Reviewerly/exHarmony huggingface dataset:
Modifying Processuality of Emotions of a Musician-Performer

"The essence of this research is to understand the professional and creative specifics of the emotional thinking of a musician-performer. The idea arose from the experience of performing activities, teaching and scientific research in the context of the traditions of performing arts and the theoretical opinion of experts in the field of musicology. The main task consists in substantiating the methodological and theoretical principles, methods of approach to determining the functional features of the emotional tone of a musician-performer as a tool for the formation of professional thinking of a creative personality. In such formulation of the question, it is necessary to proceed from the fact that the problem of human thinking is generally constantly active, meaning that it is in a procedural state as both, an individual person and his/her natural environment, assimilate and generate new emotions (feelings), and, therefore, directly carry out influence on the flow of mental processes. The processuality of this dual dynamic psychological modification is endless, and the disclosure of regularities here also has a timely processual and continuously renewed character. Keywords: feelings, emotions, performance tone, intuition, thinking of a musician-performer."
Pelatihan Penggunaan Canva dan Google Drive Bagi Masyarakat Desa Wijirejo Kecamatan Pandak Kabupaten Bantul

Specifically for the Gesikan IV area, Wijrirejo Village, there are also mosque youth. One of the programs is to develop and enhance and create the competitiveness of members so as to create superior youth accompanied by the use of technology to improve the quality of publications and documentation in supporting daily life. The problem that occurs is that not all members have the ability to use graphic design applications to make posters. Mosque youth experience difficulties in teaching graphic design applications as is currently the case, this is because youth organizations do not yet have graphic design skills. Likewise, the use of other applications in cloud-based storage, namely Google Drive. By looking at these problems, in this service there will be training on the use of graphic design applications for making posters using Canva and cloud-based storage applications using Google Drive in the Wijirejo Village area, especially Padukuhan Gesikan IV, Pandak District, Bantul Regency. The method used in this service is pre-implementation, training, mentoring and evaluation. Two students were directly involved in this PPM, namely Zulhaji Guswantoro and Lisdianto Dwi K, undergraduate students of the Informatics Study Program. Pre-implementation includes making Canva and Google Drive modules and communicating with partners regarding what needs are needed in poster making and Google Drive training. The training is hands-on practice carried out to increase youth skills in operating the Canva application for making posters and Google Drive for cloud-based storage applications. The implementation method is Offline. This training was held for 2 days starting February 28 and March 1 2023 from 15.00 to 17.00 and took place at the Baitul Ummah Mosque, Wijirejo Village. Assistance is carried out to assist youth in making posters using Canva and using cloud-based storage applications using Google Drive. Evaluation is used to make improvements for the next service. The results of this dedication are posters made using the Canva application and the ability to store data using Google Drive. With this training, 85% of teenagers found it easier to use the Canva and Google Drive applications, and found it helpful to use the application for making posters and storing data in the cloud. The output of this PPM includes training modules, articles published in online KR and Print KR
Internet use predicts Chinese character spelling performance of junior high school students: multiple mediating roles of pinyin input proficiency and net-speak experience

To examine the complex relationship between Internet use experience and character spelling performance among Chinese junior high school students, the study explored the multiple mediating roles of Pinyin input proficiency and net-speak experience. A total of 447 Chinese junior high school students aged 12-15 years old completed the Internet Use Experience and Pinyin Input Proficiency Assessment, the Net-speak Experience Questionnaire and the Chinese Spelling Test. The results showed that: (1) All investigated variables were significantly correlated with each other, but there was no direct relationship between Internet use and Chinese spelling performance. (2) Pinyin input proficiency and net-speak experience play a chain mediating role in the relationship between Internet use and Chinese character spelling performance. Teens' Internet use experience indirectly and positively predicted Chinese character spelling performance through the mediation of Pinyin input method use and net-speak experience. The implication of this study is that Chinese children should be guided to engage in Internet activities that require Pinyin typing and use net-speak creatively in order to promote the traditional Chinese character spelling skills when instructing teenagers to engage in Internet activities.
Automatic International Classification of Diseases Coding via Note-Code Interaction Network with Denoising Mechanism

