Reviewerly/exHarmony huggingface dataset:
A Comprehensive Review on Content-Aware Image Retargeting: From Classical to State-of-the-art Methods

Content-Aware Image Retargeting (CAIR) techniques are important in multimedia applications for displaying images on various display devices. This paper first provides a general classification of CAIR methods and then discusses them in more detail. In addition, the existing benchmark datasets are introduced, and the performance of CAIR approaches on the images of these datasets is qualitatively evaluated. Furthermore, Retargeted Image Quality Assessment (RIQA) metrics are introduced to quantitative evaluation of CAIR techniques. Finally, different RIQA metrics are compared to determine which can effectively evaluate the CAIR methods. In general, the primary purpose of this paper is to provide a comprehensive perspective of CAIR and RIQA techniques and to discuss the advantages and disadvantages of each method.
Predictive case-based feature importance and interaction

Feature importance and interaction are among the main issues in explainable artificial intelligence or interpretable machine learning. To measure feature importance and interaction, several methods, such as H-statistic and partial dependency, have been proposed. However, it is difficult to understand the practical implications of importance and interaction. In this paper, a new method for measuring feature importance and interaction is proposed. For the classification model, we observed correctly predicted cases in a predictive model and grouped them according to the characteristics of the cases. We derived a method for feature importance and interaction from group information. For the regression model, we grouped cases according to the change in the size of the prediction error. The proposed method supports the same rationale for feature importance and interaction. It also supports the decomposition of feature importance to feature power and feature interactions. To implement the proposed method, three visualization tools, including a feature interaction graph, are implemented. Through the proposed work, we can better understand the working mechanism of a predictive model.
Cross-domain heterogeneous residual network for single image super-resolution

Single image super-resolution is an ill-posed problem, whose purpose is to acquire a high-resolution image from its degraded observation. Existing deep learning-based methods are compromised on their performance and speed due to the heavy design (i.e., huge model size) of networks. In this paper, we propose a novel high-performance cross-domain heterogeneous residual network for super-resolved image reconstruction. Our network models heterogeneous residuals between different feature layers by hierarchical residual learning. In outer residual learning, dual-domain enhancement modules extract the frequency-domain information to reinforce the space-domain features of network mapping. In middle residual learning, wide-activated residual-in-residual dense blocks are constructed by concatenating the outputs from previous blocks as the inputs into all subsequent blocks for better parameter efficacy. In inner residual learning, wide-activated residual attention blocks are introduced to capture direction- and location-aware feature maps. The proposed method was evaluated on four benchmark datasets, indicating that it can construct the high-quality super-resolved images and achieve the state-of-the-art performance. Code and pre-trained models are available at https://github.com/zhangyongqin/HRN.
Exploration targeting of copper deposits using staged factor analysis, geochemical mineralization prospectivity index, and fractal model (Western Anti-Atlas, Morocco)

The present work aims to identify the geochemical signatures of copper deposits in the Igherm area (Anti-Atlas, Morocco) using stream sediment data. We applied factor analysis on the data normalized by log transformation. This approach identified three geochemical data associations (F1, F2, and F3) which were then used to calculate the values of the geochemical prospectivity index (GPMI). The results indicated that the large GMPI Cu values correlate strongly with the surface indices. A concentration-surface (C-A) fractal classification was applied to separate the geochemical anomalies from the regional background. To recognize and geo-position the significant geochemical signatures of the deposit, we applied the prediction zone (P-A) plot. A geographic information system (GIS) was used to define the spatial distribution of the anomalies at the sub-watershed level. The results represent the first approach to evaluating potential mineral resources at the scale of the Anti-Atlas: 90% of the anomalous sub-catchments mapped are located along the Precambrian-Cambrian transition (Base Series), 67% are traversed by major tectonic faults, and 40% contain copper showings and/or deposits. The remaining 60% are devoid of any visible surface copper occurrences. They constitute promising areas for future exploration of Cu deposits hidden under the Cambrian cover.
A Meta-Analysis of Tangible Learning Studies from the TEI Conference

Tangible learning has received increasing attention. However, in the recent decade, it has no comprehensive overview. This study aimed to fill the gap and reviewed 92 publications from all the TEI conference proceedings (2007–2021). We analysed previous studies’ characteristics (e.g., study purpose and interactive modalities) and elaborated on three common topics: collaborative tangible learning, tangibles’ impacts on learning, and comparisons between tangibles and other interfaces. Three key findings were: (1) Tangibles impacted learning because it could scaffold learning, change learning behaviour, and improve learning emotion; (2) We should see the effectiveness of tangibles with rational and critical minds. (3) Some studies emphasised too much on the interaction of tangibles and ignored their metaphor meanings. For future work, we suggest avoiding an intensive cluster on collaboration and children and consider other valuable areas, e.g., tangibles for teachers, tangibles’ social and emotional impacts on students, tangible interaction’s meaning and metaphor.
FADOHS: Framework for Detection and Integration of Unstructured Data of Hate Speech on Facebook Using Sentiment and Emotion Analysis

