Reviewerly/exHarmony huggingface dataset:
Examining the utility and usability of mobile instant messaging in a graduate-level course: A usefulness theoretical perspective

This study examined the usefulness of a mobile instant messaging (MIM) tool to support teaching and learning. Taking a usefulness theoretical perspective, we examined in detail the utility and usability of using WeChat in a postgraduate-level course. Multiple types of data were collected and analysed, including MIM interaction records, questionnaire surveys and semi-structured interviews. The results show that, overall, MIM can be utilised in a variety of ways to support teaching and learning. These include promoting transmissive, demonstrative, dialogic, journaling, helpline and notebook activities. We also found MIM to be particularly suitable for promoting affective social presence (expression of emotions, self-disclosure) and cohesive social presence (phatics, vocative). Four specific technological attributes of MIM that facilitated information sharing among students were reported. Implications for practice and research, including challenges and possible solutions, are proposed, followed by a discussion of the study’s limitations and suggestions for future research.
Rule mining in maintenance: Analysing large knowledge bases

Association rule mining is a very powerful tool for extracting knowledge from records contained in industrial databases. A difficulty is that the mining process may result in a huge set of rules that may be difficult to analyse. This problem is often addressed by an a priori filtering of the candidate rules, that does not allow the user to have access to all the potentially interesting knowledge. Another popular solution is visual mining, where visualization techniques allow to browse through the rules. We suggest in this article a different approach: generating a large number of rules as a first step, then drill-down the produced rule base using alternatively semantic analysis (based on a priori knowledge) and objective analysis (based on numerical characteristics of the rules). It will be shown on real industrial examples in the maintenance domain that UML Class Diagrams may provide an efficient support for subjective analysis, the practical management of the rules (display, sorting and filtering) being insured by a classical Spreadsheet.
Supervised graph regularization based cross media retrieval with intra and inter-class correlation

With the rapid development of internet technology, mining and retrieving the information from internet accurately is an urgent problem, among which, cross media retrieval becomes a hot spot of current research. This paper proposes a cross media retrieval approach, which learns two couples of projections based on different retrieval tasks. We first learn a common subspace to project heterogeneous media data to the isomorphic subspace, to measure the similarity of the heterogeneous media data in the isomorphic subspace. Second, we build isomorphic and heterogeneous adjacent graphs to preserve the correlations of the cross media data. Then we combine the two processes together to learn a common subspace. We also consider intra-class and inter-class similarity of images or texts in the unified framework. Third, the L2 norm is used to perform feature selection for different media data. Experimental results on three datasets demonstrate the effectiveness of the proposed approach.
Better software analytics via “DUO”: Data mining algorithms using/used-by optimizers

This paper claims that a new field of empirical software engineering research and practice is emerging: data mining using/used-by optimizers for empirical studies, or DUO. For example, data miners can generate models that are explored by optimizers. Also, optimizers can advise how to best adjust the control parameters of a data miner. This combined approach acts like an agent leaning over the shoulder of an analyst that advises "ask this question next" or "ignore that problem, it is not relevant to your goals". Further, those agents can help us build "better" predictive models, where "better" can be either greater predictive accuracy or faster modeling time (which, in turn, enables the exploration of a wider range of options). We also caution that the era of papers that just use data miners is coming to an end. Results obtained from an unoptimized data miner can be quickly refuted, just by applying an optimizer to produce a different (and better performing) model. Our conclusion, hence, is that for software analytics it is possible, useful and necessary to combine data mining and optimization using DUO.
Generative Adversarial Network-Based Method for Transforming Single RGB Image Into 3D Point Cloud

Three-dimensional (3D) point clouds are important for many applications, including object tracking and 3D scene reconstruction. Point clouds are usually obtained from laser scanners, but their high cost impedes the widespread adoption of this technology. We propose a method to generate the 3D point cloud corresponding to a single red–green–blue (RGB) image. The method retrieves high-quality 3D data from two-dimensional (2D) images captured by conventional cameras, which are generally less expensive. The proposed method comprises two stages. First, a generative adversarial network generates a depth image estimation from a single RGB image. Then, the 3D point cloud is calculated from the depth image. The estimation relies on the parameters of the depth camera employed to generate the training data. The experimental results verify that the proposed method provides high-quality 3D point clouds from single 2D images. Moreover, the method does not require a PC with outstanding computational resources, further reducing implementation costs, as only a moderate-capacity graphics processing unit can efficiently handle the calculations.
Learning from Web Data Using Adversarial Discriminative Neural Networks for Fine-Grained Classification

