OpenRaiser/Intern-Atlas huggingface dataset:
Finding Hamiltonian and Longest (s, t)-paths of C-shaped Supergrid Graphs in Linear Time
Venue: arXiv cs.CC 2019
Year: 2019

A supergrid graph is a finite vertex-induced subgraph of the infinite graph whose vertex set consists of all points of the plane with integer coordinates and in which two vertices are adjacent if the difference of their x or y coordinates is not larger than 1. The Hamiltonian path (cycle) problem is to determine whether a graph contains a simple path (cycle) in which each vertex of the graph appears exactly once. This problem is NP-complete for general graphs and it is also NP-complete for general supergrid graphs. Despite the many applications of the problem, it is still open for many classes, including solid supergrid graphs and supergrid graphs with some holes. A graph is called Hamiltonian connected if it contains a Hamiltonian path between any two distinct vertices. In this paper, first we will study the Hamiltonian cycle property of C-shaped supergrid graphs, which are a special case of rectangular supergrid graphs with a rectangular hole. Next, we will show that C-shaped supergrid graphs are Hamiltonian connected except few conditions. Finally, we will compute a longest path between two distinct vertices in these graphs. The Hamiltonian connectivity of C-shaped supergrid graphs can be applied to compute the optimal stitching trace of computer embroidery machines, and construct the minimum printing trace of 3D printers with a C-like component being printed.
Undeniable signatures based on isogenies of supersingular hyperelliptic curves
Venue: arXiv cs.CR 2019
Year: 2019

We present a proposal for an undeniable signature scheme based in supersingular hyperelliptic curves of genus 2.
Delineating Knowledge Domains in the Scientific Literature Using Visual Information
Venue: arXiv cs.DL 2019
Year: 2019

Figures are an important channel for scientific communication, used to express complex ideas, models and data in ways that words cannot. However, this visual information is mostly ignored in analyses of the scientific literature. In this paper, we demonstrate the utility of using scientific figures as markers of knowledge domains in science, which can be used for classification, recommender systems, and studies of scientific information exchange. We encode sets of images into a visual signature, then use distances between these signatures to understand how patterns of visual communication compare with patterns of jargon and citation structures. We find that figures can be as effective for differentiating communities of practice as text or citation patterns. We then consider where these metrics disagree to understand how different disciplines use visualization to express ideas. Finally, we further consider how specific figure types propagate through the literature, suggesting a new mechanism for understanding the flow of ideas apart from conventional channels of text and citations. Our ultimate aim is to better leverage these information-dense objects to improve scientific communication across disciplinary boundaries.
Championing Research Through Design in HRI
Venue: arXiv cs.HC 2019
Year: 2019

One of the challenges in conducting research on the intersection of the CHI and Human-Robot Interaction (HRI) communities is in addressing the gap of acceptable design research methods between the two. While HRI is focused on interaction with robots and includes design research in its scope, the community is not as accustomed to exploratory design methods as the CHI community. This workshop paper argues for bringing exploratory design, and specifically Research through Design (RtD) methods that have been established in CHI for the past decade to the foreground of HRI. RtD can enable design researchers in the field of HRI to conduct exploratory design work that asks what is the right thing to design and share it within the community.
Securing HPC using Federated Authentication
Venue: arXiv cs.DC 2019
Year: 2019

Federated authentication can drastically reduce the overhead of basic account maintenance while simultaneously improving overall system security. Integrating with the user's more frequently used account at their primary organization both provides a better experience to the end user and makes account compromise or changes in affiliation more likely to be noticed and acted upon. Additionally, with many organizations transitioning to multi-factor authentication for all account access, the ability to leverage external federated identity management systems provides the benefit of their efforts without the additional overhead of separately implementing a distinct multi-factor authentication process. This paper describes our experiences and the lessons we learned by enabling federated authentication with the U.S. Government PKI and InCommon Federation, scaling it up to the user base of a production HPC system, and the motivations behind those choices. We have received only positive feedback from our users.
From Text to Sound: A Preliminary Study on Retrieving Sound Effects to Radio Stories
Venue: arXiv cs.IR 2019
Year: 2019

