Reviewerly/exHarmony huggingface dataset:
The Establishment of a precise intelligent evaluation system for sports events: Diving

<h2>Abstract</h2> The introduction of action quality assessment technology in sports events to achieve precise intelligent evaluation can greatly enhance the objectivity and effectiveness of competition results. Taking diving as the specific application background, this study proposes a novel Multi-granularity Extraction Approach for Temporal-spatial features in judge scoring prediction (MEAT) under the conditions of action quality assessment. On the one hand, it uses dual-modal inflated 3D ConvNet to extract the temporal and spatial features of each modal diving video at the video granularity parallelly and to merge them to form a global feature. On the other hand, the human body pose is modeled, and the simulated athlete's three-dimensional splash state is taken as local characteristics at the object granularity. Finally, the global and local features are concatenated into the fully connected layer, and heuristic method inspired by competition rules using labeled distribution learning are employed to output the probability distribution of the average score of all referees. The maximum probability score is selected and multiplied by the difficulty coefficient to obtain the final diving score. Through comprehensive experiments, comparing the Spearman's rank correlation (SRC) evaluation results of existing methods on the UNIV-Dive dataset, this framework reflects the greater accuracy advantage and further lays the foundation for the actual implementation of the technology.
Matching recovery threshold for correlated random graphs

For two correlated graphs which are independently sub-sampled from a common Erdős–Rényi graph G(n,p), we wish to recover their latent vertex matching from the observation of these two graphs without labels. When p=n−α+o(1) for α∈(0,1], we establish a sharp information-theoretic threshold for whether it is possible to correctly match a positive fraction of vertices. Our result sharpens a constant factor in a recent work by Wu, Xu and Yu.
DTRN: Dual Transformer Residual Network for Remote Sensing Super-Resolution

The synergy of the transformer and the convolutional neural network (CNN) has been well regarded as a promising technique for single image super-resolution (SISR) based on low-quality satellite remote sensing images. In this work, a Dual Transformer Residual Network (DTRN) consisting of one transformer branch and one CNN-based residual branch is proposed. More specifically, the transformer branch is designed to capture the global relationships of feature maps by exploiting three pairs of token embedding blocks and convolutional transformer blocks (CTB). Furthermore, the residual branch employs several residual blocks (resblocks) to effectively learn hierarchical features through global feature fusion. Extensive experiments on a large-scale remote sensing dataset called OLI2MSI confirm the superior performance of the proposed DTRN as compared to the existing SISR methods.
HySpecNet-11k: a Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods

The development of learning-based hyperspectral image compression methods has recently attracted great attention in remote sensing. Such methods require a high number of hyperspectral images to be used during training to optimize all parameters and reach a high compression performance. However, existing hyperspectral datasets are not sufficient to train and evaluate learning-based compression methods, which hinders the research in this field. To address this problem, in this paper we present HySpecNet-11k that is a large-scale hyperspectral benchmark dataset made up of 11,483 nonoverlapping image patches. Each patch is a portion of 128 × 128 pixels with 224 spectral bands and a ground sample distance of 30 m. We exploit HySpecNet-11k to benchmark the current state of the art in learning-based hyperspectral image compression by focussing our attention on various 1D, 2D and 3D convolutional autoencoder architectures. Nevertheless, HySpecNet-11k can be used for any unsupervised learning task in the framework of hyperspectral image analysis. The dataset, our code and the pre-trained weights are publicly available at https://hyspecnet.rsim.berlin.
Explainable Machine Learning via Argumentation

This paper presents a general Explainable Machine Learning framework and methodology based on Argumentation (ArgEML). The flexible reasoning form of argumentation in the face of unknown and incomplete information together with the direct link of argumentation to justification and explanation enables the development of a natural form of explainable machine learning. In this form of learning the explanations are useful not only for supporting the final predictions but also play a significant role in the learning process itself. The paper defines the basic theoretical notions of ArgEML together with its main machine learning operators and method of application. It describes how such an argumentation-based approach can give a flexible way for learning that recognizes difficult cases (with respect to the current available training data) and separates these cases out not as definite predictive cases but as cases where it is more appropriate to explainably analyze the alternative predictions. Using the argumentation-based explanations we can partition the problem space into groups characterized by the basic argumentative tension between arguments for and against the alternatives. The paper presents a first evaluation of the approach by applying the ArgEML learning methodology both on artificial and on real-life datasets.
PCSS: Skull Stripping With Posture Correction From 3D Brain MRI for Diverse Imaging Environment

