Reviewerly/exHarmony huggingface dataset:
Distributed Balanced Partitioning via Linear Embedding †

Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems, for example, in some cases, a big graph can be chopped into pieces that fit on one machine to be processed independently before stitching the results together, leading to certain suboptimality from the interaction among different pieces. In other cases, links between different parts may show up in the running time and/or network communications cost, hence the desire to have small cut size. We study a distributed balanced-partitioning problem where the goal is to partition the vertices of a given graph into k pieces so as to minimize the total cut size. Our algorithm is composed of a few steps that are easily implementable in distributed computation frameworks such as MapReduce. The algorithm first embeds nodes of the graph onto a line, and then processes nodes in a distributed manner guided by the linear embedding order. We examine various ways to find the first embedding, for example, via a hierarchical clustering or Hilbert curves. Then we apply four different techniques including local swaps, and minimum cuts on the boundaries of partitions, as well as contraction and dynamic programming. As our empirical study, we compare the above techniques with each other, and also to previous work in distributed graph algorithms, for example, a label-propagation method, FENNEL and Spinner. We report our results both on a private map graph and several public social networks, and show that our results beat previous distributed algorithms: For instance, compared to the label-propagation algorithm, we report an improvement of 15–25% in the cut value. We also observe that our algorithms admit scalable distributed implementation for any number of partitions. Finally, we explain three applications of this work at Google: (1) Balanced partitioning is used to route multi-term queries to different replicas in Google Search backend in a way that reduces the cache miss rates by ≈ 0.5 % , which leads to a double-digit gain in throughput of production clusters. (2) Applied to the Google Maps Driving Directions, balanced partitioning minimizes the number of cross-shard queries with the goal of saving in CPU usage. This system achieves load balancing by dividing the world graph into several “shards”. Live experiments demonstrate an ≈ 40 % drop in the number of cross-shard queries when compared to a standard geography-based method. (3) In a job scheduling problem for our data centers, we use balanced partitioning to evenly distribute the work while minimizing the amount of communication across geographically distant servers. In fact, the hierarchical nature of our solution goes well with the layering of data center servers, where certain machines are closer to each other and have faster links to one another.
Dense '123' Color Enhancement Dehazing Network

Single image dehazing has gained much attention recently. A typical learning based approach uses example hazy and clean image pairs to train a mapping between the two. Of the learning based methods, those based on deep neural networks have shown to deliver state of the art performance. An important aspect of recovered image quality is the color information, which is severely compromised when the image is corrupted by very dense haze. While many different network architectures have been developed for recovering dehazed images, an explicit attention to recovering individual color channels with a design that ensures their quality has been missing. Our proposed work, focuses on this issue by developing a novel network structure that comprises of: a common DenseNet based feature encoder whose output branches into three distinct DensetNet based decoders to yield estimates of the R, G and B color channels of the image. A subsequent refinement block further enhances the final synthesized RGB/color image by joint processing of these color channels. Inspired by its structure, we call our approach the One-To-Three Color Enhancement Dehazing (123-CEDH) network. To ensure the recovery of physically meaningful and high quality color channels, the main network loss function is further regularized by a multi-scale structural similarity index term as well as a term that enhances color contrast. Experiments reveal that 123-CEDH has the ability to recover color information at early training stages (i.e. in the first few epochs) vs. other highly competitive methods. Validation on the benchmark datasets of the NTIRE'19 and NTIRE'18 dehazing challenges reveals the 123-CEDH to be one of the Top-3 methods based on results released in the NTIRE'19 competition.
Attention with structure regularization for action recognition

Recognizing human action in video is an important task with a wide range of applications. Recently, motivated by the findings in human visual perception, there have been numerous attempts on introducing attention mechanisms to action recognition systems. However, it is empirically observed that an implementation of attention mechanism using attention mask of free form often generates ineffective distracted attention regions caused by overfitting, which limits the benefit of attention mechanisms for action recognition. By exploiting block-structured sparsity prior on attention regions, this paper proposed an ℓ2,1-norm group sparsity regularization for learning structured attention masks. Built upon such a regularized attention module, an attention-based recurrent network is developed for action recognition. The experimental results on two benchmark datasets showed that, the proposed method can noticeably improve the accuracy of attention masks, which results in performance gain in action recognition.
A Fuzzy Based Accessibility Model for Disaster Environment

