Reviewerly/exHarmony huggingface dataset:
Pattern of Book Advertisement of Amar Ekushey Book Fair 2023: A Study of Bangladesh

Amar Ekushey Book Fair is the national book fair of Bangladesh. The book fair is held every year in February at Bangla Academy premises and Suhrawardy Udyan.This book fair is one of the largest book fairs in the world in terms of the number of readership and duration of the fair. Amar Ekushey Book Fair is associated with the movement and spirit of the mother tongue of Bengali which gives a different dimension to this book fair and makes difference from other book fairs organized in different countries of the world. Book advertising is one of the most effective ways to promote a book.In the Amar Ekushey Book Fair, book advertisements have been circulated for a long time to disseminate information among the readers.With the passage of time, the form of advertisement and various additions through advertisement have become modern.During the book fair, most of the creative publications in Bangladesh focus on the Amar Ekushey Book Fair and advertise the book.This study explores the overall picture of communication with readers through book advertisements and advertisements of the Amar Ekushey Book Fair to be held in 2023 through surveys and interviews. A total of 100 respondents were collected with an equal male-female ratio as well as 50 publications has been collected. The study found that 66 percent of publishers advertise books at book fairs. The most unprecedented result of this research is the fact that now many publications do not advertise books. It is expected that in the near future, most of the publications participating in the Amar Ekushey Book Fair will present the information of the book to the readers through book advertisements. Finally, the paper includes recommendations for publishers to effectively advertise their books and select media based on their readership preferences.
Metaverse search system: Architecture, challenges, and potential applications

Like numerous new websites created globally every day on the web, new spaces are constantly produced in the metaverse and interconnected to each other through the Internet. The continuous expansion of the metaverse space requires a search system that supports efficiently navigating and retrieving globally distributed spaces. Current metaverse search systems are limited to text-based retrieval within local platforms, and web search systems have critical limitations to apply to the metaverse. In this study, we propose an architecture for a metaverse search system that overcomes these limitations, enabling global space search across the distributed metaverse. We analyze the gaps between the web and the metaverse, identify challenges and technologies in developing a metaverse search system, and explore potential applications from different user perspectives. This study sets the foundation for a seamless browsing experience in the metaverse, enabling users to navigate three-dimensional spaces easily and freely, similar to surfing the traditional web.
Analysis and Monitoring of Maximum Solar Potential for Energy Production Optimization Using Photovoltaic Panels

This article explores the efficiency of photovoltaic (PV) panels, which is crucial in the search for sustainable energy solutions. The study presents a comprehensive analysis of the maximum solar potential achievable through photovoltaic technologies amidst the increasing global energy demands. The research examines solar radiation measurement techniques, the incidence angle of solar rays, and the intricacies of PV panel efficiency. It highlights the potential for improving the performance of solar-based energy systems. Four main sections are covered, beginning with an introduction to the importance of energy storage in sustainable energy production, especially in the context of the European Union’s energy goals and the Green Deal. The following sections discuss the precision needed in the geographical positioning of measurement systems, the impact of light physics, and variable weather conditions on energy capture. The last section presents a novel clock algorithm regulation system designed to enhance the efficiency of the measurement system.
The Efficiency of Question‐Asking Strategies in a Real‐World Visual Search Task

In recent years, a multitude of datasets of human-human conversations has been released for the main purpose of training conversational agents based on data-hungry artificial neural networks. In this paper, we argue that datasets of this sort represent a useful and underexplored source to validate, complement, and enhance cognitive studies on human behavior and language use. We present a method that leverages the recent development of powerful computational models to obtain the fine-grained annotation required to apply metrics and techniques from Cognitive Science to large datasets. Previous work in Cognitive Science has investigated the question-asking strategies of human participants by employing different variants of the so-called 20-question-game setting and proposing several evaluation methods. In our work, we focus on GuessWhat, a task proposed within the Computer Vision and Natural Language Processing communities that is similar in structure to the 20-question-game setting. Crucially, the GuessWhat dataset contains tens of thousands of dialogues based on real-world images, making it a suitable setting to investigate the question-asking strategies of human players on a large scale and in a natural setting. Our results demonstrate the effectiveness of computational tools to automatically code how the hypothesis space changes throughout the dialogue in complex visual scenes. On the one hand, we confirm findings from previous work on smaller and more controlled settings. On the other hand, our analyses allow us to highlight the presence of "uninformative" questions (in terms of Expected Information Gain) at specific rounds of the dialogue. We hypothesize that these questions fulfill pragmatic constraints that are exploited by human players to solve visual tasks in complex scenes successfully. Our work illustrates a method that brings together efforts and findings from different disciplines to gain a better understanding of human question-asking strategies on large-scale datasets, while at the same time posing new questions about the development of conversational systems.
VVA: Video Values Analysis