Hate speech is a form of expression that assaults a person or a community based on race, origin, religion, sexual orientation, or other attributes.Although it can be expressed in multiple ways, both online and offline, the increasing popularity of social media has exponentially increased both its use and severity.Therefore, the aim of this research is to locate and analyze the unstructured data of selected social media posts that intend to spread hate in the comment sections.To address this issue, we propose a novel framework called FADOHS, which combines data analysis and natural language processing strategies, to sensitize all social media providers to the pervasiveness of hate on social media.Specifically, we use sentiment and emotion analysis algorithms to analyze recent posts and comments on these pages.Posts suspected of containing dehumanizing words will be processed before fed to the clustering algorithm for further evaluation.According to the experimental results, the proposed FADOHS framework is able to surpass the state-of-the-art approach in terms of precision, recall, and F1 scores by approximately 10%.
Sememe knowledge and auxiliary information enhanced approach for sarcasm detection

Sarcasm expression is a pervasive literary technique in which people intentionally express the opposite of what is implied. Accurate detection of sarcasm in a text can facilitate the understanding of speakers' true intentions and promote other natural language processing tasks, especially sentiment analysis tasks. Since sarcasm is a kind of implicit sentiment expression and speakers deliberately confuse the audience, it is challenging to detect sarcasm only by text. Existing approaches based on machine learning and deep learning achieved unsatisfactory performance when handling sarcasm text with complex expression or needing specific background knowledge to understand. Especially, due to the characteristics of the Chinese language itself, sarcasm detection in Chinese is more difficult. To alleviate this dilemma on Chinese sarcasm detection, we propose a sememe and auxiliary enhanced attention neural model, SAAG. At the word level, we introduce sememe knowledge to enhance the representation learning of Chinese words. Sememe is the minimum unit of meaning, which is a fine-grained portrayal of a word. At the sentence level, we leverage some auxiliary information, such as the news title, to learning the representation of the context and background of sarcasm expression. Then, we construct the representation of text expression progressively and dynamically. The evaluation on a sarcasm dateset, consisting of comments on news text, reveals that our proposed approach is effective and outperforms the state-of-the-art models.
VR and AR virtual welding for psychomotor skills: a systematic review

Virtual reality (VR) and augmented reality (AR) continue to play an important role in vocational training in the current pandemic and Industrial Revolution 4.0 era. Welding is one of the highly demanded vocational skills for various manufacturing and construction industries. Students need to undergo many practical sessions to become skilful welders. However, conventional training is very costly in terms of material, time, and infrastructure. Hence, we explore the intervention of VR and AR in welding training, which includes the research purposes, VR and AR technologies, and welding concepts and activities. We performed a comprehensive search of articles from the year 2000 to 2021. After filtering through inclusion criteria and full-text assessment, a total of 42 articles were coded and evaluated. While there has been growth in VR and AR welding training research, there is little discussion in their effectiveness for supporting distance learning, and most studies targeted entry-level students. Our main contributions are classifying primary functions in the virtual welding workshops and their adaptation to the psychomotor domain. We hope these results can empower the research community to develop and improve the VR and AR system and evaluation instruments to support vocational training, especially during this pandemic.
Interaction-aware Drug Package Recommendation via Policy Gradient

Recent years have witnessed the rapid accumulation of massive electronic medical records, which highly support intelligent medical services such as drug recommendation. However, although there are multiple interaction types between drugs, e.g., synergism and antagonism, which can influence the effect of a drug package significantly, prior arts generally neglect the interaction between drugs or consider only a single type of interaction. Moreover, most existing studies generally formulate the problem of package recommendation as getting a personalized scoring function for users, despite the limits of discriminative models to achieve satisfactory performance in practical applications. To this end, in this article, we propose a novel end-to-end Drug Package Generation (DPG) framework, which develops a new generative model for drug package recommendation that considers the interaction effects between drugs that are affected by patient conditions. Specifically, we propose to formulate the drug package generation as a sequence generation process. Along this line, we first initialize the drug interaction graph based on medical records and domain knowledge. Then, we design a novel message-passing neural network to capture the drug interaction, as well as a drug package generator based on a recurrent neural network. In detail, a mask layer is utilized to capture the impact of patient condition, and the deep reinforcement learning technique is leveraged to reduce the dependence on the drug order. Finally, extensive experiments on a real-world dataset from a first-rate hospital demonstrate the effectiveness of our DPG framework compared with several competitive baseline methods.
Continual Learning Objective for Analyzing Complex Knowledge Representations

