Reviewerly/exHarmony huggingface dataset:
Optimizing Social Welfare of Live Video Streaming Services in Mobile Edge Computing

The live video streaming services have been suffered from the limited backhaul capacity of the cellular core network and occasional congestions due to the cloud-based architecture. Mobile Edge Computing (MEC) brings the services from the centralized cloud to nearby network edge to improve the Quality of Experience (QoE) of cloud services, such as live video streaming services. Nevertheless, the resource at edge devices is still limited and should be allocated economically efficiently. In this paper, we propose Edge Combinatorial Clock Auction (ECCA) and Combinatorial Clock Auction in Stream (CCAS), two auction frameworks to improve the QoE of live video streaming services in the Edge-enabled cellular system. The edge system is the auctioneer who decides the backhaul capacity and caching space allocation and streamers are the bidders who request for the backhaul capacity and caching space to improve the video quality their audiences can watch. There are two key subproblems: the caching space value evaluations and allocations. We show that both problems can be solved by the proposed dynamic programming algorithms. The truth-telling property is guaranteed in both ECCA and CCAS. The simulation results show that the overall system utility can be significantly improved through the proposed system.
Viewport Prediction for Live 360-Degree Mobile Video Streaming Using User-Content Hybrid Motion Tracking

360-degree video streaming has been gaining popularities recently with the rapid growth of adopting mobile head mounted display (HMD) devices in the consumer video market, especially for live broadcasts. The 360-degree video streaming introduces brand new bandwidth and latency challenges in live streaming due to the significantly increased video data. However, most of the existing bandwidth saving approaches based on viewport prediction have only focused on the video-on-demand (VOD) use cases leveraging historical user behavior data, which is not available in live broadcasts. We develop a new viewport prediction scheme for live 360-degree video streaming using video content-based motion tracking and dynamic user interest modeling. To obtain real-time performance, we implement the Gaussian mixture model (GMM) and optical flow algorithms for motion detection and feature tracking. Then, the user's future viewport of interest is generated by leveraging a dynamic user interest model that weighs all the features and motion information abstracted from the live video frames. Furthermore, we develop two enhancement techniques that take into consideration of user feedback for fast error recovery and view updates. Consequently, our predicted viewports are irregular and dynamically adjusted to cover the maximum portions of the actual user viewports and thus ensure a high prediction accuracy. We evaluate our viewport prediction approach using a public user head movement dataset, which contains the data of 48 users watching 6 360-degree videos. The experimental results show that the proposed approach supports sophisticated user head movement patterns and outperforms the existing velocity-based approach in terms of prediction accuracy. In addition, the motion tracking scheme introduces minimum latency overhead to ensure the quality of live streaming experience.
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory

Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Wai Lam, Shuming Shi. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Cybersecurity threat intelligence knowledge exchange based on blockchain

Although cyber threat intelligence (CTI) exchange is a theoretically useful technique for improving security of a society, the potential participants are often reluctant to share their CTI and prefer to consume only, at least in voluntary based approaches. Such behavior destroys the idea of information exchange. On the other hand, governments are forcing specific entities and operators to report them specific incidents depending on their impact, otherwise there could be sanctions to those operators which are not reporting them on time. Obligations and sanctions are usually discouraging participants to share information voluntarily which will just share and report what is strictly required. We propose a paradigm shift of cybersecurity information exchange by introducing a new way to encourage all participants involved, at all levels, to share relevant information dynamically. It will also contribute to the support and deployment of Dynamic Risk Management frameworks to keep risks under an acceptance level along the time. Participants will have new and specific incentives to share, invest and consume threat intelligence and risk intelligence information depending on their different roles (producers, consumers, investors, donors and owner). Our proposal leverages from standards like Structured Threat Information Exchange, as well as W3C semantic web standards to enable a workspace of knowledge related to behavioral threat intelligence patterning to characterize tactics, techniques and procedures. At the same time, we propose an Ethereum Blockchain Smart contract Marketplace to better incentivize the sharing of that knowledge between all parties involved as well as creating a standard CTI token as a digital asset with a promising value in the market. Simulations and an experimentation were performed to demonstrate its benefits and incentives, but also its potential limits with regard to storage and cost of transactions.
Improving Faster R-CNN Framework for Fast Vehicle Detection

