Reviewerly/exHarmony huggingface dataset:
The Scholarly Impact of Books Acquired via Approval Plan Selection, Librarian Orders, and Patron-Driven Acquisitions as Measured by Citation Counts

Patron-driven acquisition has been an important, if contentious, topic for decades, with numerous programs having been piloted, adopted, and reported on, largely favorably, in the library literature. Still, questions and doubts persist for academic libraries, especially where the composition of vendor plans and packages and the judgment of patrons are concerned. Past literature has approached the assessment of patron-driven acquisition by analyzing circulation/usage, comparing peer-library holdings, seeking patrons’ or librarians’ judgments of utility and suitability, looking for evidence of collection imbalances, and testing for overlap in patrons’ and librarians’ purchases. To contribute to this literature, this study addresses scholarly impact and examines whose selections—approval plans’, librarians’, or patrons’—have been most heavily cited. For the social sciences, the sciences, and the humanities, the authors gathered topic-matched random samples of books acquired via approval plans and librarian orders during the first five years of operation of their institutions’ interlibrary loan purchase-on-demand patron-driven acquisition program and compared their citation counts to the counts of books acquired via the program. Google Scholar was employed to tally citations.
Cornac: Tackling Huge Graph Visualization with Big Data Infrastructure

The size of available graphs has drastically increased in recent years. The real-time visualization of graphs with millions of edges is a challenge but is necessary to grasp information hidden in huge datasets. This article presents an end-to-end technique to visualize huge graphs using an established Big Data ecosystem and a lightweight client running in a Web browser. For that purpose, levels of abstraction and graph tiles are generated by a batch layer and the interactive visualization is provided using a serving layer and client-side real-time computation of edge bundling and graph splatting. A major challenge is to create techniques that work without moving data to an ad hoc system and that take advantage of the horizontal scalability of these infrastructures. We introduce two novel scalable algorithms that enable to generate a canopy clustering and to aggregate graph edges. These two algorithms are both used to produce levels of abstraction and graph tiles. We prove that our technique guarantee a quality of visualization by controlling both the necessary bandwidth required for data transfer and the quality of the produced visualization. Furthermore, we demonstrate the usability of our technique by providing a complete prototype. We present benchmarks on graphs with millions of elements and we compare our results to those obtained by state of the art techniques. Our results show that new Big Data technologies can be incorporated into visualization pipeline to push out the size limits of graphs one can visually analyze.
Gaussian process approach for metric learning

Learning appropriate distance metric from data can significantly improve the performance of machine learning tasks under investigation. In terms of the distance metric representation forms in the models, distance metric learning (DML) approaches can be generally divided into two categories: parametric and non-parametric. The first category needs to make parametric assumption on the distance metric and learns the parameters, easily leading to overfitting and limiting model flexibility. The second category abandons the above assumption and instead, directly learns a non-parametric distance metric whose complexity can be adjusted according to the number of available training data, and makes the model representation relatively flexible. In this paper we follow the idea of the latter category and develop a non-parametric DML approach. The main challenge of our work concerns the formulation and learning of non-parametric distance metric. To meet this, we use Gaussian Process (GP) to extend the bilinear similarity into a non-parametric metric (here we abuse the concept of metric) and then learn this metric for specific task. As a result, our approach learns not only nonlinear metric that inherits the flexibility of GP but also representative features for the follow-up tasks. Compared with the existing GP-based feature learning approaches, our approach can provide accurate similarity prediction in the new feature space. To the best of our knowledge, this is the first work that directly uses GP as non-parametric metric. In the experiments, we compare our approach with related GP-based feature learning approaches and DML approaches respectively. The results demonstrate the superior performance of our approach.
Structure optimization for echo state network based on contribution

Echo State Network (ESN) is a recurrent neural network with a large, randomly generated recurrent part called the dynamic reservoir.Only the output weights are modified during training.However, proper balancing of the trade-off between the structure and performance for ESN remains a difficult task.In this paper, a structure optimized method for ESN based on contribution is proposed to simplify its network structure and improve its performance.First, we evaluate the contribution of reservoir neurons.Second, we present a pruning mechanism to remove the unimportant connection weights of reservoir neurons with low contribution.Finally, the new output weights are learned with the pseudo inverse method.The novel optimized ESN, named C-ESN, is tested on a Lorenz chaotic time-series prediction and an actual municipal sewage treatment system.The simulation results show that the C-ESN can have better prediction and generalization performance than ESN.
Distracter-aware tracking via correlation filter

