OpenRaiser/Intern-Atlas huggingface dataset:
Algebraic 3D Graphic Statics: Constrained Areas
Venue: arXiv cs.CG 2020
Year: 2020

This research provides algorithms and numerical methods to geometrically control the magnitude of the internal and external forces in the reciprocal diagrams of 3D/Polyhedral Graphic statics (3DGS). In 3DGS, the form of the structure and its equilibrium of forces is represented by two polyhedral diagrams that are geometrically and topologically related. The areas of the faces of the force diagram represent the magnitude of the internal and external forces in the system. For the first time, the methods of this research allow the user to control and constrain the areas and edge lengths of the faces of general polyhedrons that can be convex, self-intersecting, or concave. As a result, a designer can explicitly control the force magnitudes in the force diagram and explore the equilibrium of a variety of compression and tension-combined funicular structural forms. In this method, a quadratic formulation is used to compute the area of a single face based on its edge lengths. The approach is applied to manipulating the face geometry with a predefined area and the edge lengths. Subsequently, the geometry of the polyhedron is updated with newly changed faces. This approach is a multi-step algorithm where each step includes computing the geometry of a single face and updating the polyhedral geometry. One of the unique results of this framework is the construction of the zero-area, self-intersecting faces, where the sum of the signed areas of a self-intersecting face is zero, representing a member with zero force in the form diagram. The methodology of this research can clarify the equilibrium of some systems that could not be previously justified using reciprocal polyhedral diagrams. Therefore, it generalizes the principle of the equilibrium of polyhedral frames and opens a completely new horizon in the design of highly-sophisticated funicular polyhedral structures beyond compression-only systems.
Benchmarking and Comparing Multi-exposure Image Fusion Algorithms
Venue: arXiv cs.CV 2020
Year: 2020

Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted increasing interests in recent years. Apart from conventional algorithms, deep learning techniques have also been applied to multi-exposure image fusion. However, although much efforts have been made on developing MEF algorithms, the lack of benchmark makes it difficult to perform fair and comprehensive performance comparison among MEF algorithms, thus significantly hindering the development of this field. In this paper, we fill this gap by proposing a benchmark for multi-exposure image fusion (MEFB) which consists of a test set of 100 image pairs, a code library of 16 algorithms, 20 evaluation metrics, 1600 fused images and a software toolkit. To the best of our knowledge, this is the first benchmark in the field of multi-exposure image fusion. Extensive experiments have been conducted using MEFB for comprehensive performance evaluation and for identifying effective algorithms. We expect that MEFB will serve as an effective platform for researchers to compare performances and investigate MEF algorithms.
Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction
Venue: arXiv cs.LG 2020
Year: 2020

Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction. Predictable ride-hailing demand can facilitate more reasonable vehicle scheduling and online car-hailing platform dispatch. Conventional deep learning methods with no external structured data can be accomplished via hybrid models of CNNs and RNNs by meshing plentiful pixel-level labeled data, but spatial data sparsity and limited learning capabilities on temporal long-term dependencies are still two striking bottlenecks. To address these limitations, we propose a new virtual graph modeling method to focus on significant demand regions and a novel Deep Multi-View Spatiotemporal Virtual Graph Neural Network (DMVST-VGNN) to strengthen learning capabilities of spatial dynamics and temporal long-term dependencies. Specifically, DMVST-VGNN integrates the structures of 1D Convolutional Neural Network, Multi Graph Attention Neural Network and Transformer layer, which correspond to short-term temporal dynamics view, spatial dynamics view and long-term temporal dynamics view respectively. In this paper, experiments are conducted on two large-scale New York City datasets in fine-grained prediction scenes. And the experimental results demonstrate effectiveness and superiority of DMVST-VGNN framework in significant citywide ride-hailing demand prediction.
NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets
Venue: arXiv cs.CL 2020
Year: 2020

Existing tools for Question Answering (QA) have challenges that limit their use in practice. They can be complex to set up or integrate with existing infrastructure, do not offer configurable interactive interfaces, and do not cover the full set of subtasks that frequently comprise the QA pipeline (query expansion, retrieval, reading, and explanation/sensemaking). To help address these issues, we introduce NeuralQA - a usable library for QA on large datasets. NeuralQA integrates well with existing infrastructure (e.g., ElasticSearch instances and reader models trained with the HuggingFace Transformers API) and offers helpful defaults for QA subtasks. It introduces and implements contextual query expansion (CQE) using a masked language model (MLM) as well as relevant snippets (RelSnip) - a method for condensing large documents into smaller passages that can be speedily processed by a document reader model. Finally, it offers a flexible user interface to support workflows for research explorations (e.g., visualization of gradient-based explanations to support qualitative inspection of model behaviour) and large scale search deployment. Code and documentation for NeuralQA is available as open source on Github (https://github.com/victordibia/neuralqa}{Github).
Learner's Dilemma: IoT Devices Training Strategies in Collaborative Deep Learning
Venue: arXiv cs.CR 2020
Year: 2020

