Reviewerly/exHarmony huggingface dataset:
Instance-Based Deep Transfer Learning

Deep transfer learning recently has acquired significant research interest. It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain. Model-based deep transfer learning is probably the most frequently used method. However, very little research work has been devoted to enhancing deep transfer learning by focusing on the influence of data. In this paper, we propose an instance-based approach to improve deep transfer learning in a target domain. Specifically, we choose a pre-trained model from a source domain and apply this model to estimate the influence of training samples in a target domain. Then we optimize the training data of the target domain by removing the training samples that will lower the performance of the pre-trained model. We later either fine-tune the pre-trained model with the optimized training data in the target domain, or build a new model which is initialized partially based on the pre-trained model, and fine-tune it with the optimized training data in the target domain. Using this approach, transfer learning can help deep learning models to capture more useful features. Extensive experiments demonstrate the effectiveness of our approach on boosting the quality of deep learning models for some common computer vision tasks, such as image classification.
Mean-field theory of graph neural networks in graph partitioning

A theoretical performance analysis of the graph neural network (GNN) is presented. For classification tasks, the neural network approach has the advantage in terms of flexibility that it can be employed in a data-driven manner, whereas Bayesian inference requires the assumption of a specific model. A fundamental question is then whether GNN has a high accuracy in addition to this flexibility. Moreover, whether the achieved performance is predominately a result of the backpropagation or the architecture itself is a matter of considerable interest. To gain a better insight into these questions, a mean-field theory of a minimal GNN architecture is developed for the graph partitioning problem. This demonstrates a good agreement with numerical experiments.
Order-Preserving Optimal Transport for Distances between Sequences

We present new distance measures between sequences that can tackle local temporal distortion and periodic sequences with arbitrary starting points. Through viewing the instances of each sequence as empirical samples of an unknown distribution, we cast the calculations of distances between sequences as optimal transport problems. To preserve the inherent temporal relationships of the instances in sequences, we propose two methods through incorporating the temporal information into the spatial ground metric and concentrating the transport with two novel temporal regularization terms, respectively. The inverse difference moment regularization enforces local homogeneous structures in the transport, and the KL-divergence with a prior distribution regularization prevents transport between instances with far temporal positions. We show that the resulting problems can be efficiently solved by the matrix scaling algorithm. Extensive experiments on eight datasets with different classifiers and performance measures show the effectiveness and generality of the proposed distances.
MobileFace: 3D Face Reconstruction with Efficient CNN Regression

Estimation of facial shapes plays a central role for face transfer and animation. Accurate 3D face reconstruction, however, often deploys iterative and costly methods preventing real-time applications. In this work we design a compact and fast CNN model enabling real-time face reconstruction on mobile devices. For this purpose, we first study more traditional but slow morphable face models and use them to automatically annotate a large set of images for CNN training. We then investigate a class of efficient MobileNet CNNs and adapt such models for the task of shape regression. Our evaluation on three datasets demonstrates significant improvements in the speed and the size of our model while maintaining state-of-the-art reconstruction accuracy.
A High Efficient Approach for Power Disturbance Waveform Compression in the View of Heisenberg Uncertainty

This paper proposes a highly efficient approach for power disturbance waveform (PDW) compression in the view of Heisenberg uncertainty. The key idea is to represent each signal component of PDW using as few nonzero coefficients as possible by the uncertainty principle restriction. PDWs are projected in a union of bases (UB), and each signal component of the PDWs can be represented very sparsely. The UB decomposition is solved by orthogonal matching pursuit. The features and cross correlation of subbases of the UB guarantee the PDW compression with high a compression ratio and recovered accuracy. With various simulated and field PDWs tests, the compressed data size of the new method is proven with good characteristics such as low sensitivity to sampling frequency increment and types of signal components contained in PDWs. Moreover, it is found that the new method and methods that employ wavelet techniques share the similar effect of noise for PDW compression. The proposed method is also applied at a 220-kV power substation for field PDW compression from fault recorders. The comparisons, analyses, and experiments indicate that the proposed method has a high PDW compression efficiency for future power grid monitoring.
Watershed Algorithm for Medical Image Segmentation Based on Morphology and Total Variation Model

