Reviewerly/exHarmony huggingface dataset:
Representation learning using Attention Network and CNN for Heterogeneous networks

Network embedding (NE), also known as network representation learning (NRL), is a method to learn a low-dimensional latent representation of nodes in an information network. The real-world data is usually presented in the form of heterogeneous information network (HIN) with multiple types of nodes and edges. Because of the rich information in HINs, it is necessary for a network embedding method to incorporate this information into the low-dimensional potential representation of the nodes as much as possible. In this paper, we propose a semi-supervised representation learning model using a graph attention network and a convolutional neural network (CNN) for HINs, called RANCH. In the part of the graph attention network, we construct a heterogeneous graph attention network using heterogeneous edges to preserve the features of nodes and the structure of network. In the part of the CNN, we leverage a 1D-CNN sentence classification model from natural language processing (NLP) community by adopting edge-constrained truncated random walks to generate node sequences, which can be treated as a corpus of words and sentences. The latter part further integrates the structural information of the network on the basis of the previous part and strengthens the influence of the node’s label information on the node representation. We have performed experiments of node classification on three real-world datasets, and the result shows that our model performs better than the state-of-the-arts. • A network embedding method for heterogeneous information network is proposed. • Most network embedding methods require the use of meta-paths for semantic learning. • The semantic information is learned by multi-typed edges without meta-paths here. • The embeddings of all types of nodes in the network are learned at the same time. • Our model performs better in node classification than most state-of-the-art methods.
A Study on the Relative Importance of Convolutional Neural Networks in Visually-Aware Recommender Systems

Visually-aware recommender systems (VRSs) enhance the semantics of user-item interactions with visual features extracted from item images when they are available. Traditionally, VRSs leverage the representational power of pretrained convolutional neural networks (CNNs) to perform the item recommendation task. The adoption of CNNs is mainly attributed to their outstanding performance in representing visual data for supervised learning tasks, such as image classification. Their main drawback is that the learned representation of these networks is not entirely in line with the RS tasks — learning users’ preferences.This work aims to provide a better understanding of the representation power of pretrained CNNs commonly adopted by the community when integrated with state-of-the-art VRSs algorithms. In particular, we evaluate the recommendation performance of a suite of VRSs using several pretrained CNNs as the image feature extractors on two datasets from a real-world e-commerce platform. Additionally, we propose a novel qualitative and quantitative evaluation paradigm to assess the visual diversity of recommended items compared to the interacted user’s items.
A no-reference metric to assess quality of denoising for Magnetic Resonance images

Performance evaluation of algorithms used for denoising real-time Magnetic Resonance (MR) images and the selection of their operational parameters are difficult as noise-free ground-truth is unavailable. No-reference metrics that can reflect the quality of denoised images with respect to the strength of residual noise and inadvertent blur at edges are required in this context. A no-reference metric, termed as Objective Measure of Quality of Denoised Images (OMQDI), for assessing the quality of denoised MR images is proposed in this paper. OMQDI is a sum of two quality factors termed as Edge-Preservation Factor (EPF) and Noise Suppression Factor (NSF). EPF is computed from the sharpness of edges in the noisy input and denoised images and NSF is computed from the noise power in them. The sharpness is computed in the Wavelet domain. The sharpness is the nonlinearly weighted sum of cumulative log-energy of wavelet coefficients from three different levels of decomposition. Cumulative log-energy at any particular level of decomposition is computed from log-energy from LH, HL and HH sub-bands corresponding to that level of decomposition. The noise power is estimated from the global mean of local grey level variance with the help of a non-parametric model. The Pearson’s correlation with the subjective quality rating exhibited by No-Reference Structure Similarity (NRSS), Sparsity and Dominant orientation-based Quality Index (SDQI), MetricQ, Anisotropic Quality Index (AQI), Optimum Denoising Index (ODI) and OMQDI on 100 test datasets are 0.245 ± 0.1032, 0.5757 ± 0.2735, 0.5615 ± 0.2959, 0.6566 ± 0.2361, 0.8391 ± 0.085 and 0.9619 ± 0.0293, respectively. The computational time (in sec) of NRSS, SDQI, MetricQ, AQI, ODI and OMQDI are 0.0777 ± 0.0032, 0.1998 ± 0.1155, 2.4347 ± 0.0757, 5.4859 ± 1.4862, 0.2914 ± 0.0508 and 0.1382 ± 0.0164, respectively. OMQDI has good agreement with the subjective quality rating and it is fast in computation.
Federated Learning-based Active Authentication on Mobile Devices