Sound effects play an essential role in producing high-quality radio stories but require enormous labor cost to add. In this paper, we address the problem of automatically adding sound effects to radio stories with a retrieval-based model. However, directly implementing a tag-based retrieval model leads to high false positives due to the ambiguity of story contents. To solve this problem, we introduce a retrieval-based framework hybridized with a semantic inference model which helps to achieve robust retrieval results. Our model relies on fine-designed features extracted from the context of candidate triggers. We collect two story dubbing datasets through crowdsourcing to analyze the setting of adding sound effects and to train and test our proposed methods. We further discuss the importance of each feature and introduce several heuristic rules for the trade-off between precision and recall. Together with the text-to-speech technology, our results reveal a promising automatic pipeline on producing high-quality radio stories.
Covert Millimeter-Wave Communication via a Dual-Beam Transmitter
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we investigate covert communication over millimeter-wave (mmWave) frequencies. In particular, a dual-beam mmWave transmitter, comprised of two independent antenna arrays, attempts to reliably communicate to a receiver Bob when hiding the existence of transmission from a warden Willie. In this regard, operating over mmWave bands not only increases the covertness thanks to directional beams, but also increases the transmission data rates given much more available bandwidths and enables ultra-low form factor transceivers due to the lower wavelengths used compared to the conventional radio frequency (RF) counterpart. We assume that the transmitter Alice employs one of its antenna arrays to form a directive beam for transmission to Bob. The other antenna array is used by Alice to generate another beam toward Willie as a jamming signal with its transmit power changing independently from a transmission block to another block. We characterize Willie's detection performance with the optimal detector and the closed-form of its expected value from Alice's perspective. We further derive the closed-form expression for the outage probability of the Alice-Bob link, which enables characterizing the optimal covert rate that can be achieved using the proposed setup. Our results demonstrate the superiority of mmWave covert communication, in terms of covertness and rate, compared to the RF counterpart.
AdaCliP: Adaptive Clipping for Private SGD
Venue: arXiv cs.LG 2019
Year: 2019

Privacy preserving machine learning algorithms are crucial for learning models over user data to protect sensitive information. Motivated by this, differentially private stochastic gradient descent (SGD) algorithms for training machine learning models have been proposed. At each step, these algorithms modify the gradients and add noise proportional to the sensitivity of the modified gradients. Under this framework, we propose AdaCliP, a theoretically motivated differentially private SGD algorithm that provably adds less noise compared to the previous methods, by using coordinate-wise adaptive clipping of the gradient. We empirically demonstrate that AdaCliP reduces the amount of added noise and produces models with better accuracy.
Line and Plane Cover Numbers Revisited
Venue: arXiv cs.CG 2019
Year: 2019

A measure for the visual complexity of a straight-line crossing-free drawing of a graph is the minimum number of lines needed to cover all vertices. For a given graph $G$, the minimum such number (over all drawings in dimension $d \in \{2,3\}$) is called the \emph{$d$-dimensional weak line cover number} and denoted by $π^1_d(G)$. In 3D, the minimum number of \emph{planes} needed to cover all vertices of~$G$ is denoted by $π^2_3(G)$. When edges are also required to be covered, the corresponding numbers $ρ^1_d(G)$ and $ρ^2_3(G)$ are called the \emph{(strong) line cover number} and the \emph{(strong) plane cover number}. Computing any of these cover numbers -- except $π^1_2(G)$ -- is known to be NP-hard. The complexity of computing $π^1_2(G)$ was posed as an open problem by Chaplick et al. [WADS 2017]. We show that it is NP-hard to decide, for a given planar graph~$G$, whether $π^1_2(G)=2$. We further show that the universal stacked triangulation of depth~$d$, $G_d$, has $π^1_2(G_d)=d+1$. Concerning~3D, we show that any $n$-vertex graph~$G$ with $ρ^2_3(G)=2$ has at most $5n-19$ edges, which is tight.
Learning Joint Embedding for Cross-Modal Retrieval
Venue: arXiv cs.IR 2019
Year: 2019

A cross-modal retrieval process is to use a query in one modality to obtain relevant data in another modality. The challenging issue of cross-modal retrieval lies in bridging the heterogeneous gap for similarity computation, which has been broadly discussed in image-text, audio-text, and video-text cross-modal multimedia data mining and retrieval. However, the gap in temporal structures of different data modalities is not well addressed due to the lack of alignment relationship between temporal cross-modal structures. Our research focuses on learning the correlation between different modalities for the task of cross-modal retrieval. We have proposed an architecture: Supervised-Deep Canonical Correlation Analysis (S-DCCA), for cross-modal retrieval. In this forum paper, we will talk about how to exploit triplet neural networks (TNN) to enhance the correlation learning for cross-modal retrieval. The experimental result shows the proposed TNN-based supervised correlation learning architecture can get the best result when the data representation extracted by supervised learning.
Copy-Enhanced Heterogeneous Information Learning for Dialogue State Tracking
Venue: arXiv cs.CL 2019
Year: 2019

