Reviewerly/exHarmony huggingface dataset:
Heap-based optimizer based on three new updating strategies

Heap-based optimizer (HBO) is a novel meta-heuristic algorithm proposed in 2020, and it has a unique search mechanism and good performance, but still some deficiencies, such as poor search ability and low search efficiency in solving complex optimization problems. Therefore, this paper proposes an improved HBO, namely, HBO based on Three new Updating Strategies (TUS-HBO). Firstly, a differential self-learning strategy is adopted for the best agent since it does not update its position in HBO, which leads to low search efficiency. Secondly, a best example learning strategy is proposed to replace the original position-updating strategy of a sub-best agent to strengthen the exploitation. Finally, an opposition differential perturbation strategy is used to replace the original position-updating strategy of an ordinary agent to improve the exploration and avoid falling into local optima. A lot of experimental results on 30 classic functions with different (30, 50, 1000 and 2000) dimensions, 30 complex functions from CEC2017 test set show that TUS-HBO outperforms HBO and quite a few other state-of-the-art algorithms in search ability and efficiency. The results of k-means clustering and engineering optimization problems indicate that TUS-HBO can cope with real-life problems better.
ModelWise: Interactive Model Comparison for Model Diagnosis, Improvement and Selection

Abstract Model comparison is an important process to facilitate model diagnosis, improvement, and selection when multiple models are developed for a classification task. It involves careful comparison concerning model performance and interpretation. Current visual analytics solutions often ignore the feature selection process. They either do not support detailed analysis of multiple multi‐class classifiers or rely on feature analysis alone to interpret model results. Understanding how different models make classification decisions, especially classification disagreements of the same instances, requires a deeper model understanding. We present ModelWise, a visual analytics method to compare multiple multi‐class classifiers in terms of model performance, feature space, and model explanation. ModelWise adapts visualizations with rich interactions to support multiple workflows to achieve model diagnosis, improvement, and selection. It considers feature subspaces generated for use in different models and improves model understanding by model explanation. We demonstrate the usability of ModelWise with two case studies, one with a small exemplar dataset and another developed with a machine learning expert with real‐world perioperative data.
A Grammar‐Based Approach for Applying Visualization Taxonomies to Interaction Logs

Abstract Researchers collect large amounts of user interaction data with the goal of mapping user's workflows and behaviors to their high‐level motivations, intuitions, and goals. Although the visual analytics community has proposed numerous taxonomies to facilitate this mapping process, no formal methods exist for systematically applying these existing theories to user interaction logs. This paper seeks to bridge the gap between visualization task taxonomies and interaction log data by making the taxonomies more actionable for interaction log analysis. To achieve this, we leverage structural parallels between how people express themselves through interactions and language by reformulating existing theories as regular grammars. We represent interactions as terminals within a regular grammar, similar to the role of individual words in a language, and patterns of interactions or non‐terminals as regular expressions over these terminals to capture common language patterns. To demonstrate our approach, we generate regular grammars for seven existing visualization taxonomies and develop code to apply them to three public interaction log datasets. In analyzing these regular grammars, we find that the taxonomies at the low‐level (i.e., terminals) show mixed results in expressing multiple interaction log datasets, and taxonomies at the high‐level (i.e., regular expressions) have limited expressiveness, due to primarily two challenges: inconsistencies in interaction log dataset granularity and structure, and under‐expressiveness of certain terminals. Based on our findings, we suggest new research directions for the visualization community to augment existing taxonomies, develop new ones, and build better interaction log recording processes to facilitate the data‐driven development of user behavior taxonomies.
Leveraging Analysis History for Improved In Situ Visualization Recommendation

Abstract Existing visualization recommendation systems commonly rely on a single snapshot of a dataset to suggest visualizations to users. However, exploratory data analysis involves a series of related interactions with a dataset over time rather than one‐off analytical steps. We present Solas, a tool that tracks the history of a user's data analysis, models their interest in each column, and uses this information to provide visualization recommendations, all within the user's native analytical environment. Recommending with analysis history improves visualizations in three primary ways: task‐specific visualizations use the provenance of data to provide sensible encodings for common analysis functions, aggregated history is used to rank visualizations by our model of a user's interest in each column, and column data types are inferred based on applied operations. We present a usage scenario and a user evaluation demonstrating how leveraging analysis history improves in situ visualization recommendations on real‐world analysis tasks.
Leveraging Integrated Learning for Open-Domain Chinese Named Entity Recognition