User active authentication on mobile devices aims to learn a model that can correctly recognize the enrolled user based on device sensor information. Due to lack of negative class data, it is often modeled as a one-class classification problem. In practice, mobile devices are connected to a central server, e.g, all android-based devices are connected to Google server through internet. This device-server structure can be exploited by recently proposed Federated Learning (FL) and Split Learning (SL) frameworks to perform collaborative learning over the data distributed among multiple devices. Using FL/SL frameworks, we can alleviate the lack of negative data problem by training a user authentication model over multiple user data distributed across devices. To this end, we propose a novel user active authentication training, termed as Federated Active Authentication (FAA), that utilizes the principles of FL/SL. We first show that existing FL/SL methods are suboptimal for FAA as they rely on the data to be distributed homogeneously (i.e. IID) across devices, which is not true in the case of FAA. Subsequently, we propose a novel method that is able to tackle heterogeneous/non-IID distribution of data in FAA. Specifically, we first extract feature statistics such as mean and variance corresponding to data from each user which are later combined in a central server to learn a multi-class classifier and sent back to the individual devices. We conduct extensive experiments using three active authentication benchmark datasets (MOBIO, UMDAA-01, UMDAA-02) and show that such approach performs better than state-of-the-art one-class based FAA methods and is also able to outperform traditional FL/SL methods.
DeepAssociate: A deep learning model exploring sequential influence and history-candidate association for sequence recommendation

A remarkable progress in sequential recommendation field lies on deep learning techniques, where deep learning was widely used to capture user preference from behavior records. However, researchers usually place emphasis on sequential change while ignore the correlation between user's historical behaviors and candidate item's characteristics (history-candidate association), which leads to the inaccurate matching between target users and candidate items. In this paper, we proposed a deep learning model to explore both sequential influence and history-candidate association in sequential recommendation, namely DeepAssociate. First, we considered the history-candidate association in user preference representation and obtained it by two steps, including sequential influence extraction and association feature extraction. Then, by defining a weighted objective function, we introduced an integrated framework which makes a combination of sequential and association features extraction and prediction module to enhance recommendation performance. Experimental results on four real-world datasets demonstrated that DeepAssociate model outperformed state-of-the-art methods on recommendation performance. Furthermore, a series of extensive experiments indicated the benefit of utilizing history-candidate association feature in reducing model complexity and accelerating model convergence.
Biomedical Named Entity Recognition via Knowledge Guidance and Question Answering

In this work, we formulated the named entity recognition (NER) task as a multi-answer knowledge guided question-answer task (KGQA) and showed that the knowledge guidance helps to achieve state-of-the-art results for 11 of 18 biomedical NER datasets. We prepended five different knowledge contexts—entity types, questions, definitions, and examples—to the input text and trained and tested BERT-based neural models on such input sequences from a combined dataset of the 18 different datasets. This novel formulation of the task (a) improved named entity recognition and illustrated the impact of different knowledge contexts, (b) reduced system confusion by limiting prediction to a single entity-class for each input token (i.e., B , I , O only) compared to multiple entity-classes in traditional NER (i.e., B entity 1, B entity 2, I entity 1, I , O ), (c) made detection of nested entities easier, and (d) enabled the models to jointly learn NER-specific features from a large number of datasets. We performed extensive experiments of this KGQA formulation on the biomedical datasets, and through the experiments, we showed when knowledge improved named entity recognition. We analyzed the effect of the task formulation, the impact of the different knowledge contexts, the multi-task aspect of the generic format, and the generalization ability of KGQA. We also probed the model to better understand the key contributors for these improvements.
BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. Here, we propose BrcaSeg, an image analysis pipeline based on a convolutional neural network (CNN) model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin (H&E) stained histopathological images. The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas (TCGA) Program. BrcaSeg achieves a classification accuracy of 91.02%, which outperforms other state-of-the-art methods. Using this model, we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data. We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios. Gene Ontology (GO) enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes, whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues. Taken all together, our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors. BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.
A novel regularized asymmetric non-negative matrix factorization for text clustering

