Reviewerly/exHarmony huggingface dataset:
Purifying naturalistic images through a real-time style transfer semantics network

Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distribution of synthetic images compared to real images, the desired performance cannot still be achieved. Real images consist of multiple forms of light orientation, while synthetic images consist of a uniform light orientation. These features are considered to be characteristic of outdoor and indoor scenes, respectively. To solve this problem, the previous method learned a model to improve the realism of the synthetic image. Different from the previous methods, this paper takes the first step to purify real images. Through the style transfer task, the distribution of outdoor real images is converted into indoor synthetic images, thereby reducing the influence of light. Therefore, this paper proposes a real-time style transfer network that preserves image content information (e.g., gaze direction, pupil center position) of an input image (real image) while inferring style information (e.g., image color structure, semantic features) of style image (synthetic image). In addition, the network accelerates the convergence speed of the model and adapts to multi-scale images. Experiments were performed using mixed studies (qualitative and quantitative) methods to demonstrate the possibility of purifying real images in complex directions. Qualitatively, it compares the proposed method with the available methods in a series of indoor and outdoor scenarios of the LPW dataset. In quantitative terms, it evaluates the purified image by training a gaze estimation model on the cross data set. The results show a significant improvement over the baseline method compared to the raw real image.
Enhanced Local Binary Patterns for Automatic Face Recognition

This paper presents a novel automatic face recognition approach based on local binary patterns. This descriptor considers a local neighbourhood of a pixel to compute the feature vector values. This method is not very robust to handle image noise, variances and different illumination conditions. We address these issues by proposing a novel descriptor which considers more pixels and different neighbourhoods to compute the feature vector values. The proposed method is evaluated on two benchmark corpora, namely UFI and FERET face datasets. We experimentally show that our approach outperforms state-of-the-art methods and is efficient particularly in the real conditions where the above mentioned issues are obvious. We further show that the proposed method handles well one training sample issue and is also robust to the image resolution.
Terrain Matching by Fusing HOG With Zernike Moments

A new matching algorithm is proposed by fusing the histogram of oriented gradients' and the Zernike moments' descriptors extracted from the real-time synthetic aperture radar (SAR) images and the real-time elevation maps (REMs), respectively. Both the SAR images and the REMs are acquired by an on-board interferometric SAR simultaneously. The two reciprocal descriptors are fused by weighting their Canberra distances. The numerical experiments demonstrate the advantage of the proposed matching algorithm preliminarily.
A novel approach for performance-based clustering and management of network traffic flows

Management of network performance comprises numerous functions such as measuring, modelling, planning and optimising networks to ensure that they transmit traffic with the speed, capacity and reliability expected by the applications, each with different requirements for bandwidth and delay. Overall, the objective of this paper is to propose a novel mechanism to optimise the network resource allocation through supporting the routing of individual flows, by clustering them based on performance and integrating the respective clusters with an SDN scheme. In this paper we have employed a particular set of traffic features then applied data reduction and unsupervised machine learning techniques, to derive an Internet traffic performance-based clustering model. Finally, the resulting data clusters are integrated within a unified SDN architectural solution, which improves network management by finding nearly optimal flow routing, to be evaluated against a number of traffic data sources.
VisualBib: A novel Web app for supporting researchers in the creation, visualization and sharing of bibliographies

In this paper, we present VisualBib, a Web application, which allows users to create, visualize, modify, explore, and share bibliographies and the related citation networks, using innovative diagrams, called narrative views. The metadata are retrieved in real-time from four existing bibliographic indexes, Scopus, OpenCitations, and CrossRef/Orcid. Bibliographies and views are formally described and modeled using zz-structures, a semantic, not-hierarchical data model. VisualBib has been evaluated through two evaluation studies, one focused on the quantitative side and another on the qualitative side. Taking into account both studies, they evaluate the tool regarding the effectiveness performing tasks, usability, graphic layout and other questions specific to the VisualBib features. The evaluation throws positive significant results in all areas when compared to Scopus searching features.
West: Word Encoded Sequence Transducers

Most of the parameters in large vocabulary models are used in embedding layer to map categorical features to vectors and in softmax layer for classification weights. This is a bottleneck in memory constraint on-device training applications like federated learning and on-device inference applications like automatic speech recognition (ASR). One way of compressing the embedding and softmax layers is to substitute larger units such as words with smaller sub-units such as characters. However, often the sub-unit models perform poorly compared to the larger unit models. We propose WEST, an algorithm for encoding categorical features and output classes with a sequence of random or domain dependent sub-units and demonstrate that this transduction can lead to significant compression without compromising performance.
Latent Question Interpretation Through Variational Adaptation

