Reviewerly/exHarmony huggingface dataset:
Accuracy rate of relevance vector machine with modified algorithm: A meta-analysis

This research aims to analyze the accuracy of the Relevance Vector Machine (RVM) method, including algorithm modification and unmodified algorithms or other methods in the field of prediction. The data is collected from indexing databases such as Scopus, Sciencedirect, and Google Scholar. The criteria set are (1) articles published in 2010-2021, (2) search keywords "prediction, forecasting, relevance vector machine, RVM"; (3) existence coefficient correlation (R) value, accuracy rate, and the amount of data predicted (N). In addition, the data is analyzed using JASP software based on effect size (ES) and summary effect (SE) values. The data analysis showed that for the unmodified RVM method case, the accuracy rate of up to 30 data that meets the standard is 87% (range 0.80-0.93), using the Random Effects (RE) model, and if the RVM is modified, then the average accuracy rate is 93% (range 0.93-0.96). Finally, modifying the algorithm of the RVM method will have a great impact on the accuracy level in the prediction process.
User preferences for video conferencing using the analytical hierarchical process (AHP)

The spread of the Covid-19 virus has changed the social order and led to the emergence of a new era. One of the new habits that emerged is the use of video conferencing for meetings and learning. There are many alternatives to video conferencing that can be found today. This study aims to determine user preferences for video conferencing with criteria based on web equal 4.0. the method used is the Analytical Hierarchical Process (AHP) method with the help of super decision software, which is free and easy to use. Based on the results of this study, it was found that the highest criterion preference was for information criteria followed by interaction and usability. Based on criteria, Google meet is preferred by users, followed by BBB and Zoom. Based on the sensitivity analysis results, it can be concluded that the value of alternative preferences for criteria will not change, regardless of the value of the criteria.
Implementation of support vector machine (SVM) based on particle swarm optimization (PSO) with synthetic minority over-sampling technique (SMOTE) on tweet data

In the current period, almost all individuals utilize social media to discover information. One of them is Twitter. Many details can be obtained from Twitter about technological advances, such as the launch of the Livin’ by Mandiri application by Bank Mandiri. The public submitted various positive, negative, and neutral comments, especially Bank Mandiri customers, regarding the application by tweeting it on Twitter. In this study, a classification analysis was carried out on these comments using two methods, namely the Support Vector Machine (SVM) and the SVM-Particle Swarm Optimization (PSO), to get the best performance and classification model. PSO-based SVM classifier is able to optimize SVM parameters to improve classification accuracy. The dataset used consists of 4,131 positive comments, 746 negative comments, and 5,746 neutral comments. Because there is an unbalanced amount of data, a data balancing process is carried out using the Synthetic Minority Oversampling Technique (SMOTE). However, the data analyzed are only positive and negative comments to see the tendency of people to have an opinion about the Livin’ by Mandiri application. Compared to SVM, SVM-PSO provides the best classification performance with an accuracy of 98.77%, the sensitivity of 99.64%, specificity of 93.57%, the precision of 98.93%, F1 score of 99.28%, and AUC of 0.966. Based on the AUC value, the model is included in the excellent classification category, meaning that the model is good in classification accuracy and predicts positive and negative comments.
Analysis of problems on zoom cloud meetings application based on end user reviews using text mining and fishbone diagram

In the era of the industrial revolution 4.0 as it is today, there has been a rapid technological development that has penetrated into various fields, especially in the telecommunications sector. One of the advances in telecommunications technology is a technology that can support audio and video communication simultaneously and in real-time, namely video conferencing. One of the companies engaged in video conference-based telecommunications services is Zoom Technologies, Inc. which launched the Zoom cloud meetings application which can be downloaded via the Google Play Store. However, until December 2020 the Zoom Cloud Meetings application only received a rating of 3.6 stars out of a total rating of 5.0. Therefore, there is a need for research related to Zoom Cloud Meetings using user reviews from the Google Play Store. The reviews analyzed in the study amounted to 75,454 which had been classified into positive and negative sentiment classes using the Naïve Bayes Classifier algorithm and association rules were carried out. Classification with Naïve Bayes Classifier with training data of 85% and test data of 15% produces an optimum accuracy of 86.67%. From the results of the review analysis, eight key words were found, namely, “meeting”, “video”, “audio”, “class”, “work”, “update”, “time” and “connect”. Some of the causes of problems include connecting to meetings for a long time, complaints about the quality of sound, video and limited time for free users, and problems related to applications that cannot be run.
State of the Art

This chapter provides a review of the relevant literature and research. A classification of the Web is derived, which on the one hand highlights the structure of the Web and the distribution of content and, on the other hand depicts relevant aspects of Web search and navigation.
Video Hashing with Tensor Robust PCA and Histogram of Optical Flow for Copy Detection