Non-negative matrix factorization (NMF) is a dimension reduction method that extracts semantic features from high-dimensional data. Most of the developed optimization methods for NMF only pay attention to how each feature vector of factorized matrices should be modeled, and ignore the relationships among feature vectors. Such a relationship among documents’ feature vectors provides better factorization for text clustering. This paper proposes a novel regularized asymmetric non-negative matrix factorization (RANMF) for text clustering. The proposed method puts regularized constraints on pairwise feature vectors by applying penalties using distance-based measures. We design a new cost function based on the Kullback–Leibler divergence and develop an optimization scheme to solve the cost function by suggesting novel multiplicative updating rules. The proposed method considers the documents from the same cluster closely together in the new representation space. Hence, the acquired parts-based representation has consistent cluster labeling with the original space and has a more discriminating ability. The complexity analysis showed that RANMF does not increase time cost by applying regularizers when comparing with the original NMF. Regarding experiments, the proposed RANMF converges very fast because it terminates in less than ten iterations. The complete proof of convergence and experimental results on the benchmark data sets demonstrate that the proposed multiplicative updating rules converge fast and achieve superior results compared to other algorithms.
Adaptive Two-Step Bearing-Only Underwater Uncooperative Target Tracking with Uncertain Underwater Disturbances

The bearing-only tracking of an underwater uncooperative target can protect maritime territories and allows for the utilization of sea resources. Considering the influences of an unknown underwater environment, this work aimed to estimate 2-D locations and velocities of an underwater target with uncertain underwater disturbances. In this paper, an adaptive two-step bearing-only underwater uncooperative target tracking filter (ATSF) for uncertain underwater disturbances is proposed. Considering the nonlinearities of the target’s kinematics and the bearing-only measurements, in addition to the uncertain noise caused by an unknown underwater environment, the proposed ATSF consists of two major components, namely, an online noise estimator and a robust extended two-step filter. First, using a modified Sage-Husa online noise estimator, the uncertain process and measurement noise are estimated at each tracking step. Then, by adopting an extended state and by using a robust negative matrix-correcting method in conjunction with a regularized Newton-Gauss iteration scheme, the current state of the underwater uncooperative target is estimated. Finally, the proposed ATSF was tested via simulations of a 2-D underwater uncooperative target tracking scenario. The Monte Carlo simulation results demonstrated the reliability and accuracy of the proposed ATSF in bearing-only underwater uncooperative tracking missions.
Integrating big data and cloud computing topics into the computing curricula: A modular approach

Big data and cloud computing collectively offer a paradigm shift in the way businesses are now acquiring, using, and managing information technology. This creates the need for every CS student to be equipped with foundational knowledge in this collective paradigm and possess some hands-on experience in deploying and managing big data applications in the cloud. This study argues that, for substantial coverage of big data and cloud computing concepts and skills, the relevant topics need to be integrated into multiple core courses across the CS curriculum rather than creating additional courses and performing a major overhaul of the curriculum. Our approach to including these topics is to develop autonomous competency-based learning modules for specific core courses in which their coverage might find an appropriate context. In this paper, four such modules are discussed, and our classroom experiences during these interventions are documented. Student performance data and survey results show reasonable success in attaining student learning outcomes, enhanced engagement, and interests.
FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems

We present FACESEC, a framework for fine-grained robustness evaluation of face recognition systems. FACESEC evaluation is performed along four dimensions of adversarial modeling: the nature of perturbation (e.g., pixel-level or face accessories), the attacker’s system knowledge (about training data and learning architecture), goals (dodging or impersonation), and capability (tailored to individual inputs or across sets of these). We use FACESEC to study five face recognition systems in both closed-set and open-set settings, and to evaluate the state-of-the-art approach for defending against physically realizable attacks on these. We find that accurate knowledge of neural architecture is significantly more important than knowledge of the training data in black-box attacks. Moreover, we observe that open-set face recognition systems are more vulnerable than closed-set systems under different types of attacks. The efficacy of attacks for other threat model variations, however, appears highly dependent on both the nature of perturbation and the neural network architecture. For example, attacks that involve adversarial face masks are usually more potent, even against adversarially trained models, and the ArcFace architecture tends to be more robust than the others.
Creating synthetic minority class samples based on autoencoder extreme learning machine

This paper reports a new method (simplified as AE-ELM-SynMin) to create the Synthetic Minority class samples for imbalanced classification based on AutoEncoder Extreme Learning Machine (AE-ELM). AE-ELM-SynMin first trains an AE-ELM which is a special ELM with the same input and output, i.e., the original minority class samples. Second, the crossover, mutation and filtration operations are conducted on the hidden-layer output of AE-ELM and then the synthetic hidden-layer output is obtained. Third, the synthetic minority class samples are created by decoding the synthetic hidden-layer output with output-layer weights of AE-ELM. AE-ELM-SynMin guarantees that the synthetic minority class has the higher information amount than original minority class and meanwhile keeps the consistent probability distribution with the original minority class. The experimental results demonstrate the better imbalanced classification performances of AE-ELM-SynMin in comparison with the regular synthetic minority over-sampling technique (Regular-SMOTE) and its variants, e.g., Borderline-SMOTE, Random-SMOTE, and SMOTE-IPF.
Superpixel alpha-expansion and normal adjustment for stereo matching