With the growth of Internet of Things (IoT) and mo-bile edge computing, billions of smart devices are interconnected to develop applications used in various domains including smart homes, healthcare and smart manufacturing. Deep learning has been extensively utilized in various IoT applications which require huge amount of data for model training. Due to privacy requirements, smart IoT devices do not release data to a remote third party for their use. To overcome this problem, collaborative approach to deep learning, also known as Collaborative DeepLearning (CDL) has been largely employed in data-driven applications. This approach enables multiple edge IoT devices to train their models locally on mobile edge devices. In this paper,we address IoT device training problem in CDL by analyzing the behavior of mobile edge devices using a game-theoretic model,where each mobile edge device aims at maximizing the accuracy of its local model at the same time limiting the overhead of participating in CDL. We analyze the Nash Equilibrium in anN-player static game model. We further present a novel cluster-based fair strategy to approximately solve the CDL game to enforce mobile edge devices for cooperation. Our experimental results and evaluation analysis in a real-world smart home deployment show that 80% mobile edge devices are ready to cooperate in CDL, while 20% of them do not train their local models collaboratively.
kth Distance Distributions of n-Dimensional Matérn Cluster Process
Venue: arXiv cs.IT 2020
Year: 2020

In this letter, we derive the CDF (cumulative distribution function) of $k$th contact distance (CD) and nearest neighbor distance (NND) of the $n$-dimensional ($n$-D) Matérn cluster process (MCP). We present a new approach based on the probability generating function (PGF) of the random variable (RV) denoting the number of points in a ball of arbitrary radius to derive its probability mass function (PMF). The proposed method is general and can be used for any point process with known probability generating functional (PGFL). We also validate our analysis via numerical simulations and provide insights using the presented analysis. We also discuss two applications, namely: macro-diversity in cellular networks and caching in D2D networks, to study the impact of clustering on the performance.
Repairing Reed-Solomon Codes via Subspace Polynomials
Venue: arXiv cs.IT 2020
Year: 2020

We propose new repair schemes for Reed-Solomon codes that use subspace polynomials and hence generalize previous works in the literature that employ trace polynomials. The Reed-Solomon codes are over $\mathbb{F}_{q^\ell}$ and have redundancy $r = n-k \geq q^m$, $1\leq m\leq \ell$, where $n$ and $k$ are the code length and dimension, respectively. In particular, for one erasure, we show that our schemes can achieve optimal repair bandwidths whenever $n=q^\ell$ and $r = q^m,$ for all $1 \leq m \leq \ell$. For two erasures, our schemes use the same bandwidth per erasure as the single erasure schemes, for $\ell/m$ is a power of $q$, and for $\ell=q^a$, $m=q^b-1>1$ ($a \geq b \geq 1$), and for $m\geq \ell/2$ when $\ell$ is even and $q$ is a power of two.
Topics as Clusters of Citation Links to Highly Cited Sources: The Case of Research on International Relations
Venue: arXiv cs.DL 2020
Year: 2020

Following Henry Small in his approach to co-citation analysis, highly cited sources are seen as concept symbols of research fronts. But instead of co-cited sources I cluster citation links, which are the thematically least heterogenous elements in bibliometric studies. To obtain clusters representing topics characterised by concepts I restrict link clustering to citation links to highly cited sources. Clusters of citation links between papers in a political-science subfield (International Relations) and 300 of their sources most cited in the period 2006-2015 are constructed by a local memetic algorithm. It finds local minima in a cost landscape corresponding to clusters, which can overlap each other pervasively. The clusters obtained are well separated from the rest of the network but can have suboptimal cohesion. Cohesive cores of topics are found by applying an algorithm that constructs core-periphery structures in link sets. In this methodological paper I only discuss some first clustering results for the second half of the 10-years period.
The Blessing and the Curse of the Noise behind Facial Landmark Annotations
Venue: arXiv cs.CV 2020
Year: 2020

The evolving algorithms for 2D facial landmark detection empower people to recognize faces, analyze facial expressions, etc. However, existing methods still encounter problems of unstable facial landmarks when applied to videos. Because previous research shows that the instability of facial landmarks is caused by the inconsistency of labeling quality among the public datasets, we want to have a better understanding of the influence of annotation noise in them. In this paper, we make the following contributions: 1) we propose two metrics that quantitatively measure the stability of detected facial landmarks, 2) we model the annotation noise in an existing public dataset, 3) we investigate the influence of different types of noise in training face alignment neural networks, and propose corresponding solutions. Our results demonstrate improvements in both accuracy and stability of detected facial landmarks.
Dynamic texture analysis for detecting fake faces in video sequences
Venue: arXiv cs.CV 2020
Year: 2020