The traditional watershed algorithm has the limitation of false mark in medical image segmentation, which causes over-segmentation and images to be contaminated by noise possibly during acquisition. In this study, we proposed an improved watershed segmentation algorithm based on morphological processing and total variation model (TV) for medical image segmentation. First of all, morphological gradient preprocessing is performed on MRI images of brain lesions. Secondly, the gradient images are denoised by the all-variational model. While retaining the edge information of MRI images of brain lesions, the image noise is reduced. And then, the internal and external markers are obtained by forced minimum technique, and the gradient amplitude images are corrected by using these markers. Finally, the modified gradient image is subjected to watershed transformation. The experiment of segmentation and simulation of brain lesion MRI image is carried out on MATLAB. And the segmentation results are compared with other watershed algrothims. The experimental results demonstrate that our method obtains the least number of regions, which can extract MRI images of brain lesions effectively. In addition, this method can inhibit over-segmentation, improving the segmentation results of lesions in MRI images of brain lesions.
Enhancing generative conversational service agents with dialog history and external knowledge

For generative conversational agents, especially service-oriented systems, it is of great importance to improve the informativeness of generated responses and avoid bland results. In this paper, we describe our attempt at generating natural and informative responses for customer service oriented dialog systems, by incorporating dialog history related information and external knowledge. Two improved sequence-to-sequence frameworks are proposed to generate responses based on extra information in addition to the current user input, one encodes the entire dialogue history, while the other integrates external knowledge extracted from a search engine. The experimental results on the DSCT6-Track2 and Ubuntu Dialog corpora demonstrate that the proposed systems are promising to generate more informative responses. However, case studies suggest that some particular features of the proposed systems and the datasets might restrict the systems to fully exploit such extra information.
SCGAN: Disentangled Representation Learning by Adding Similarity Constraint on Generative Adversarial Nets

We proposed a novel generative adversarial net called similarity constraint generative adversarial network (SCGAN), which is capable of learning the disentangled representation in a completely unsupervised manner. Inspired by the smoothness assumption and our assumption on the content and the representation of images, we design an effective similarity constraint. SCGAN can disentangle interpretable representations by adding this similarity constraint between conditions and synthetic images. In fact, similarity constraint works as a tutor to instruct generator network to comprehend the difference of representations based on conditions. SCGAN successfully distinguishes different representations on a number of datasets. Specifically, SCGAN captures digit type on MNIST, clothing type on Fashion-MNIST, lighting on SVHN, and object size on CIFAR10. On the CelebA dataset, SCGAN captures more semantic representations, e.g., poses, emotions, and hair styles. Experiments show that SCGAN is comparable with InfoGAN (another generative adversarial net disentangles interpretable representations on these datasets unsupervisedly) on disentangled representation learning. Code is available at https://github.com/gauss-clb/SCGAN.
Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer

In this paper, we propose a pathological image compression framework to address the needs of Big Data image analysis in digital pathology. Big Data image analytics require analysis of large databases of high-resolution images using distributed storage and computing resources along with transmission of large amounts of data between the storage and computing nodes that can create a major processing bottleneck. The proposed image compression framework is based on the JPEG2000 Interactive Protocol and aims to minimize the amount of data transfer between the storage and computing nodes as well as to considerably reduce the computational demands of the decompression engine. The proposed framework was integrated into hotspot detection from images of breast biopsies, yielding considerable reduction of data and computing requirements.
Toward a unified framework for Cloud Computing governance: An approach for evaluating and integrating IT management and governance models

Cloud Computing is currently one of the major trends in the computer industry. It offers a wide range of both opportunities and challenges. The lack of governance causes a slowdown in the adoption process, especially with an absence of a comprehensive approach supporting the Cloud Computing governance objectives. Regarding this issue, our goal is to develop a new management and governance framework based on endorsed IT models; namely ITIL, COBIT, and ISO/IEC 27001/2. These models have been developed for different purposes and aspects of IT governance. The proposed framework is an attempt to a unified approach by taking into consideration the models’ dissimilarities. Before we go further with the final unification stage, this study's main objective is to develop both the evaluation and the integration approaches with regwards to the IT models. The results, therefore, are to shed light on the relevant outcomes emanate from an elaboration within an entirely unified CC governance framework.
Digital blind watermarking based on depth variation prediction map and DWT for DIBR free-viewpoint image