User-generated content videos have attracted increasingly attention due to its dominant role in social platforms. It is crucial to analyze values in videos because the extensive range of video content results in significant variations in the subjective quality of videos. However, the research literature on Video Values Analysis (VVA) is very scarce, which aims to evaluate the compatibility between video content and the social mainstream values. Meanwhile, existing video content analysis methods are mainly based on classification techniques, which can not adequate VVA due to their coarse-grained manners. To tackle this challenge, we propose a framework to generate more fine-grained scores for diverse videos, termed as Video Values Analysis Model (VVAM), which consists of a feature extractor based on R3D, a feature aggregation module based on Transformer and a regression head based on MLP. In addition, considered texts in videos can be key clues to improve VVA, we design a new pipeline, termed as Text-Guided Video Values Analysis Model (TG-VVAM), in which texts in videos are spotted by OCR tools and a cross-modal fusion module is used to combine the vision and text features. To further facilitate the VVA, we construct a large-scale dataset, termed as Video Values Analysis Dataset (VVAD), which contains 53,705 short videos of various types from main social platforms. Experiments demonstrate that our proposed VVAM and TG-VVAM achieves promising results in the VVAD.
Text Causal Discovery Based on Sequence Structure Information

Causality forms the basis for reasoning and decision-making in artificial intelligence systems. To take advantage of the vast amount of textual data available today, causal discovery from text has become a significant challenge in recent years. Text data contains rich contextual semantic information. However, traditional causal discovery methods only handle structured data and do not consider serial relationships and semantic relevance between words on textual variables. To address this problem, in this paper, we propose a causal discovery method Text Causal Discovery Based on Sequence Structure Information (TCDSS) discovers strongly correlated text word pairs with semantic relevance and statistical causality and finally constructs lexical causal graphs by introducing sequence-structure information in the causal discovery algorithm. We tested our method TCDSS on the DXY-COVID-19-Data and the Chinese Emergency Corpus (CEC) and compared it with other existing causal discovery methods. The experimental results show that PC, IGCI, RECI, and other forms have improved in precision, recall, and structural Hamming distance (SHD) after the introduction of TCDSS.
Dual-Stream Context-Aware Neural Network for Survival Prediction from Whole Slide Images

Whole slide images (WSI) encompass a wealth of information about the tumor micro-environment, which holds prognostic value for patients’ survival. While significant progress has been made in predicting patients’ survival risks from WSI, existing studies often overlook the importance of incorporating multi-resolution and multi-scale histological image features, as well as their interactions, in the prediction process. This paper introduces the dual-stream context-aware (DSCA) model, which aims to enhance survival risk prediction by leveraging multi-resolution histological images and multi-scale feature maps, along with their contextual information. The DSCA model comprises three prediction branches: two ResNet50 branches that learn features from multi-resolution images, and one feature fusion branch that aggregates multi-scale features by exploring their interactions. The feature fusion branch of the DSCA model incorporates a mixed attention module, which performs adaptive spatial fusion to enhance the multi-scale feature maps. Subsequently, the self-attention mechanism is developed to learn contextual and interactive information from the enhanced feature maps. The ordinal Cox loss is employed to optimize the model for generating patch-level predictions. Patient-level predictions are obtained by mean-pooling patch-level results. Experimental results conducted on colorectal cancer cohorts demonstrate that the proposed DSCA model achieves significant improvements over state-of-the-art methods in survival prognosis.
Self-guided Transformer for Video Super-Resolution

The challenge of video super-resolution (VSR) is to leverage the long-range spatial-temporal correlation between low-resolution (LR) frames to generate high-resolution (LR) video frames. However, CNN-based video super-resolution approaches show limitations in modeling using long-range dependencies and non-local self-similarity. In this paper. For further spatio-temporal learning, we propose a novel self-guided transformer for video super-resolution (SGTVSR). In this framework, we customize a multi-headed self-attention based on offset-guided window (OGW-MSA). For each query element on a low-resolution reference frame, the OGW-MSA enjoys offset guidance to globally sample highly relevant key elements throughout the video. In addition, we propose a feature aggregation module that aggregates the favorable spatial information of adjacent frame features at different scales as a way to improve the video reconstruction quality. Comprehensive experiments show that our proposed self-guided transformer for video super-resolution outperforms the state-of-the-art (SOTA) method on several public datasets and produces good results visually.
Computing 2D Skeleton via Generalized Electric Potential

