Reviewerly/exHarmony huggingface dataset:
Global calibration method for non-overlapping cameras based on rigidly connected stereo targets

In complex field of view (FOV) environments, a single camera’s FOV measurement range is limited and cannot cover the entire object under test for global calibration. Multiple cameras are used mostly for large FOV environment measurements, but the traditional one- and two-dimensional targets used for global calibration in large FOV environments are prone to overlapping FOV. Furthermore, other large-sized targets are difficult to produce and process, and the laser projection method and plane mirror calibration methods are easily affected by the outdoor environment. To solve this problem, a non-common FOV binocular calibration method based on rigidly connected stereo targets is proposed. The calibration process is as follows: First, the rigidly connected target, which is composed of two plane targets with a checkerboard, is placed in front of the two cameras, and the vision sensor captures the corresponding sub-target image; then, the target is moved multiple times, and the transformation relationship between multiple vision sensors is obtained according to the spatial constraint characteristics of the rigidly connected target. Hence, the method overcomes the limitation of the non-overlapping FOV calibration method that relies on large measuring instruments. The experimental results show that the RMS error of the 13 mm distance is 0.16 mm. The proposed method is effective, simpler to operate than other methods, and does not rely on the constraint of complex targets. More importantly, this measurement method solves the difficult problem of measurement in non-public FOV, meeting the requirements of large FOV measurement ranges.
Faster object detector for drone-captured images

Object detection based on computer vision is becoming popular in drone-captured images. However, real-time object detection in unmanned aerial vehicle (UAV) scenarios is a huge challenge for low-end devices. To deal with the problem, we have improved YOLOv3-tiny in the following aspects. First, the label rewriting problem, which is caused by network structure and dataset of YOLOv3-tiny in drone-captured images detection, is very serious. The method of increasing the size of the predicted feature map is used to reduce the ratio of label rewriting. Second, the features of small targets will be reduced in a small feature map, but the context information with large receptive fields in it can improve the performance of small target detection. So we use dilated convolution to expand the receptive field without reducing the size of the feature map. Third, multiscale feature fusion is very helpful for small target detection. The multidilated module is adopted to merge features in earlier layer and deeper layers. Finally, a pretraining strategy combining copy-paste data augmentation method is proposed to learn more features from categories with a small number of samples. We evaluated our model on the VisDrone2019-Det test set. It achieves compelling results compared to the counterparts of YOLOv3-tiny, including ∼86.1 % decline in model size, increasing ∼19.2 % AP50. Although our model is slower than YOLOv3-tiny, it is 2.96 times faster than YOLOv3. The results of experiments verify that our network is more effective than YOLOv3-tiny. It is more suitable for UAV object detection applications on low-end devices.
Evaluation of communIT, a large-scale, cyber-physical artifact supporting diverse subgroups building community

Many subgroups remain marginalized from their larger communities. Technologies supporting community building have focused on apps, but many apps fall short of making subgroups such as the LGTBQ+, immigrant and black populations visible and heard. We report on design iterations and evaluations of communIT, a large-scale, cyber-physical platform for helping subgroups build community by making them visible and heard in public places. We conducted a design studio study ( N = 57), co-design activities with to-scale and full-scale prototypes ( N = 12 and 28), and an online study ( N = 197). We learned: preferences for communIT’s design; that communIT may be suitable and useful for diverse groups to engage, share, and interact; that communIT may make an impact on how the larger community perceives diverse groups; and that communIT may be helpful for groups to express their ideas, concerns, and aspirations to the larger community. Our research suggests the promise of large-scale, cyber-physical artifacts for building community.
Evidence of metal migration over concealed gold deposit in loess terrain and its prospecting significance

There is a growing demand for sensitive penetrating geochemical techniques for prospecting transported covered terrains. However, it is difficult to determine whether geochemical anomalies are the result of deep-seated orebodies or lateral transportation. It remains unclear how metal elements migrate through the concealed layer to the surface, which has called into question these methods and limited their use. In this study, we used three approaches, including trace element patterns, isotope tracers, and microscopic observations, to provide evidence of metal migration over a concealed Au deposit in loess terrain. The results of the survey strongly suggest that the surface ore-forming elements are most likely derived from deep-seated orebodies in the study area. Metal migration to the surface resulted in the accumulation of metals in the surface media. Based on microscopic observations and discussion, we developed a model reflecting the main migration mechanism of metallic nanoparticle movement concerning ascending geogas and migration processes. Additionally, this study demonstrates that fine-fraction soil could be an effective approach for prospecting concealed deposits in loess terrains. We anticipate that this assay will enhance the confidence in using and developing penetrating geochemical techniques and enable them to play a greater role in mineral exploration.
An embedding and interactions learning approach for ID feature in deep recommender system

