Reviewerly/exHarmony huggingface dataset:
Lightweight Human Pose Estimation Using Loss Weighted by Target Heatmap

Recent research on human pose estimation exploits complex structures to improve performance on benchmark datasets, ignoring the resource overhead and inference speed when the model is actually deployed. In this paper, we lighten the computation cost and parameters of the deconvolution head network in SimpleBaseline and introduce an attention mechanism that utilizes original, inter-level, and intra-level information to intensify the accuracy. Additionally, we propose a novel loss function called heatmap weighting loss, which generates weights for each pixel on the heatmap that makes the model more focused on keypoints. Experiments demonstrate our method achieves a balance between performance, resource volume, and inference speed. Specifically, our method can achieve 65.3 AP score on COCO test-dev, while the inference speed is 55 FPS and 18 FPS on the mobile GPU and CPU, respectively.
Transition-Based Mention Representation for Neural Coreference Resolution

Coreference resolution plays an important role in text understanding. Recently, various neural approaches have been proposed and achieved success. Although most researches agree that mention extraction and representation much impact the performance of coreference resolution, existing neural architectures consider all possible spans up to a maximum length and only employ a simple ffat word-level span representation. In this way, those information which has been proved to be effective in previous non-neural coreference resolution, such as structural information, has been largely ignored. In this paper, for coreference resolution, we propose a uniffed transition-based approach to extract and represent mentions simultaneously. In particular, we propose a Simplified Constituent Parse Tree (SCPT) scheme for each sentence by only keeping the local detail inside the mentions and the coarse frame structure outside the mentions. That is each mention corresponds to a constituent of the obtained SCPT. Then we employ a transition-based strategy to construct the SCPT structure in a bottom-up manner. In this way, various potential mentions (i.e., constituents) can be obtained and the corresponding transition action sequences embedded with internal structural information can be viewed as their proper representations. After that, we employ such achieved potential mentions and their transition-based representations for neural coreference resolution.
Tree-Mining: Understanding Applications and Challenges

Tree-mining is an essential system of techniques and software technologies for multi-level and multi-angled operations in databases. Pertaining to the purview of this manuscript, several applications of various sub-techniques of tree mining have been explored. The current write-up is aimed at investigating the major applications and challenges of different types and techniques of tree mining, as there have been patchy and scanty investigations so far in this context. To accomplish these tasks, the author has reviewed some of the latest and most pertinent research articles of the last two decades to investigate the titled aspects of this technique.
IoT- Edge Deep Learning EHealth Monitoring System

This research aims to investigate the Possibility with viability of open-source Internet of Things (IoT) and edge-compatible equipment in the field of health monitoring. The focus is on exploring various IoT health monitoring environ- ments used in e-health applications, taking into account crucial aspects such as sensor integration, data collection methods, communication protocols, secu- rity measures, scalability, and regulatory requirements. The research begins by examining existing IoT health monitoring environments to gain a compre- hensive understanding of their strengths and limitations. This analysis helps identify the gaps and challenges that open-source IoT and edge devices can address in the context of health monitoring. Building upon this groundwork, a novel IoT-edge-powered deep learning system will be developed specifically for a targeted health monitoring environment. The system will leverage the capabilities of IoT devices, integrate various sensors to capture relevant health data, harness edge computing techniques to process data locally, and utilize deep learning algorithms for advanced analysis and inference. Special empha- sis will be placed on optimizing data preprocessing, feature extraction, model training, and overall system performance. To assess the effectiveness of the proposed IoT-edge-deep learning environment, A comparison with currently implemented solutions will be done. Key indicators will be the focus of the assessment such as precision, accuracy, and efficiency, aiming to highlight the advantages and improvements offered by the developed system. The results of this research project are anticipated to make major contributions to the implementation of IoT and edge computing in health monitoring. By explor- ing the feasibility of open-source devices, the research will demonstrate their potential for democratizing access to health monitoring technology. Addition- ally, the exploration of e-health monitoring environments will provide valu- able insights into best practices, challenges, and regulatory considerations. Finally, the introduction of an innovative deep learning system will enhance health monitoring capabilities, enabling more accurate and timely detection of health-related conditions. This research holds promise for advancing the field of health monitoring by combining IoT, edge computing, and deep learning, ultimately raising the standard of healthcare as a whole services and patient outcomes.
Accounting Information and Role It on Financial Reports Quality in Jordanian Hotels, and Social Performance as a Mediating Effect

