Reviewerly/exHarmony huggingface dataset:
The Quest for Omnioculars: Embedded Visualization for Augmenting Basketball Game Viewing Experiences

Sports game data is becoming increasingly complex, often consisting of multivariate data such as player performance stats, historical team records, and athletes' positional tracking information. While numerous visual analytics systems have been developed for sports analysts to derive insights, few tools target fans to improve their understanding and engagement of sports data during live games. By presenting extra data in the actual game views, embedded visualization has the potential to enhance fans' game-viewing experience. However, little is known about how to design such kinds of visualizations embedded into live games. In this work, we present a user-centered design study of developing interactive embedded visualizations for basketball fans to improve their live game-watching experiences. We first conducted a formative study to characterize basketball fans' in-game analysis behaviors and tasks. Based on our findings, we propose a design framework to inform the design of embedded visualizations based on specific data-seeking contexts. Following the design framework, we present five novel embedded visualization designs targeting five representative contexts identified by the fans, including shooting, offense, defense, player evaluation, and team comparison. We then developed Omnioculars, an interactive basketball game-viewing prototype that features the proposed embedded visualizations for fans' in-game data analysis. We evaluated Omnioculars in a simulated basketball game with basketball fans. The study results suggest that our design supports personalized in-game data analysis and enhances game understanding and engagement.
Iterative Class Prototype Calibration for Transductive Zero-Shot Learning

Zero-shot learning (ZSL) typically suffers from the domain shift issue since the projected feature embedding of unseen samples mismatch with the corresponding class semantic prototypes, making it very challenging to fine-tune an optimal visual-semantic mapping for the unseen domain. Some existing transductive ZSL methods solve this problem by introducing unlabeled samples of the unseen domain, in which the projected features of unseen samples are still not discriminative and tend to be distributed around prototypes of seen classes. Therefore, how to effectively align the projection features of samples in unseen classes with corresponding predefined class prototypes is crucial for promoting the generalization of ZSL models. In this paper, we propose a novel Iterative Class Prototype Calibration (ICPC) framework for transductive ZSL which consists of a pseudo-labeling stage and a model retraining stage to address the above key issue. First, in the labeling stage, we devise a Class Prototype Calibration (CPC) module to calibrate the predefined class prototypes of the unseen domain by estimating the real center of projected feature distribution, which achieves better matching of sample points and class prototypes. Next, in the retraining stage, we devise a Certain Samples Screening (CSS) module to select relatively certain unseen samples with high confidence and align them with predefined class prototypes in the embedding space. A progressive training strategy is adopted to select more certain samples and update the proposed model with augmented training data. Extensive experiments on AwA2, CUB, and SUN datasets demonstrate that the proposed scheme achieves new state-of-the-art in the conventional setting under both standard split (SS) and proposed split (PS).
OCSTN: One-class time-series classification approach using a signal transformation network into a goal signal

• One-class time series classification algorithm , OCSTN, discriminates between seen and unseen classes. • OCSTN is evaluated using two ballistocardiography (BCG) datasets to demonstrate its performance. • OCSTN shows fair AUC score and good processing speed for the authentication task. One-class classification (OCC) is a classification task where the training data have only one class. The goal is to classify input data into one seen class or other unseen classes. This paper proposes an OCC approach using a signal transformation network (OCSTN), which aims to process univariate time-series data. The main contribution is developing a signal transformation network (STN) that aims to transform input signals into one signal, namely the goal signal. Moreover, the model error of the STN is a distance metric between the goal signal and the model output. The STN model learns from one-class signals. Therefore, model error for one class is small relative to other classes. Accordingly, OCSTN could discriminate between seen and unseen classes using the model errors. The proposed OCSTN is evaluated using two ballistocardiography (BCG) datasets. The OCSTN achieves fair results in both AUC scores and processing speed. OCSTN has a weak point in training diverse signals. In addition, the entropy and smoothness of the goal signal are highly related to the AUC score.
Reconstructing Intimate Contexts through Data Donation: A Case Study in Menstrual Tracking Technologies

