Reviewerly/exHarmony huggingface dataset:
Data-driven flexibility assessment for internet data center towards periodic batch workloads

Considering its unique operational and power consumption characteristics, internet data center (IDC) has been intensively investigated as a promising candidate to provide flexibility for electric power system. In this paper, a data-driven flexibility assessment scheme for IDC is proposed by investigating the temporal shifting capability of periodic batch workloads, which are the major flexibility source in the workload scheduling and execution process. We develop a four-step assessment procedure by identifying the periodic jobs, extracting key operational patterns, mapping the power consumption with workload execution, and quantifying the flexibility associated with power system operation, all of which are established in a data-driven manner. In addition, we adopt real-world production workload trace to verify and demonstrate the effectiveness of the proposed flexibility assessment scheme.
Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted Model Aggregation

Federated learning enables distributed model training over various computing nodes, e.g., mobile devices, where instead of sharing raw user data, computing nodes can solely commit model updates without compromising data privacy. The quality of federated learning relies on the model updates contributed by computing nodes training with their local data. However, with various factors (e.g., training data size, mislabeled data samples, skewed data distributions), the model update qualities of computing nodes can vary dramatically, while inclusively aggregating low-quality model updates can deteriorate the global model quality. To achieve efficient federated learning, in this paper, we propose a novel framework named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FAIR</i> , i.e., <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> ederated le <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> rning with qual <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> ty awa <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> eness. Particularly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FAIR</i> integrates three major components: 1) learning quality estimation: we adopt the model aggregation weight (learned in the third component) to reversely quantify the individual learning quality of nodes in a privacy-preserving manner, and leverage the historical learning records to infer the next-round learning quality; 2) quality-aware incentive mechanism: within the recruiting budget, we model a reverse auction problem to stimulate the participation of high-quality and low-cost computing nodes, and the method is proved to be truthful, individually rational, and computationally efficient; and 3) auto-weighted model aggregation: based on the gradient descent method, we devise an auto-weighted model aggregation algorithm to automatically learn the optimal aggregation weights to further enhance the global model quality. Based on real-world datasets and learning tasks, extensive experiments are conducted to demonstrate the efficacy of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FAIR</i> .
ClusterFL: A Clustering-based Federated Learning System for Human Activity Recognition

Federated Learning (FL) has recently received significant interest, thanks to its capability of protecting data privacy. However, existing FL paradigms yield unsatisfactory performance for a wide class of human activity recognition (HAR) applications, since they are oblivious to the intrinsic relationship between data of different users. We propose ClusterFL, a clustering-based federated learning system that can provide high model accuracy and low communication overhead for HAR applications. ClusterFL features a novel clustered multi-task federated learning framework that minimizes the empirical training loss of multiple learned models while automatically capturing the intrinsic clustering relationship among the nodes. We theoretically prove the convergence of proposed FL framework for non-convex and strongly convex models and provide the guidance on selection of hyper-parameters for achieving such convergence. Based on the learned cluster relationship, ClusterFL can efficiently drop the nodes that converge slower or have little correlations with others in each cluster, significantly speeding up the convergence while maintaining the accuracy performance. We evaluate the performance of ClusterFL on an NVIDIA edge testbed using four new HAR datasets collected from 145 users. The results show that ClusterFL outperforms several state-of-the-art FL paradigms in terms of overall accuracy and can save more than 50% communication overhead.
Promoting students' artwork appreciation: An experiential learning‐based virtual reality approach