Named entity recognition (NER) is a fundamental technique in natural language processing that provides preconditions for tasks, such as natural language question reasoning, text matching, and semantic text similarity. Compared to English, the challenge of Chinese NER lies in the noise impact caused by the complex meanings, diverse structures, and ambiguous semantic boundaries of the Chinese language itself. At the same time, compared with specific domains, open-domain entity types are more complex and changeable, and the number of entities is considerably larger. Thus, the task of Chinese NER is more difficult. However, existing open-domain NER methods have low recognition rates. Therefore, this paper proposes a method based on the bidirectional long short-term memory conditional random field (BiLSTM-CRF) model, which leverages integrated learning to improve the efficiency of Chinese NER. Compared with single models, including CRF, BiLSTM-CRF, and gated recurrent unit-CRF, the proposed method can significantly improve the accuracy of open-domain Chinese NER.
Non-cooperative LEO Satellite Orbit Determination Based on Single Pass Doppler Measurements

Precise orbit information is the premise of the wide application of low-Earth-orbit (LEO) satellites. Traditional orbit determination (OD) methods have disadvantages of poor reliability, high hardware cost, and long orbit determination time. To account for these issues, we propose an OD framework, using several distributed facilities to acquire Doppler shift values of signals of opportunity (SOPs) during the same pass.Firstly, we propose a search-least squares (LS) algorithm for initial orbit determination (IOD), providing a reliable accurate initial value for precise orbit determination (POD). Moreover, a non-recursive cubature batch filter (NR-CBF) is proposed for POD to reduce linearization errors, avoid tuning parameters, and save the calculation time. Besides, we modify the measurement model by considering signal propagation delays and provide the corresponding theoretical basis. Finally, the real experiment result verifies the feasibility of the proposed method, whose velocity accuracy is at m&#x002F;s grade.
TAG: Joint Triple-Hierarchical Attention and GCN for Review-Based Social Recommender System

Recommender systems across many Internet services have become a critical part of online businesses, as consumers would refer to them before making decisions. However, the lack of explicit ratings for items on many services makes it challenging to capture user preferences and item characteristics. Both academia and the industry have drawn attention to rating predications as a fundamental problem in recommendation systems. With the emergence of social networks, social recommender systems have been proposed to utilize the relationship between users and items to alleviate the data sparsity problem for rating predictions. However, they either concentrate on the opinion mining for each user and item, or consider the connections between users only. In this paper, we present an effective framework, Triple-hierarchical Attention Graph-based social rating prediction (TAG), to exploit the social relationships between users, the user-item interest relationships, the correlation relationships between items, and reviews for rating predictions. In order to consider opinions from reviews and these complex relationships, we first employ two triple-hierarchical attention to extract user and item features from reviews. We then design an inductive GNN, which generates effective embedding for users and items. Experiments over Yelp show that TAG outperforms state-of-the-art methods across RMSE, MAE, and NDCG metrics.
An Anonymous and Outsourcing-Supported Multiauthority Access Control Scheme With Revocation for Edge-Enabled IIoT System

With the application of the Internet of Things and intelligent technologies in industrial systems, the manufacturing efficiency and product quality have been improved, leading to the development of the Industrial Internet of Things (IIoT). Uploading private data to the cloud may lead to data leakage without security protection. Thus, attribute-based encryption (ABE) is widely used to ensure data security and implement data access control, and some multiauthority ABE schemes are proposed to meet the requirement of attributes from different authorities, such as factory and government, which is more suitable for the IIoT system. However, the current multiauthority ABE schemes are with privacy leakage problems on attributes and low-efficiency issues. To address these problems, we propose a multiauthority ABE scheme, which protects users’ privacy by anonymizing attributes in authentication, reduces the computing burden of IIoT devices by adapting online/offline technique and outsourcing decryption to edge devices, and realizes effective attribute revocation. A formal security proof is presented that our scheme is replayable chosen ciphertext attack secure. Finally, we implement the proposed scheme, and experimental results show that our scheme is more efficient than the existing schemes.
Fuzzy Clustering With Knowledge Extraction and Granulation