Fine-grained classification is absorbed in recognizing the subordinate categories of one field, which need a large number of labeled images, while it is expensive to label these images. Utilizing web data has been an attractive option to meet the demands of training data for convolutional neural networks (CNNs), especially when the well-labeled data is not enough. However, directly training on such easily obtained images often leads to unsatisfactory performance due to factors such as noisy labels. This has been conventionally addressed by reducing the noise level of web data. In this paper, we take a fundamentally different view and propose an adversarial discriminative loss to advocate representation coherence between standard and web data. This is further encapsulated in a simple, scalable and end-to-end trainable multi-task learning framework. We experiment on three public datasets using large-scale web data to evaluate the effectiveness and generalizability of the proposed approach. Extensive experiments demonstrate that our approach performs favorably against the state-of-the-art methods.
A Two-Individual Based Evolutionary Algorithm for the Flexible Job Shop Scheduling Problem

Population-based evolutionary algorithms usually manage a large number of individuals to maintain the diversity of the search, which is complex and time-consuming. In this paper, we propose an evolutionary algorithm using only two individuals, called master-apprentice evolutionary algorithm (MAE), for solving the flexible job shop scheduling problem (FJSP). To ensure the diversity and the quality of the evolution, MAE integrates a tabu search procedure, a recombination operator based on path relinking using a novel distance definition, and an effective individual updating strategy, taking into account the multiple complex constraints of FJSP. Experiments on 313 widely-used public instances show that MAE improves the previous best known results for 47 instances and matches the best known results on all except 3 of the remaining instances while consuming the same computational time as current state-of-the-art metaheuristics. MAE additionally establishes solution quality records for 10 hard instances whose previous best values were established by a well-known industrial solver and a state-of-the-art exact method.
Dirichlet Multinomial Mixture with Variational Manifold Regularization: Topic Modeling over Short Texts

Conventional topic models suffer from a severe sparsity problem when facing extremely short texts such as social media posts. The family of Dirichlet multinomial mixture (DMM) can handle the sparsity problem, however, they are still very sensitive to ordinary and noisy words, resulting in inaccurate topic representations at the document level. In this paper, we alleviate this problem by preserving local neighborhood structure of short texts, enabling to spread topical signals among neighboring documents, so as to correct the inaccurate topic representations. This is achieved by using variational manifold regularization, constraining the close short texts should have similar variational topic representations. Upon this idea, we propose a novel Laplacian DMM (LapDMM) topic model. During the document graph construction, we further use the word mover’s distance with word embeddings to measure document similarities at the semantic level. To evaluate LapDMM, we compare it against the state-of-theart short text topic models on several traditional tasks. Experimental results demonstrate that our LapDMM achieves very significant performance gains over baseline models, e.g., achieving even about 0.2 higher scores on clustering and classification tasks in many cases.
Dual Semi-Supervised Learning for Facial Action Unit Recognition

Current works on facial action unit (AU) recognition typically require fully AU-labeled training samples. To reduce the reliance on time-consuming manual AU annotations, we propose a novel semi-supervised AU recognition method leveraging two kinds of readily available auxiliary information. The method leverages the dependencies between AUs and expressions as well as the dependencies among AUs, which are caused by facial anatomy and therefore embedded in all facial images, independent on their AU annotation status. The other auxiliary information is facial image synthesis given AUs, the dual task of AU recognition from facial images, and therefore has intrinsic probabilistic connections with AU recognition, regardless of AU annotations. Specifically, we propose a dual semi-supervised generative adversarial network for AU recognition from partially AU-labeled and fully expressionlabeled facial images. The proposed network consists of an AU classifier C, an image generator G, and a discriminator D. In addition to minimize the supervised losses of the AU classifier and the face generator for labeled training data, we explore the probabilistic duality between the tasks using adversary learning to force the convergence of the face-AU-expression tuples generated from the AU classifier and the face generator, and the ground-truth distribution in labeled data for all training data. This joint distribution also includes the inherent AU dependencies. Furthermore, we reconstruct the facial image using the output of the AU classifier as the input of the face generator, and create AU labels by feeding the output of the face generator to the AU classifier. We minimize reconstruction losses for all training data, thus exploiting the informative feedback provided by the dual tasks. Within-database and cross-database experiments on three benchmark databases demonstrate the superiority of our method in both AU recognition and face synthesis compared to state-of-the-art works.
Bayesian Deep Collaborative Matrix Factorization