Abstract In artwork appreciation activities, visiting physical exhibitions in art museums is often limited by time, distance and cost. Thus, conventional teaching is mostly conducted through textbooks, presentation slides or videos. However, by presenting artwork appreciation through a lecture, it is difficult for students to feel the beauty of artworks, and they can hardly develop accurate appreciation knowledge and critical thinking. To tackle this problem, this study proposed an experiential learning‐based spherical video‐based virtual reality (EL‐SVVR) learning approach based on the experiential learning theory to guide students to browse world‐famous artworks and experience the features of artwork creation. This study developed an EL‐SVVR learning system and adopted a quasi‐experimental design to determine whether the EL‐SVVR learning approach improved students' performance in artwork appreciation in comparison with the conventional teaching approach. A total of 45 university students from two classes who participated in an elective art course were recruited in the study. One class was assigned to be the experimental group and adopted the EL‐SVVR learning approach, while the other class was assigned to be the control group and adopted the conventional technology‐supported learning (CTL) approach. The results showed that in comparison with the CTL approach, the EL‐SVVR learning approach enhanced students' learning achievement, learning motivation, self‐efficacy, critical thinking and performance in digital painting creation during artwork appreciation. Practitioner notes What is already known about this topic Spherical video‐based virtual reality (SVVR) situates students in a simulated environment that enables them to have deep experience regarding the learning contexts. SVVR has great potential in promoting students' learning performance by providing them situational contexts. What this paper adds An SVVR‐based experiential learning approach is proposed to facilitate students' artwork creation. In addition to promoting students' learning outcomes, the EL‐SVVR learning approach could better facilitate students' learning achievement, learning motivation, critical thinking, self‐efficacy, and performance in painting creation. Implications for practice and/or policy SVVR is a potential technology for implementing experiential learning activities; in particular, in improving students' creative thinking for artwork creation. It is potentially worth trying to apply the SVVR technology to other courses related to artwork creation to promote learners' learning performances and perceptions. It is worth promoting the SVVR approach in school settings since it is a low‐tech and low‐cost solution with potentially positive impacts on students' learning outcomes.
Object Detection in Medical Images Based on Hierarchical Transformer and Mask Mechanism

The object detection task in the medical field is challenging in terms of classification and regression. Due to its crucial applications in computer-aided diagnosis and computer-aided detection techniques, an increasing number of researchers are transferring the object detection techniques to the medical field. However, in existing work on object detection, researchers do not consider the low resolution of medical images, the high amount of noise, and the small size of the objects to be detected. Based on this, this paper proposes a new algorithmic model called the MS Transformer, where a self-supervised learning approach is used to perform a random mask on the input image to reconstruct the input features, learn a richer feature vector, and filter out excessive noise. To focus the model on the small objects that are being detected, the hierarchical transformer model is introduced in this paper, and a sliding window with a local self-attention mechanism is used to give a higher attention score to the small objects to be detected. Finally, a single-stage object detection framework is used to predict the sequence of sets at the location of the bounding box and the class of objects to be detected. On the DeepLesion and BCDD benchmark dataset, the model proposed in this paper achieves better performance improvement on multiple evaluation metric categories.
DMCGNet: A Novel Network for Medical Image Segmentation With Dense Self-Mimic and Channel Grouping Mechanism

Automatic Medical Image Segmentation (MIS) can assist doctors by reducing labor and providing a unified standard. Nowadays, approaches based on Deep Learning have become mainstream for MIS because of their ability of automatic feature extraction. However, due to the plain network design and targets variety in medical images, the semantic features can hardly be extracted adequately. In this work, we propose a novel Dense Self-Mimic and Channel Grouping based Network (DMCGNet) for MIS for better feature extraction. Specifically, we introduce a Pyramid Target-aware Dense Self Mimic (PTDSM) module, which is capable of exploring deeper and better feature representation with no parameter increase. Then, to utilize features efficiently, an effective Channel Split based Feature Fusion Module (CSFFM) is proposed for feature reuse, which strengthens the adaptation of multi-scale targets by utilizing the channel grouping mechanism. Finally, to train the proposed method adequately, Deep Supervision with Group Ensemble Learning (DSGEL) is equipped to the network. Extensive experiments demonstrate that our proposed model achieves state-of-the-art performance on 4 medical image segmentation datasets.
FuAlign: Cross-lingual entity alignment via multi-view representation learning of fused knowledge graphs

Cross-lingual entity alignment is essential in knowledge graph completion. Recently, knowledge representation learning (KRL) has gained remarkable achievements in cross-lingual entity alignment. Most existing methods first learn entity representations of different knowledge graphs and transform them into a unified space based on a seed set of pre-aligned entity pairs. However, the transformation is error-prone due to the distribution differences between the seed set and the whole entity set. This paper presents FuAlign, a novel cross-lingual entity alignment framework based on multi-view KRL of a pre-fused knowledge graph. FuAlign first fuses two matching knowledge graphs based on the given seed set. Then, it exploits multi-view representation learning to map the fused knowledge graph into a unified space. FuAlign improves the performance from two aspects. First, it represents entities in a unified embedding space, thus avoiding the error-prone transformation between different embedding spaces. Second, the proposed multi-view representation learning model captures different kinds of information such as semantics, entity context, and long-term entity dependency in knowledge graphs. We conduct extensive experiments to evaluate the performance of the proposed method. Experimental results show that FuAlign outperforms most baseline methods and achieves comparable performance with the Bert-based model.
DemoHash: Hashtag recommendation based on user demographic information