A subject’s head position in magnetic resonance imaging (MRI) scanners can vary significantly with the imaging environment and disease status. This variation is known to influence the accuracy of skull stripping (SS), a method to extract the brain region from the whole head image, which is an essential initial step to attain high performance in various neuroimaging applications. However, existing SS methods have failed to accommodate this wide range of variation. To achieve accurate, consistent, and fast SS, we introduce a novel two-stage methodology that we call posture correction skull stripping (PCSS): the first involves adjusting the subject’s head angle and position, and the second involves the actual SS to generate the brain mask. PCSS also incorporates various machine learning techniques, such as a weighted loss function, adversarial training from generative adversarial networks, and ensemble methods. Thorough evaluations conducted on five publicly accessible datasets show that the PCSS method outperforms current state-of-the-art techniques in SS performance, achieving an average increase of 1.38 points on the Dice score and demonstrating the contributions of each PCSS component technique.
Olio: A Semantic Search Interface for Data Repositories

Search and information retrieval systems are becoming more expressive in interpreting user queries beyond the traditional weighted bag-of-words model of document retrieval. For example, searching for a flight status or a game score returns a dynamically generated response along with supporting, pre-authored documents contextually relevant to the query. In this paper, we extend this hybrid search paradigm to data repositories that contain curated data sources and visualization content. We introduce a semantic search interface, Olio, that provides a hybrid set of results comprising both auto-generated visualization responses and pre-authored charts to blend analytical question-answering with content discovery search goals. We specifically explore three search scenarios - question-and-answering, exploratory search, and design search over data repositories. The interface also provides faceted search support for users to refine and filter the conventional best-first search results based on parameters such as author name, time, and chart type. A preliminary user evaluation of the system demonstrates that Olio's interface and the hybrid search paradigm collectively afford greater expressivity in how users discover insights and visualization content in data repositories.
Video2Action: Reducing Human Interactions in Action Annotation of App Tutorial Videos

Tutorial videos of mobile apps have become a popular and compelling way for users to learn unfamiliar app features. To make the video accessible to the users, video creators always need to annotate the actions in the video, including what actions are performed and where to tap. However, this process can be time-consuming and labor-intensive. In this paper, we introduce a lightweight approach Video2Action, to automatically generate the action scenes and predict the action locations from the video by using image-processing and deep-learning methods. The automated experiments demonstrate the good performance of Video2Action in acquiring actions from the videos, and a user study shows the usefulness of our generated action cues in assisting video creators with action annotation.
EU-Net: Automatic U-Net neural architecture search with differential evolutionary algorithm for medical image segmentation

Medical images are crucial in clinical practice, providing essential information for patient assessment and treatment planning. However, manual extraction of information from images is both time-consuming and prone to errors. The emergence of U-Net addresses this challenge by automating the segmentation of anatomical structures and pathological lesions in medical images, thereby significantly enhancing the accuracy of image interpretation and diagnosis. However, the performance of U-Net largely depends on its encoder–decoder structure, which requires researchers with knowledge of neural network architecture design and an in-depth understanding of medical images. In this paper, we propose an automatic U-Net Neural Architecture Search (NAS) algorithm using the differential evolutionary (DE) algorithm, named EU-Net, to segment critical information in medical images to assist physicians in diagnosis. Specifically, by presenting the variable-length strategy, the proposed EU-Net algorithm can sufficiently and automatically search for the neural network architecture without expertise. Moreover, the utilization of crossover, mutation, and selection strategies of DE takes account of the trade-off between exploration and exploitation in the search space. Finally, in the encoding and decoding phases of the proposed algorithm, different block-based and layer-based structures are introduced for architectural optimization. The proposed EU-Net algorithm is validated on two widely used medical datasets, i.e., CHAOS and BUSI, for image segmentation tasks. Extensive experimental results show that the proposed EU-Net algorithm outperforms the chosen peer competitors in both two datasets. In particular, compared to the original U-Net, our proposed method improves the metric mIou by at least 6%.
Web 3.0 and Sustainability: Challenges and Research Opportunities