The creation of manipulated multimedia content involving human characters has reached in the last years unprecedented realism, calling for automated techniques to expose synthetically generated faces in images and videos. This work explores the analysis of spatio-temporal texture dynamics of the video signal, with the goal of characterizing and distinguishing real and fake sequences. We propose to build a binary decision on the joint analysis of multiple temporal segments and, in contrast to previous approaches, to exploit the textural dynamics of both the spatial and temporal dimensions. This is achieved through the use of Local Derivative Patterns on Three Orthogonal Planes (LDP-TOP), a compact feature representation known to be an important asset for the detection of face spoofing attacks. Experimental analyses on state-of-the-art datasets of manipulated videos show the discriminative power of such descriptors in separating real and fake sequences, and also identifying the creation method used. Linear Support Vector Machines (SVMs) are used which, despite the lower complexity, yield comparable performance to previously proposed deep models for fake content detection.
Photon: A Robust Cross-Domain Text-to-SQL System
Venue: arXiv cs.CL 2020
Year: 2020

Natural language interfaces to databases (NLIDB) democratize end user access to relational data. Due to fundamental differences between natural language communication and programming, it is common for end users to issue questions that are ambiguous to the system or fall outside the semantic scope of its underlying query language. We present Photon, a robust, modular, cross-domain NLIDB that can flag natural language input to which a SQL mapping cannot be immediately determined. Photon consists of a strong neural semantic parser (63.2\% structure accuracy on the Spider dev benchmark), a human-in-the-loop question corrector, a SQL executor and a response generator. The question corrector is a discriminative neural sequence editor which detects confusion span(s) in the input question and suggests rephrasing until a translatable input is given by the user or a maximum number of iterations are conducted. Experiments on simulated data show that the proposed method effectively improves the robustness of text-to-SQL system against untranslatable user input. The live demo of our system is available at http://naturalsql.com.
Design Guidelines for Blockchain-Assisted 5G-UAV Networks
Venue: arXiv cs.NI 2020
Year: 2020

Fifth Generation (5G) wireless networks are designed to meet various end-user Quality of Service (QoS) requirements through high data rates (typically of Gbps order) and low latencies. Coupled with Fog and Mobile Edge Computing (MEC), 5G can achieve high data rates, enabling complex autonomous smart city services such as the large deployment of self-driving vehicles and large-scale Artificial Intelligence (AI)-enabled industrial manufacturing. However, to meet the exponentially growing number of connected IoT devices and irregular data and service requests in both low and highly dense locations, the process of enacting traditional cells supported through fixed and costly base stations requires rethought to enable on-demand mobile access points in the form of Unmanned Aerial Vehicles (UAV) for diversified smart city scenarios. This article envisions a 5G network environment that is supported by blockchain-enabled UAVs to meet dynamic user demands with network access supply. The solution enables decentralized service delivery (Drones as a Service) and routing to and from end-users in a reliable and secure manner. Both public and private blockchains are deployed within the UAVs, supported by fog and cloud computing devices and data centers to provide wide range of complex authenticated service and data availability. Particular attention is paid tocomparing data delivery success rates and message exchange in the proposed solution against traditional UAV-supported cellular networks. Challenges and future research are also discussed with highlights on emerging technologies such as Federated Learning.
Leverage Unlabeled Data for Abstractive Speech Summarization with Self-Supervised Learning and Back-Summarization
Venue: arXiv cs.CL 2020
Year: 2020

Supervised approaches for Neural Abstractive Summarization require large annotated corpora that are costly to build. We present a French meeting summarization task where reports are predicted based on the automatic transcription of the meeting audio recordings. In order to build a corpus for this task, it is necessary to obtain the (automatic or manual) transcription of each meeting, and then to segment and align it with the corresponding manual report to produce training examples suitable for training. On the other hand, we have access to a very large amount of unaligned data, in particular reports without corresponding transcription. Reports are professionally written and well formatted making pre-processing straightforward. In this context, we study how to take advantage of this massive amount of unaligned data using two approaches (i) self-supervised pre-training using a target-side denoising encoder-decoder model; (ii) back-summarization i.e. reversing the summarization process by learning to predict the transcription given the report, in order to align single reports with generated transcription, and use this synthetic dataset for further training. We report large improvements compared to the previous baseline (trained on aligned data only) for both approaches on two evaluation sets. Moreover, combining the two gives even better results, outperforming the baseline by a large margin of +6 ROUGE-1 and ROUGE-L and +5 ROUGE-2 on two evaluation sets
Mixed-Reality Robotic Games: Design Guidelines for Effective Entertainment with Consumer Robots
Venue: arXiv cs.HC 2020
Year: 2020