Robots that perform autonomous maneuvering in a disaster environment usually dont have perfect understanding of the environment in advance. The robot is continuously evaluating the environment as it proceeds, deciding the optimal way to traverse the environment to get to the goal. A critical aspect of this decision is the robot estimate of the terrain accessibility index, which quantifies how easy it is to navigate through the immediate terrain. This paper represents a new method to calculate terrain accessibility index based on obstacle distance to the robots position. In addition, a Fuzzy Inference System Vector Field Histogram (FISVFH) method has been designed for automating the selection of the robots linear and angular velocities in the VFH (Vector Field Histogram) algorithm, based on the calculated sector accessibility index. The proposed method is a two-input and two-output Fuzzy Inference System, where the current robot heading, and sector accessibility index serve as the input, and the corresponding linear and angular velocities to the VFH algorithm are outputs. The VFH, VFH + and FISVFH are tested both in simulation and experimentation in 4 environments that are known to result in failures in VFH and VFH +, for comparison purposes. In addition, the algorithm was verified through experimental setup of a disaster prone environment. In both simulation and experimentation the results show that FISVFH outperforms VFH and VFH +. It is also shown that the FISVFH algorithm is capable of handling the disaster prone environment. Overall, the FISVFH algorithm enables the robot to get to the goal faster and also produces a smoother path while doing so.
Analysis of Adapted Films and Stories Based on Social Network

Adaptation of a film is the transfer of a novel to its visual medium. The characters of a story or its adapted film are connected and form two different social networks. The underlying theme of the story may remain unaltered but the relationship among the characters generally deviates in the adapted movie. The difference in the alliance of the characters in a story and its corresponding film is reflected in the extracted networks. The study of these character networks exhibits the deviation as per the demands of the visual medium. We propose an efficient method using different weighted graph centrality metrics such as degree centrality, betweenness centrality, closeness centrality, and edge contribution factor to explore the dissimilarity in the character categorization within a story and its adapted film. We also examine the deviation of the association among the characters according to the time frames using the Mantel test. We analyze two distinguished novels, Nastanirh and Harry Potter and the Philosopher's Stone, and their celebrated film versions as the case study to indicate how the successful filmmakers interpret the characters in their visual version. The difference in the distribution of the characters in each chapter and the corresponding scene is also analyzed to manifest the convergences and divergences between the two art of expressions. Our approach is distinctive and the analytical results are satisfactory and unbiased than the human perspective.
Efficient Integrity Monitoring for KF-based Localization

This paper presents a new method to efficiently monitor localization safety in mobile robots. Localization safety is quantified by measuring the system's integrity risk, which is a well-known aviation performance metric. However, aviation integrity monitoring solutions almost exclusively rely on the Global Navigation Satellite System (GNSS) while robot navigation usually needs the additional information provided by a state evolution model and/or relative positioning sensors, which makes previously established approaches impractical. In response, this paper develops an efficient integrity monitoring methodology applicable to Kalman Filter-based localization. The work is intended for life-or mission-critical operations such as co-robot applications where ignoring the impact of faults can jeopardize human safety.
Intra-continental deformation and tectonic evolution of the West Junggar Orogenic Belt, Central Asia: Evidence from remote sensing and structural geological analyses

The West Junggar Orogenic Belt (WJOB) in northwestern Xinjiang, China, is located in the core of the western part of the Central Asian Orogenic Belt (CAOB). It has suffered two stage tectonic evolutions in Phanerozoic, before and after the ocean–continental conversion in Late Paleozoic. The later on intracontinental deformation, characterized by the development of the NE-trending West Junggar sinistral strike-slip fault system (WJFS) since Late Carboniferous and Early Permian, and the NW-trending Chingiz-Junggar dextral strike-slip fault (CJF) in Mesozoic and Cenozoic, has an important significance for the tectonic evolution of the WJOB and the CAOB. In this paper, we conduct geometric and kinematic analyses of the WJOB, based on field geological survey and structural interpretation of remote sensing image data. Using some piercing points such as truncated plutons and anticlines, an average magnitude of ~73 ​km for the left-lateral strike-slip is calculated for the Darabut Fault, a major fault of the WJFS. Some partial of the displacement should be accommodated by strike-slip fault-related folds developed during the strike-slip faulting. Circular and curved faults, asymmetrical folds, and irregular contribution of ultramafic bodies, implies potential opposite vertical rotation of the Miao'ergou and the Akebasitao batholiths, resulted from the sinistral strike-slipping along the Darabut Fault. Due to conjugate shearing set of the sinistral WJFS and the dextral CJF since Early Mesozoic, superimposed folds formed with N–S convergence in southwestern part of the WJOB.
The Collaborative Virtual Reality Neurorobotics Lab