Most artificial neural network models for question-answering rely on complex attention mechanisms. These techniques demonstrate high performance on existing datasets; however, they are limited in their ability to capture natural language variability, and to generate diverse relevant answers. To address this limitation, we propose a model that learns multiple interpretations of a given question. This diversity is ensured by our interpretation policy module which automatically adapts the parameters of a question-answering model with respect to a discrete latent variable. This variable follows the distribution of interpretations learned by the interpretation policy through a semi-supervised variational inference framework. To boost the performance further, the resulting policy is fine-tuned using the rewards from the answer accuracy with a policy gradient. We demonstrate the relevance and efficiency of our model through a large panel of experiments. Qualitative results, in particular, underline the ability of the proposed architecture to discover multiple interpretations of a question. When tested using the Stanford Question Answering Dataset 1.1, our model outperforms the baseline methods in finding multiple and diverse answers. To assess our strategy from a human standpoint, we also conduct a large-scale user study. This study highlights the ability of our network to produce diverse and coherent answers compared to existing approaches. Our Pytorch implementation is available as open source. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> github.com/parshakova/APIP.
A Potts‐mixture spatiotemporal joint model for combined magnetoencephalography and electroencephalography data

Abstract We develop a new methodology for determining the location and dynamics of brain activity from combined magnetoencephalography (MEG) and electroencephalography (EEG) data. The resulting inverse problem is ill‐posed and is one of the most difficult problems in neuroimaging data analysis. In our development we propose a solution that combines the data from three different modalities, magnetic resonance imaging (MRI), MEG and EEG, together. We propose a new Bayesian spatial finite mixture model that builds on the mesostate‐space model developed by Daunizeau &amp; Friston [Daunizeau and Friston, NeuroImage 2007; 38, 67–81]. Our new model incorporates two major extensions: (i) We combine EEG and MEG data together and formulate a joint model for dealing with the two modalities simultaneously; (ii) we incorporate the Potts model to represent the spatial dependence in an allocation process that partitions the cortical surface into a small number of latent states termed mesostates. The cortical surface is obtained from MRI. We formulate the new spatiotemporal model and derive an efficient procedure for simultaneous point estimation and model selection based on the iterated conditional modes algorithm combined with local polynomial smoothing. The proposed method results in a novel estimator for the number of mixture components and is able to select active brain regions, which correspond to active variables in a high‐dimensional dynamic linear model. The methodology is investigated using synthetic data and simulation studies and then demonstrated on an application examining the neural response to the perception of scrambled faces. R software implementing the methodology along with several sample datasets are available at the following GitHub repository https://github.com/v2south/PottsMix . The Canadian Journal of Statistics 47: 688–711; 2019 © 2019 Statistical Society of Canada
Direct Fitting of Gaussian Mixture Models

When fitting Gaussian Mixture Models to 3D geometry, the model is typically fit to point clouds, even when the shapes were obtained as 3D meshes. Here we present a formulation for fitting Gaussian Mixture Models (GMMs) directly to geometric objects, using the triangles of triangular mesh instead of using points sampled from its surface. We demonstrate that this modification enables fitting higher-quality GMMs under a wider range of initialization conditions. Additionally, models obtained from this fitting method are shown to produce an improvement in 3D registration for both meshes and RGB-D frames.
Preprocessing Role in Analyzing Tweets Towards Requirement Engineering

Twitter is an important source for crowdsourcing software requirements and can be used as a communication channel between technical stakeholders and end users of software products. Previous studies have shown that software users share their opinions about the software through short messages called tweets. These tweets might contain worthwhile information (such as bug reports and user requests) which relates to the software requirements. Machine learning and Natural language processing approaches have been applied to elicit these technical tweets. Particularly, when learning techniques are considered, the success rate of the preprocessing process to overcome shortcomings in analyzing tweets becomes crucial. Therefore, in this paper, we propose a method which involves more than 6000 experiments in order to systematically analyze the effects of eleven preprocessing techniques on the classification performance of requirements-related tweets. We train and test three popular supervised learning algorithms on a dataset of 4000 tweets which is a sample of twitter feeds. Finally, we analyze our evaluation results from three different viewpoints to answer the research question. Our findings have demonstrated the crucial role of preprocessing, and also are feasible to provide more insights in designing approaches which can deal more accurately in case of elicitation such tweets.
RESA: A Real-Time Evaluation System for ABR