Knowledge-based clustering algorithms can improve traditional clustering models by introducing domain knowledge to identify the underlying data structure. While there have been several approaches to clustering with the guidance of knowledge tidbits, most of them mainly focus on numeric knowledge without considering the uncertain nature of information. To capture the uncertainty of information, pure numeric knowledge tidbits are expanded to knowledge granules in this article. Then, two questions arise: how to obtain granular knowledge and how to use those knowledge granules in clustering. To the end, a novel knowledge extraction and granulation (KEG) method and a granular knowledge-based fuzzy clustering model are proposed in this study. First, inspired by the concept of natural neighbors, an automatic KEG is developed. In KEG, high-density points are filtered from the dataset and then merged with their natural neighbors to form several dense areas, i.e., granular knowledge. Furthermore, the granular knowledge expressed by interval or triangular numbers is leveraged into the clustering algorithm, which is the framework of fuzzy clustering with granular knowledge. To concretize this model into clustering algorithms, the classical fuzzy C-Means clustering algorithm has been selected to incorporate the granular knowledge produced by KEG. Then, the corresponding fuzzy C-Means clustering with interval knowledge granules (IKG-FCM) and triangular knowledge granules (TKG-FCM) are proposed. Experiments on synthetic and real-world datasets demonstrate that IKG-FCM and TKG-FCM always achieve better clustering performance with less time cost, especially on imbalanced data, compared with state-of-the-art algorithms.
Individual Surveillance Around Parked Aircraft at Nighttime: Thermal Infrared Vision-Based Human Action Recognition

The COVID-19 crisis has led to an unusually large number of commercial aircraft being currently parked or stored. For airlines, airports, and civil aviation authorities around the world, monitoring, and protecting these parked aircraft to prevent them from causing human-made damage are becoming urgent problems that are receiving increasing attention. In this study, we use thermal infrared monitoring videos to establish a framework for individual surveillance around parked aircraft by proposing a human action recognition (HAR) algorithm. As the focus of this article, the proposed HAR algorithm seamlessly integrates a preprocessing module in which a novel data structure is constructed to introduce spatiotemporal information of the action; a convolutional neural network-based module for spatial feature extraction; a triple-layer convolutional long short-term memory network for temporal feature extraction; and two fully connected layers for classification. Moreover, because no infrared dataset is available for the HAR task on airport grounds at nighttime, we present a dataset called IIAR-30, which consists of eight action categories that frequently occur on airport grounds and 2000 video clips. The experimental results on the IIAR-30 dataset demonstrated that the recognition accuracy of the proposed method was higher than 96%. We also further evaluated the effectiveness of the proposed method by comparing it with five baselines and four other methods.
Augmentations: An Insight into Their Effectiveness on Convolution Neural Networks

Augmentations are the key factor in determining the performance of any neural network as they provide a model with a critical edge in boosting its performance. Their ability to boost a model’s robustness depends on two factors, viz-a-viz, the model architecture, and the type of augmentations. Augmentations are very specific to a dataset, and it is not imperative that all kinds of augmentation would necessarily produce a positive effect on a model’s performance. Hence there is a need to identify augmentations that perform consistently well across a variety of datasets and also remain invariant to the type of architecture, convolutions, and the number of parameters used. This paper evaluates the effect of parameters using 3 × 3 and depth-wise separable convolutions on different augmentation techniques on MNIST, FMNIST, and CIFAR10 datasets. Statistical Evidence shows that techniques such as Cutouts and Random horizontal flip were consistent on both parametrically low and high architectures. Depth-wise separable convolutions outperformed 3 × 3 convolutions at higher parameters due to their ability to create deeper networks. Augmentations resulted in bridging the accuracy gap between the 3 × 3 and depth-wise separable convolutions, thus establishing their role in model generalization. At higher number augmentations did not produce a significant change in performance. The synergistic effect of multiple augmentations at higher parameters, with antagonistic effect at lower parameters, was also evaluated. The work proves that a delicate balance between architectural supremacy and augmentations needs to be achieved to enhance a model’s performance in any given deep learning task.
A Comprehensive Survey on Sentiment Analysis in Twitter Data

The literature scrutinizes on diverse techniques that are associated with sentiment analysis in twitter data. It reviews several research papers and states the significant analysis. Initially, the analysis depicts various schemes that are contributed in different papers. Subsequently, the analysis also focuses on various features and it also analyses the sentiment analysis in twitter data that is exploited in each paper. Furthermore, this paper provides the detailed study regarding the performance measures and maximum performance achievements in each contribution. Finally, it extends the various research issues which can be useful for the researchers to accomplish further research on sentiment analysis in twitter data.
Rectification of Syntactic and Semantic Privacy Mechanisms

We highlight that syntactic and semantic mechanisms used for preserving individual privacy in data analysis have various technical shortcomings with respect to the nature of personal data, workflow, and evaluation metrics. We suggest technical guidelines to rectify these shortcomings to meet both privacy and utility expectations.
CrossTrainer