Abstract This paper proposes a novel video hashing with tensor robust Principal Component Analysis (PCA) and Histogram of Optical Flow (HOF) for copy detection. In the proposed hashing, a video is divided into some video groups. For each video group, a low-rank secondary frame is constructed from the low-rank component decomposed by applying tensor robust PCA to the video group. Since the low-rank component can well indicate spatial-temporal intrinsic structure of the video group and it is slightly disturbed by digital operations, feature extraction from the low-rank secondary frames is discriminative and stable. Next, spatial features and temporal features are extracted from low-rank secondary frames by Charlier moments and HOF, respectively. Since the Charlier moments are robust to geometric transform and they can efficiently distinguish video frames with different contents, the use of Charlier moments can make robust and discriminative spatial features. As the HOF can measure the distribution of motion information between frames, the temporal features formed by HOFs can provide good discrimination. Hash is ultimately determined by quantizing the spatial and temporal features and concatenating the quantized results. Numerous experiments on open video datasets indicate that the proposed hashing is superior to some hashing baseline schemes in terms of classification and copy detection.
SFNet: Saliency fast Fourier convolutional Network for medical image segmentation

Due to the limitation of local characteristics of convolution operations, the encoder of U-Net cannot effectively capture global context information; furthermore, the skip connections of U-Net fail to capture salient features in image segmentation tasks. Therefore, we put forward a Saliency fast Fourier convolutional Network (SFNet) for medical image segmentation. To begin with, we propose a SCAU attention module, which can highlight both spatial and channel attention for capturing not only global but local information, paying more attention to the whole target regions of an image, and creating strong information association among samples to extract the characteristics of the overall dataset. Subsequently, instead of employing the convolution to set the encoder and decoder, we introduce a Fourier convolution module, FFconv, which owns the non-local receptive field to fulfil cross-scale fusion in the convolution unit properly. Experimental results show that, on BUSI and Kvasir-SEG datasets, the mIOU and F1-score of our network reach 75.08% and 84.75%, respectively; our network greatly promote medical image segmentation performance.
Reimagining 3D Visual Grounding: Instance Segmentation and Transformers for Fragmented Point Cloud Scenarios

This work introduces a pioneering, engineerable approach to 3D visual localization(3DVG). Current challenges for 2D Visual Grounding (2DVG) and 3DVG are summarized: Absence of Depth Information in 2DVG, Memory and Computational Demands of Global Point Clouds, Limitations in Dynamic Scenarios, and Limited Understanding of Spatial Localization Reference Frames. Our solution proposes a Re_3DVG method for fragmented point cloud scenarios. Utilizing instance segmentation and transformer models, our approach offers a potent mechanism for establishing robust correspondences between text queries and object instances within the shared visible range. The introduction of a FragCloud3DRef dataset, grounded in ScanNet and supplemented with RGB data, object segmentation, and textual descriptions, fortifies the effectiveness of our proposed model. Experimental outcomes display that our model excels beyond conventional 3DVG and 2DVG models, establishing a formidable benchmark for future research within this discipline. The code source and dataset are open at https://github.com/zehantan6970/Reimagining_3DVG.
Cross-modal Image-Recipe Retrieval via Multimodal Fusion

Cross-modal image-recipe retrieval aims to capture the correlation between food images and recipes. While existing methods have demonstrated good performance on retrieval tasks, they often overlook two crucial aspects: (1) the capture of fine-grained recipe information and (2) the consideration of correlations between embeddings from different modalities. We introduce the Multimodal Fusion Retrieval Framework (MFRF) to address these issues. The proposed framework utilizes a deep learning-based encoder to process recipe and image data effectively, incorporates a fusion network to learn cross-modal semantic alignment, and ultimately achieves image-recipe retrieval. MFRF comprises three integral modules. The recipe preprocessing module utilizes various levels of Transformer to extract essential features such as the title and ingredients from the recipe. Additionally, it employs LSTM based on BERT to establish contextual relationships and dependencies among sentences in the recipe instructions. The multimodal fusion module incorporates visual-linguistic contrastive losses to align the representations of both images and recipes. Moreover, it leverages cross-modal attention mechanisms to facilitate effective interaction between the two modalities. Lastly, the cross-modal retrieval module employs a triple loss function to enable cross-modal retrieval of image-recipe pairs. Experimental evaluations conducted on the widely-used Recipe1M benchmark dataset demonstrate the effectiveness of the proposed MFRF, achieving substantial performance improvements on both the 1k and 10k test sets. Specifically, the results indicate an increase of +9.9% (64.8 R@1) and +8.4% (33.7 R@1) respectively.
Guided Spatio-Temporal Learning Method for 4K Video Super-Resolution