We examine how social performance moderates the relationship between accounting data and the accuracy of financial reports from the perspective of accountants in Jordanian hotels.A 39-item survey was prepared and sent to hotel accountants in Jordan to gauge the strength of this relationship, and a total of 220 questionnaires were completed.The results show that accounting information (relevance, faithful representation, comparability, understandability, and timeliness) has a positive and statistically significant relationship with the quality of financial reports.Furthermore, the regression findings indicate that social performance mediates the relationship between accounting data and the quality of financial reports.This study demonstrates how a socially responsible approach to environmental preservation can motivate people to protect the environment and fulfill their environmental responsibilities.It is also suggested that by paying closer attention to and identifying environmental expenses, hotel operators may save money and provide more accurate financial reporting as eco-tourism grows in Jordan.
AutoFe-Sel: A Meta-learning based methodology for Recommending Feature Subset Selection Algorithms

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models.There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyperparameter optimization, and preprocessing method selection.Among them, preprocessing method selection is a relatively new and fast growing research area.The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms.One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling.Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs).Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling.This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel.To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets.Results show that the proposed method achieves better performance compared to three baseline methods.The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.
On the Power of Gradual Network Alignment Using Dual-Perception Similarities

Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs <i>at once</i> , they do not harness information enriched through <i>interim</i> discovery of node correspondences to more accurately find the next correspondences during the node matching. To tackle this challenge, we propose <inline-formula><tex-math notation="LaTeX">${\sf Grad\text{-}Align}$</tex-math></inline-formula> , a new NA method that <i>gradually</i> discovers node pairs by making full use of node pairs exhibiting strong consistency, which are easy to be discovered in the early stage of gradual matching. Specifically, <inline-formula><tex-math notation="LaTeX">${\sf Grad\text{-}Align}$</tex-math></inline-formula> first generates node embeddings of the two networks based on graph neural networks along with our <i>layer-wise reconstruction loss</i> , a loss built upon capturing the first-order and higher-order neighborhood structures. Then, nodes are gradually aligned by computing <i>dual-perception similarity</i> measures including the <i>multi-layer embedding similarity</i> as well as the <i>Tversky similarity</i> , an asymmetric set similarity using the Tversky index applicable to networks with different scales. Additionally, we incorporate an edge augmentation module into <inline-formula><tex-math notation="LaTeX">${\sf Grad\text{-}Align}$</tex-math></inline-formula> to reinforce the structural consistency. Through comprehensive experiments using real-world and synthetic datasets, we empirically demonstrate that <inline-formula><tex-math notation="LaTeX">${\sf Grad\text{-}Align}$</tex-math></inline-formula> consistently outperforms state-of-the-art NA methods.
First Successes and Failures of Universities Participating in “Leadership in the Region and/or Industry” Track of the “Priority 2030” Program

The article presents the results of quantitative indicators analysis of the main activities of three groups of universities participating in the “Priority 2030” program of the “Leadership in the region and/or industry” track (hereinafter referred to as the territorial and industrial track) during the first year of their development programs implementation. The Displaced Ideal Method was used in the research. Dynamics of indicators series of special and basic parts has being analyzed for universities groups in 2017-2021. The strengths and weaknesses of the universities groups of the territorial and industrial track are revealed. It was established that the special indicators values achieved by universities during the first year had a significant impact on the assessment of the Council for universities development programs support in frame of Priority-2030 Project, which taking into account the performances of the universities teams led to significant changes in the groups composition of the studied track – more than half of universities changed their positions. A number of reasons were considered that led to a change in universities’ positions in the groups of the researched track.
Dungeon level generation using generative adversarial network: an experimental study for top-down view games