As a fundamental step in visual shape analysis, finding a good shape representation has attracted a lot of interest. A skeleton, which not only retains the geometric characteristics of the shape but also has its complete topology, is regarded as a very efficient tool in visual shape analysis. Although a variety of skeleton computation methods have been proposed, the obtained skeletons still suffer from some limitations, i.e., being sensitive to noise or being inconsistent. In this study, we propose a method for computing the 2D skeleton based on a generalized electric field. Upon evenly placing unit positive charges on the shape contour, we find that the skeleton of the shape can be accurately extracted by a novel definition of electric potential and later pruned by the distribution of charge on the shape when the state of electrostatic equilibrium is reached. Extensive qualitative experiments are conducted on two public datasets, demonstrating that our method can produce a stable and concise skeleton and outperform several state-of-the-art methods.
MCKIE: Multi-class Key Information Extraction from Complex Documents Based on Graph Convolutional Network

The majority of key information extraction in document analysis work relies on simple layout scenes with few classes, such as the date and amount on invoices or receipts. However, many document applications entail sophisticated layouts with various types of elements. The mortgage contract agreement, for example, often has over ninety entities, and it is expensive to design template for each document. To this end, we propose an efficient multi-class key information extraction (MCKIE) method based on graph convolutional network. In detail, we design a graph construction strategy to generate a text box graph. Then, MCKIE utilizes the message-passing mechanism to learn node representation by aggregating information from neighborhoods. Besides, we compare the performance of various graph construction methods and verify the effectiveness of MCKIE on the realistic contract document dataset. Extensive experimental results show that the proposed model significantly outperforms other baseline models.
AIE-KB: Information Extraction Technology with Knowledge Base for Chinese Archival Scenario

The current visual information extraction (VIE) methods usually focus only on textual features or image features of the document modality, ignoring the segment-level structural information in the document, thus limiting the accuracy of information extraction. To address the problem of information extraction in the scenario of scanned personnel archival images, the paper proposes an Archive Information Extraction model with Knowledge Base (AIE-KB), which injects knowledge into the model in the coarse relationship prediction module (CRP). Our model undergoes several modules, including entity recognition, CRP, and entity linking, after encoding the features for multi-modal fusion. As detailed in Fig. 2. Specifically, we (1) add new intermediate entity types and (2) design a new relationship prediction head to explicitly exploit structural information in scanned images, solving the problem of nested entity relationships specific to personnel archives. To address the lack of a certain amount of annotated data on personnel files, we also introduce a benchmark dataset of personnel archives in which the “answer” types in each scanned archival image are handwritten. The experimental results show that our model AIE-KB achieves excellent results in two tasks with different datasets, demonstrating the effectiveness and robustness of the model. All metrics for both tasks exceeded the baseline model, with the F1 metric for entity recognition on the Visually rich scanned Images in the personnel Archive (VIA) (from 84.36 to 94.61) and entity linking on VIA (from 69.79 to 87.94).
Unsupervised Feature Selection via Nonlinear Representation and Adaptive Structure Preservation

Unsupervised feature selection has attracted increasing attention for its promising performance on high dimensional data with higher dimensionality and more expensive labeling costs. Existing unsupervised feature selection methods mostly assume that linear relationships can interpret all feature associations. However, data with exclusively linear relationships are rare and impractical. Moreover, the quality of the similarity matrix significantly affects the effectiveness of conventional spectral-based methods. Real-world data contains lots of noise and redundancy, making the similarity matrix built using the raw data unreliable. To address these problems, we propose a novel and robust method for feature selection over a novel nonlinear mapping function, aiming to mine the nonlinear relationships among features. Furthermore, we incorporated manifold learning into our training process, embedded with adaptive graph constraints based on the principle of maximum entropy, to maintain the intrinsic structure of the data and simultaneously capture more accurate information. An efficient and effective algorithm was designed to perform our method. Experiments with eight benchmark datasets from face images, biology, and time series outperformed nine state-of-the-art algorithms, validating the superiority and effectiveness of our method. The source code is available at https://github.com/aasdlaca/NRASP .
TEFNet: Target-Aware Enhanced Fusion Network for RGB-T Tracking

RGB-T tracking leverages the fusion of visible (RGB) and thermal (T) modalities to achieve more robust object tracking. Existing popular RGB-T trackers often fail to fully leverage background information and complementary information from different modalities. To address these issues, we propose the target-aware enhanced fusion network (TEFNet). TEFNet concatenates the features of template and search regions from each modality and then utilizes self-attention operations to enhance the single-modality features for the target by discriminating it from the background. Additionally, a background elimination module is introduced to reduce the background regions. To further fuse the complementary information across different modalities, a dual-layer fusion module based on channel attention, self-attention, and bidirectional cross-attention is constructed. This module diminishes the feature information of the inferior modality, and amplifies the feature information of the dominant modality, effectively eliminating the adverse effects caused by modality differences. Experimental results on the LasHeR and VTUAV datasets demonstrate that our method outperforms other representative RGB-T tracking approaches, with significant improvements of 6.6% and 7.1% in MPR and MSR on the VTUAV dataset respectively.
A Few-Shot Medical Image Segmentation Network with Boundary Category Correction