This paper presents a continuous stereo disparity estimation method based on superpixel segmentation and graph-cuts. We re-parameterize the disparity with a 3D tangent plane, and propose two algorithms to optimize the Markov Random Field (MRF) energy. The first algorithm, called superpixel α-expansion, is built on superpixel segmentation to localize the label proposal and the expansion scope. Three levels of superpixels with increasing granularity are generated for acceleration. The second algorithm, called normal adjustment, optimizes the 3D planes for the regions with low texture and/or illumination changes. The normal adjustment is performed along a depth-first similarity path of superpixels. We evaluate our method on the Middlebury 3.0 evaluation benchmark and the Eth3d benchmark. Experimental results show that our method achieves high accuracy on both evaluation benchmarks. (Middlebury 3.0 evaluation benchmark: http://vision.middlebury.edu/stereo/eval3/. Eth3d benchmark: https://www.eth3d.net/low_res_two_view.)
Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

The large number of visual applications in multimedia sharing websites and social networks contribute to the increasing amounts of multimedia data in cyberspace.Video data is a rich source of information and considered the most demanding in terms of storage space.With the huge development of digital video production, video management becomes a challenging task.Video content analysis (VCA) aims to provide big data solutions by automating the video management.To this end, shot boundary detection (SBD) is considered an essential step in VCA.It aims to partition the video sequence into shots by detecting shot transitions.High computational cost in transition detection is considered a bottleneck for real-time applications.Thus, in this paper, a balance between detection accuracy and speed for SBD is addressed by presenting a new method for fast video processing.The proposed SBD framework is based on the concept of candidate segment selection with frame active area and separable moments.First, for each frame, the active area is selected such that only the informative content is considered.This leads to a reduction in the computational cost and disturbance factors.Second, for each active area, the moments are computed using orthogonal polynomials.Then, an adaptive threshold and inequality criteria are used to eliminate most of the non-transition frames and preserve candidate segments.For further elimination, two rounds of bisection comparisons are applied.As a result, the computational cost is reduced in the subsequent stages.Finally, machine learning statistics based on the support vector machine is implemented to detect the cut transitions.The enhancement of the proposed fast video processing method over existing methods in terms of computational complexity and accuracy is verified.The average improvements in terms of frame percentage and transition accuracy percentage are 1.63% and 2.05%, respectively.Moreover, for the proposed SBD algorithm, a comparative study is performed with state-of-the-art algorithms.The comparison results confirm the superiority of the proposed algorithm in computation time with improvement of over 38%.
UAV based accurate displacement monitoring through automatic filtering out its camera's translations and rotations

In this paper, three translations and three rotations of the unmanned aerial vehicle (UAV) were derived through the suggested Normalized Cross Correlation (NCC) based template matching computer vision method and their effect on the monitored structural displacement is analyzed. The NCC-based template matching is used to obtain the vibration of UAVs by tracing far-field background points at the recorded images. The images captured at any general orientations are rotated back to their original positions which could detect errors in the monitored displacement induced by the rotation angles. A fast NCC-based template matching method was also proposed in this paper, which could accelerate the original NCC-based template matching method significantly and reaches the same level accuracy as that of the original NCC-based template matching method. In order to verify the concept, a series of experiments were performed on a Multipurpose Testing System (MTS) machine with amplitudes of 10 mm, 5 mm, and 2.5 mm and frequencies of 2 Hz, 1 Hz and 0.5 Hz, respectively, at the same time a UAV and a fixed camera were used to record the motions of the MTS piston. The derived displacements through the UAV and the fixed camera are compared with the true motions of the MTS machine. Excellent precision and consistence were obtained for the UAV monitored displacement, the MTS piston motion, and the fixed camera derived displacement. Comparing to the case of the fixed camera, scales and rotation angles of the UAV are critical and largely affect the accuracy of monitored displacements. A field experiment was conducted to test the proposed method in a windy environment. Excellent agreement was found between the monitored displacement obtained by the UAV and that derived from the fixed camera.