Clinical notes are comprehensive files containing explicit information about a patient's visit. However, accurately assigning medical codes from clinical documents can be a persistent challenge due to the complexity of clinical data and the vast range of medical codes. Moreover, the large volume of medical records, the noisy medical records, and the uneven quality of coders all negatively impact the quality of the final codes. Deep learning technology has recently been integrated into automatic International Classification of Diseases (ICD) coding tasks to improve accuracy. Nevertheless, the imbalanced class problem, the complexness of code associations, and the noise in lengthy records still restrict the advancement of ICD coding tasks in deep learning. Thus, we present the Note-code Interaction Denoising Network (NIDN) that employs the self-attention mechanism to pull critical semantic features in electronic medical records (EMRs). Our model utilizes the label attention mechanism for retaining code-specific text expression. We introduce Clinical Classifications Software coding for multitask learning, capturing the functional relationships of medical coding to oblige in model prediction. To minimize the impact of noise on model prediction and improve the label distribution imbalance, a denoising module is introduced to filter noise. Our practical consequences indicate that the model NIDN exceeds competitive models on a third version of Medical Information Mart for Intensive Care data set.
Diverse Image Captioning via Conditional Variational Autoencoder and Dual Contrastive Learning

Diverse image captioning has achieved substantial progress in recent years. However, the discriminability of generative models and the limitation of cross entropy loss are generally overlooked in the traditional diverse image captioning models, which seriously hurts both the diversity and accuracy of image captioning. In this article, aiming to improve diversity and accuracy simultaneously, we propose a novel Conditional Variational Autoencoder (DCL-CVAE) framework for diverse image captioning by seamlessly integrating sequential variational autoencoder with contrastive learning. In the encoding stage, we first build conditional variational autoencoders to separately learn the sequential latent spaces for a pair of captions. Then, we introduce contrastive learning in the sequential latent spaces to enhance the discriminability of latent representations for both image-caption pairs and mismatched pairs. In the decoding stage, we leverage the captions sampled from the pre-trained Long Short-Term Memory (LSTM), LSTM decoder as the negative examples and perform contrastive learning with the greedily sampled positive examples, which can restrain the generation of common words and phrases induced by the cross entropy loss. By virtue of dual constrastive learning, DCL-CVAE is capable of encouraging the discriminability and facilitating the diversity, while promoting the accuracy of the generated captions. Extensive experiments are conducted on the challenging MSCOCO dataset, showing that our proposed methods can achieve a better balance between accuracy and diversity compared to the state-of-the-art diverse image captioning models.
Max Markov Chain

In this paper, we introduce Max Markov Chain (MMC), a novel model for sequential data with sparse correlations among the state variables. It may also be viewed as a special class of approximate models for High-order Markov Chains (HMCs). MMC is desirable for domains where the sparse correlations are long-term and vary in their temporal stretches. Although generally intractable, parameter optimization for MMC can be solved analytically. However, based on this result, we derive an approximate solution that is highly efficient empirically. When compared with HMC and approximate HMC models, MMC combines better sample efficiency, model parsimony, and an outstanding computational advantage. Such a quality allows MMC to scale to large domains where the competing models would struggle to perform. We compare MMC with several baselines with synthetic and real-world datasets to demonstrate MMC as a valuable alternative for stochastic modeling.
Adaptive Estimation Q-learning with Uncertainty and Familiarity

One of the key problems in model-free deep reinforcement learning is how to obtain more accurate value estimations. Current most widely-used off-policy algorithms suffer from over- or underestimation bias which may lead to unstable policy. In this paper, we propose a novel method, Adaptive Estimation Q-learning (AEQ), which uses uncertainty and familiarity to control the value estimation naturally and can adaptively change for specific state-action pair. We theoretically prove the property of our familiarity term which can even keep the expected estimation bias approximate to 0, and experimentally demonstrate our dynamic estimation can improve the performance and prevent the bias continuously increasing. We evaluate AEQ on several continuous control tasks, outperforming state-of-the-art performance. Moreover, AEQ is simple to implement and can be applied in any off-policy actor-critic algorithm.
Bias On Demand: Investigating Bias with a Synthetic Data Generator

Machine Learning (ML) systems are increasingly being adopted to make decisions that might have a significant impact on people's lives. Because these decision-making systems rely on data-driven learning, the risk is that they will systematically propagate the bias embedded in the data. To prevent harmful consequences, it is essential to comprehend how and where bias is introduced and possibly how to mitigate it. We demonstrate Bias on Demand, a framework to generate synthetic datasets with different types of bias, which is available as an open-source toolkit and as a pip package. We include a demo of our proposed synthetic data generator, in which we illustrate experiments on different scenarios to showcase the interconnection between biases and their effect on performance and fairness evaluations. We encourage readers to explore the full paper for a more detailed analysis.
Incomplete Multi-view Clustering via Prototype-based Imputation