The free-view image could suffer from a viewpoint change attack, although it may be not malicious. This paper focuses not only on this viewpoint change attack, but also on the other traditional and the geometric attacks. We propose a digital watermarking scheme to protect the ownership of a free-view 2D or 3D image, such that the viewer watches the image(s) by rendering an arbitrary viewpoint image (or multiple images) with the received texture image and its depth image. First, the proposed scheme generates a special map, termed the depth variation prediction map (DVPM), to find locations that are safe from viewpoint change. A 3-level Mallat-tree 2-dimensional discrete wavelet transform (2D DWT) is also used, from which the three horizontally low-pass filtered and vertically high-pass filtered subbands are used as the watermark embedding regions, in conjunction with the DVPM. Next, Multiples of the watermark data are embedded into the three subbands, and each watermark bit is embedded into a 2D DWT coefficient by a quantization index modulation (QIM) method, where the quantization step is decided by considering the energy of each subband. In extracting the watermark data, the multiple watermark data are extracted, and the most frequent value in each bit position is taken from the extracted data to form the final data. We experiment the proposed method with various test images for the various attacks, and compare our scheme with the previous methods, to show that the proposed method has excellent performance.
Designing Spatial UI as a Solution of the Narrow FOV of Microsoft HoloLens: Prototype of Virtual Museum Guide

Since the Augmented Reality (AR) headset ‘Microsoft HoloLens’ released in 2016, the academic and the industrial community witnessed an obvious transformation and changes in the perception of AR applications. Despite this breakthrough, most of the HoloLens users have explicitly reported the narrow field of view (FOV) that crops the virtual augmentation from the viewer’s sight to a small window of 34° (Bimber & Bruns in PhoneGuide: Adaptive image classification for mobile museum guidance, 2011). This limitation can result in losing pre-made functions and visuals in the AR application. Therefore, this study introduced attempts to design a spatial UI representing a way around the narrow FOV that HoloLens suffers from. The UI was a crucial part of AR museum system which was evaluated by 9 experts in HCI, visual communication and museum engaging studies. Results showed a positive feedback on the usability of the system and users’ experience. This method can help HoloLens developers to extend their applications’ functionalities with avoiding missing content.
Context Driven Optimized Perceptual Video Summarization and Retrieval

Video summarization is an economical way of representing video contents and is useful for effective and quick browsing of the relevant activity present in the video. Most current video summarization approaches do not provide the correct insight of the events occurring in the video, such as moving the objects in a nonchronological order and tampering with the background and size of the objects. In this paper, we present an approach to create a video summarization that is a precise representation of the video content. First, our approach finds out the salient activities that are taking place in the video using an optimization framework for static and dynamic scenes. Second, the frames with the salient activities are stitched using alpha matting to form a single frame. Third, the summarized frame over multiple video shots obtained by our approach gives superior retrieval performance with image queries while reducing retrieval latency and memory requirement. This paper proposes a single frame-based indexing of the video database instead of multi-frame indexing as it is an interesting and potentially promising approach. Finally, several experiments are carried out to evaluate the proposed approach. The experiments demonstrate that the video summarization produced by our approach gives a realistic view of the events in the video, while the reduction in computational complexity and memory requirement is high.
Flexible calibration method for line-scan cameras using a stereo target with hollow stripes

With the advantages of ultrahigh resolution and acquisition rate, stereo line-scan sensors are gradually being developed for three-dimensional (3D) measurements. Calibration is crucial for line-scan cameras. However, viewing targets of line-scan cameras from different angles cause eccentricity errors. This paper presents a flexible calibration method for line-scan cameras to solve the eccentricity error problem. The calibration quality is improved by the accurate extraction of one-dimensional (1D) points on line-scan images, and the corresponding 3D points in space. Through the analysis of the eccentricity error in 1D image point extraction when utilizing targets with solid stripes, a stereo target with hollow stripes is designed. An algorithm is described to calculate the 3D points of intersection between the centerlines of hollow lines on the target and the viewing plane of the camera. A robust optimization is proposed to enhance the stability of parameter estimation. Experiments are performed to compare the re-projection error between the cross-ratio-based method and proposed method. A simulation is also performed to estimate the combined uncertainty. The comparative results of the experiments verify that the proposed method is accurate, low cost, and easy to implement.
Integrating Data and Model Space in Ensemble Learning by Visual Analytics