The embedding of features plays a critical role in the neural network-based recommendation model. The ID feature, which is discrete and high-dimensional sparse, contains the significant information for relevance reasoning in recommender system. However, most models use the same embedding method to address the encoding of all features. This results in inaccurate expression of ID features, as well as reduces the generalization ability of the model. Based on the assumption that learning higher-order information from the mixture of ID and other features by crossover and extraction operations provides limited benefits, or negative results, we propose an ID feature learning model (IFLM) to embed the ID tags independently and learn their interactions efficiently. IFLM separates the ID tags from the complete inputs as its exclusive input, then outputs the vectors with general data structure, thus, making it easily parallel to other deep recommendation models. Besides, IFLM generates the second-order cross feature from pairwise IDs and captures higher-order feature through the specific hidden layer, which is more efficient in learning the knowledge of ID features. We used our approach on three typical deep recommendation models. The experimental results showed that the optimized model produced better results than the corresponding baseline, in terms of prediction accuracy and convergence time.
Prediction of diffuse solar radiation using machine learning models based on sunshine period and sky‐clearness index for the humid‐subtropical climate of India

Abstract Machine learning (ML) models were developed to estimate monthly average diffuse solar radiation (DSR) with sky‐clearness index and relative sunshine period as inputs for the humid subtropical climate of India. Three categories of ML models were defined, each having six models. The solar radiation data was split into two parts, the “Training dataset” used to develop the models and the “Validation dataset” used to test the models. Model accuracy was investigated as a function of various commonly used statistical pointers. A comparison was made between the ML models and well‐known empirical models from prior research. Global performance indicator was used to rank ML models within each category. The projected values from the ML models and solar radiation data were in reasonable agreement. Thus, DSR can be accurately predicted using ML models in the area under consideration.
Exploring Strategies for Quality Assurance in Online-Offline Hybrid Teaching under the Background of Smart Cities

The development of smart city construction and IT education and training are closely related and promote each other. The construction of a smart city is based on the application of information technology and IT education, and training is an important part of the construction of a smart city. Effective teaching needs the support of appropriate and scientific teaching strategies. Compared with the traditional offline classroom, the spatio-temporal structure of online and online hybrid teaching is significantly different. Therefore, we aim to explore the reform of our teaching and take online teaching as an example. Starting from the essence of online and offline blended teaching, this paper clarifies the differences between online and offline blended teaching and traditional course teaching and online network teaching, puts forward feasible strategies for the main problems existing in blended teaching, and provides new ideas for the implementation of online and offline blended teaching.
Joint global metric learning and local manifold preservation for scene recognition

Scene recognition plays an important role in many computer vision tasks. However, the recognition performance hardly meets the development of computer vision, since scene images show large variations in spatial position, illumination, and scale. To address this issue, a joint global metric learning and local manifold preservation (JGML-LMP) approach is proposed. First, we formulate a new global metric learning problem based on the cluster centers of each specific class, allowing to capture the global discriminative information with more informative samples. Second, in order to exploit the local manifold structure, we introduce an adaptive nearest neighbors constraint through which the local intrinsic relationships can be preserved in the new metric space instead of the Euclidean space. Third, through performing global metric learning and local manifold preservation jointly within a unified optimization framework, our approach can take advantage of both global and local information, and hence produces more discriminative and robust feature representations for scene recognition. Extensive experiments on four benchmark scene datasets demonstrate the superiority of the proposed method over state-of-the-art methods.
The Construction of a Prediction Model for the Teaching Effect of Two Courses Education in Colleges and Universities Based on Machine Learning Algorithms