Domain adaptation provides a powerful set of model training techniques given domain-specific training data and supplemental data with unknown relevance. The techniques are useful when users need to develop models with data from varying sources, of varying quality, or from different time ranges. We build CrossTrainer, a system for practical domain adaptation. CrossTrainer utilizes loss reweighting from [6], which provides consistently high model accuracy across a variety of datasets in our empirical analysis. However, loss reweighting is sensitive to the choice of a weight hyperparameter that is expensive to tune. We develop optimizations leveraging unique properties of loss reweighting that allow CrossTrainer to output accurate models while improving training time compared to naïve hyperparameter search.
METransE: Manifold-like mechanism enhanced embedding for reasoning over knowledge graphs

The knowledge graph (KG) has attracted much concern due to its positive effect on AI-related applications. While even for those large-scale KGs widely used, they are still far from being complete and comprehensive. This prompts reasoning over KGs to be one of the most basic and attention-grabbing tasks. However, most existing reasoning methods only focus on the semantics explicitly indicated by triples, but ignore the other valuable information hidden in the KGs that can assist in reasoning. In addition, such one-sided focus on triples also makes the embedding space completely induced by triples independently, rather than reproducing the semantics in KGs fully. All these constrain the reasoning performance. Motivated by these, we propose a more reasonable and better-performing reasoning framework that can discovery and utilize the reasoning-related experience in the KG to guide reasoning. To cooperate with such reasoning mechanism and make the embedding space more interpretable, a type-aware embedding model, METransE, is proposed. It introduces type information through our manifold-like mechanism, which considers restraining excessive aggregation of similar entities to avoid mutual confusion in reasoning, and is more suitable for the needs of KG reasoning. Furthermore, different from the previous work that highly relies on explicit types, we also take the automatic acquisition of types into account to ensure the universality of our model. Extensive experimental results demonstrate the ideal reasoning performance of our model and the type-related interpretability of the embedding space. • A type-aware reasoning framework over KGs is proposed. • Embedding model learns KG in semantic-view and entity-type-view jointly. • Manifold-like mechanism is proposed to effectively embed type constraints. • Type acquisition is proposed to overcome type-aware models’ generality defects. • The embedding space is equipped with better type-related interpretability.
Image generation with self pixel-wise normalization

Region-adaptive normalization (RAN) methods have been widely used in the generative adversarial network (GAN)-based image-to-image translation technique. However, since these approaches need a mask image to infer pixel-wise affine transformation parameters, they are not applicable to general image generation models having no paired mask images. To resolve this problem, this paper presents a novel normalization method, called self pixel-wise normalization (SPN), which effectively boosts the generative performance by carrying out the pixel-adaptive affine transformation without an external guidance map. In our method, the transforming parameters are derived from a self-latent mask that divides the feature map into foreground and background regions. The visualization of the self-latent masks shows that SPN effectively captures a single object to be generated as the foreground. Since the proposed method produces the self-latent mask without external data, it is easily adaptable to existing generative models. Extensive experiments on various datasets reveal that our SPN significantly improves the performance of image generation technique in terms of Frechet inception distance (FID) and Inception score (IS).
Gaussian Process Regression Machine Learning Models for Photonic Sintering

Novel solid-state thermoelectric (TE) materials have the potential to improve energy efficiency by converting waste heat into electricity. However, the performance of many state-of-the-art TE materials remains inadequate for adoption beyond niche applications. Current efforts to optimize photonic sintering, an important step in additive manufacturing of TE devices, rely on expert-driven trial-and-error search which is often extremely time-consuming and without the guarantee of improvement. Emerging Bayesian optimization frameworks offer a principled approach to intelligentially recommend optimized experimental conditions by balancing exploitation and exploration. In this paper, we develop a Gaussian Process Regression (GPR) machine learning model to predict the thermoelectric power factor of aerosol jet printed n-type Bi2Te2.7Se0.3 TE films. We compare hyperparameter tuning methods and perform retrospective analysis to quantify the predictivity of GPR. Finally, we discuss the challenges and opportunities of adopting Bayesian optimization for photonic sintering and fabrication of high-performance TE devices.
Use Cases for Augmented Reality Applications in Enterprise Modeling: A Morphological Analysis

With the more-widespread availability and cost effectiveness of advanced computer vision technologies, first attempts have recently been made for applying augmented reality in enterprise modeling. Despite these first steps, a systematic analysis of the potential opportunities of this technology for enterprise modeling has so far not been conducted. Therefore, we describe in this paper the results of a morphological analysis that has been performed in a series of expert workshops for deriving according use cases. Based on the technological dimensions of augmented reality and the traditional dimensions of enterprise modeling, we show the potential of this combination by means of three selected use cases.