In this paper, we propose a Bayesian Deep Collaborative Matrix Factorization (BDCMF) algorithm for collaborative filtering (CF). BDCMF is a novel Bayesian deep generative model that learns user and item latent vectors from users’ social interactions, contents of items as the auxiliary information and user-item rating (feedback) matrix. It alleviates the problem of matrix sparsity by incorporating items’ auxiliary and users’ social information into the model. It can learn more robust and dense latent representations by integrating deep learning into Bayesian probabilistic framework. As being one of deep generative models, it has both non-linearity and Bayesian nature. Additionally, in BDCMF, we derive an efficient EM-style point estimation algorithm for parameter learning. To further improve recommendation performance, we also derive a full Bayesian posterior estimation algorithm for inference. Experiments conducted on two sparse datasets show that BDCMF can significantly outperform the state-of-the-art CF methods.
Weighted Oblique Decision Trees

Decision trees have attracted much attention during the past decades. Previous decision trees include axis-parallel and oblique decision trees; both of them try to find the best splits via exhaustive search or heuristic algorithms in each iteration. Oblique decision trees generally simplify tree structure and take better performance, but are always accompanied with higher computation, as well as the initialization with the best axis-parallel splits. This work presents the Weighted Oblique Decision Tree (WODT) based on continuous optimization with random initialization. We consider different weights of each instance for child nodes at all internal nodes, and then obtain a split by optimizing the continuous and differentiable objective function of weighted information entropy. Extensive experiments show the effectiveness of the proposed algorithm.
Towards Automated Semi-Supervised Learning

Automated Machine Learning (AutoML) aims to build an appropriate machine learning model for any unseen dataset automatically, i.e., without human intervention. Great efforts have been devoted on AutoML while they typically focus on supervised learning. In many applications, however, semisupervised learning (SSL) are widespread and current AutoML systems could not well address SSL problems. In this paper, we propose to present an automated learning system for SSL (AUTO-SSL). First, meta-learning with enhanced meta-features is employed to quickly suggest some instantiations of the SSL techniques which are likely to perform quite well. Second, a large margin separation method is proposed to fine-tune the hyperparameters and more importantly, alleviate performance deterioration. The basic idea is that, if a certain hyperparameter owns a high quality, its predictive results on unlabeled data may have a large margin separation. Extensive empirical results over 200 cases demonstrate that our proposal on one side achieves highly competitive or better performance compared to the state-of-the-art AutoML system AUTO-SKLEARN and classical SSL techniques, on the other side unlike classical SSL techniques which often significantly degenerate performance, our proposal seldom suffers from such deficiency.
Automatic detection technology of sports athletes based on image recognition technology

In order to improve the motion recognition effect of sports athletes based on image recognition technology, this study takes the current common diving athletes as the research material in the actual research, and combines the research status of image recognition to study the athlete's motion recognition from image processing. Simultaneously, in this study, the gradient segmentation method is used to segment the image, the research object is segmented from the video image, the traditional image grayscale method is improved, and the image segmentation algorithm adapted to the diving motion is obtained. On this basis, this study combines Gaussian mixture background modeling and background subtraction to achieve the detection and extraction of target human body regions, and uses morphological operators to deal with noise and void phenomena in foreground images. The example analysis shows that the proposed method has certain practicality and can provide theoretical reference for subsequent related research.
Kinematic Analysis of Pianists' Expressive Performances of Romantic Excerpts: Applications for Enhanced Pedagogical Approaches

