art restoration, ensuring the enduring legacy of our cultural treasures.
Gesture Controlled Virtual Mouse and Keyboard Using OpenCV

A gesture-controlled computer makes it easier to connect with computers by utilizing hand gestures, voice commands, and eye movements. In this gesture approach computers seldom ever require direct contact, static and dynamic hand gestures, voice assistance, and eye motions can be used to virtually control all operations. In the existing systems it uses different parameters for gesture control such as voice, eye and hand. In this research paper, we are implementing a hybrid model used to perform gesture controlled by using voice, hand and eyes. We are implementing a Convolutional Neural Network (CNN) model by using Media Pipe running on top of pybind 11. The webcam, which is the only piece of hardware mandatorily required in the proposed algorithm, is used to record images, recognize hand gestures, and eye movements, and receive voice instructions using the Pyttsx3 module. It has been observed that the proposed algorithm provides more efficient results. it provides different form of gestures under hybrid model with only webcam as the input resource, it can be utilized for smart security and surveillance system.
Semantic and Depth Learning for Autonomous Forest Mapping

While overhead data can provide general information about a forest, inside the forest we can identify understory plants, measure the diameter and count the trunks of each tree and check the number of dry leaves on the ground, which is an indication of the risk of fire. To get these measurements, we rely on human expeditions, which can be slow, expensive, and even dangerous. For this reason, robots that can autonomously navigate and extract data from inside the forest could revolutionize the way to monitor forests around the world and the amount of information we have about them. With no reliable GPS signal, uneven terrain covered by plants and leaves, and trees with branches moving with the wind, classic algorithms developed with urban environments in mind may fail in forests as their assumptions may not be valid in this environment. Semantic information such as classes and forms of objects expected in the environment can be a promising way to increase the robustness and performance of autonomous systems. In this paper, we propose a learning-based approach to the stereo depth estimation problem that simultaneously outputs semantic labels and stereo disparity estimates. This method can obtain pseudo-Light Detection and Ranging (LiDAR) readings that can be incorporated into localization and mapping frameworks to create a semantic map of a forest. The proposed method balances between cost map resolution and speed while learning two tasks. Simultaneously, our architecture maintains an inference speed performance acceptable for real-time semantic mapping. Several experiments in simulated forests are performed, demonstrating that our method achieves a good balance between inference speed and accuracy that would be suitable for real-time semantic mapping.
AN ANALYSIS OF PATHS TO RECOVER DEFICIENCIES IN THE LOGICAL-MATHEMATICAL FIELD DURING TWO YEARS OF THE PANDEMIC

Two short university courses of recovery of logical-mathematical deficiencies were completed by 210 students of a degree course in Primary Education Sciences, for future elementary school teachers. The courses were held during the years of the
Calibration for Industrial Optical High-Resolution Microimaging Systems

Due to issues stemming from unclear models, limited microfeatures, and the poor robustness of algorithms, estimating calibration parameters for optical high-resolution microimaging systems has proven to be a challenging task. In this article, we propose a grid-full-size active-based calibration (GAC) method to address these aforementioned limitations. We establish a parallel projection model within a high-resolution microimaging system to describe the camera calibration process. The full size of a single grid on the resolution board is accurately obtained through a robust subpixel feature extraction method, allowing us to estimate the homography matrix. In conjunction with the parallel projection model, we introduce a novel homography matrix decomposition method to provide initial parameter estimation. To enhance feature richness, we employ active control of the piezoelectric microplatform and improve global optimization to achieve precise parameter estimation. A comprehensive series of experiments has been conducted, and the results demonstrate a mean square error of 5.25 nm. Furthermore, in large-stroke experiments, the GAC method enables a displacement precision of 658 nm for standard deviation errors within a measurement range of 0–100 mm.
Resilience and visual memory. An educational look at the great 1960 Valdivia earthquake.

The photographic exhibition Journey to the Epicenter is an exercise of heritage education developed by the Dirección Museológica UACh within the framework of the 60th anniversary of the Valdivia earthquake. The visual understanding of the seismic phenomenon experienced in 1960 is a contribution to the heritage field, by highlighting the value of resilience in the formation of cultural identities, leaving at the disposal of the territories the articulation of scientific, social and emotional knowledge to face catastrophic situations.