Reviewerly/exHarmony huggingface dataset:
Optimizing solar power plant efficiency through advanced analytical framework and comparative analysis

Authors:
raw_author_name: V. Senthil Kumar
raw_affiliation_string: Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India
raw_author_name: R. Aruna
raw_affiliation_string: Department of Electronics and Communication Engineering, AMC Engineering College, Bengaluru, Karnataka, India
raw_author_name: M. Varalatchoumy
raw_affiliation_string: Department of Computer Science and Engineering, Cambridge Institute of Technology, Bengaluru, Karnataka, India
raw_author_name: Ashok Kumar
raw_affiliation_string: Department of Computer Science, Banasthali Vidyapith, Rajasthan, India
raw_author_name: A. Rajaram
raw_affiliation_string: Department of Electronics and Communication Engineering, E.G.S Pillay Engineering College, Nagapattinam, India

Abstract: As the world embraces the transition towards renewable energy, the optimization of solar power plants becomes paramount. In this research, we present a comprehensive framework that leverages advanced analytical methodologies to address critical operational challenges and elevate the efficiency of solar power generation. Our framework encompasses data preprocessing, time series analysis, anomaly detection, and equipment performance assessment, synergistically combining their strengths to offer a holistic solution. The heart of our proposed approach lies in the precision and efficacy of anomaly detection. We introduce two powerful techniques—LSTM Autoencoder and Isolation Forest—to identify anomalies and equipment underperformance. Through meticulous evaluation, we showcase their comparative performance, revealing the nuanced strengths of each. Visualizations depict the model’s proficiency in pinpointing anomalies, with LSTM Autoencoder emerging as a standout performer, adept at capturing even subtle deviations from expected patterns. Our research extends beyond detection to equip stakeholders with real-time insights. The visualization of daily yield trends uncovers potential data anomalies, enabling timely intervention and rectification. Additionally, we address equipment failures by harnessing random forest modeling to establish a robust relationship between irradiance, temperature, and DC power. This approach provides a powerful tool for real-time condition monitoring and fault detection, enabling proactive maintenance and enhancing operational resilience.
A hybrid multi-criteria meta-learner based classifier for imbalanced data

Authors:
raw_author_name: Hasna Chamlal
raw_affiliation_string: Department of Mathematics and Informatics, Fundamental and Applied Mathematics Laboratory, Faculty of Sciences Ain Chock, Hassan II University, Casablanca, Morocco
raw_author_name: Tayeb Ouaderhman
raw_affiliation_string: Department of Mathematics and Informatics, Fundamental and Applied Mathematics Laboratory, Faculty of Sciences Ain Chock, Hassan II University, Casablanca, Morocco

Abstract: Numerous imbalanced datasets exist in modern machine learning dilemmas. Challenges of generalization and fairness stem from the existence of underrepresented classes with sensitive characteristics. Additionally, defining an optimal criterion is crucial when dealing with imbalanced data. To address this, it is common in practice to employ composite metrics and loss functions that evenly distribute the cost across each class. However, relying solely on a single metric or loss function, frequently yields sub-par outcomes due to the tendency of the optimization criteria to be overly responsive to imbalanced data, resulting in excessive adaptation and flawed generalization. In order to overcome these limitations, various heuristics can be applied to optimize the criterion. To this end, we aim at addressing the problem of imbalanced data classification by leveraging a multicriteria ensemble procedure, the Hybrid Multi-criteria Meta-learner (HML). The suggested approach focuses on optimizing precision, recall together with balanced accuracy in the Multi-Objective optimization phase and brings forth optimal ensembles, from which the concluding Meta-learner can be chosen after a clustering step, based on a multicriteria decision-making aspect contingent on the selected metrics. Comprehensive experiments portrayed the effectiveness of HML, offering both quality assurance and addresses a vast array of imbalanced data problems.
Appearance flow estimation for online virtual clothing warping via optimal feature linear assignment

Authors:
raw_author_name: Kexin Sun
raw_affiliation_string: Institute of Artificial Intelligence, Donghua University, Shanghai 201620, China
raw_author_name: Jie Zhang
raw_affiliation_string: Institute of Artificial Intelligence, Donghua University, Shanghai 201620, China