Dialogue state tracking (DST) is an essential component in task-oriented dialogue systems, which estimates user goals at every dialogue turn. However, most previous approaches usually suffer from the following problems. Many discriminative models, especially end-to-end (E2E) models, are difficult to extract unknown values that are not in the candidate ontology; previous generative models, which can extract unknown values from utterances, degrade the performance due to ignoring the semantic information of pre-defined ontology. Besides, previous generative models usually need a hand-crafted list to normalize the generated values. How to integrate the semantic information of pre-defined ontology and dialogue text (heterogeneous texts) to generate unknown values and improve performance becomes a severe challenge. In this paper, we propose a Copy-Enhanced Heterogeneous Information Learning model with multiple encoder-decoder for DST (CEDST), which can effectively generate all possible values including unknown values by copying values from heterogeneous texts. Meanwhile, CEDST can effectively decompose the large state space into several small state spaces through multi-encoder, and employ multi-decoder to make full use of the reduced spaces to generate values. Multi-encoder-decoder architecture can significantly improve performance. Experiments show that CEDST can achieve state-of-the-art results on two datasets and our constructed datasets with many unknown values.
Video-based Bottleneck Detection utilizing Lagrangian Dynamics in Crowded Scenes
Venue: arXiv cs.CV 2019
Year: 2019

Avoiding bottleneck situations in crowds is critical for the safety and comfort of people at large events or in public transportation. Based on the work of Lagrangian motion analysis we propose a novel video-based bottleneckdetector by identifying characteristic stowage patterns in crowd-movements captured by optical flow fields. The Lagrangian framework allows to assess complex timedependent crowd-motion dynamics at large temporal scales near the bottleneck by two dimensional Lagrangian fields. In particular we propose long-term temporal filtered Finite Time Lyapunov Exponents (FTLE) fields that provide towards a more global segmentation of the crowd movements and allows to capture its deformations when a crowd is passing a bottleneck. Finally, these deformations are used for an automatic spatio-temporal detection of such situations. The performance of the proposed approach is shown in extensive evaluations on the existing Jülich and AGORASET datasets, that we have updated with ground truth data for spatio-temporal bottleneck analysis.
Leveraging creativity in requirements elicitation within agile software development: a systematic literature review
Venue: arXiv cs.SE 2019
Year: 2019

Agile approaches tend to focus solely on scoping and simplicity rather than on problem solving and discovery. This hampers the development of innovative solutions. Additionally, little has been said about how to capture and represent the real user needs. To fill this gap, some authors argue in favor of the application of Creative thinking for requirements elicitation within agile software development. This synergy between creativeness and agility has arisen as a new means of bringing innovation and flexibility to increasingly demanding software. The aim of the present study is therefore to employ a systematic review to investigate the state-of-the-art of those approaches that leverage creativity in requirements elicitation within Agile Software Development, as well as the benefits, limitations and strength of evidence of these approaches. The search strategy identified 1451 studies. The selected studies contained 13 different and unique proposals. These approaches provide evidence that enhanced creativity in requirements elicitation can be successfully implemented in real software projects. We specifically observed that projects related to user interface development, such as those for mobile or web applications, are good candidates for the use of these approaches. We have also found that agile methodologies are preferred when introducing creativity into requirements elicitation. Despite this being a new research field, there is a mixture of techniques, tools and processes that have already been and are currently being successfully tested in industry. Finally, we have found that, although creativity is an important ingredient with which to bring about innovation, it is not always sufficient to generate new requirements because this needs to be followed by user engagement and a specific context in which proper conditions, such as flexibility, time or resources, have to be met.
Exploring Offline Policy Evaluation for the Continuous-Armed Bandit Problem
Venue: arXiv cs.LG 2019
Year: 2019

The (contextual) multi-armed bandit problem (MAB) provides a formalization of sequential decision-making which has many applications. However, validly evaluating MAB policies is challenging; we either resort to simulations which inherently include debatable assumptions, or we resort to expensive field trials. Recently an offline evaluation method has been suggested that is based on empirical data, thus relaxing the assumptions, and can be used to evaluate multiple competing policies in parallel. This method is however not directly suited for the continuous armed (CAB) problem; an often encountered version of the MAB problem in which the action set is continuous instead of discrete. We propose and evaluate an extension of the existing method such that it can be used to evaluate CAB policies. We empirically demonstrate that our method provides a relatively consistent ranking of policies. Furthermore, we detail how our method can be used to select policies in a real-life CAB problem.
Analog circuits for mixed-signal neuromorphic computing architectures in 28 nm FD-SOI technology
Venue: arXiv cs.ET 2019
Year: 2019