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand.However, the gain in performance comes together with the lack of comprehensibility, posing a challenge to understand how each model affects the classification outputs and from where the errors come.We propose a tight visual integration of the data and the model space for exploring and combining classifier models.We introduce an interactive workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models.The involvement of the user is key to our approach.Therefore, we elaborate on the role of the human and connect our approach to theoretical frameworks on human-centered machine learning.We showcase the usefulness of our approach and the integration of the user via binary and multiclass classification problems.Based on ensembles automatically selected by a standard ensemble selection algorithm, the user can manipulate models and alternative combinations.
A Contrast Metric for Fraud Detection in Rich Graphs

How can we detect fraud in a big graph with rich properties, as online fraudsters invest more resources, including purchasing large pools of fake user accounts and dedicated IPs, to hide their fraudulent attacks? To achieve robustness, existing approaches detected dense sub-graphs as suspicious patterns in an unsupervised way, such as average degree maximization. However, such approaches suffer from the bias of including more nodes than necessary, resulting in lower accuracy and increased need for manual verification. Therefore, we propose HoloScope, which introduces a novel metric “contrast suspiciousness” integrating information from graph topology and spikes to more accurately detect fraudulent users and objects. Contrast suspiciousness dynamically emphasizes the contrasting patterns between fraudsters and normal users, making HoloScope capable of distinguishing the synchronized and strange behaviors of fraudsters by means of topology, bursts and drops, and rating scores. In addition, we provide theoretical bounds for how much this method increases the time cost needed for fraudsters to conduct adversarial attacks. Moreover, HoloScope has a concise framework and sub-quadratic time complexity, making the algorithm reproducible and scalable. In extensive experiments, HoloScope achieved significant accuracy improvements on real data with injected labels and true labels, when compared with state-of-the-art fraud detection methods.
Multi-channel fusion convolutional neural network to classify syntactic anomaly from language-related ERP components

Event-Related Potential (ERP) analyses have revealed several language-related components in sentence processing literature. More recently, researchers attempted to apply machine-learning techniques to classify the language-structure dependent ERP signals in a more reliable and efficient way. The purpose of the current paper is to propose a classification technique based on data-driven approach to detect syntactic anomaly from language-related ERP components. We specifically examined whether sentences with syntactic violations elicited differential patterns of ERP signals and the abnormal patterns can be reliably classified by machine-learning techniques. The specific aim of the study is to develop a multi-channel fusion convolutional neural network (MCF-CNN) including two branches of CNNs and a trunk merged by an intermediate fusion layer to obtain trained linguistic features from the raw data and perform the classification. We extracted different linguistic ERP components from syntactic violations and put them in the fusion. As a next procedure we combined the features in the fusion layer of the proposed neural network architecture. Experimental results demonstrate that the proposed method provides more than 92% classification accuracy.
Self-paced and soft-weighted nonnegative matrix factorization for data representation

Nonnegative matrix factorization (NMF) has received intensive attention due to producing a parts-based representation of the data. However, because of the non-convexity of NMF models, these methods easily obtain a bad local solution. To alleviate this deficiency, this paper presents a novel NMF method by gradually including data points into NMF from easy to complex, namely self-paced learning (SPL), which is shown to be beneficial in avoiding a bad local solution. Furthermore, instead of using the conventional hard weighting scheme, we adopt the soft weighting strategy of SPL to further improve the performance of our model. An iterative updating algorithm is proposed to solve the optimization problem of our method. The convergence of the updating rules is also theoretically guaranteed. Experiments on both toy data and real-world benchmark datasets demonstrate the effectiveness of the proposed method.
QoE-Aware wireless video communications for emotion-aware intelligent systems: A multi-layered collaboration approach

With the ever increasing demand on high-quality visual information for emotion-aware intelligent systems, wireless video traffic explosively grows and causes great energy consumption. Therefore, providing high quality of experience (QoE) for connected users becomes increasingly important. Aiming to establish a new paradigm to solve this challenging problem, in this article we propose a multi-layered collaboration approach to provide energy-efficient QoE-aware wireless video communications by efficiently utilizing the limited transmission resources of wireless networks for 5G. We first investigate the emotion-aware intelligent system QoE measurement based on objective metrics of quality of service (QoS). Then, we utilize the multi-layered collaborations of physical, network and application layers among the connected users to achieve energy-efficient QoE-aware video communications. By developing a profound understanding of the interplay between the video applications and wireless networks, we qualitatively analyze how QoE can benefit from the multi-layered collaborations, and quantitatively assess the achievable gains in a typical wireless-connected emotion-aware application scenario.