We present the collaborative Virtual Reality Neurorobotics Lab, which allows multiple collocated and remote users to experience, discuss and participate in neurorobotic experiments in immersive virtual reality. We describe the coupling of the Neurorobotics Platform of the Human Brain Project with our collaborative virtual reality and 3D telepresence infrastructure and highlight future opportunities arising from our work for research on direct human interaction with simulated robots and brains.
Proposal for a new communicative model in immersive journalism

Different forms of communication have emerged throughout history and most of them have been linked to technological innovations. This fact has allowed the development, since the 1950s, of a powerful theoretical tradition specialized in the analysis of communication processes, which has resulted in the formulation of different communication theories. Recently, the emergence of Virtual Reality and 360-degree video has opened new analytical perspectives in relation to communication mediated by these technologies. Precisely, this article analyzes the narrative elements that are modified by the intervention of Virtual Reality and the peculiarities caused by the introduction of this technology in the communication process within a specific context such as journalism. This area of study has been selected because Virtual Reality and 360-degree video technologies are increasingly being used in the creation of journalistic content, which has resulted in the emergence of the so-called ‘immersive journalism’. Based on this analysis, the article proposes a new model for ‘immersive communication’ and a model for ‘the structure of immersive journalism’, which is totally novel in the journalistic context.
Applying Eye-Tracking Technology to Measure Interactive Experience Toward the Navigation Interface of Mobile Games Considering Different Visual Attention Mechanisms

As an initial channel for users learning about a mobile game, the interactive experience of the navigation interface will directly affect the first impression of the users on the game and their subsequent behaviors and willingness to use. This study aims to investigate players’ visual attention mechanisms of various interactive levels of mobile games’ interfaces under free-browsing and task-oriented conditions. Eye-tracking glasses and a questionnaire were used to measure the interactive experience of mobile games. The results show that in the free-browsing condition, the fixation count, saccade count and average saccade amplitude can be used to reflect and predict the interactive experiences of mobile games’ navigation interface; while in the task-oriented condition, the fixation count, first fixation duration, dwell time ratio and saccade count can be used to reflect and predict the interactive experience of mobile games’ navigation interface. These findings suggest that apart from the different eye movement indicators, players’ motivations should also be considered during the process of the games’ navigation interface design.
Blockchain Trilemma Solver Algorand has Dilemma over Undecidable Messages

A variety of solutions, e.g., Proof-of-Work (PoW), Proof-of-Stake (PoS), Proof-of-Burn (PoB), and Proof-of-Elapsed-Time (PoET), have been proposed to make consensus mechanism used by the blockchain technology more democratic, efficient, and scalable. However, these solutions have a number of limitations, e.g., PoW approach requires a huge amount of computational power, scales poorly, and wastes a lot of electrical energy. Recently, an innovative protocol called Algorand has been proposed to overcome these limitations. Algorand not only guarantees an overwhelming probability of linearity of the blockchain, but it also aims to solve the "blockchain trilemma" of decentralization, scalability, and security.
Exploring spatial and channel contribution for object based image retrieval

With the rapid development of deep learning methods, researchers have gradually shifted the research focus from hand-crafted features to deep features in the field of the content-based image retrieval (CBIR). A great deal of attention has been paid to aggregate the extracted features from the convolutional layer in the deep convolutional neural network (CNN) into a global representation vector for CBIR. In this paper, we propose a simple but effective method which called Strong-Response-Stack-Contribution (SRSC) to generate the global representation vector for object retrieval. As we know, for object retrieval, when using CNN to extract features, what we want is to extract features in the region of interest (ROI). So we explored spatial and channel contribution to help us focus more on ROI and make the global image representation vector more representative. The process of the approach SRSC is to first generate spatial contribution according to the degree of channel response intensity. Then, we generate channel contribution by joining the sparsity information and the element-value information together. Finally, the global representation vector is generated according to spatial and channel contribution to perform image retrieval. Experiments on Oxford and Paris buildings datasets show the effectiveness of the proposed approach.
Detection of Breast Cancer Using Fusion of MLO and CC View Features Through a Hybrid Technique Based on Binary Firefly Algorithm and Optimum-Path Forest Classifier