Accurate medical image segmentation is the foundation of clinical imaging diagnosis and 3D image reconstruction. However, medical images often have low contrast between target objects, greatly affected by organ movement, and suffer from limited annotated samples. To address these issues, we propose a few-shot medical image segmentation network with boundary category correction named Boundary Category Correction Network (BCC-Net). Of overall medical few-shot learning framework, we first propose the Prior Mask Generation Module (PRGM) and Multi-scale Feature Fusion Module (MFFM). PRGM can better localize the query target, while MFFM can adaptively fuse the support set prototype, the prior mask and the query set features at different scales to solve the problem of the spatial inconsistency between the support set and the query set. To improve segmentation accuracy, we construct an additional base-learning branch, which, together with the meta-learning branch, forms the Boundary Category Correction Framework (BCCF). It corrects the boundary category of the meta-learning branch prediction mask by predicting the region of the base categories in the query set. Experiments are conducted on the mainstream ABD-MR and ABD-CT medical image segmentation public datasets. Comparative analysis and ablation experiments are performed with a variety of existing state-of-the-art few-shot segmentation methods. The results demonstrate that the effectiveness of the proposed method with significant enhance the segmentation performance on medical images.
Global Selection and Local Attention Network for Referring Image Segmentation

Referring image segmentation (RIS) aims to segment the target object based on a natural language expression. The challenge lies in comprehending both the image and the referring expression simultaneously, while establishing the alignment between these two modalities. Recently, the visual-language large-scale pre-trained model CLIP can well align the modalities. However, the alignment in these models is based on the global image. And RIS requires aligning global text features with local visual features, rather than global visual features. To this end, features extracted by CLIP can not be directly applied to RIS. In this paper, we propose a novel framework called Global Selection and Local Attention Network (GLNet), which builds upon CLIP. GLNet comprises two modules: Global Selection and Fusion Module (GSFM) and Local Attention Module (LAM). GSFM utilizes text information to adaptively select and fuse visual features from low-level and middle-level. LAM leverages attention mechanisms on both local visual features and local text features to establish relationships between objects and text. Extensive experiments demonstrate the exceptional performance of our proposed method in referring image segmentation. On RefCOCO+, GLNet achieves significant performance gains of $$+2.38\%$$ , $$+2.78\%$$ , and $$+2.50\%$$ on the three splits compared to SADLR.
MKB: Multi-Kernel Bures Metric for Nighttime Aerial Tracking

In recent years, many advanced visual object tracking algorithms have achieved significant performance improvements in daytime scenes. However, when these algorithms are applied at night on unmanned aerial vehicles, they often exhibit poor tracking accuracy due to the domain shift. Therefore, designing an effective and stable domain adaptation tracking framework is in great demand. Besides, as the complexity of the tracking task scene and a large number of samples, it is difficult to accurately characterize the feature differences. Moreover, the method should avoid excessive manual parameters. To solve above three challenges, this study proposes a metric-based domain alignment framework for nighttime object tracking of unmanned aerial vehicles. It achieves transfer learning from daytime to nighttime by introducing a multi-kernel Bures metric (MKB). MKB quantifies the distribution distance by calculating the difference between two domain covariance operators in the latent space. MKB also uses a linear combination of a series of kernel functions to accommodate complex samples of different scenes and categories, enhancing the ability of the metric to represent various sequences in the latent space. Besides, we introduce an alternating update mechanism to optimize the weights of kernels avoiding manual parameters. Experimental results demonstrate that the proposed method has significant advantages in improving tracking accuracy and robustness at nighttime.
MetaSelection: A Learnable Masked AutoEncoder for Multimodal Sentiment Feature Selection

Multimodal learning has demonstrated a great advantage in sentimental analysis tasks due to the richer information from different modalities, especially the complementary information. However, our study shows that multimodal data not only provides useful complementary information, but also contains some information that is irrelevant to or conflicts with the task of sentiment prediction. It can degrade the training effectiveness of multimodal models. To tackle this problem, we propose a Learnable Masked AutoEncoder (LMAE) to eliminate the irrelevant or conflicting features of each modality by a learned mask. Afterward, the selected features from modalities are fused by a cross-modal attention. Experiments on samples with conflicting information across modalities and two benchmark datasets, CMU-MOSI and CMU-MOSEI, demonstrate the superiority of our proposal over seven state-of-the-art methods.