Ubiquitous wearable and mobile technologies generate vast amounts of data from sensors and self-logging applications. This data creates opportunities to better understand people’s behavior and inform research on intimate topics such as menstruation. However, in design and HCI research, reconstructing the context in which data was collected and understanding the lived experience behind the data often requires the active participation of people. In this paper, we augment the concept of data donation beyond data collection to explore the possibilities of actively engaging data donors in the (intimate) interpretation of their data. Specifically, we define and implement a menstrual logs data donation journey. We received data sets from 35 donors over five weeks, 13 of whom participated in reconstructing the context of their data. We translate our experience into a conceptualization of designerly data donation around the data, data donors, and data receivers, which we discuss along with its implications.
An ensemble multi-swarm teaching–learning-based optimization algorithm for function optimization and image segmentation

Intelligent optimization algorithms are widely utilized to deal with complex optimization problems in various areas. However, a single intelligent optimization algorithm cannot handle well more and more complex optimization problems. The ensemble strategy can integrate several different operators and algorithms by using some appropriate strategies and maybe obtain better optimization performance. In this paper, a new ensemble multi-swarm method based on teaching–learning-based optimization (EMTLBO) was proposed by integrating three different algorithms including the original teaching–learning-based optimization algorithm, its variant with neighborhood search and the variant with differential evolution. In EMTLBO, a new evaluating mechanism based on the fitness-based and diversity-based metrics (FDEM) for each sub-swarm was proposed to evaluate the optimization performance after a continuous generation interval. Moreover, an algorithm matching mechanism based on ranking for sub-swarms (AMM) is adapted to re-divide the population into three sub-swarms and match a suitable algorithm for each sub-swarm so as to increase the whole optimization performance. Furthermore, the experimental results on CEC2014 and CEC2017 test suits verify the feasibility and optimization performance of EMTLBO. Finally, the proposed algorithm is extended to optimize the segmentation thresholds of images and the segmentation performances on different benchmark images show that EMTLBO has good performance in most cases. • A new evaluating mechanism was proposed to evaluate the optimization performance. • Algorithm matching mechanism is adopted to match a suitable algorithm for each sub-swarm. • The experimental results on CEC2014 and CEC2017 test suits verify EMTLBO. • The proposed EMTLBO algorithm is extended to deal with image segmentation problems.
Towards effective semantic annotation for mobile and edge services for Internet-of-Things ecosystems

As a popular and typical representative of services, Web APIs have been widely used to provide mobile and edge services in Internet-of-Things (IoT) ecosystems. As a combination of Web APIs, mashups are also popularly used to provide more complicated services in IoT ecosystems. Tags are one of the core attributes of Web APIs, and semantic annotation or automatic tagging takes a core role in understanding and management of the large volume of services. But currently, semantic annotation heavily relies on manual work and costs a lot of time and human resources. There have been some annotation methods for services, but the existing methods suffer from low accuracy and other problems. In this paper, we propose a neural semantic annotation framework for automatic tagging for mobile and edge services in IoT ecosystems. We propose to accomplish semantic annotation as the multi-label classification problem, and develop an automatic annotation framework based on multi-head self-attention mechanism. We crawled two large real-world datasets and gave a deep analysis of the distributions of annotations. We performed sufficient experiments on the two datasets and the experimental results demonstrate that our framework yields superior performance in semantic annotation for mobile and edge services. • Propose a novel framework for semantic annotation in IoT ecosystems. • Facilitate the organization and management of services in IoT. • Give a comprehensive analysis of the distributions and correlations of annotations. • Crawled and collected two large real-world datasets.
Negative samples selecting strategy for graph contrastive learning

Graph neural networks (GNNs) have emerged as a successful method on graph structured data. Limited by expensive labeled data, contrastive learning has been adopted to the graph domain. In most existing node-level graph contrastive learning methods, when applying contrastive learning to a certain unlabeled node (the center node), its corresponding “similar” node (positive sample) is usually generated by data augmentation. Other nodes in the graph are served as the “dissimilar” nodes (negative samples), which leads to two major problems. First, the computational cost can be prohibitively expensive, especially when the graph is large. Second, utilizing some nodes which share the same label with the center node as the negative samples will damage the learning process. Hence, to address these issues, we explore the feasibility of only sampling a part of nodes for graph contrastive learning process. And unlike the previous self-supervised contrastive methods, we use joint training to exploit supervised signals as much as possible in contrastive learning. Hence, we propose a Negative Samples Selecting Strategy to utilize the classification prediction to guide the selection of the negative samples for sampled nodes. Then, we further incorporate this strategy for performing contrastive learning on graphs and propose a framework named Graph Contrastive Learning with Negative Samples Selecting Strategy (GCNSS). We demonstrate that GCNSS can be trained much faster with much less computation memory than graph contrastive learning baselines, and GCNSS can effectively boost the performance of existing GNN models on semi-supervised node classification tasks across many different datasets. The code is in: https://github.com/MR9812/GCNSS.
Should a luxury <scp>Brand's Chatbot</scp> use emoticons? Impact on brand status