Developing mixed-signal analog-digital neuromorphic circuits in advanced scaled processes poses significant design challenges. We present compact and energy efficient sub-threshold analog synapse and neuron circuits, optimized for a 28 nm FD-SOI process, to implement massively parallel large-scale neuromorphic computing systems. We describe the techniques used for maximizing density with mixed-mode analog/digital synaptic weight configurations, and the methods adopted for minimizing the effect of channel leakage current, in order to implement efficient analog computation based on pA-nA small currents. We present circuit simulation results, based on a new chip that has been recently taped out, to demonstrate how the circuits can be useful for both low-frequency operation in systems that need to interact with the environment in real-time, and for high-frequency operation for fast data processing in different types of spiking neural network architectures.
Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. As a result, TP-DPL unifies the salient feature extraction, representation and classification. The twin-incoherence constraint on codes and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve the local neighborhood of the coefficients and salient features within each class explicitly. For efficiency, TP-DPL uses Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.
Universal Reconfiguration of Facet-Connected Modular Robots by Pivots: The $O(1)$ Musketeers
Venue: arXiv cs.CG 2019
Year: 2019

We present the first universal reconfiguration algorithm for transforming a modular robot between any two facet-connected square-grid configurations using pivot moves. More precisely, we show that five extra "helper" modules ("musketeers") suffice to reconfigure the remaining $n$ modules between any two given configurations. Our algorithm uses $O(n^2)$ pivot moves, which is worst-case optimal. Previous reconfiguration algorithms either require less restrictive "sliding" moves, do not preserve facet-connectivity, or for the setting we consider, could only handle a small subset of configurations defined by a local forbidden pattern. Configurations with the forbidden pattern do have disconnected reconfiguration graphs (discrete configuration spaces), and indeed we show that they can have an exponential number of connected components. But forbidding the local pattern throughout the configuration is far from necessary, as we show that just a constant number of added modules (placed to be freely reconfigurable) suffice for universal reconfigurability. We also classify three different models of natural pivot moves that preserve facet-connectivity, and show separations between these models.
Visualization in the preprocessing phase: an interview study with enterprise professionals
Venue: arXiv cs.HC 2019
Year: 2019

The current information age has increasingly required organizations to become data-driven. However, analyzing and managing raw data is still a challenging part of the data mining process. Even though we can find interview studies proposing design implications or recommendations for future visualization solutions in the data mining scope, they cover the entire workflow and do not fully focus on the challenges during the preprocessing phase and on how visualization can support it. Moreover, they do not organize a final list of insights consolidating the findings of other related studies. Hence, to better understand the current practice of enterprise professionals in data mining workflows, in particular during the preprocessing phase, and how visualization supports this process, we conducted semi-structured interviews with thirteen data analysts. The discussion about the challenges and opportunities based on the responses of the interviewees resulted in a list of ten insights. This list was compared with the closest related works, improving the reliability of our findings and providing background, as a consolidated set of requirements, for future visualization research papers applied to visual data exploration in data mining. Furthermore, we provide greater details on the profile of the data analysts, the main challenges they face, and the opportunities that arise while they are engaged in data mining projects in diverse organizational areas.
PCRNet: Point Cloud Registration Network using PointNet Encoding
Venue: arXiv cs.CV 2019
Year: 2019

PointNet has recently emerged as a popular representation for unstructured point cloud data, allowing application of deep learning to tasks such as object detection, segmentation and shape completion. However, recent works in literature have shown the sensitivity of the PointNet representation to pose misalignment. This paper presents a novel framework that uses the PointNet representation to align point clouds and perform registration for applications such as tracking, 3D reconstruction and pose estimation. We develop a framework that compares PointNet features of template and source point clouds to find the transformation that aligns them accurately. Depending on the prior information about the shape of the object formed by the point clouds, our framework can produce approaches that are shape specific or general to unseen shapes. The shape specific approach uses a Siamese architecture with fully connected (FC) layers and is robust to noise and initial misalignment in data. We perform extensive simulation and real-world experiments to validate the efficacy of our approach and compare the performance with state-of-art approaches.