Web 3.0 is the next generation of the web and is still in its early stages. Web 3.0 uses many technical components to create an innovative Internet ecosystem. It is characterized by features such as decentralization, interoperability, data ownership, and smart contracts. Web 3.0 aims to enhance user experiences, privacy, and data control while fostering an equitable digital future. Web 3.0 holds significant potential for accelerating progress towards SDGs. As Web 3.0 technologies gain attraction, concerns arise about their potential impact on sustainability, encompassing energy consumption, hardware waste, and carbon footprint. By examining the current state of research about Web 3.0 and sustainability, this opinion paper discusses key Web 3.0 sustainability projects, sustainability challenges, and future directions to build a more sustainable digital era.
Pose2Trajectory: Using transformers on body pose to predict tennis player’s trajectory

Tracking the trajectory of tennis players can help camera operators in production. Predicting future movement enables cameras to automatically track and predict a player's future trajectory without human intervention. It is also intellectually satisfying to predict future human movement in the context of complex physical tasks. Swift advancements in sports analytics and the wide availability of videos for tennis have inspired us to propose a novel method called Pose2Trajectory, which predicts a tennis player's future trajectory as a sequence derived from their body joints' data and ball position. Demonstrating impressive accuracy, our approach capitalizes on body joint information to provide a comprehensive understanding of the human body's geometry and motion, thereby enhancing the prediction of the player's trajectory. We use encoder–decoder Transformer architecture trained on the joints and trajectory information of the players with ball positions. The predicted sequence can provide information to help close-up cameras to keep tracking the tennis player, following centroid coordinates. We generate a high-quality dataset from multiple videos to assist tennis player movement prediction using object detection and human pose estimation methods. It contains bounding boxes and joint information for tennis players and ball positions in singles tennis games. Our method shows promising results in predicting the tennis player's movement trajectory with different sequence prediction lengths using the joints and trajectory information with the ball position.
Diffusion Augmentation for Sequential Recommendation

Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems. Besides, most users only interact with a few items, but existing SRS models often underperform these users. Such a problem, named the long-tail user problem, is still to be resolved. Data augmentation is a distinct way to alleviate these two problems, but they often need fabricated training strategies or are hindered by poor-quality generated interactions. To address these problems, we propose a Diffusion Augmentation for Sequential Recommendation (DiffuASR) for a higher quality generation. The augmented dataset by DiffuASR can be used to train the sequential recommendation models directly, free from complex training procedures. To make the best of the generation ability of the diffusion model, we first propose a diffusion-based pseudo sequence generation framework to fill the gap between image and sequence generation. Then, a sequential U-Net is designed to adapt the diffusion noise prediction model U-Net to the discrete sequence generation task. At last, we develop two guide strategies to assimilate the preference between generated and origin sequences. To validate the proposed DiffuASR, we conduct extensive experiments on three real-world datasets with three sequential recommendation models. The experimental results illustrate the effectiveness of DiffuASR. As far as we know, DiffuASR is one pioneer that introduce the diffusion model to the recommendation.The implementation code is available online.
Task-Difficulty-Aware Meta-Learning with Adaptive Update Strategies for User Cold-Start Recommendation

User cold-start recommendation is one of the most challenging problems that limit the effectiveness of recommender systems. Meta-learning-based methods are introduced to address this problem by learning initialization parameters for cold-start tasks. Recent studies attempt to enhance the initialization methods. They first represent each task by the cold-start user and interacted items. Then they distinguish tasks based on the task relevance to learn adaptive initialization. However, this manner is based on the assumption that user preferences can be reflected by the interacted items saliently, which is not always true in reality. In addition, we argue that previous approaches suffer from their adaptive framework (e.g., adaptive initialization), which reduces the adaptability in the process of transferring meta-knowledge to personalized RSs. In response to the issues, we propose a task-difficulty-aware meta-learning with adaptive update strategies (TDAS) for user cold-start recommendation. First, we design a task difficulty encoder, which can represent user preference salience, task relevance, and other task characteristics by modeling task difficulty information. Second, we adopt a novel framework with task-adaptive local update strategies by optimizing the initialization parameters with task-adaptive per-step and per-layer hyperparameters. Extensive experiments based on three real-world datasets demonstrate that our TDAS outperforms the state-of-the-art methods. The source code is available at https://github.com/XuHao-bit/TDAS.
Seq-HyGAN: Sequence Classification via Hypergraph Attention Network