Manual designing levels for games is a complex task, often demanding time and effort from the game designer. An option for this is using algorithms to generate such levels, improving its scalability. Currently, such procedural content generation methods can be guided by hand-crafted rules or, as in more recent approaches, by learning from existing data. In deep learning, generative adversarial networks have been proven to effectively generate samples for a given distribution in an unsupervised manner. Unfortunately, the training and usage of GANs show many challenges, such as convergence problems, instability, and mode collapse. In addition to these problems, we show that learning to generate valid levels is not trivial. In this paper, we demonstrate an empirical analysis of the use of GANs, pointing out the best state-of-the-art practices and highlighting future directions in this research field.
UIQI: A Comprehensive Quality Evaluation Index for Underwater Images

Due to the light absorption and scattering in waterbodies, acquired underwater images frequently suffer from color cast, blur, low contrast, noise, etc., which seriously degrade the image quality and affect their subsequent applications. Therefore, it is necessary to propose a reliable and practical underwater image quality assessment (IQA) model that can faithfully evaluate underwater image quality. To this end, in this paper, we establish a novel quality assessment model for underwater images by in-depth analysis and characterization of multiple image properties. Specifically, we propose characterizing the image luminance, color cast, sharpness, contrast, fog density and noise to comprehensively describe the image quality to evaluate the underwater image quality more accurately. Dedicated features are elaborately investigated to characterize those quality-aware image properties. After feature extraction, we employ support vector regression (SVR) to integrate all the quality-aware features and regress them onto the underwater image quality score. Extensive tests performed on standard underwater image quality databases demonstrate the superior prediction performance of the proposed underwater IQA model to state-of-the-art congeneric quality assessment models.
FW-ECPE: An Emotion-Cause Pair Extraction Model Based on Fusion Word Vectors

Emotion-Cause Pair Extraction (ECPE) aims to extract potential emotion-cause pairs from text without emotion labels. It lays an important foundation for downstream research such as causal reasoning, public opinion prediction, and reason detection. However, the ECPE task now faces two dilemmas: 1) insufficient utilization of word sequence information, and 2) inadequate use of position information between clauses. To address the above problems, we proposed an Emotion-Cause Pair Extraction model based on Fusion Word Vectors named FW-ECPE. It is a two-stage model that first extracts emotion clauses and cause clauses respectively then combines them into pairs and filters out the right emotion-cause pairs. The Fusion Word Vector is reflected in two aspects. Firstly, we integrate the clause context vectors and the emotion clauses prediction results with cause context vectors in cause clauses extraction. Secondly, in the emotion-cause pair extraction stage, we fuse the position information between clauses and contextual information. Finally, we extend Easy Data Augmentation, a corpus enhancement algorithm, to enlarge the amount of data and alleviate the risk of overfitting. The experiment results show that our proposed approach outperforms the previous methods on a benchmark dataset.
Underwater image co-enhancement based on physical-guided transformer interaction

Most existing underwater image enhancement methods only focus on enhancing a single image. However, underwater images taken in the same scene often exhibit similar degradation characteristics, which can provide richer complementary information to each other. In this paper, a novel underwater image co-enhancement based on physical-guided Transformer interaction (UICE-PTI), which adopts a multi-scale encoder–decoder structure to effective mine the rich semantic information, is proposed. Specifically, considering that the degradation of underwater image is directly related to scene depth, the Dark Channel Prior-guided Transformer (DCPT) module is embedded into the framework before the preliminary feature extraction. Then, the convolution operation in the preliminary feature extraction is proposed for the local degradation of the underwater image. After that, considering the non-local and heterogeneous degradation of the underwater images across different channels and pixels, the CS-Transformer block with second-order statistics is proposed, which incorporates both channel and spatial Transformer modules. Furthermore, considering the rich complementary information between images of the same scene for enhancement, the Feature Transformer Interaction Module (FTIM) is proposed to capture the correlation between two branches in the network bottleneck layer. Additionally, the proposed UICE-PTI can also be extended to underwater stereo image enhancement. Finally, the experimental results demonstrate the superior performance of the proposed UICE-PTI and the effectiveness of each module.
Low-Resource Language Information Processing using Dwarf Mongoose Optimization with Deep Learning Based Sentiment Classification