Vision-based vehicle detection plays an important role in intelligent transportation systems. With the fast development of deep convolutional neural networks (CNNs), vision-based vehicle detection approaches have achieved significant improvements compared to traditional approaches. However, due to large vehicle scale variation, heavy occlusion, or truncation of the vehicle in an image, recent deep CNN-based object detectors still showed a limited performance. This paper proposes an improved framework based on Faster R-CNN for fast vehicle detection. Firstly, MobileNet architecture is adopted to build the base convolution layer in Faster R-CNN. Then, NMS algorithm after the region proposal network in the original Faster R-CNN is replaced by the soft-NMS algorithm to solve the issue of duplicate proposals. Next, context-aware RoI pooling layer is adopted to adjust the proposals to the specified size without sacrificing important contextual information. Finally, the structure of depthwise separable convolution in MobileNet architecture is adopted to build the classifier at the final stage of the Faster R-CNN framework to classify proposals and adjust the bounding box for each of the detected vehicle. Experimental results on the KITTI vehicle dataset and LSVH dataset show that the proposed approach achieved better performance compared to original Faster R-CNN in both detection accuracy and inference time. More specific, the performance of the proposed method is improved comparing with the original Faster R-CNN framework by 4% on the KITTI test set and 24.5% on the LSVH test set.
Challenges and Strategies for Educational Virtual Reality

Virtual reality (VR) is a rich visualization and analytic platform that furthers the library’s mission of providing access to all forms of information and supporting pedagogy and scholarship across disciplines. Academic libraries are increasingly adopting VR technology for a variety of research and teaching purposes, which include providing enhanced access to digital collections, offering new research tools, and constructing new immersive learning environments for students. This trend suggests that positive technological innovation is flourishing in libraries, but there remains a lack of clear guidance in the library community on how to introduce these technologies in effective ways and make them sustainable within different types of institutions. In June 2018, the University of Oklahoma hosted the second of three forums on the use of 3D and VR for visualization and analysis in academic libraries, as part of the project Developing Library Strategy for 3D and Virtual Reality Collection Development and Reuse(LIB3DVR), funded by a grant from the Institute of Museum and Library Services. This qualitative study invited experts from a range of disciplines and sectors to identify common challenges in the visualization and analysis of 3D data, and the management of VR programs, for the purpose of developing a national library strategy.
Robust Distance Measures for <i>k</i>NN Classification of Cancer Data

The k-Nearest Neighbor ( kNN) classifier represents a simple and very general approach to classification. Still, the performance of kNN classifiers can often compete with more complex machine-learning algorithms. The core of kNN depends on a “guilt by association” principle where classification is performed by measuring the similarity between a query and a set of training patterns, often computed as distances. The relative performance of kNN classifiers is closely linked to the choice of distance or similarity measure, and it is therefore relevant to investigate the effect of using different distance measures when comparing biomedical data. In this study on classification of cancer data sets, we have used both common and novel distance measures, including the novel distance measures Sobolev and Fisher, and we have evaluated the performance of kNN with these distances on 4 cancer data sets of different type. We find that the performance when using the novel distance measures is comparable to the performance with more well-established measures, in particular for the Sobolev distance. We define a robust ranking of all the distance measures according to overall performance. Several distance measures show robust performance in kNN over several data sets, in particular the Hassanat, Sobolev, and Manhattan measures. Some of the other measures show good performance on selected data sets but seem to be more sensitive to the nature of the classification data. It is therefore important to benchmark distance measures on similar data prior to classification to identify the most suitable measure in each case.
What Should Not Be Contrastive in Contrastive Learning

Recent self-supervised contrastive methods have been able to produce impressive transferable visual representations by learning to be invariant to different data augmentations. However, these methods implicitly assume a particular set of representational invariances (e.g., invariance to color), and can perform poorly when a downstream task violates this assumption (e.g., distinguishing red vs. yellow cars). We introduce a contrastive learning framework which does not require prior knowledge of specific, task-dependent invariances. Our model learns to capture varying and invariant factors for visual representations by constructing separate embedding spaces, each of which is invariant to all but one augmentation. We use a multi-head network with a shared backbone which captures information across each augmentation and alone outperforms all baselines on downstream tasks. We further find that the concatenation of the invariant and varying spaces performs best across all tasks we investigate, including coarse-grained, fine-grained, and few-shot downstream classification tasks, and various data corruptions.
Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation

Group recommendation aims to recommend items to a group of users. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, where groups are formed ad hoc and users may just constitute a group for the first time—that is, the historical group-item interaction records are highly limited. Most state-of-the-art works have addressed the challenge by aggregating group members’ personal preferences to learn the group representation. However, the representation learning for a group is most complex beyond the aggregation or fusion of group member representation, as the personal preferences and group preferences may be in different spaces and even orthogonal. In addition, the learned user representation is not accurate due to the sparsity of users’ interaction data. Moreover, the group similarity in terms of common group members has been overlooked, which, however, has the great potential to improve the group representation learning. In this work, we focus on addressing the aforementioned challenges in the group representation learning task, and devise a hierarchical hyperedge embedding-based group recommender, namely HyperGroup. Specifically, we propose to leverage the user-user interactions to alleviate the sparsity issue of user-item interactions, and design a graph neural network-based representation learning network to enhance the learning of individuals’ preferences from their friends’ preferences, which provides a solid foundation for learning groups’ preferences. To exploit the group similarity (i.e., overlapping relationships among groups) to learn a more accurate group representation from highly limited group-item interactions, we connect all groups as a network of overlapping sets (a.k.a. hypergraph), and treat the task of group preference learning as embedding hyperedges (i.e., user sets/groups) in a hypergraph, where an inductive hyperedge embedding method is proposed. To further enhance the group-level preference modeling, we develop a joint training strategy to learn both user-item and group-item interactions in the same process. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our proposed HyperGroup in comparison to the state-of-the-art baselines.
Extending Embodied Lexical Development

This paper describes an implemented computational model of lexical development for the case of action verbs. A simulated agent is trained by an informant labeling the agent’s actions (here hand motions), and the system learns to both label and carry out similar actions. The verb learning model is placed in the broader context of the NTL project on embodied natural language and its acquisition. Based on experimental results and projections to the full range of early lexemes, a significantly enriched model is proposed and its properties discussed.
Evolution of Experts in Question Answering Communities

Community Question Answering (CQA) services thrive as a result of a small number of highly active users, typically called experts, who provide a large number of high quality useful answers. Understanding the temporal dynamics and interactions between experts can present key insights into how community members evolve over time. In this paper, we present a temporal study of experts in CQA and analyze the changes in their behavioral patterns over time. Further, using unsupervised machine learning methods, we show the interesting evolution patterns that can help us distinguish experts from one another. Using supervised classification methods, we show that the models based on evolutionary data of users can be more effective at expert identification than the models that ignore evolution. We run our experiments on two large online CQA to show the generality of our proposed approach.
Trapped-Ion Spin-Motion Coupling with Microwaves and a Near-Motional Oscillating Magnetic Field Gradient

We present a new method of spin-motion coupling for trapped ions using microwaves and a magnetic field gradient oscillating close to the ions’ motional frequency. We demonstrate and characterize this coupling experimentally using a single ion in a surface-electrode trap that incorporates current-carrying electrodes to generate the microwave field and the oscillating magnetic field gradient. Using this method, we perform resolved-sideband cooling of a single motional mode to its ground state.Received 17 December 2018DOI:https://doi.org/10.1103/PhysRevLett.122.163201© 2019 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAtom & ion coolingCoherent controlQuantum information with trapped ionsPhysical SystemsTrapped ionsTechniquesMicrowave techniquesQuantum InformationAtomic, Molecular & Optical
Few-shot Learning: A Survey

The quest of `can machines think' and `can machines do what human do' arequests that drive the development of artificial intelligence. Although recentartificial intelligence succeeds in many data intensive applications, it stilllacks the ability of learning from limited exemplars and fast generalizing tonew tasks. To tackle this problem, one has to turn to machine learning, whichsupports the scientific study of artificial intelligence. Particularly, amachine learning problem called Few-Shot Learning (FSL) targets at this case.It can rapidly generalize to new tasks of limited supervised experience byturning to prior knowledge, which mimics human's ability to acquire knowledgefrom few examples through generalization and analogy. It has been seen as atest-bed for real artificial intelligence, a way to reduce laborious datagathering and computationally costly training, and antidote for rare caseslearning. With extensive works on emerging, we give a comprehensive surveyfor it. We first give the formal definition for FSL. Then we point out the coreissues of FSL, which turns the problem from how to solve FSL to how to dealwith the core issues. Accordingly, existing works from the birth of to themost recent published ones are categorized in a unified taxonomy, with thoroughdiscussion of the pros and cons for different categories. Finally, we envisionpossible future directions for in terms of problem setup, techniques,applications and theory, hoping to provide insights to both beginners andexperienced researchers.
Solar PV on mosque rooftops: Results from a pilot study in Saudi Arabia