Human beings tend to incrementally learn from the rapidly changing environment without comprising or forgetting the already learned representations. Although deep learning also has the potential to mimic such human behaviors to some extent, it suffers from catastrophic forgetting due to which its performance on already learned tasks drastically decreases while learning about newer knowledge. Many researchers have proposed promising solutions to eliminate such catastrophic forgetting during the knowledge distillation process. However, to our best knowledge, there is no literature available to date that exploits the complex relationships between these solutions and utilizes them for the effective learning that spans over multiple datasets and even multiple domains. In this paper, we propose a continual learning objective that encompasses mutual distillation loss to understand such complex relationships and allows deep learning models to effectively retain the prior knowledge while adapting to the new classes, new datasets, and even new applications. The proposed objective was rigorously tested on nine publicly available, multi-vendor, and multimodal datasets that span over three applications, and it achieved the top-1 accuracy of 0.9863% and an F1-score of 0.9930.
Meta-Learning for Multi-Label Few-Shot Classification

Even with the luxury of having abundant data, multi-label classification is widely known to be a challenging task to address. This work targets the problem of multi-label meta-learning, where a model learns to predict multiple labels within a query (e.g., an image) by just observing a few supporting examples. In doing so, we first propose a benchmark for Few-Shot Learning (FSL) with multiple labels per sample. Next, we discuss and extend several solutions specifically designed to address the conventional and single-label FSL, to work in the multi-label regime. Lastly, we introduce a neural module to estimate the label count of a given sample by exploiting the relational inference. We will show empirically the benefit of the label count module, the label propagation algorithm, and the extensions of conventional FSL methods on three challenging datasets, namely MS-COCO, iMaterialist, and Open MIC. Overall, our thorough experiments suggest that the proposed label-propagation algorithm in conjunction with the neural label count module (NLC) shall be considered as the method of choice.
UAV Image Stitching Based on Optimal Seam and Half-Projective Warp

This paper introduces an Unmanned Aerial Vehicle (UAV) image stitching method, based on the optimal seam algorithm and half-projective warp, that can effectively retain the original information of the image and obtain the ideal stitching effect. The existing seam stitching algorithms can eliminate the ghosting and blurring problems on the stitched images, but the deformation and angle distortion caused by image registration will remain in the stitching results. To overcome this situation, we propose a stitching strategy based on optimal seam and half-projective warp. Firstly, we define a new difference matrix in the overlapping region of the aligned image, which includes the color, structural and line difference information. Then, we constrain the search range of the seam by the minimum energy, and propose a seam search algorithm based on the global minimum energy to obtain the seam. Finally, combined with the seam position and half-projective warp, the shape of the stitched image is rectified to keep more regions in their original shape. The experimental results of several groups of UAV images show that our method has a superior stitching effect.
VirtualCube: An Immersive 3D Video Communication System

The VirtualCube system is a 3D video conference system that attempts to overcome some limitations of conventional technologies. The key ingredient is VirtualCube, an abstract representation of a real-world cubicle instrumented with RGBD cameras for capturing the user's 3D geometry and texture. We design VirtualCube so that the task of data capturing is standardized and significantly simplified, and everything can be built using off-the-shelf hardware. We use VirtualCubes as the basic building blocks of a virtual conferencing environment, and we provide each VirtualCube user with a surrounding display showing life-size videos of remote participants. To achieve real-time rendering of remote participants, we develop the V-Cube View algorithm, which uses multi-view stereo for more accurate depth estimation and Lumi-Net rendering for better rendering quality. The VirtualCube system correctly preserves the mutual eye gaze between participants, allowing them to establish eye contact and be aware of who is visually paying attention to them. The system also allows a participant to have side discussions with remote participants as if they were in the same room. Finally, the system sheds lights on how to support the shared space of work items (e.g., documents and applications) and track participants' visual attention to work items.
DeU-Net 2.0: Enhanced deformable U-Net for 3D cardiac cine MRI segmentation