Visual object tracking is an attractive issue in the field of computer vision. Recently, correlation filters (CF) based trackers formulate the training process by solving the regression in the Fourier domain and show a great efficiency for tracking task. However, its efficiency collapsed when the distracters appear in the background. To improve the robustness of these trackers, we propose a distracter-aware tracking method via correlation filter, which utilizes the positions of target center and distracters. Since most of CF based trackers directly use the Gaussian-shaped label map as the regression target, which may lead the discriminability of tracker reduced. Differing from previous work, we first use the response map of CF model to detect the information of distracters and then utilize these information to design a distracter-aware label map as the regression target for the training process. To further promote the robustness of the tracker, we design a re-detection scheme which uses positive and negative filters to refine the tracking results when the distracters appear. The proposed method not only detect and capture the discriminative information to learn a distracter-aware label map for the filter training, but also utilize these information to train the positive and negative filters for object re-detection, which enhances the robustness of the tracker. We evaluate our proposed method on both the standard OTB2013 and OTB2015 benchmarks, the experimental results show the effectiveness and robustness of the proposed method.
Collaborative filtering recommendation algorithm towards intelligent community

Collaborative filtering recommendation algorithm is a successful and widely used recommendation method in recommender system. In the collaborative filtering recommendation algorithm, the key step is to find the nearest neighbor. Combined with the application scenario of the intelligent community, Pearson Correlation Coefficient is introduced to improve the accuracy of similarity calculation. At the same time, considering that the residents are relatively fixed, the K-means clustering algorithm can be combined with the user-based collaborative filtering recommendation algorithm to improve the sparsity of the matrix and improve the speed of recommendation. Validation results on MovieLens dataset show that the collaborative filtering recommendation algorithm integrating with K-means clustering algorithm and community factors can more effectively predict the actual user rating in the community application scenario, and improve the recommendation accuracy and recommendation speed, compared with the traditional collaborative filtering recommendation algorithm.
Improved Performance Measures for Video Quality Assessment Algorithms Using Training and Validation Sets

The training and performance analysis of objective video quality assessment algorithms is complex due to the huge variety of possible content classes and transmission distortions. Several secondary issues such as free parameters in machine learning algorithms and alignment of subjective datasets put an additional burden on the developer. In this paper, three subsequent steps are presented to address such issues. First, the content and coding parameter space of a large-scale database is used to select dedicated subsets for training objective algorithms. This aims at providing a method for selecting the most significant contents and coding parameters from all imaginable combinations. In the practical case where only a limited set is available, it also helps us to avoid redundancy in the training subset selection. The second step is a discussion on performance measures for algorithms that employ machine-learning methods. The particularity of the performance measures is that the quality of the training and verification datasets is taken into consideration. Common issues that often use existing measures are presented, and improved or complementary methods are proposed. The measures are applied to two examples of no-reference objective assessment algorithms using the aforementioned subsets of the large-scale database. While limited in terms of practical applications, this sandbox approach of objectively predicting an objectively evaluated video sequences allows for eliminating additional influence factors from subjective studies. In the third step, the proposed performance measures are applied to the practical case of training and analyzing assessment algorithms on readily available subjectively annotated image datasets. The presentation method in this part of the paper can also be used as an exemplified recommendation for reporting in-depth information on the performance. Using this presentation method, future publications presenting newly developed quality assessment algorithms may be significantly improved.
Data encryption algorithm for e-commerce platform based on blockchain technology

Aiming at the poor encryption effect existing in the data encryption algorithm of e-commerce platform, and the data lost and distorted easily after encrypting, a data encryption algorithm based on blockchain technology is proposed in this paper. By analyzing the symmetric key algorithm and the public key algorithm, the DES encryption algorithm is described in detail. The two related technologies of digital envelopes and message authentication are analyzed to ensure the accuracy of the data and the one time encryption of the data. Based on this, in order to ensure the effectiveness of encryption, the process of asymmetric encryption algorithm based on chaotic sequence of neural network and asymmetric encryption algorithm based on neural network chaotic attractor are analyzed, and the security is tested. While ensuring the accuracy of data, it improves the effect of data encryption and realizes the encryption of e-commerce platform data, which is to realize data encryption algorithm based on blockchain technology. Experimental results show that the~proposed algorithm can encrypt the data of e-commerce platform, and the encryption process is relatively simple, the encryption effect is better, and the accuracy of the encrypted data is relatively high, which provides a theoretical basis for further research of the subject.
Performance evaluation of a hybrid fuzzy logic controller based on genetic algorithm for three phase induction motor drive