In this paper, we study how to achieve two characteristics highly-expected by incomplete multi-view clustering (IMvC). Namely, i) instance commonality refers to that within-cluster instances should share a common pattern, and ii) view versatility refers to that cross-view samples should own view-specific patterns. To this end, we design a novel dual-stream model which employs a dual attention layer and a dual contrastive learning loss to learn view-specific prototypes and model the sample-prototype relationship. When the view is missed, our model performs data recovery using the prototypes in the missing view and the sample-prototype relationship inherited from the observed view. Thanks to our dual-stream model, both cluster- and view-specific information could be captured, and thus the instance commonality and view versatility could be preserved to facilitate IMvC. Extensive experiments demonstrate the superiority of our method on five challenging benchmarks compared with 11 approaches. The code could be accessed from https://pengxi.me.
Sistem Informasi Permintaan Barang dan Jasa Fasilitas Umum

Abstract&#x0D; The Banten Province Land Transportation Management Office (BPTD) Region VIII oversees many Service Units (SATPEL) such as type A bus terminals, crossing ports, and weigh stations in Banten Province. The General Bureau is a Work Unit (Satker) tasked with handling requests for goods/services. Some problems occur in the process of requesting goods/services in the Office of the Land Transportation Management Center (BPTD) Region VIII of Banten Province, namely the submission process is still manual, so it takes a long time and the status of approval from each party who has a decision-making policy cannot be known directly. So to overcome the problems faced, it is necessary to design an information system for requests for goods and services that use the Waterfall method and the PHP and MySQL programming languages ​​to create an expected information system, so that data processing can be processed more quickly and in control. Thus each Service Unit (Satpel) can submit a letter of submission of goods by accessing the goods/services procurement web online.&#x0D; &#x0D; Keywords: Service Goods, Demand, Information Systems, Transportation, Waterfall.
IMPLEMENTASI DIGITAL IMAGE PROCESSING SEBAGAI PENDETEKSI POSISI MATAHARI PADA PERANCANGAN DUAL AXIS SOLAR TRACKER

One of the uses of new renewable energy (EBT) in Indonesia which has great potential is solar panels. Solar panels are electrical devices that are used to convert solar energy into electrical energy. The limited surface area of the panels requires users to be able to pay attention to the work efficiency of solar panels. The aspect that has the most influence on optimizing the efficiency of solar panel performance is the placement of the surface of the solar panel against incoming solar radiation. Therefore, it is necessary to have a method to detect the position of the sun to provide input to the actuator so that the surface of the solar panel is positioned in the direction of the sun's rays. This study describes the accuracy of detecting the position of the sun using digital image processing methods. This process needs to be done as a reference in designing a dual axis solar tracker. Detection of the sun's position using this method gets 100% accurate results in cloudless sky conditions and gets 80% success results in cloudy sky conditions.
RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation

Glass-like objects are widespread in daily life but remain intractable to be segmented for most existing methods. The transparent property makes it difficult to be distinguished from background, while the tiny separation boundary further impedes the acquisition of their exact contour. In this paper, by revealing the key co-evolution demand of semantic and boundary learning, we propose a Selective Mutual Evolution (SME) module to enable the reciprocal feature learning between them. Then to exploit the global shape context, we propose a Structurally Attentive Refinement (SAR) module to conduct a fine-grained feature refinement for those ambiguous points around the boundary. Finally, to further utilize the multi-scale representation, we integrate the above two modules into a cascaded structure and then introduce a Reciprocal Feature Evolution Network (RFENet) for effective glass-like object segmentation. Extensive experiments demonstrate that our RFENet achieves state-of-the-art performance on three popular public datasets. Code is available at https://github.com/VankouF/RFENet.
Helpful Information Sharing for Partially Informed Planning Agents

In many real-world settings, an autonomous agent may not have sufficient information or sensory capabilities to accomplish its goals, even when they are achievable. In some cases, the needed information can be provided by another agent, but information sharing might be costly due to limited communication bandwidth and other constraints. We address the problem of Helpful Information Sharing (HIS), which focuses on selecting minimal information to reveal to a partially informed agent in order to guarantee it can achieve its goal. We offer a novel compilation of HIS to a classical planning problem, which can be solved efficiently by any off-the-shelf planner. We provide guarantees of optimality for our approach and describe its extensions to maximize robustness and support settings in which the agent needs to decide which sensors to deploy in the environment. We demonstrate the power of our approaches on a set of standard benchmarks as well as on a novel benchmark.
Learning Object Consistency and Interaction in Image Generation from Scene Graphs