Automatic segmentation of cardiac magnetic resonance imaging (MRI) facilitates efficient and accurate volume measurement in clinical applications. However, due to anisotropic resolution, ambiguous borders and complicated shapes, existing methods suffer from the degradation of accuracy and robustness in cardiac MRI segmentation. In this paper, we propose an enhanced Deformable U-Net (DeU-Net) for 3D cardiac cine MRI segmentation, composed of three modules, namely Temporal Deformable Aggregation Module (TDAM), Enhanced Deformable Attention Network (EDAN), and Probabilistic Noise Correction Module (PNCM). TDAM first takes consecutive cardiac MR slices (including a target slice and its neighboring reference slices) as input, and extracts spatio-temporal information by an offset prediction network to generate fused features of the target slice. Then the fused features are also fed into EDAN that exploits several flexible deformable convolutional layers and generates clear borders of every segmentation map. A Multi-Scale Attention Module (MSAM) in EDAN is proposed to capture long range dependencies between features of different scales. Meanwhile, PNCM treats the fused features as a distribution to quantify uncertainty. Experimental results show that our DeU-Net achieves the state-of-the-art performance in terms of the commonly used evaluation metrics on the Extended ACDC dataset and competitive performance on other two datasets, validating the robustness and generalization of DeU-Net.
Multi-view unsupervised feature selection with tensor low-rank minimization

To describe objects more comprehensively and accurately, multi-view learning has attracted considerable attention. Recently, graph embedding based multi-view feature selection methods have been proposed and shown efficient in many real applications. The existing methods generally construct one common graph matrix to exploit the local structure of multi-view data via the linear weight fusion or learning one common graph matrix across all views. However, since all views share the identical graph structure, this emphasizes the consistency too much, resulting in restricting the diversity among different views. In this paper, a tensor low-rank constrained graph embedding method is proposed for multi-view unsupervised feature selection. To embody the view-specific information of each view, our model constructs the graph structure for each corresponding view, respectively. To capture the consistency across views, a tensor low-rank regularization constraint is imposed on the tensor data formed by these graph matrices. An efficient optimization algorithm with theoretical convergence guarantee is designed to solve the proposed method. Extensive experimental results validate that the proposed method outperforms some state-of-the-art methods. The code of our model can be found athttps://www.researchgate.net/publication/353902948_demoTLR.
Ship weather routing optimization based on improved fractional order particle swarm optimization

Based on the original FOPSO (fractional order particle swarm optimization) algorithm, this paper improves new coefficients of the fractional order velocity update formula to avoid falling into local optimization and premature problem. The coefficients change adaptively with the swarm evolution state to make the fractional order velocity occur some disturbances to jump out of the local optimal solution and the search space more comprehensively. In addition, by comparing with other PSO (particle swarm optimization) algorithms for solving typical benchmark functions, the superiority of the improved FOPSO algorithm in solving multi-dimensional nonlinear problems is confirmed. Then, taking a VLCC oil tanker as a research example, it is analyzed that under the objective function of minimum fuel oil consumption (FOC) and Estimated time of arrival (ETA), the maximum profit calculated by the proposed improved algorithm is 9.83 $/nm and 4.17 $/nm higher than that of the actual navigation, respectively. Meanwhile, when the weather forecast data is updated or the ship deviates from the voyage, the improved FOPSO algorithm will update the optimized route according to the current ship's position and sea conditions. Consequently, the improved FOPSO algorithm is quite suitable to solve the ship weather routing optimization problem with constraints.
Zero-shot Hashing via Asymmetric Ratio Similarity Matrix

Zero-shot hashing targets to learn the hash codes of images in unseen classes based on the limited training data provided by seen classes. In zero-shot hashing, transferring the supervised knowledge, such as attributes and semantic relations, from seen classes to unseen ones is a widely employed method, where the performance is always subject to the ability to capture these supervised knowledge (which is always difficult to obtain). Therefore, in this study, we propose a new methodology for zero-shot hashing via an asymmetric ratio similarity matrix (ASZH), which only needs to calculate the semantic similarity among seen classes for hash learning. Specifically, we use an asymmetric ratio matrix in the similarity calculation to further explore the influence of similarity, where the values of positive weights for similar samples are not equivalent to those of negative ones for dissimilar samples. Additionally, a theoretical analysis regarding the utilization of an asymmetric ratio matrix is provided in this study. The experiments on three large benchmark datasets indicate that the proposed method achieves excellent performance than several state-of-the-art hashing methods.
A Context-Independent Ontological Linked Data Alignment Approach to Instance Matching

Linking data by finding matching instances in different datasets requires considering many characteristics, such as structural heterogeneity, implicit knowledge, and URI (Uniform Resource Identifier)-oriented identification. The authors propose a context-independent approach to align Linked data through an alignment process based on the ontological model’s components and considering data’s multidimensionality. The researchers experimented with the proposed approach against two methods for aligning linked data in two datasets and evaluated precision, recall, and f-measure metrics. The authors also conducted a case study in a real scenario considering a Brazilian publication dataset on computers and education. This study’s results indicate that the proposed approach overcomes the other methods (regarding the precision, recall, and f-measure metrics), requiring less work when changing the dataset domain. This work’s main contributions include enabling real datasets to be semi-automatically linked, presenting an approach capable of calculating resource similarity.