Adaptive Bitrate Algorithm (ABR) is a category of important technique to improve the Quality of Experience (QoE) of video streaming service. Many ABR algorithms have been proposed recently and achieved good results. However, there are still some problems to be solved: (1) These ABR algorithms are based on open source data and evaluated by only one simulated player; (2) The QoE metrics are defined differently and their parameters are set by experience with little explanation; (3) These algorithms consider only the QoE of users but ignore the bandwidth cost of video service providers. In order to solve these problems, we propose a Real-time Evaluation System for ABR (RESA) which do evaluations by real online users. A reasonable QoE metric is also introduced by setting its parameters based on user preferences during video watching. We evaluate the state-of-the-art ABR algorithms on RESA with real video streaming service. Finally, we further introduce bitrate controlling into the Adaptive Bitrate Algorithm to solve the bandwidth cost problem for online ABR.
Bringing Engineering Rigor to Deep Learning

Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including autonomous driving, robotics, and malware detection, where the correctness and predictability of a system on corner-case inputs are of great importance. Unfortunately, the common practice to validating a deep neural network (DNN) - measuring overall accuracy on a randomly selected test set - is not designed to surface corner-case errors. As recent work shows, even DNNs with state-of-the-art accuracy are easily fooled by human-imperceptible, adversarial perturbations to the inputs. Questions such as how to test corner-case behaviors more thoroughly and whether all adversarial samples have been found remain unanswered. In the last few years, we have been working on bringing more engineering rigor into deep learning. Towards this goal, we have built five systems to test DNNs more thoroughly and verify the absence of adversarial samples for given datasets. These systems check a broad spectrum of properties (e.g., rotating an image should never change its classification) and find thousands of error-inducing samples for popular DNNs in critical domains (e.g., ImageNet, autonomous driving, and malware detection). Our DNN verifiers are also orders of magnitude (e.g., 5,000×) faster than similar tools. This article overviews our systems and discusses three open research challenges to hopefully inspire more future research towards testing and verifying DNNs.
Experience-Centric Mobile Video Scheduling Through Machine Learning

Providing a high quality video streaming experience in a mobile data network via the ubiquitous HTTP Adaptive Streaming (HAS) protocol is challenging. This is largely because HAS traffic arrives as regular Internet Protocol (IP) packets, indistinguishable from those of other data services. This paper presents real-time network-based Machine Learning (ML) classifiers incurring low overhead and capable of (a) detecting the service type of different flows including HAS, and (b)detecting the player status for users with HAS flows. We utilize random forests, an ensemble classifier, relying only upon standard unencrypted packet headers. By applying the ML classifier outputs to derive scheduling metrics, we show how existing LTE base-station schedulers can improve video Quality-of-Experience (QoE) while incurring minimal overhead. For a simulated LTE cellular network, we present quantitative performance results that include misclassification errors. Our classification and scheduling framework is shown to provide an improved video QoE with tolerable impact on other non-video best effort services. These design insights can be applied to optimize video delivery in current and future wireless networks.
Observations on Darwiche and Pearl's Approach for Iterated Belief Revision

Notwithstanding the extensive work on iterated belief revision, there is, still, no fully satisfactory solution within the classical AGM paradigm. The seminal work of Darwiche and Pearl (DP approach, for short) remains the most dominant, despite its well-documented shortcomings. In this article, we make further observations on the DP approach. Firstly, we prove that the DP postulates are, in a strong sense, inconsistent with Parikh's relevance-sensitive axiom (P), extending previous initial conflicts. Immediate consequences of this result are that an entire class of intuitive revision operators, which includes Dalal's operator, violates the DP postulates, as well as that the Independence postulate and Spohn's conditionalization are inconsistent with (P). Lastly, we show that the DP postulates allow for more revision polices than the ones that can be captured by identifying belief states with total preorders over possible worlds, a fact implying that a preference ordering (over possible worlds) is an insufficient representation for a belief state.
Fast Compressive Spectral Clustering for Large-Scale Sparse Graph