Social network services have become widely used, and hashtags, which are implicitly involved in delivering specific information, have shown to greatly improve user engagement. A number of prior studies have attempted to recommend appropriate hashtags for each social media user considering his/her posts by consequently extracting the important features from text and images. To develop this multi-dimensionality with hashtag recommendation, user demographic information also plays a significant role in the manner of personalized hashtag recommendation. Thus, this paper proposes the demographic hashtag recommendation ( DemoHash ) model to utilize users’ demographic information extracted from their selfie images, in addition to textual and visual information. The experimental results with the datasets from Instagram show that our proposed model achieves a greater performance with F 1 -score, Precision, and Recall than the existing hashtag recommendation methods by average of 4.19%, 18.45%, and 3.91%, respectively. Our approach effectively combined the content-based as well as user-oriented modeling for personalized hashtag recommendation. • This paper proposes the demographic hashtag recommendation model (DemoHash). • Compared to other state-of-art models, Demohash achieves a greater performance. • Both effective and simple integrated modeling techniques are proposed and employed.
Single candidate optimizer: a novel optimization algorithm

Abstract Single-solution-based optimization algorithms have gained little to no attention by the research community, unlike population-based approaches. This paper proposes a novel optimization algorithm, called Single Candidate Optimizer (SCO), that relies only on a single candidate solution throughout the whole optimization process. The proposed algorithm implements a unique set of equations to effectively update the position of the candidate solution. To balance exploration and exploitation, SCO is integrated with the two-phase strategy where the candidate solution updates its position differently in each phase. The effectiveness of the proposed approach is validated by testing it on thirty three classical benchmarking functions and four real-world engineering problems. SCO is compared with three well-known optimization algorithms, i.e., Particle Swarm Optimization, Grey Wolf Optimizer, and Gravitational Search Algorithm and with four recent high-performance algorithms: Equilibrium Optimizer, Archimedes Optimization Algorithm, Mayfly Algorithm, and Salp Swarm Algorithm. According to Friedman and Wilcoxon rank-sum tests, SCO can significantly outperform all other algorithms for the majority of the investigated problems. The results achieved by SCO motivates the design and development of new single-solution-based optimization algorithms to further improve the performance. The source code of SCO is publicly available at: https://uk.mathworks.com/matlabcentral/fileexchange/116100-single-candidate-optimizer .
Forecast daily tourist volumes during the epidemic period using COVID-19 data, search engine data and weather data

The COVID-19 epidemic has brought a devastating blow to the tourism industry. Affected by the epidemic situation, the change of tourism volume of scenic spots is very unstable. Therefore, forecasting tourist volume in the context of COVID-19 epidemic is a new and challenging problem. In response, a novel multivariate time series forecasting framework based on variational mode decomposition (VMD) and gated recurrent unit network (GRU), i.e., VMD-GRU, is proposed to forecast daily tourist volumes during the epidemic. It takes the lead in using COVID-19 data, search traffic data and weather data. Through sufficient experiments and comparisons, the superiority of the approach is illustrated, and the predictive power of the above three types of data, especially the COVID-19 data, is revealed. Accurate forecast results from the method can help relevant government officials and tourism practitioners to better adjust tourism resources, cooperate with anti-epidemic work and reduce operational risks.
Improving Social Network Embedding via New Second-Order Continuous Graph Neural Networks