Muslims congregate for prayers in mosques five times daily: at dawn, noon, afternoon, sunset, and evening. Because these times are governed by the sun, they change within the year but are perpetual. As such, mosque load profiles are highly predictable and witness little variation year over year. The relatively large size of mosque rooftops and their ubiquity in the Muslim world make them ideal candidates for solar photovoltaic (PV) installations. We perform a technoeconomic analysis on a 124 kW PV system commissioned in 2017 on a mosque rooftop in Riyadh, Saudi Arabia, under a net metering mechanism. At a capital cost of 1.18 US$/watt, it was found that net metering reduces the annual energy bill by more than 50%. If some prior planning were incorporated in the early stages of the mosque’s construction, PV can bring down the electricity bill to zero. We compare our theoretical modeling results with an actual commissioned system, serving as a pilot-project, and the field results confirm our a priori theoretical expectations. The capacity factor obtained from the physical system was 18.2%. The results of this study can be extended to other houses of worship and community halls with appropriate modifications.
Ensemble of optimal trees, random forest and random projection ensemble classification

The predictive performance of a random forest ensemble is highly associated with the strength of individual trees and their diversity. Ensemble of a small number of accurate and diverse trees, if prediction accuracy is not compromised, will also reduce computational burden. We investigate the idea of integrating trees that are accurate and diverse. For this purpose, we utilize out-of-bag observations as a validation sample from the training bootstrap samples, to choose the best trees based on their individual performance and then assess these trees for diversity using the Brier score on an independent validation sample. Starting from the first best tree, a tree is selected for the final ensemble if its addition to the forest reduces error of the trees that have already been added. Our approach does not use an implicit dimension reduction for each tree as random project ensemble classification. A total of 35 bench mark problems on classification and regression are used to assess the performance of the proposed method and compare it with random forest, random projection ensemble, node harvest, support vector machine, kNN and classification and regression tree. We compute unexplained variances or classification error rates for all the methods on the corresponding data sets. Our experiments reveal that the size of the ensemble is reduced significantly and better results are obtained in most of the cases. Results of a simulation study are also given where four tree style scenarios are considered to generate data sets with several structures.
Maintaining Natural Image Statistics with the Contextual Loss

Maintaining natural image statistics is a crucial factor in restoration and generation of realistic looking images. When training CNNs, photorealism is usually attempted by adversarial training (GAN), that pushes the output images to lie on the manifold of natural images. GANs are very powerful, but not perfect. They are hard to train and the results still often suffer from artifacts. In this paper we propose a complementary approach, that could be applied with or without GAN, whose goal is to train a feed-forward CNN to maintain natural internal statistics. We look explicitly at the distribution of features in an image and train the network to generate images with natural feature distributions. Our approach reduces by orders of magnitude the number of images required for training and achieves state-of-the-art results on both single-image super-resolution, and high-resolution surface normal estimation. Project page: https://www.github.com/roimehrez/contextualLoss .
Analyzing Dynamic Hypergraphs with Parallel Aggregated Ordered Hypergraph Visualization

Parallel Aggregated Ordered Hypergraph(PAOH) is a novel technique to visualize dynamic hypergraphs. Hypergraphs are a generalization of graphs where edges can connect several vertices. Hypergraphs can be used to model networks of business partners or co-authorship networks with multiple authors per article. A dynamic hypergraph evolves over discrete time slots. PAOH represents vertices as parallel horizontal bars and hyperedges as vertical lines, using dots to depict the connections to one or more vertices. We describe a prototype implementation of Parallel Aggregated Ordered Hypergraph, report on a usability study with 9 participants analyzing publication data, and summarize the improvements made. Two case studies and several examples are provided. We believe that PAOH is the first technique to provide a highly readable representation of dynamic hypergraphs. It is easy to learn and well suited for medium size dynamic hypergraphs (50-500 vertices) such as those commonly generated by digital humanities projects-our driving application domain.
RustBelt meets relaxed memory

The Rust programming language supports safe systems programming by means of a strong ownership-tracking type system. In their prior work on RustBelt, Jung et al. began the task of setting Rust’s safety claims on a more rigorous formal foundation. Specifically, they used Iris, a Coq-based separation logic framework, to build a machine-checked proof of semantic soundness for a λ-calculus model of Rust, as well as for a number of widely-used Rust libraries that internally employ unsafe language features. However, they also made the significant simplifying assumption that the language is sequentially consistent. In this paper, we adapt RustBelt to account for the relaxed-memory operations that concurrent Rust libraries actually use, in the process uncovering a data race in the Arc library. We focus on the most interesting technical problem: how to reason about resource reclamation under relaxed memory , using a logical construction we call synchronized ghost state .