4K Video Super-Resolution (VSR) presents a challenging task in video processing, as most existing VSR models have high computational complexity, limiting their application to high-resolution videos, particularly for 4K resolution videos. To address this issue, we propose a novel Guided Spatio-Temporal Video Super-Resolution network (GST-VSR) designed to perform 4K VSR on a single GPU. The proposed method comprises two key components: the Spatio-Temporal Alignment Network (STAN) and the Super-resolution Reconstruction Network (SRN), which work together to enhance the quality of the output frames. The STAN is responsible for extracting highly relevant features in frames and aligning the reference frame with the neighboring frames at the feature level to maintain temporal consistency. The SRN fuses high-quality features into the final high-resolution frames. Unlike existing methods, our proposed approach does not require explicit optical flow estimation, making it more efficient and less computationally demanding. To facilitate the training and testing of the compared models, we have established a new dataset, Pixabay-Set, consisting of 145 videos suitable for the 4K VSR task. Experimental results on the test dataset show that the proposed method achieves competitive performance compared to state-of-the-art models. In summary, our proposed GST-VSR network provides an effective solution to the challenging task of 4K VSR.
Learning Surface-awareness Network for X-Ray Prohibited Item Detection

X-ray image security detection is a crucial method used to identify various types of prohibited items in luggage. However, the unique characteristics of X-ray imaging can result in the loss of intricate surface details, leading to subpar detection of prohibited items within X-ray images. In this paper, a Surface-aware Prohibited Item X-ray Detection Network (SPIXDet) is proposed to address this issue, which incorporates two key components: the Boundary Aggregation Module (BAM) and the Global Cross-Feature Downsampling layer (GCFD). The BAM module effectively mines image edge information while minimizing the number of parameters involved. Meanwhile, the GCFD module is introduced to mitigate chaotic interference caused by undifferentiated boundary boosting. The surface-aware capability of the model can be enhanced through the BAM and GCFD module. Furthermore, the Focal-SIoU loss function is introduced to increase positioning accuracy and optimize the model training process. To validate the effectiveness of our model, extensive experiments are conducted on the SIXray100 dataset, and the results demonstrate the advantages of SPIXDet compared to other X-ray prohibited item detection methods.
SASSM: Semantic Awareness and Self-Support Matching for Semi-Supervised Video Object Segmentation

Matching-based methods have becamed popular in semi-supervised video object segmentation (VOS), by maintaining a memory bank to predict object masks. However, these methods encounter challenges for fast motions and appearance changes, resulting in blurred predictions and missing boundaries. Then we introduce an innovative network that exploits the self-feature of the query frame to improve the masks prediction. We propose a semantic-aware branch (SAB) for precise semantic guidance during readout decoding and an enhanced feature memory matching module with a self-support matching (SSM) mechanism. Ablations demonstrate the strong collaboration between the semantic-aware branch and the self-support matching mechanism. Our approach achieves a favourable performance on popular datasets, demonstrating a acceptable accuracy and speed performance of 86.3 J&F and 26 FPS on DAVIS 2017 validation. Code will be available.
Self-supervised anomaly detection of medical images based on dual-module discrepancy

Medical images anomaly detection plays a very important role in modern health care, which helps to improve the quality and efficiency of medical services and promote the development of human health. Due to the high cost of annotation in anomaly images and the fact that most existing methods do not fully utilize information from unlabeled images. Therefore, we propose a new reconstruction network and loss function that can better utilize unlabelled and normal images for anomaly identification. The framework used in this paper consists of two modules, each consisting of three reconstruction networks with the same architecture but different inputs. One module is trained only on normal images and is called the normal module (NM). The other module is trained on both normal images and unlabeled images, and is called the unknown module (UM). Furthermore, the internal differences of the normal module and the differences between the two modules will be used as two powerful anomaly scores, and these two anomaly scores will be refined to indicate anomalies. Experiments on four medical datasets show the state-of-the-art performance by the proposed approach.
Joint Coordinate Regression and Association For Multi-Person Pose Estimation, A Pure Neural Network Approach

We introduce a novel one-stage end-to-end multi-person 2D pose estimation algorithm, known as Joint Coordinate Regression and Association (JCRA), that produces human pose joints and associations without requiring any post-processing. The proposed algorithm is fast, accurate, effective, and simple. The one-stage end-to-end network architecture significantly improves the inference speed of JCRA. Meanwhile, we devised a symmetric network structure for both the encoder and decoder, which ensures high accuracy in identifying keypoints. It follows an architecture that directly outputs part positions via a transformer network, resulting in a significant improvement in performance. Extensive experiments on the MS COCO and CrowdPose benchmarks demonstrate that JCRA outperforms state-of-the-art approaches in both accuracy and efficiency. Moreover, JCRA demonstrates 69.2 mAP and is 78% faster at inference acceleration than previous state-of-the-art bottom-up algorithms.
Adapting Hierarchical Transformer for Scene-Level Sketch-Based Image Retrieval