&lt;p class="Author"&gt;&lt;span&gt;It is known that controlling the speed of a three phase Induction Motor (IM) under different operating conditions is an important task and this can be accomplished through the process of controlling the applied voltage on its stator circuit. Conventional Proportional- Integral- Differeantional (PID) controller takes long time in selecting the error signal gain values. In this paper a hybrid Fuzzy Logic Controller (FLC) with Genetic Algorithm (GA) is proposed to reduce the selected time for the optimized error signal gain values and as a result inhances the controller and system performance. The proposed controller FL with GA is designed, modeled and simulated using MATLAB/ software under different load torque motor operating condition. The simulation result shows that the closed loop system performance efficiency under the controller has a maximum value of 95.92%. In terms of efficiency and at reference speed signal of 146.53 rad/sec, this system performance shows an inhancement of 0.67%,0.49% and 0.05% with respect to the closed loop system efficiency performance of the PID, FL, and PID with GA controllers respectively. Also the simulation result of the well designed and efficient GA in speeding up the process of selecting the gain values, makes the system to have an efficiency improvement of 14.42% with respect to the open loop system performance.&lt;/span&gt;&lt;/p&gt;
Face detection in the operating room: comparison of state-of-the-art methods and a self-supervised approach

Face detection is a needed component for the automatic analysis and assistance of human activities during surgical procedures. Efficient face detection algorithms can indeed help to detect and identify the persons present in the room and also be used to automatically anonymize the data. However, current algorithms trained on natural images do not generalize well to the operating room (OR) images. In this work, we provide a comparison of state-of-the-art face detectors on OR data and also present an approach to train a face detector for the OR by exploiting non-annotated OR images.We propose a comparison of six state-of-the-art face detectors on clinical data using multi-view OR faces, a dataset of OR images capturing real surgical activities. We then propose to use self-supervision, a domain adaptation method, for the task of face detection in the OR. The approach makes use of non-annotated images to fine-tune a state-of-the-art detector for the OR without using any human supervision.The results show that the best model, namely the tiny face detector, yields an average precision of 0.556 at intersection over union of 0.5. Our self-supervised model using non-annotated clinical data outperforms this result by 9.2%.We present the first comparison of state-of-the-art face detectors on OR images and show that results can be significantly improved by using self-supervision on non-annotated data.
Multistream Classification with Relative Density Ratio Estimation

In supervised learning, availability of sufficient labeled data is of prime importance. Unfortunately, they are sparingly available in many real-world applications. Particularly when performing classification over a non-stationary data stream, unavailability of sufficient labeled data undermines the classifier’s long-term performance by limiting its adaptability to changes in data distribution over time. Recently, studies in such settings have appealed to transfer learning techniques over a data stream while detecting drifts in data distribution over time. Here, the data stream is represented by two independent non-stationary streams, one containing labeled data instances (called source stream) having a biased distribution compared to the unlabeled data instances (called target stream). The task of label prediction under this representation is called Multistream Classification, where instances in the two streams occur independently. While these studies have addressed various challenges in the multistream setting, it still suffers from large computational overhead mainly due to frequent bias correction and drift adaptation methods employed. In this paper, we focus on utilizing an alternative bias correction technique, called relative density-ratio estimation, which is known to be computationally faster. Importantly, we propose a novel mechanism to automatically learn an appropriate mixture of relative density that adapts to changes in the multistream setting over time. We theoretically study its properties and empirically demonstrate its superior performance, within a multistream framework called MSCRDR, on benchmark datasets by comparing with other competing methods.
Hypergraph Optimization for Multi-Structural Geometric Model Fitting

Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between data points. However, a hypergraph becomes extremely complicated when the input data include a large number of data points (usually contaminated with noises and outliers), which will significantly increase the computational burden. In order to overcome the above problem, we propose a novel hypergraph optimization based model fitting (HOMF) method to construct a simple but effective hypergraph. Specifically, HOMF includes two main parts: an adaptive inlier estimation algorithm for vertex optimization and an iterative hyperedge optimization algorithm for hyperedge optimization. The proposed method is highly efficient, and it can obtain accurate model fitting results within a few iterations. Moreover, HOMF can then directly apply spectral clustering, to achieve good fitting performance. Extensive experimental results show that HOMF outperforms several state-of-the-art model fitting methods on both synthetic data and real images, especially in sampling efficiency and in handling data with severe outliers.
Accurate and Interpretable Factorization Machines

Factorization Machines (FMs), a general predictor that can efficiently model high-order feature interactions, have been widely used for regression, classification and ranking problems. However, despite many successful applications of FMs, there are two main limitations of FMs: (1) FMs consider feature interactions among input features by using only polynomial expansion which fail to capture complex nonlinear patterns in data. (2) Existing FMs do not provide interpretable prediction to users. In this paper, we present a novel method named Subspace Encoding Factorization Machines (SEFM) to overcome these two limitations by using non-parametric subspace feature mapping. Due to the high sparsity of new feature representation, our proposed method achieves the same time complexity as the standard FMs but can capture more complex nonlinear patterns. Moreover, since the prediction score of our proposed model for a sample is a sum of contribution scores of the bins and grid cells that this sample lies in low-dimensional subspaces, it works similar like a scoring system which only involves data binning and score addition. Therefore, our proposed method naturally provides interpretable prediction. Our experimental results demonstrate that our proposed method efficiently provides accurate and interpretable prediction.
Adding Constraints to Bayesian Inverse Problems

Using observation data to estimate unknown parameters in computational models is broadly important. This task is often challenging because solutions are non-unique due to the complexity of the model and limited observation data. However, the parameters or states of the model are often known to satisfy additional constraints beyond the model. Thus, we propose an approach to improve parameter estimation in such inverse problems by incorporating constraints in a Bayesian inference framework. Constraints are imposed by constructing a likelihood function based on fitness of the solution to the constraints. The posterior distribution of the parameters conditioned on (1) the observed data and (2) satisfaction of the constraints is obtained, and the estimate of the parameters is given by the maximum a posteriori estimation or posterior mean. Both equality and inequality constraints can be considered by this framework, and the strictness of the constraints can be controlled by constraint uncertainty denoting a confidence on its correctness. Furthermore, we extend this framework to an approximate Bayesian inference framework in terms of the ensemble Kalman filter method, where the constraint is imposed by re-weighing the ensemble members based on the likelihood function. A synthetic model is presented to demonstrate the effectiveness of the proposed method and in both the exact Bayesian inference and ensemble Kalman filter scenarios, numerical simulations show that imposing constraints using the method presented improves identification of the true parameter solution among multiple local minima.
Deep Hierarchical Graph Convolution for Election Prediction from Geospatial Census Data

Geographic information systems’ (GIS) research is widely used within the social and physical sciences and plays a crucial role in the development and implementation by governments of economic, education, environment and transportation policy. While machine learning methods have been applied to GIS datasets, the uptake of powerful deep learning CNN methodologies has been limited in part due to challenges posed by the complex and often poorly structured nature of the data. In this paper, we demonstrate the utility of GCNNs for GIS analysis via a multi-graph hierarchical spatial-filter GCNN network model in the context of GIS systems to predict election outcomes using socio-economic features drawn from the 2016 Australian Census. We report a marked improvement in performance accuracy of Hierarchical GCNNs over benchmark generalised linear models and standard GCNNs, especially in semi-supervised tasks. These results indicate the widespread potential for GIS-GCNN research methods to enrich socio-economic GIS analysis, aiding the social sciences and policy development.
TransConv: Relationship Embedding in Social Networks

Representation learning (RL) for social networks facilitates real-world tasks such as visualization, link prediction and friend recommendation. Traditional knowledge graph embedding models learn continuous low-dimensional embedding of entities and relations. However, when applied to social networks, existing approaches do not consider the rich textual communications between users, which contains valuable information to describe social relationships. In this paper, we propose TransConv, a novel approach that incorporates textual interactions between pair of users to improve representation learning of both users and relationships. Our experiments on real social network data show TransConv learns better user and relationship embeddings compared to other state-of-theart knowledge graph embedding models. Moreover, the results illustrate that our model is more robust for sparse relationships where there are fewer examples.