In this era of machine learning being applied to various fields, its use in college Two courses education has gradually become the next outlet. The reform and innovation based on machine learning is an upgrade that must be carried out in Two courses education. In this experiment, the purpose of predicting the effect of Two courses classroom teaching is achieved by rationally using the Apriori algorithm in the machine learning algorithm to construct a prediction model. After modeling and verification, it has been shown the prediction model of Two courses classroom and extracurricular teaching effect using the machine. Apriori algorithm technology can enhance the participation and interaction of Two courses education, stimulate the interest of college students in Two courses education, and improve their enthusiasm. The selection and basic information of Apriori algorithm and the prediction for practical application have been studied. The accuracy of the prediction model is investigated. Compared with the logistic regression algorithm, it is concluded that the model established by the Apriori algorithm selected in this experiment has higher accuracy. Finally, the prediction model is organically integrated into the Two courses of a university carefully selected by the experiment. After a semester of intensive use, and then algorithm effects, the difference in accuracy is no more than 2%, far better than the expected 6%.
Selection of unlabeled source domains for domain adaptation in remote sensing

—In the context of supervised learning techniques, it can be desirable to utilize existing prior knowledge from a source domain to estimate a target variable in a target domain by exploiting the concept of domain adaptation. This is done to alleviate the costly compilation of prior knowledge, i.e., training data. Here, our goal is to select a single source domain for domain adaptation from multiple potentially helpful but unlabeled source domains. The training data is solely obtained for a source domain if it was identified as being relevant for estimating the target variable in the corresponding target domain by a selection mechanism. From a methodological point of view, we propose unsupervised source selection by voting from (an ensemble of) similarity metrics that follow aligned marginal distributions regarding image features of source and target domains. Thereby, we also propose an unsupervised pruning heuristic to solely include robust similarity metrics in an ensemble voting scheme. We provide an evaluation of the methods by learning models from training data sets created with Level-of-Detail-1 building models and regress built-up density and height on Sentinel-2 satellite imagery. To evaluate the domain adaptation capability, we learn and apply models interchangeably for the four largest cities in Germany. Experimental results underline the capability of the methods to obtain more frequently higher accuracy levels with an improvement of up to 10% regarding the most robust selection mechanisms compared to random source-target domain selections.
The Status Quo of College Students’ Participation in English Online Learning in the Blended Learning Environment and the Ways to Improve It

In college English teaching, online resources can meet students' self-study needs and show them a variety of English knowledge. Teachers should reasonably design teaching according to the characteristics of disciplines, and give students sufficient participation to conduct online self-study and online training. Therefore, online learning can make full use of students' spare time. In this paper, the current situation of college students' participation in online English learning and the ways to improve it are studied under the blended learning environment. The research shows that more than 88% of learners can finish online learning assignments on time. When the number of experiments reaches 60, 42.97% of them can finish online learning assignments on time, 29.51% of them can partially finish online learning assignments on time, and 21.94% of them cannot. In the process of online learning, autonomy and participation are high, and few of them are in a passive state, so they follow up online learning in time. The richness of college students' English online learning content under the blended learning environment provides a foundation, and college English teachers can optimize the learning content according to their lack of practical knowledge, which greatly improves college students' participation in online English learning.
A Pattern Dictionary Method for Anomaly Detection

In this paper, we propose a compression-based anomaly detection method for time series and sequence data using a pattern dictionary. The proposed method is capable of learning complex patterns in a training data sequence, using these learned patterns to detect potentially anomalous patterns in a test data sequence. The proposed pattern dictionary method uses a measure of complexity of the test sequence as an anomaly score that can be used to perform stand-alone anomaly detection. We also show that when combined with a universal source coder, the proposed pattern dictionary yields a powerful atypicality detector that is equally applicable to anomaly detection. The pattern dictionary-based atypicality detector uses an anomaly score defined as the difference between the complexity of the test sequence data encoded by the trained pattern dictionary (typical) encoder and the universal (atypical) encoder, respectively. We consider two complexity measures: the number of parsed phrases in the sequence, and the length of the encoded sequence (codelength). Specializing to a particular type of universal encoder, the Tree-Structured Lempel-Ziv (LZ78), we obtain a novel non-asymptotic upper bound, in terms of the Lambert W function, on the number of distinct phrases resulting from the LZ78 parser. This non-asymptotic bound determines the range of anomaly score. As a concrete application, we illustrate the pattern dictionary framework for constructing a baseline of health against which anomalous deviations can be detected.
Learning to recommend journals for submission based on embedding models