Spectral clustering (SC) is an unsupervised learning method that has been widely used in industrial product analysis. Compressive spectral clustering (CSC) effectively accelerates clustering by leveraging graph filter and random sampling techniques. However, CSC suffers from two major problems. First, the direct use of the dichotomy and eigencount techniques for estimating Laplacian matrix&#x0027;s <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq1-2931532.gif"/></alternatives></inline-formula>th eigenvalue is expensive. Second, the computation of interpolation is time-consuming because it requires to repeat matrix-vector product for every cluster in each iteration. To address these problems, we propose a new method called <i>fast compressive spectral clustering</i> (FCSC). Our method addresses the first problem by assuming that the eigenvalues approximately satisfy local uniform distribution, and addresses the second problem by recalculating the pairwise similarity between nodes with low-dimensional representation to reconstruct denoised laplacian matrix. The time complexity of reconstruction is linear with the number of non-zeros in Laplacian matrix. As experimentally demonstrated on both artificial and real-world datasets, our method significantly reduces the computation time while preserving high clustering accuracy comparable to previous designs, demonstrating the effectiveness of FCSC.
Global horizontal irradiance forecasting using online sparse Gaussian process regression based on quasiperiodic kernels

The application of Gaussian process models is intractable for large datasets, because of time complexity and storage. To overcome this limitation, online sparse Gaussian process regression (OSGPR) based on quasiperiodic kernels is used to model and forecast global horizontal irradiance (GHI), at three forecast horizons (30 min, 4 h and 24 h). Using two years of GHI data, OSGPR models are trained. Forecasting accuracy is evaluated for various levels of sparsity in training data.
Deep Stacked Ensemble Recommender

Collaborative filtering techniques remain a staple in recommender systems research and applications. With the plethora of research done in recommender systems, some more recent works applied deep learning with great success. We stack a deep neural network recommender onto a shallow one for item recommendations in this work. Our experiments with popular datasets indicate that the proposed technique outperforms other state-of-the art techniques and baselines in key performance metrics.
Fast Image Clustering Based on Camera Fingerprint Ordering

This work presents a new camera fingerprint-based image clustering algorithm. The proposed algorithm is based on sorting the camera fingerprints according to information that is inherently present in images. A ranking index is constructed for each image, taking into account the combined effect of gray-level, saturation and texture on camera fingerprint estimation. Then, camera fingerprints are ordered according to this ranking index and clusters are iteratively constructed using as reference fingerprint the top-ranked fingerprint among the currently un-clustered fingerprints. The algorithm can be optionally implemented with an additional attraction stage to refine clustering. The results confirm that the proposed method achieves a performance comparable to state of the art approaches, with a significantly lower computational complexity. The method can also handle cases in which the number of clusters is much larger than the average size of the clusters.
A New Image Watermarking Scheme for Efficient Tamper Detection, Localization and Recovery

Due to the powerful image editing tools, image integrity protection is becoming important, for instance, in a court of law, news reports, insurance claims or telemedicine. Digital image watermarking is a technique to detect, localize and recover tampered image but the accuracy of performance by the existing methods is still an issue. This paper proposes a new watermarking embedding mechanism for effective image authentication with a recovery scheme. In the method, a host image is divided into blocks and the feature of each block is embedded in the block itself and another block as watermark. For detecting tampering, the extracted watermarked data is checked with source and destination block leading to more accuracy. The recovery data is extracted from the destination block which is intact. The proposed scheme achieves better results in terms of tamper detection accuracy and the quality of recovered image compared with the state-of-art schemes.
Derivation of Parameters of Quantum optimization in Resource Distribution Management

In the recent decades, the ubiquity of computational complexity in the process of a large scale of resource distribution management became one of the key interest of scientific research community, so to solve this large computationally hard problem, it requires selecting the best and the optimum computing framework. In this paper we will present a resource distribution strategy based on the quantum method, where we explained how we can use the quantum algorithm as a computational platform for resource distribution, then a mathematical model has been built, in order to define the stochastic parameters of the quantum strategy related to the given resource distribution model, finally.
Quadruplet-based deep hashing for image retrieval

Hashing methods have been widely used for large-scale image retrieval. Learning deep hashing networks with a pairwise loss or a triplet ranking loss has become a common framework. The pairwise loss and triplet ranking loss, respectively, focus on preserving the pairwise similarity and the relative similarity ordering. In this paper, we design a quadruplet loss that can fully explore the similarity relation between image pairs to decrease the intraclass variation and increase the interclass variation. Moreover, we propose a deep architecture based on quadruplet loss and optimal adaptive margins for learning hash codes. Extensive experimental results show that our method achieves state-of-the-art performance on several benchmark image retrieval datasets.