Abstract: Clothing warping to spatially align source garments with the corresponding body parts is crucial in clothing media tasks such as virtual try-on and pose-guided person generation. Recent pioneering work has utilized flow fields with additional dimensions of freedom to simulate clothing warping flexibly. However, current methods for estimating appearance flow typically rely on calculating just the local cost volume which contains multiple noisy matching points, potentially leading to a mismatch between clothing and body parts. To address this issue, we propose a novel appearance flow estimation network(Warping-Flow) for clothing warping based on optimal features linear assignment. Specially, two remarkable contributions are made to improve feature matching precision. First, a local context feature aggregation module is proposed to enhance the semantic feature distinction of the source cloth and target pose. Second, Warping-Flow estimates a hard attention mask through the cost volume to filter irrelevant features, followed by the optimal linear assignment algorithm to normalize the cost volume to a discrete permutation matrix that explicitly models the most contributing bipartite matches. Experiments conducted on the VITON and VITON-HD datasets demonstrate that Warping-Flow outperforms existing state-of-the-art algorithms, particularly in cases involving complex clothing deformation. Furthermore, Warping-Flow can serve as a plug-in to improve existing garment media technologies.
Delta robot control by learning systems: Harnessing the power of deep reinforcement learning algorithms

Authors:
raw_author_name: Victor Augusto Kich
raw_affiliation_string: University of Tsukuba, Tsukuba, Japan
raw_author_name: Raul Steinmetz
raw_affiliation_string: Federal University of Santa Maria (UFSM), Santa Maria, Brazil
raw_author_name: Daniel Fernando Tello Gamarra
raw_affiliation_string: Federal University of Santa Maria (UFSM), Santa Maria, Brazil

Abstract: The present study focuses on the implementation of Deep Reinforcement Learning (Deep-RL) techniques for a parallel manipulator robot, specifically the Delta Robot, within a simulated setting. We introduced a simulation framework designed to guide the Delta Robot’s end-effector to a designated spatial point accurately. Within this environment, the robotic agent undergoes a learning process grounded in trial and error. It garners positive rewards for successful predictions regarding the next action and faces negative repercussions for inaccuracies. Through this iterative learning mechanism, the robot refines its strategies, thereby establishing improved decision-making rules based on the ever-evolving environment states. Our investigation delved into three distinct Deep-RL algorithms: the Deep Q-Network Algorithm (DQN), the Double Deep Q-Network (DDQN), and the Trust Region Policy Optimization Algorithm (TRPO). All three methodologies were adept at addressing the challenge presented, and a comprehensive discussion of the findings is encapsulated in the subsequent sections of the paper.
Pyramid hybrid pooling quantization for efficient fine-grained image retrieval

Authors:
raw_author_name: Ziyun Zeng
raw_affiliation_string: Research Center of Artificial Intelligence, Peng Cheng Laboratory, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, China
raw_author_name: Jinpeng Wang
raw_affiliation_string: Research Center of Artificial Intelligence, Peng Cheng Laboratory, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, China
raw_author_name: Bin Chen
raw_affiliation_string: Harbin Institute of Technology, Shenzhen, China; Research Center of Artificial Intelligence, Peng Cheng Laboratory, China
raw_author_name: Tao Dai
raw_affiliation_string: Shenzhen University, China
raw_author_name: Shu-Tao Xia
raw_affiliation_string: Research Center of Artificial Intelligence, Peng Cheng Laboratory, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, China
raw_author_name: Zhi Wang
raw_affiliation_string: Tsinghua Shenzhen International Graduate School, Tsinghua University, China

Abstract: Deep hashing approaches, including deep quantization and deep binary hashing, have become a common solution to large-scale image retrieval due to their high computation and storage efficiency. Most existing hashing methods cannot produce satisfactory results for fine-grained retrieval, because they usually adopt the outputs of the last CNN layer to generate binary codes. Since deeper layers tend to summarize visual clues, e.g., texture, into abstract semantics, e.g., dogs and cats, the feature produced by the last CNN layer is less effective in capturing subtle but discriminative visual details that mostly exist in shallow layers. To improve fine-grained image hashing, we propose Pyramid Hybrid Pooling Quantization (PHPQ). Specifically, we propose a Pyramid Hybrid Pooling (PHP) module to capture and preserve fine-grained semantic information from multi-level features, which emphasizes the subtle discrimination of different sub-categories. Besides, we propose a learnable quantization module with a partial codebook attention mechanism, which helps to optimize the most relevant codewords and improves the quantization. Comprehensive experiments on two widely-used public benchmarks, i.e., CUB-200-2011 and Stanford Dogs, demonstrate that PHPQ outperforms state-of-the-art methods.
Music curriculum integration and reconstruction model based on advanced music waveform iterative reconstruction algorithm

Authors:
raw_author_name: Miaomiao Gao
raw_affiliation_string: Tsai Chi-Kun Academy of Music, Minjiang University, Fuzhou, China