Sketch-based image retrieval (SBIR) is an essential application of sketches. Research on object-level SBIR is relatively mature, but the study of more complex scene-level SBIR is still in its early stages. In order to advance this research, we investigate previous works and identify two main shortcomings: (1) insufficient utilization of multi-scale features from sketches and images, and (2) lack of effective modules to eliminate the substantial domain gap between them. To address these issues, we propose SketchRetriever, a hierarchical Transformer-based scene-level SBIR model. In our model, the hierarchical Transformer and compressors are capable of efficiently capturing feature maps at various granularities and compressing them into corresponding feature vectors, and the modality-specific Adapters can project the feature embeddings of sketches and images into the same feature space, thereby closing the domain gap between them. We adopt the adapter-tuning strategy, which not only considerably reduces the number of tunable parameters but also effectively avoids overfitting. Extensive experiments demonstrate that SketchRetriever significantly outperforms state-of-the-art methods on two benchmark datasets with lower fine-tuning overhead.
Exploring User-oriented Social Recommendation System through Granting Users Control over a Social Group

The limitations of accuracy-focused recommendation systems in improving user experiences have become apparent since user preferences change over time. Despite efforts to solve this issue through the examination of social information (e.g., relational data pertaining to users), capturing temporal user preferences remains a challenge. This study proposes a novel interaction method for integrating temporal user preferences into a social recommendation system. Users can highlight their preferred users within their social interactions. Through this interaction, preferences-integrated social information is incorporated into recommendations. We conducted a user test to validate the value of our proposed interaction method and found that the proposed approach had a positive impact on users’ subjective responses. The implications showed practical and theoretical improvements in user-centered recommendations.
An Efficient CNN-based Prediction for Reversible Data Hiding

In the field of reversible data hiding (RDH), how to design an efficient image prediction method is an enduring research topic. In this paper, we propose a new CNN-based predictor consisting of an efficient image division strategy and a well-designed prediction network. The image division strategy optimizes the distribution of pixels belonging to different sets, which increases the amount of available adjacent pixels in the image prediction. In addition, with the utilization of the well-designed compensation module, the prediction network performs better and consumes less memory. The experiment results demonstrate that our approach achieves better prediction performance compared with the existing predictors. Furthermore, we have developed an RDH algorithm by combining the proposed CNN-based predictor with the location-based pixel value ordering (LPVO) embedding strategy. This RDH algorithm outperforms the state-of-the-art predictor-based RDH algorithm in embedding performance.
Learning Snippet-to-Motion Progression for Skeleton-based Human Motion Prediction

Existing Graph Convolutional Networks to achieve human motion prediction largely adopt a one-step scheme, which output the prediction straight from history input, failing to exploit human motion patterns. We observe that human motions have transitional patterns and can be split into snippets representative of each transition. Each snippet can be reconstructed from its starting and ending poses referred to as the transitional poses. We propose a snippet-to-motion multi-stage framework that breaks motion prediction into sub-tasks easier to accomplish. Each sub-task integrates three modules: transitional pose prediction, snippet reconstruction, and snippet-to-motion prediction. Specifically, we propose to first predict only the transitional poses. Then we use them to reconstruct the corresponding snippets, obtaining a close approximation to the true motion sequence. Finally we refine them to produce the final prediction output. To implement the network, we propose a novel unified graph modeling, which allows for direct and effective feature propagation compared to existing approaches which rely on separate space-time modeling. Extensive experiments on Human 3.6M, CMU Mocap and 3DPW datasets verify the effectiveness of our method which achieves state-of-the-art performance.
History-Detr: Optimize Query Initialization Strategy by Using Historical Information and Kinematics

Recent 3D object detectors leverage multi-frame data, including past and future data, to enhance performance. However, the method of temporal data fusion they employ has not fully tapped into its potential for improving performance. Existing works make use of multi-frame data which only fuse specific features according to ego-motion and cannot be directly applied to long sequences due to the huge computation and memory cost. We find that the present methods do not efficiently exploit history information including history predictions and object-motion. Building on our investigations, we present a novel hybrid query formulation comprised of the history queries and original queries. The history queries consist of inferred position and content queries obtained from the historical predictions and features, which take into account the motion of all objects in the current scene. What’s more, our method can be simply applied into other DETR-like models to boost performance without introducing huge computation and memory cost. As a result, our History-DETR results in a remarkable improvement(+1.1% NDS) under negligible inference time increase.