Breast cancer is a leading killer disease among women of the new era. As per the GLOBOCAN project, the breast cancer incidences showed an increase from 22.2 to 27% globally from 2008 to 2012. Many times, no obvious symptoms were identified in breast cancer patients. Accurate detection of breast cancer at the earliest stage is very much essential to reduce mortality. Mammography has been used as a gold standard for over 40 years in diagnosing breast diseases. Computer-Aided Detection (CAD) systems have been developed to avoid the subjective analysis of screening mammograms made by radiologist. Craniocaudal (CC) view and Mediolateral Oblique (MLO) view are commonly used for breast cancer detection and diagnosis. Detection accuracy of breast cancers can be improved as the number of views is increased. This work is focused to improve the detection performance by fusing Local Binary Pattern (LBP) features extracted from MLO and CC view mammograms through a hybrid feature fusion technique based on Firefly algorithm and Optimum-Path Forest classifier. Seven performance metrics such as accuracy, sensitivity, specificity, precision, F1 score, Mathews Correlation Coefficient (MCC) and Balanced Classification Rate (BCR) were used to analyse the detection performance. The proposed work shows better performance when compared to existing work in literature.
Deep Learning Based Sentiment Analysis on Product Reviews on Twitter

Sentiment analysis is the process of extracting an opinion about a particular subject from text documents. The immense quantity of text documents contain opinions or reviews towards a particular entity. The identification of sentiment can be useful for individual decision makers, business organizations and governments. Sentiment analysis is an important research direction. Deep learning is a recent research direction in machine learning, which builds learning models based on multiple layers of representations and features of data. Deep learning based frameworks can be employed in a wide range of applications, including natural language processing tasks, with encouraging prediction results. In this paper, we present a deep learning based scheme for sentiment analysis on Twitter messages. In the presented scheme, three-word embeddings based schemes (namely, GloVe, fastText and word2vec) and convolutional neural network (CNN) have been utilized. In the empirical analysis, different subsets of Twitter messages, ranging from 5000 to 50.000 are taken into consideration. The prediction results obtained by deep-learning based schemes have been compared to conventional classifiers (such as, Naïve Bayes and support vector machines).
Evaluating sentence-level relevance feedback for high-recall information retrieval

This study uses a novel simulation framework to evaluate whether the time and effort necessary to achieve high recall using active learning is reduced by presenting the reviewer with isolated sentences, as opposed to full documents, for relevance feedback. Under the weak assumption that more time and effort is required to review an entire document than a single sentence, simulation results indicate that the use of isolated sentences for relevance feedback can yield comparable accuracy and higher efficiency, relative to the state-of-the-art baseline model implementation (BMI) of the AutoTAR continuous active learning ("CAL") method employed in the TREC 2015 and 2016 Total Recall Track.
Resolution-Preserving Generative Adversarial Networks for Image Enhancement

Generative adversarial networks (GANs) are used for image enhancement such as single image super-resolution (SISR) and deblurring. The conventional GANs-based image enhancement suffers from two drawbacks that cause a quality degradation due to a loss of detailed information. First, the conventional discriminator network adopts strided convolution layers which cause a reduction in the resolution of the feature map, and thereby resulting in a loss of detailed information. Second, the previous GANs for image enhancement use the feature map of the visual geometry group (VGG) network for generating a content loss, which also causes visual artifacts because the maxpooling layers in the VGG network result in a loss of detailed information. To overcome these two drawbacks, this paper presents a proposal of a new resolution-preserving discriminator network architecture which removes the strided convolution layers, and a new content loss generated from the VGG network without maxpooling layers. The proposed discriminator network is applied to the super-resolution generative adversarial network (SRGAN), which is called a resolution-preserving SRGAN (RPSRGAN). Experimental results show that RPSRGAN generates more realistic super-resolution images than SRGAN does, and consequently, RPSRGAN with the new content loss improves the average peak signal-to-noise ratio (PSNR) by 0.75 dB and 0.32 dB for super-resolution images with the scale factors of 2 and 4, respectively. For deblurring, the visual appearance is also significantly improved, and the average PSNR is increased by 1.54 dB when the proposed discriminator and content loss are applied to the deblurring adversarial network.
The Impact of Avatar Tracking Errors on User Experience in VR

There is evidence that adding motion-tracked avatars to virtual environments increases users' sense of presence. High quality motion capture systems are cost sensitive for the average user and low cost resource-constrained systems introduce various forms of error to the tracking. Much research has looked at the impact of particular kinds of error, primarily latency, on factors such as body ownership, but it is still not known what level of tracking error is permissible in these systems to afford compelling social interaction. This paper presents a series of experiments employing a sizable subject pool (n=96) that study the impact of motion tracking errors on user experience for activities including social interaction and virtual object manipulation. Diverse forms of error that arise in tracking are examined, including latency, popping (jumps in position), stuttering (positions held in time) and constant noise. The focus is on error on a person's own avatar, but some conditions also include error on an interlocutor, which appears underexplored. The picture that emerges is complex. Certain forms of error impact performance, a person's sense of embodiment' enjoyment and perceived usability, while others do not. Notably, evidence was not found that tracking errors impact social presence, even when those errors are severe.