Asian and low-resource language information processing refers to the field of computational linguistics that aims to develop natural language processing (NLP) technologies for languages that have fewer available language resources or are less commonly spoken. This is an important field of study because many languages in Asia and other parts of the world are underrepresented in the field of NLP, which may limit access to information and technology for speakers of these languages. The growing volume of user-generated content on the web has made sentiment analysis (SA) a significant tool for extracting data regarding human emotional states. Twitter sentiment detectors provide a superior solution for assessing the quality of products and services compared to other conventional technologies. The detection performance and classifier accuracy of SA, which can be highly dependent on classifier methods and the quality of input features have been utilised. Deep learning (DL) methods use distinct techniques to extract data from raw data such as tweets or texts and represent them in different forms of models. Therefore, this article presents a Dwarf Mongoose Optimization with Deep Learning-Based Twitter Sentiment Classification (DMODL-TSC) technique to classify sentiments based on tweets. The presented DMODL-TSC technique leverages the concepts of natural language processing (NLP) and DL. Primarily, the raw tweets are preprocessed to transform them into a useful format. Next, the DMODL-TSC technique uses the advanced FastText word embedding technique. Moreover, the bidirectional recurrent neural network (BiRNN) method is utilized for the recognition of sentiments. Finally, the DMO technique is utilized for the optimal hyperparameter optimization of the BiRNN method, which leads to effective classification performance. The comprehensive result examination of the DMODL-TSC system was tested on three datasets, and the obtained outcomes illustrate the supremacy of the DMODL-TSC approach.
Multiple Knowledge-Enhanced Meteorological Social Briefing Generation

Frequent meteorological disasters present new challenges for decision-making in disaster response. As a timely and effective source of intelligent information, social media plays a vital role in detecting and monitoring these situations. Meteorological social briefings summarize valuable information from numerous social media posts, providing essential decision-support services. This article proposes a multi-knowledge-enhanced summarization (MKES) model for automatically generating meteorological social briefing content from multiple Sina Weibo posts. The MKES model consists of a summary generation module and a knowledge enhancement module. The knowledge enhancement module guides and constrains the summary generation process using meteorological events and geographical location knowledge, resulting in summaries that focus on describing specific knowledge from the source text. The MKES model outperforms baseline models in content evaluation, as measured by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{ROUGE-1}$</tex-math> </inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{ROUGE-2}$</tex-math> </inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{ROUGE-L}$</tex-math> </inline-formula> scores, and in sentiment evaluation, as measured by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_1$</tex-math> </inline-formula> scores. Based on the MKES model, a framework for generating meteorological social briefings is developed, providing decision support services for the China Meteorological Administration (CMA).
Modulating Pretrained Diffusion Models for Multimodal Image Synthesis

We present multimodal conditioning modules (MCM) for enabling conditional image synthesis using pretrained diffusion models. Previous multimodal synthesis works rely on training networks from scratch or fine-tuning pretrained networks, both of which are computationally expensive for large, state-of-the-art diffusion models. Our method uses pretrained networks but does not require any updates to the diffusion network’s parameters. MCM is a small module trained to modulate the diffusion network’s predictions during sampling using 2D modalities (e.g., semantic segmentation maps, sketches) that were unseen during the original training of the diffusion model. We show that MCM enables user control over the spatial layout of the image and leads to increased control over the image generation process. Training MCM is cheap as it does not require gradients from the original diffusion net, consists of only ∼ 1% of the number of parameters of the base diffusion model, and is trained using only a limited number of training examples. We evaluate our method on unconditional and text-conditional models to demonstrate the improved control over the generated images and their alignment with respect to the conditioning inputs.