Abstract: To improve the effect of intelligent teaching in music classrooms, this paper combines the advanced music waveform iterative reconstruction algorithm to analyze the integration and reconstruction of the music curriculum. Aiming at the problem that the projection matrix occupies a large space and takes a long time to calculate in iterative reconstruction, a fast and real-time incremental method for generating a music wave matrix is proposed. The improved method avoids the judgment and comparison calculations performed by the incremental method when calculating the length and number of each voxel that the ray passes through. The research results show that the music curriculum integration and reconstruction model based on the advanced music waveform iterative reconstruction algorithm can effectively improve the teaching effect of modern music classrooms.
Bayesian network structure learning based on HC-PSO algorithm

Authors:
raw_author_name: Wenlong Gao
raw_affiliation_string: Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, P. R. China; Institute of Health Statistics and Intelligent Analysis, School of Public Health, Lanzhou University, Lanzhou, Gansu, P. R. China
raw_author_name: Minqian Zhi
raw_affiliation_string: E-mail:; Fax: +31 2436 68754; School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu, P. R. China
raw_author_name: Yongsong Ke
raw_affiliation_string: E-mail:; Fax: +31 2436 68754; School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu, P. R. China
raw_author_name: Yun Zhuo
raw_affiliation_string: E-mail:; Fax: +31 2436 68754; School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu, P. R. China
raw_author_name: Anping Liu
raw_affiliation_string: E-mail:; Fax: +31 2436 68754; School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu, P. R. China
raw_author_name: Yi Yang
raw_affiliation_string: E-mail:; Fax: +31 2436 68754; School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu, P. R. China

Abstract: Structure learning is the core of graph model Bayesian Network learning, and the current mainstream single search algorithm has problems such as poor learning effect, fuzzy initial network, and easy falling into local optimum. In this paper, we propose a heuristic learning algorithm HC-PSO combining the HC (Hill Climbing) algorithm and PSO (Particle Swarm Optimization) algorithm, which firstly uses HC algorithm to search for locally optimal network structures, takes these networks as the initial networks, then introduces mutation operator and crossover operator, and uses PSO algorithm for global search. Meanwhile, we use the DE (Differential Evolution) strategy to select the mutation operator and crossover operator. Finally, experiments are conducted in four different datasets to calculate BIC (Bayesian Information Criterion) and HD (Hamming Distance), and comparative analysis is made with other algorithms, the structure shows that the HC-PSO algorithm is superior in feasibility and accuracy.
GLOCAL: A self-supervised learning framework for global and local motion estimation

Authors:
raw_author_name: Kunming Luo
raw_affiliation_string: Megvii Technology, Beijing 100190, China
raw_author_name: Shuaicheng Liu
raw_affiliation_string: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
raw_author_name: Zun Li
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Ye Xiang
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Lifang Wu
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Bing Zeng
raw_affiliation_string: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
raw_author_name: Chang Wen Chen
raw_affiliation_string: Department of Computing, The Hong Kong Polytechnic University, Hong Kong 999077, China

Abstract: Motions in videos are typically a mixture of local dynamic object motions and global camera motion, which are inconsistent in some cases, and even interfere with each other, causing difficulties in various downstream applications, such as video stabilization that requires the global motion, and action recognition that consumes local motions. Therefore, it is crucial to estimate them separately. Existing methods separate two motions from the mixed motion fields, such as optical flow. However, the quality of mixed motion determines the higher bounds of the performance. In this work, we propose a framework, GLOCAL, to directly estimate global and local motions simultaneously from adjacent frames in a self-supervised manner. Our GLOCAL consists of a Global Motion Estimation (GME) module and a Local Motion Estimation (LME) module. The GME module involves a mixed motion estimation backbone, an implicit bottleneck structure for feature dimension reduction, and an explicit bottleneck for global motion recovery based on the global motion bases with foreground mask under the training guidance of proposed global reconstruction loss. An attention U-Net is adopted for LME which produces local motions while excluding motion of irrelevant regions under the guidance of proposed local reconstruction loss. Our method can achieve better performance than the existing methods on the homography estimation dataset DHE and the action recognition dataset NCAA and UCF-101.
Structural Discovery with Partial Ordering Information for Time-Dependent Data with Convergence Guarantees

Authors:
raw_author_name: Jiahe Lin
raw_affiliation_string: Machine Learning Research, Morgan Stanley, New York, NY
raw_author_name: Huitian Lei
raw_affiliation_string: Lyft, Inc., San Francisco, CA
raw_author_name: George Michailidis
raw_affiliation_string: Department of Statistics &amp; Data Science, University of California, Los Angeles, CA