This paper is concerned with synthesizing images conditioned on a scene graph (SG), a set of object nodes and their edges of interactive relations. We divide existing works into image-oriented and code-oriented methods. In our analysis, the image-oriented methods do not consider object interaction in spatial hidden feature. On the other hand, in empirical study, the code-oriented methods lose object consistency as their generated images miss certain objects in the input scene graph. To alleviate these two issues, we propose Learning Object Consistency and Interaction (LOCI). To preserve object consistency, we design a consistency module with a weighted augmentation strategy for objects easy to be ignored and a matching loss between scene graphs and image codes. To learn object interaction, we design an interaction module consisting of three kinds of message propagation between the input scene graph and the learned image code. Experiments on COCO-stuff and Visual Genome datasets show our proposed method alleviates the ignorance of objects and outperforms the state-of-the-art on visual fidelity of generated images and objects.
Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size

Tsetlin Machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they become less interpretable. Further, longer clauses increase the switching activity of the clause logic in hardware, consuming more power. This paper introduces a novel variant of TM learning -- Clause Size Constrained TMs (CSC-TMs) -- where one can set a soft constraint on the clause size. As soon as a clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate CSC-TM, we conduct classification, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches one literal. We finally analyze CSC-TM power consumption and derive new convergence properties.
Improving Heterogeneous Model Reuse by Density Estimation

This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party. Considering the potential sample selection bias among different parties, some heterogeneous model reuse approaches have been developed. However, although pre-trained local classifiers are utilized in these approaches, the characteristics of the local data are not well exploited. This motivates us to estimate the density of local data and design an auxiliary model together with the local classifiers for reuse. To address the scenarios where some local models are not well pre-trained, we further design a multiparty cross-entropy loss for calibration. Upon existing works, we address a challenging problem of heterogeneous model reuse from a decision theory perspective and take advantage of recent advances in density estimation. Experimental results on both synthetic and benchmark data demonstrate the superiority of the proposed method.
Learning Prototype Classifiers for Long-Tailed Recognition

The problem of long-tailed recognition (LTR) has received attention in recent years due to the fundamental power-law distribution of objects in the real-world. Most recent works in LTR use softmax classifiers that are biased in that they correlate classifier norm with the amount of training data for a given class. In this work, we show that learning prototype classifiers addresses the biased softmax problem in LTR. Prototype classifiers can deliver promising results simply using Nearest-Class-Mean (NCM), a special case where prototypes are empirical centroids. We go one step further and propose to jointly learn prototypes by using distances to prototypes in representation space as the logit scores for classification. Further, we theoretically analyze the properties of Euclidean distance based prototype classifiers that lead to stable gradient-based optimization which is robust to outliers. To enable independent distance scales along each channel, we enhance Prototype classifiers by learning channel-dependent temperature parameters. Our analysis shows that prototypes learned by Prototype classifiers are better separated than empirical centroids. Results on four LTR benchmarks show that Prototype classifier outperforms or is comparable to state-of-the-art methods. Our code is made available at https://github.com/saurabhsharma1993/prototype-classifier-ltr.
Multi-Task Learning via Time-Aware Neural ODE

Multi-Task Learning (MTL) is a well-established paradigm for learning shared models for a diverse set of tasks. Moreover, MTL improves data efficiency by jointly training all tasks simultaneously. However, directly optimizing the losses of all the tasks may lead to imbalanced performance on all the tasks due to the competition among tasks for the shared parameters in MTL models. Many MTL methods try to mitigate this problem by dynamically weighting task losses or manipulating task gradients. Different from existing studies, in this paper, we propose a Neural Ordinal diffeRential equation based Multi-tAsk Learning (NORMAL) method to alleviate this issue by modeling task-specific feature transformations from the perspective of dynamic flows built on the Neural Ordinary Differential Equation (NODE). Specifically, the proposed NORMAL model designs a time-aware neural ODE block to learn task-specific time information, which determines task positions of feature transformations in the dynamic flow, in NODE automatically via gradient descent methods. In this way, the proposed NORMAL model handles the problem of competing shared parameters by learning task positions. Moreover, the learned task positions can be used to measure the relevance among different tasks. Extensive experiments show that the proposed NORMAL model outperforms state-of-the-art MTL models.