Graph neural networks (GNN) are powerful tools in many web research problems. However, existing GNNs are not fully suitable for many real-world web applications. For example, over-smoothing may affect personalized recommendations and the lack of an explanation for the GNN prediction hind the understanding of many business scenarios. To address these problems, in this paper, we propose a new second-order continuous GNN which naturally avoids over-smoothing and enjoys better interpretability. There is some research interest in continuous graph neural networks inspired by the recent success of neural ordinary differential equations (ODEs). However, there are some remaining problems w.r.t. the prevailing first-order continuous GNN frameworks. Firstly, augmenting node features is an essential, however heuristic step for the numerical stability of current frameworks; secondly, first-order methods characterize a diffusion process, in which the over-smoothing effect w.r.t. node representations are intrinsic; and thirdly, there are some difficulties to integrate the topology of graphs into the ODEs. Therefore, we propose a framework employing second-order graph neural networks, which usually learn a less stiff transformation than the first-order counterpart. Our method can also be viewed as a coupled first-order model, which is easy to implement. We propose a semi-model-agnostic method based on our model to enhance the prediction explanation using high-order information. We construct an analog between continuous GNNs and some famous partial differential equations and discuss some properties of the first and second-order models. Extensive experiments demonstrate the effectiveness of our proposed method, and the results outperform related baselines.
Multi-Aspect Dense Retrieval

Prior work in Dense Retrieval usually encodes queries and documents using single-vector representations (also called embeddings) and performs retrieval in the embedding space using approximate nearest neighbor search. This paradigm enables efficient semantic retrieval. However, the single-vector representations can be ineffective at capturing different aspects of the queries and documents in relevance matching, especially for some vertical domains. For example, in e-commerce search, these aspects could be category, brand and color. Given a query ''white nike socks", a Dense Retrieval model may mistakenly retrieve some ''white adidas socks" while missing out the intended brand. We propose to explicitly represent multiple aspects using one embedding per aspect. We introduce an aspect prediction task to teach the model to capture aspect information with particular aspect embeddings. We design a lightweight network to fuse the aspect embeddings for representing queries and documents. Our evaluation using an e-commerce dataset shows impressive improvements over strong Dense Retrieval baselines. We also discover that the proposed aspect embeddings can enhance the interpretability of Dense Retrieval models as a byproduct.
Of Course it's Political! A Critical Inquiry into Underemphasized Dimensions in Civic Text Visualization

Abstract Recent developments in critical information visualization have brought the field's attention to political, feminist, ethical, and rhetorical aspects of data visualization. However, less work has explored the interplay between design decisions and political ramifications—structures of authority, means of representation, etc. In this paper, we build upon these critical perspectives and highlight the political aspect of civic text visualization especially in the context of democratic decision‐making. Based on a critical analysis of survey papers about text visualization in general, followed by a review on the status quo of text visualization in civics, we argue that civic text visualization inherits an exclusively analytic framing. This framing leads to a series of issues and challenges in the fundamentally political context of civics, such as misinterpretation of data, missing minority voices, and excluding the public from decision making processes. To span this gap between political context and analytic framing, we provide a series of two‐pole conceptual dimensions, such as from singular user to multiple relationships, and from complexity to inclusivity of visualization design. For each dimension, we discuss how the tensions between these poles can help surface the political ramifications of design decisions in civic text visualization. These dimensions can thus help visualization researchers, designers, and practitioners attend more intentionally to these political aspects and inspire their design choices. We conclude by suggesting that these dimensions may be useful for visualization design across a variety of application domains, beyond civic text visualization.
EdgePhase: A Deep Learning Model for Multi‐Station Seismic Phase Picking

Abstract In this study, we build a multi‐station phase‐picking model named EdgePhase by integrating an Edge Convolutional module with a state‐of‐the‐art single‐station phase‐picking model, EQTransformer. The Edge Convolutional module, a variant of Graph Neural Network, exchanges information relevant to seismic phases between neighboring stations. In EdgePhase, seismograms are first encoded into the latent representations, then converted into enhanced representations by the Edge Convolutional module, and finally decoded into the P‐ and S‐phase probabilities. Compared to the standard EQTransformer, EdgePhase increases the precision (fraction of phase identifications that are real) and recall (fraction of phase arrivals that are identified) rate by 5% on our training and test data sets of Southern California earthquakes. To evaluate its performance in regions of different tectonic settings, we applied EdgePhase to detect the early aftershocks following the 2020 M7.0 Samos, Greece earthquake. Compared to a local earthquake catalog, EdgePhase produced 190% additional detections with an event distribution more conformative to a planar fault interface, suggesting higher fidelity in event locations. This case study indicates that EdgePhase provides a strong regional generalization capability in real‐world applications.