Established pedagogical theories for classical piano usually do not consider the essential relationship between the musical structure, whole body movements, and expression. Research focusing on musicians' expression has shown that body movements reflect the performer's understanding of the musical structure. However, most studies to date focus on the performance of a single piece at a time, leaving unanswered the question on how structural parameters of pieces with varied technical difficulties influence pianists' movements. In this study, ten pianists performed three contrasting Romantic excerpts in terms of technical level and character, while motion data was collected with a passive infrared motion capture system. We observed how pianists modulate their performances for each of the three pieces and measured the absolute difference in percentage of duration and quantity of motion (QoM) between four expressive conditions (normal, deadpan, exaggerated, immobile). We analyzed common patterns within the time-series of position data to investigate whether pianists embody musical structure in similar ways. A survey was filled in by pianists to understand how they conceive the relationship between body movements and musical structure. Results show that the variation in duration between the exaggerated and deadpan conditions was significant in one measure for one of the excerpts, and that tempo was less affected by the QoM used than by the level of expression. By applying PCA on the pianists' position data, we found that the head QoM is an important parameter for communicating different expressions and structural features. Significant variations in head QoM were found in the immobile and deadpan conditions if compared to the normal condition, only in specific regions of the score. Recurrent head movements occurred along with certain structural parameters for two of the excerpts only. Altogether, these results indicate that the analysis of pianists' body movements and expressive intentions should be carried out in relation to the specific musical context, being dependent on the technical level of the pieces and the repertoire. These results, combined with piano teaching methods, may lead to the development of new approaches in instrumental lessons to help students make independent choices regarding body movements and expression.
Addressing the issue of processing element under-utilization in general-purpose systolic deep learning accelerators

As an energy-efficient hardware solution for deep neural network (DNN) inference, systolic accelerators are particularly popular in both embedded and datacenter computing scenarios. Despite their excellent performance and energy efficiency, however, systolic DNN accelerators are naturally facing a resource under-utilization problem - not all DNN models can well match the fixed processing elements (PEs) in a systolic array implementation, because typical DNN models vary significantly from applications to applications. Consequently, state-of-the-art hardware solutions are not expected to deliver the nominal (peak) performance and energy efficiency as claimed because of resource under-utilization. To deal with this dilemma, this study proposes a novel systolic DNN accelerator with a flexible computation mapping and dataflow scheme. By providing three types of parallelism and dynamically switching among them: channel-direction mapping, planar mapping, and hybrid, our accelerator offers the adaptability to match various DNN models to the fixed hardware resources, and thus, enables flexibly exploiting PE provision and data reuse for a wide range of DNN models to achieve optimal performance and energy efficiency.
Video Summarization via Nonlinear Sparse Dictionary Selection

Video summarization (VS) is to identify important content from a given video, which can help users quickly comprehend video content. Recently, sparse dictionary selection (SDS) has demonstrated to be an effective solution for VS problems, which generally assumes a linear relationship between keyframes and non-keyframes. However, this assumption is not always true for video frames which possess intrinsic nonlinear structures and properties. In this paper, by exploiting the nonlinearity between video frames, a nonlinear SDS model is formulated for VS, in which the nonlinearity is transformed to linearity by projecting a video to a high-dimensional feature space induced by a kernel function. We also propose two greedy optimization algorithms to solve the resulting model, namely the standard kernel SDS (KSDS) greedy algorithm and the robust KSDS greedy algorithm with a backtracking strategy. In order to achieve an intuitive and flexible configuration of the VS process, an adaptive criterion, namely energy ratio, is devised to produce video summaries with different lengths for different video contents. Experimental results on two different benchmark video datasets demonstrate that the proposed algorithm outperforms several state-of-the-art VS algorithms.
SSPA-LBS: Scalable and Social-Friendly Privacy-Aware Location-Based Services

Privacy-aware location-based service (PA-LBS) preserves LBS users' privacy but undesirably sacrifices service quality. In order to balance the two factors with satisfactory user experience, existing frameworks are faced with two barriers, that is, scalability and social-friendliness. First, existing schemes do not enable LBS users to flexibly scale their privacy level on service provision. Such a lack of scalability easily results in either unacceptable service-quality degradation or insufficient privacy protection and fails to meet dynamic user requirements. Second, existing schemes handle privacy protection by merely considering the trust relationship between users and servers but ignore the complex trust relationships among users. As a result, users cannot preserve privacy in location-based social services that involve user-to-user interactions. In this paper, we present the first scalable and social-friendly PA-LBS system. In particular, we propose a novel camouflage algorithm with a formal privacy guarantee that enables LBS users to expose their location information by scaling two privacy related factors, that is, camouflage range and place type. Furthermore, we apply the scalable ciphertext policy attribute-based encryption algorithm to enable LBS users to effectively control the access from other users to their location information. Moreover, we also demonstrated the operational efficiency of the proposed system through successful implementations on Android devices.