Abstract: Structural discovery among a set of variables is of interest in both static and dynamic settings. In the presence of lead-lag dependencies in the data, the dynamics of the system can be represented through a structural equation model (SEM) that simultaneously captures the contemporaneous and temporal relationships amongst the variables, with the former encoded through a directed acyclic graph (DAG) for model identification. In many real applications, a partial ordering amongst the nodes of the DAG is available, which makes it either beneficial or imperative to incorporate it as a constraint in the problem formulation. This article develops an algorithm that can seamlessly incorporate a priori partial ordering information for solving a linear SEM (also known as Structural Vector Autoregression) under a high-dimensional setting. The proposed algorithm is provably convergent to a stationary point, and exhibits competitive performance on both synthetic and real datasets. Supplementary materials for this article are available online.
Dilated convolution for enhanced extractive summarization: A GAN-based approach with BERT word embedding

Authors:
raw_author_name: Huimin Wu
raw_affiliation_string: Henan Polytechnic Institute, Nanyang Henan, China

Abstract: Text summarization (TS) plays a crucial role in natural language processing (NLP) by automatically condensing and capturing key information from text documents. Its significance extends to diverse fields, including engineering, healthcare, and others, where it offers substantial time and resource savings. However, manual summarization is a laborious task, prompting the need for automated text summarization systems. In this paper, we propose a novel strategy for extractive summarization that leverages a generative adversarial network (GAN)-based method and Bidirectional Encoder Representations from Transformers (BERT) word embedding. BERT, a transformer-based architecture, processes sentence bidirectionally, considering both preceding and following words. This contextual understanding empowers BERT to generate word representations that carry a deeper meaning and accurately reflect their usage within specific contexts. Our method adopts a generator and discriminator within the GAN framework. The generator assesses the likelihood of each sentence in the summary while the discriminator evaluates the generated summary. To extract meaningful features in parallel, we introduce three dilated convolution layers in the generator and discriminator. Dilated convolution allows for capturing a larger context and incorporating long-range dependencies. By introducing gaps between filter weights, dilated convolution expands the receptive field, enabling the model to consider a broader context of words. To encourage the generator to explore diverse sentence combinations that lead to high-quality summaries, we introduce various noises to each document within our proposed GAN. This approach allows the generator to learn from a range of sentence permutations and select the most suitable ones. We evaluate the performance of our proposed model using the CNN/Daily Mail dataset. The results, measured using the ROUGE metric, demonstrate the superiority of our approach compared to other tested methods. This confirms the effectiveness of our GAN-based strategy, which integrates dilated convolution layers, BERT word embedding, and a generator-discriminator framework in achieving enhanced extractive summarization performance.
Transliteration Characteristics in Romanized Assamese Language Social Media Text and Machine Transliteration

Authors:
raw_author_name: Sanasam Ranbir Singh
raw_affiliation_string: Indian Institute of Technology Guwahati, India
raw_author_name: Priyankoo Sarmah
raw_affiliation_string: Indian Institute of Technology Guwahati, India

Abstract: This article aims to understand different transliteration behaviors of Romanized Assamese text on social media. Assamese, a language that belongs to the Indo-Aryan language family, is also among the 22 scheduled languages in India. With the increasing popularity of social media in India and also the common use of the English Qwerty keyboard, Indian users on social media express themselves in their native languages, but using the Roman/Latin script. Unlike some other popular South Asian languages (say Pinyin for Chinese), Indian languages do not have a common standard romanization convention for writing on social media platforms. Assamese and English are two very different orthographical languages. Thus, considering both orthographic and phonemic characteristics of the language, this study tries to explain how Assamese vowels, vowel diacritics, and consonants are represented in Roman transliterated form. From a dataset of romanized Assamese social media texts collected from three popular social media sites: (Facebook, YouTube, and X (formerly known as Twitter)), 1 we have manually labeled them with their native Assamese script. A comparison analysis is also carried out between the transliterated Assamese social media texts with six different Assamese romanization schemes that reflect how Assamese users on social media do not adhere to any fixed romanization scheme. We have built three separate character-level transliteration models from our dataset. One using a traditional phrase-based statistical machine transliteration model, (1) PBSMT model and two separate neural transliteration models, (2) BiLSTM neural seq2seq model with attention, and (3) Neural transformer model. A thorough error analysis has been performed on the transliteration result obtained from the three state-of-the-art models mentioned above. This may help to build a more robust machine transliteration system for the Assamese social media domain in the future. Finally, an attention analysis experiment is also carried out with the help of attention weight scores taken from the character-level BiLSTM neural seq2seq transliteration model built from our dataset.