Abstract Luxury brands are increasingly adopting chatbots for online customer service. But, little is known about the role of adding design features such as emoticons on customers' luxury experience. This study fills this research gap by exploring the influence of a luxury brand chatbot's adoption of emoticons on status perception and its underlying mechanisms. Results from two experiments suggest that luxury brands might be better off not using emoticons in chatbot communications because it dampens the brand status perception due to perceived unexpectedness, which in turn decreases the perception of the appropriateness of the interaction with chatbots. However, this negative effect of luxury brand's use of emoticons in chatbot communication only exists for traditional luxury brands, not for masstige brands. This study advances the literature on AI, particularly regarding luxury brand‐specific chatbot applications. It also offers insights for luxury brand managers that they should be cautious in adopting emoticons in chatbot communication given the risk of ruining the brand status, especially when the brand is a traditional luxury brand as opposed to a masstige brand.
Deep Learning-Based Automatic Extraction of Cyanobacterial Blooms from Sentinel-2 MSI Satellite Data

Cyanobacterial harmful algal blooms (CyanoHABs) in inland water have emerged as a major global environmental challenge. Although satellite remote sensing technology has been widely used to monitor CyanoHABs, there are also some automatic extraction methods of CyanoHABs based on spectral indices (such as gradient mode, fixed threshold, and the Otsu method, etc.), the accuracy is generally not very high. This study developed a high-precision automatic extraction model for CyanoHABs using a deep learning (DL) network based on Sentinel-2 multi-spectral instrument (MSI) data of Chaohu Lake, China. First, we generated the CyanoHABs “ground truth” dataset based on visual interpretation. Thereafter, we trained the CyanoHABs extraction model based on a DL image segmentation network (U-Net) and extracted CyanoHABs. Then, we compared three previous automatic CyanoHABs extraction methods based on spectral index threshold segmentation and evaluated the accuracy of the results. Based on “ground truth”, at the pixel level, the F1 score and relative error (RE) of the DL model extraction results are 0.90 and 3%, respectively, which are better than that of the gradient mode (0.81,40%), the fixed threshold (0.81, 31%), and the Otsu method (0.53, 62%); at CyanoHABs area level, the R2 of the scatter fitting between DL model result and the “ground truth” is 0.99, which is also higher than the other three methods (0.90, 0.92, 0.84, respectively). Finally, we produced the annual CyanoHABs frequency map based on DL model results. The frequency map showed that the CyanoHABs on the northwest bank are significantly higher than in the center and east of Chaohu Lake, and the most serious CyanoHABs occurred in 2018 and 2019. Furthermore, CyanoHAB extraction based on this model did not cause cloud misjudgment and exhibited good promotion ability in Taihu Lake, China. Hence, our findings indicate the high potential of the CyanoHABs extraction model based on DL in further high-precision and automatic extraction of CyanoHABs from large-scale water bodies.
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-Based Beam Search

To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be selected as the tracking result of each frame. However, we found that this may be not an optimal choice, especially when encountering challenging tracking scenarios such as heavy occlusion and fast motion. In particular, if a tracker drifts, errors will be accumulated and would further make response scores estimated by the tracker unreliable in future frames. To address this issue, we propose to maintain multiple tracking trajectories and apply beam search strategy for visual tracking, so that the trajectory with fewer accumulated errors can be identified. Accordingly, this paper introduces a novel multi-agent reinforcement learning based beam search tracking strategy, termed BeamTracking. It is mainly inspired by the image captioning task, which takes an image as input and generates diverse descriptions using beam search algorithm. Accordingly, we formulate the tracking as a sample selection problem fulfilled by multiple parallel decision-making processes, each of which aims at picking out one sample as their tracking result in each frame. Each maintained trajectory is associated with an agent to perform the decision-making and determine what actions should be taken to update related information. More specifically, using the classification-based tracker as the baseline, we first adopt bi-GRU to encode the target feature, proposal feature, and its response score into a unified state representation. The state feature and greedy search result are then fed into the first agent for independent action selection. Afterwards, the output action and state features are fed into the subsequent agent for diverse results prediction. When all the frames are processed, we select the trajectory with the maximum accumulated score as the tracking result. Extensive experiments on seven popular tracking benchmark datasets validated the effectiveness of the proposed algorithm.
Evaluating the performance of meta-heuristic algorithms on CEC 2021 benchmark problems