Extracting meaningful features from sequences and devising effective similarity measures are vital for sequence data mining tasks, particularly sequence classification. While neural network models are commonly used to automatically learn sequence features, they are limited to capturing adjacent structural connection information and ignoring global, higher-order information between the sequences. To address these challenges, we propose a novel Hypergraph Attention Network model, namely Seq-HyGAN for sequence classification problems. To capture the complex structural similarity between sequence data, we create a novel hypergraph model by defining higher-order relations between subsequences extracted from sequences. Subsequently, we introduce a Sequence Hypergraph Attention Network that learns sequence features by considering the significance of subsequences and sequences to one another. Through extensive experiments, we demonstrate the effectiveness of our proposed Seq-HyGAN model in accurately classifying sequence data, outperforming several state-of-the-art methods by a significant margin.
Prompt Distillation for Efficient LLM-based Recommendation

Large language models (LLM) have manifested unparalleled modeling capability on various tasks, e.g., multi-step reasoning, but the input to these models is mostly limited to plain text, which could be very long and contain noisy information. Long text could take long time to process, and thus may not be efficient enough for recommender systems that require immediate response. In LLM-based recommendation models, user and item IDs are usually filled in a template (i.e., discrete prompt) to allow the models to understand a given task, but the models usually need extensive fine-tuning to bridge the user/item IDs and the template words and to unleash the power of LLM for recommendation. To address the problems, we propose to distill the discrete prompt for a specific task to a set of continuous prompt vectors so as to bridge IDs and words and to reduce the inference time. We also design a training strategy with an attempt to improve the efficiency of training these models. Experimental results on three real-world datasets demonstrate the effectiveness of our PrOmpt Distillation (POD) approach on both sequential recommendation and top-N recommendation tasks. Although the training efficiency can be significantly improved, the improvement of inference efficiency is limited. This finding may inspire researchers in the community to further improve the inference efficiency of LLM-based recommendation models.
Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation

Information Retrieval (IR) and Recommender Systems (RSs) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving these problems leads to a set of Pareto-optimal solutions, known as Pareto frontier, in which no objective can be further improved without hurting the others. In principle, all the points on the Pareto frontier are potential candidates to represent the best model selected with respect to the combination of two, or more, metrics. To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier in IR and RSs. In this paper, we propose a novel, post-hoc, theoretically-justified technique, named "Population Distance from Utopia" (PDU), to identify and select the one-best Pareto-optimal solution. PDU considers fine-grained utopia points, and measures how far each point is from its utopia point, allowing to select solutions tailored to user preferences, a novel feature we call "calibration". We compare PDU against state-of-the-art strategies through extensive experiments on tasks from both IR and RS, showing that PDU combined with calibration notably impacts the solution selection.
Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks

Abstraction is a key verification technique to improve scalability. However, its use for neural networks is so far extremely limited. Previous approaches for abstracting classification networks replace several neurons with one of them that is similar enough. We can classify the similarity as defined either syntactically (using quantities on the connections between neurons) or semantically (on the activation values of neurons for various inputs). Unfortunately, the previous approaches only achieve moderate reductions, when implemented at all. In this work, we provide a more flexible framework, where a neuron can be replaced with a linear combination of other neurons, improving the reduction. We apply this approach both on syntactic and semantic abstractions, and implement and evaluate them experimentally. Further, we introduce a refinement method for our abstractions, allowing for finding a better balance between reduction and precision.
Semantic clustering on common list-learning tasks: a systematic review of the state of the literature and recommendations for future directions

Introduction On some list-learning tasks, such as the California Verbal Learning Test (CVLT) or Hopkins Verbal Learning Test (HVLT), examinees have the opportunity to group words based on semantically related categories (i.e., semantic clustering). Semantic clustering (SC) is often considered the most efficient organizational strategy and adopting SC is presumed to improve learning and memory. In addition, SC is conceptualized as reflecting higher-order executive functioning skills. Although SC measures have intuitive appeal, to date, there are no comprehensive reviews of the SC literature base that summarize its psychometric utility. In this systematic review, we synthesize the literature to judge the validity of SC scores.