Due to the rapid development of electronic journals, selecting appropriate journals to publish research papers has become a significant challenge to researchers. Sometimes, even a high-quality paper may get rejected from the editor due to the mismatch between the topic of the paper and the scope of the journal. To address this issue, we present a framework of learning to recommend journals for submission based on embedding models to assist researchers in journal selection. Specifically, the journal recommendation problem is formulated in the context of multi-class classification, where the Bidirectional Encoder Representations from Transformers (BERT) is deployed to extract the text-level features of representing papers and the AutoEncoder (AE) network is adopted to obtain the feature representation of each journal from the relationship matrix of the paper-journal bipartite graph. The final recommendation of journals is made by using a scoring function and a Softmax classifier. Experimental results obtained on the closed dataset of 10 different journals and the DBLP dataset indicate that we proposed method outperforms several classical approaches in terms of accuracy, F1, MRR, etc. Furthermore, we introduce information entropy as an evaluation index and analyze the model performance from the perspective of prediction uncertainty. This study provides a new approach to the journal recommendation task, and researchers can choose the appropriate embedding methods according to the actual problem.
Dual Bidirectional Graph Convolutional Networks for Zero-shot Node Classification

Zero-shot node classification is a very important challenge for classical semi-supervised node classification algorithms, such as Graph Convolutional Network (GCN) which has been widely applied to node classification. In order to predict the unlabeled nodes from unseen classes, zero-shot node classification needs to transfer knowledge from seen classes to unseen classes. It is crucial to consider the relations between the classes in zero-shot node classification. However, the GCN only considers the relations between the nodes, not the relations between the classes. Therefore, the GCN can not handle the zero-shot node classification effectively. This paper proposes a Dual Bidirectional Graph Convolutional Networks (DBiGCN) that consists of dual BiGCNs from the perspective of the nodes and the classes, respectively. The BiGCN can integrate the relations between the nodes and between the classes simultaneously in an united network. In addition, to make the dual BiGCNs work collaboratively, a label consistency loss is introduced, which can achieve mutual guidance and mutual improvement between the dual BiGCNs. Finally, the experimental results on real-world graph data sets verify the effectiveness of the proposed method.
A Generalized Backward Compatibility Metric

Retraining a classifier with new data is inseparable from ML/AI applications, but most of the existing ML methods do not take into account the backward compatibility of predictions. That is, although the overall performance of a new classifier is improved, users will be confused by the wrong predictions of the new classifier, especially when the predictions of the old classifier are correct for the same samples. To this end, several metrics and learning methods for backward compatibility have been actively studied recently. Despite significant interest in backward compatibility, the metrics and methods are not well known from a theoretical perspective. In this paper, we first analyze the existing backward compatibility metrics and reveal that these metrics essentially assess the same quantity between old and new models. In addition, to obtain a unified view of backward compatibility metrics, we propose a generalized backward compatibility (GBC) metric that can represent the existing backward compatibility metrics. We formulate a learning objective based on the GBC metric and derive the estimation error bound, and the result is applied to one of the existing methods. Through further analysis, we reveal that the existing backward compatibility metrics are not suitable for imbalanced classification. We then design a backward compatibility metric for imbalanced classification on the basis of the GBC metric and empirically demonstrate the practicality of the proposed metric.
Pretraining Representations of Multi-modal Multi-query E-commerce Search

The importance of modeling contextual information within a search session has been widely acknowledged. However, learning representations of multi-query multi-modal (MM) search, in which Mobile Taobao users repeatedly submit textual and visual queries, remains unexplored in literature. Previous work which learns task-specific representations of textual query sessions fails to capture diverse query types and correlations in MM search sessions. This paper presents to represent MM search sessions by heterogeneous graph neural network (HGN). A multi-view contrastive learning framework is proposed to pretrain the HGN, with two views to model different intra-query, inter-query, and inter-modality information diffusion in MM search. Extensive experiments demonstrate that, the pretrained session representation can benefit state-of-the-art baselines on various downstream tasks, such as personalized click prediction, query suggestion, and intent classification.
Integrity Authentication in Tree Models

Tree models are very widely used in practice of machine learning and data mining. In this paper, we study the problem of model integrity authentication in tree models. In general, the task of model integrity authentication is the design & implementation of mechanisms for checking/detecting whether the model deployed for the end-users has been tampered with or compromised, e.g., malicious modifications on the model. We propose an authentication framework that enables the model builders/distributors to embed a signature to the tree model and authenticate the existence of the signature by only making a small number of black-box queries to the model. To the best of our knowledge, this is the first study of signature embedding on tree models. Our proposed method simply locates a collection of leaves and modifies their prediction values, which does not require any training/testing data nor any re-training. The experiments on a large number of public classification datasets confirm that the proposed signature embedding process has a high success rate while only introducing a minimal accuracy loss.