Abstract To develop new meta-heuristic algorithms and evaluate on the benchmark functions is the most challenging task. In this paper, performance of the various developed meta-heuristic algorithms are evaluated on the recently developed CEC 2021 benchmark functions. The objective functions are parametrized by inclusion of the operators, such as bias, shift and rotation. The different combinations of the binary operators are applied to the objective functions which leads to the CEC2021 benchmark functions. Therefore, different meta-heuristic algorithms are considered which solve the benchmark functions with different dimensions. The performance of some basic, advanced meta-heuristics algorithms and the algorithms that participated in the CEC2021 competition have been experimentally investigated and many observations, recommendations, conclusions have been reached. The experimental results show the performance of meta-heuristic algorithms on the different combinations of binary parameterized operators.
Comparative Study and Improvement Analysis of Sparrow Search Algorithm

To solve the problem that the emerging sparrow search algorithm (SSA) lacks systematic comparison and analysis with other classical algorithms, this paper first introduces the principle of the sparrow search algorithm and then describes the mathematical model and algorithm description of the sparrow search algorithm. By comparing several classical intelligent algorithms with particle swarm optimization (PSO), differential evolution (DE), and gray wolf optimizer (GWO), the sparrow search algorithm’s theory and model are systematically compared and analyzed, and the advantages and disadvantages of SSA are summarized. Finally, based on the above research and previous research, the limitations of SSA and current improved SSA are analyzed, which provides ideas for further improvement of the algorithm.
VisionCervix: Papanicolaou cervical smears classification using novel CNN-Vision ensemble approach

About half a million women in the world are affected by cervical cancer and about 0.3 million deaths occur per year due to cervical cancer. Cytologists perform Pap-smear tests to screen the Pap Smear images of the cervical cells. This manual screening is prone also to error. Therefore, an automated computer-aided detection systems have been proposed for the classification of cervical cancer cell images. In the proposed work, an ensemble of Vision Transformer network (ViT) and convolution neural network (CNN) has been proposed for the classification of cervical cell Pap smear images. ViT has been known for its minimal inductive bias and its competitive classification performance in comparison to the state-of-the-art convolution neural network. Fine-tuning large ViT network is a computationally intensive procedure; therefore, as an alternative to ViT-CNN approach, another transfer learning-based approach has also been proposed in which the features extracted from the pre-trained CNNs are combined and classified with the resource-efficient Long Short Term Memory (LSTM) network. Comparison between both the approaches has been made on the basis of their classification performance, test time, generalization ability and attention maps. Experimental results show that the ViT-CNN ensemble approach achieved 97.65% classification accuracy whereas the LSTM-based approach achieved 95.80% classification accuracy. ViT-CNN ensemble approach achieves better classification accuracy at the cost of the huge demand for computation since it takes more computational resources in terms of high amount of random access memory (RAM) in the graphical processing unit (GPU); whereas, the CNN-LSTM approach is less accurate and computationally cheaper.
Feature selection using Decomposed Mutual Information Maximization

Feature selection has been recognized for long as an important preprocessing technique to reduce dimensionality and improve the performance of regression and classification tasks. The class of sequential forward feature selection methods based on Mutual Information (MI) is widely used in practice, mainly due to its computational efficiency and independence from the specific classifier. A recent work introduced a theoretical framework for this class of methods which explains the existing proposals as approximations to an optimal target objective function. Such framework made clear the advantages and drawbacks of each proposal. Methods accounting for the redundancy of candidate features using a maximization function and considering the so-called complementary effect are among the best ones. However, they still penalize the complementarity, which is an important drawback. This paper proposes the Decomposed Mutual Information Maximization (DMIM) method, which keeps the good theoretical properties of the best methods proposed so far but overcomes the complementarity penalization by applying the maximization separately to the inter-feature and class-relevant redundancies. DMIM was extensively evaluated and compared with other methods, both theoretically and using two synthetic scenarios and 20 publicly available real datasets applied to specific classifiers. Our results show that DMIM achieves a better classification performance than the remaining forward feature selection methods based on MI.