In recent years, there has been an increasing interest in the use of robotic technology at home. A number of service robots appeared on the market, supporting customers in the execution of everyday tasks. Roughly at the same time, consumer level robots started to be used also as toys or gaming companions. However, gaming possibilities provided by current off-the-shelf robotic products are generally quite limited, and this fact makes them quickly loose their attractiveness. A way that has been proven capable to boost robotic gaming and related devices consists in creating playful experiences in which physical and digital elements are combined together using Mixed Reality technologies. However, these games differ significantly from digital- or physical only experiences, and new design principles are required to support developers in their creative work. This papers addresses such need, by drafting a set of guidelines which summarize developments carried out by the research community and their findings.
Epipolar-Guided Deep Object Matching for Scene Change Detection
Venue: arXiv cs.CV 2020
Year: 2020

This paper describes a viewpoint-robust object-based change detection network (OBJ-CDNet). Mobile cameras such as drive recorders capture images from different viewpoints each time due to differences in camera trajectory and shutter timing. However, previous methods for pixel-wise change detection are vulnerable to the viewpoint differences because they assume aligned image pairs as inputs. To cope with the difficulty, we introduce a deep graph matching network that establishes object correspondence between an image pair. The introduction enables us to detect object-wise scene changes without precise image alignment. For more accurate object matching, we propose an epipolar-guided deep graph matching network (EGMNet), which incorporates the epipolar constraint into the deep graph matching layer used in OBJCDNet. To evaluate our network's robustness against viewpoint differences, we created synthetic and real datasets for scene change detection from an image pair. The experimental results verified the effectiveness of our network.
Quantitative Distortion Analysis of Flattening Applied to the Scroll from En-Gedi
Venue: arXiv cs.CV 2020
Year: 2020

Non-invasive volumetric imaging can now capture the internal structure and detailed evidence of ink-based writing from within the confines of damaged and deteriorated manuscripts that cannot be physically opened. As demonstrated recently on the En-Gedi scroll, our "virtual unwrapping" software pipeline enables the recovery of substantial ink-based text from damaged artifacts at a quality high enough for serious critical textual analysis. However, the quality of the resulting images is defined by the subjective evaluation of scholars, and a choice of specific algorithms and parameters must be available at each stage in the pipeline in order to maximize the output quality.
Beyond $\mathcal{H}$-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence
Venue: arXiv cs.LG 2020
Year: 2020

We reveal the incoherence between the widely-adopted empirical domain adversarial training and its generally-assumed theoretical counterpart based on $\mathcal{H}$-divergence. Concretely, we find that $\mathcal{H}$-divergence is not equivalent to Jensen-Shannon divergence, the optimization objective in domain adversarial training. To this end, we establish a new theoretical framework by directly proving the upper and lower target risk bounds based on joint distributional Jensen-Shannon divergence. We further derive bi-directional upper bounds for marginal and conditional shifts. Our framework exhibits inherent flexibilities for different transfer learning problems, which is usable for various scenarios where $\mathcal{H}$-divergence-based theory fails to adapt. From an algorithmic perspective, our theory enables a generic guideline unifying principles of semantic conditional matching, feature marginal matching, and label marginal shift correction. We employ algorithms for each principle and empirically validate the benefits of our framework on real datasets.
SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization
Venue: arXiv cs.CR 2020
Year: 2020

Nowadays, as data becomes increasingly complex and distributed, data analyses often involve several related datasets that are stored on different servers and probably owned by different stakeholders. While there is an emerging need to provide these stakeholders with a full picture of their data under a global context, conventional visual analytical methods, such as dimensionality reduction, could expose data privacy when multi-party datasets are fused into a single site to build point-level relationships. In this paper, we reformulate the conventional t-SNE method from the single-site mode into a secure distributed infrastructure. We present a secure multi-party scheme for joint t-SNE computation, which can minimize the risk of data leakage. Aggregated visualization can be optionally employed to hide disclosure of point-level relationships. We build a prototype system based on our method, SMAP, to support the organization, computation, and exploration of secure joint embedding. We demonstrate the effectiveness of our approach with three case studies, one of which is based on the deployment of our system in real-world applications.
A Core Calculus for Static Latency Tracking with Placement Types
Venue: arXiv cs.PL 2020
Year: 2020

Developing efficient geo-distributed applications is challenging as programmers can easily introduce computations that entail high latency communication. We propose a language design which makes latency explicit and extracts type-level bounds for a computation's runtime latency. We present our initial steps with a core calculus that enables extracting provably correct latency bounds and outline future work.