OpenRaiser/Intern-Atlas huggingface dataset:
'Alexa, Do You Know Anything?' The Impact of an Intelligent Assistant on Team Interactions and Creative Performance Under Time Scarcity
Venue: arXiv cs.HC 2019
Year: 2019

Human-AI collaboration is on the rise with the deployment of AI-enabled intelligent assistants (e.g. Amazon Echo, Cortana, Siri, etc.) across organizational contexts. It is claimed that intelligent assistants can help people achieve more in less time (Personal Digital Assistant - Cortana, n.d.). However, despite the increasing presence of intelligent assistants in collaborative settings, there is a void in the literature on how the deployment of this technology intersects with time scarcity to impact team behaviors and performance. To fill this gap in the literature, we collected behavioral data from 56 teams who participated in a between-subjects 2 (Intelligent Assistant: Available vs. Not Available) x 2 (Time: Scarce vs. Not Scarce/Control) lab experiment. The results show that teams with an intelligent assistant had significantly fewer interactions between its members compared to teams without an intelligent assistant. Teams who faced time scarcity also used the intelligent assistant more often to seek its assistance during task completion compared to those in the control condition. Lastly, teams with an intelligent assistant underperformed on a creative task compared to those without the device. We discuss implications of this technology from theoretical, empirical, and practical perspectives.
Performance Evaluation of Dynamic Scaling on MPI
Venue: arXiv cs.DC 2019
Year: 2019

Dynamic scaling aims to elastically change the number of processes during runtime to tune the performance of the distributed applications. This report briefly presents a performance evaluation of MPI process provisioning / de-provisioning for dynamic scaling by using 16 to 128 cores. Our dynamic scaling implementation allows the new MPI processes from new hosts to communicate with the original ones immediately. Moreover, it forbids the removing MPI processes to communicate with others as well as gets the information whether the host node can be terminated or not. Such a simple feature is not supported as a single-line API in MPI-2 such as MPI_Comm_spawn(). We provide our implementation as a simple library to extend a non-dynamic-scalable MPI program into a dynamic-scalable one by adding only several lines of codes.
Consistency-Aware Recommendation for User-Generated ItemList Continuation
Venue: arXiv cs.IR 2019
Year: 2019

User-generated item lists are popular on many platforms. Examples include video-based playlists on YouTube, image-based lists (or"boards") on Pinterest, book-based lists on Goodreads, and answer-based lists on question-answer forums like Zhihu. As users create these lists, a common challenge is in identifying what items to curate next. Some lists are organized around particular genres or topics, while others are seemingly incoherent, reflecting individual preferences for what items belong together. Furthermore, this heterogeneity in item consistency may vary from platform to platform, and from sub-community to sub-community. Hence, this paper proposes a generalizable approach for user-generated item list continuation. Complementary to methods that exploit specific content patterns (e.g., as in song-based playlists that rely on audio features), the proposed approach models the consistency of item lists based on human curation patterns, and so can be deployed across a wide range of varying item types (e.g., videos, images, books). A key contribution is in intelligently combining two preference models via a novel consistency-aware gating network - a general user preference model that captures a user's overall interests, and a current preference priority model that captures a user's current (as of the most recent item) interests. In this way, the proposed consistency-aware recommender can dynamically adapt as user preferences evolve. Evaluation over four datasets(of songs, books, and answers) confirms these observations and demonstrates the effectiveness of the proposed model versus state-of-the-art alternatives. Further, all code and data are available at https://github.com/heyunh2015/ListContinuation_WSDM2020.
Physical Layer Security Protocol for Poisson Channels for Passive Man-in-the-middle Attack
Venue: arXiv cs.CR 2019
Year: 2019

In this work, we focus on the classical optical channel having Poissonian statistical behavior and propose a novel secrecy coding-based physical layer protocol. Our protocol is different but complementary to both (computationally secure) quantum immune cryptographic protocols and (information theoretically secure) quantum cryptographic protocols. Specifically, our (information theoretical) secrecy coding protocol secures classical digital information bits at photonic level exploiting the random nature of the Poisson channel. It is known that secrecy coding techniques for the Poisson channel based on the classical one-way wiretap channel (introduced by Wyner in 1975) ensure secret communication only if the mutual information to the eavesdropper is smaller than that to the legitimate receiver. In order to overcome such a strong limitation, we introduce a two-way protocol that always ensures secret communication independently of the conditions of legitimate and eavesdropper channels. We prove this claim showing rigorous comparative derivation and analysis of the information theoretical secrecy capacity of the classical one-way and of the proposed two-way protocols. We also show numerical calculations that prove drastic gains and strong practical potential of our proposed two-way protocol to secure information transmission over optical channels.
NOMA-Aided Mobile Edge Computing via User Cooperation
Venue: arXiv cs.IT 2019
Year: 2019

Exploiting the idle computation resources of mobile devices in mobile edge computing (MEC) system can achieve both channel diversity and computing diversity as mobile devices can offload their computation tasks to nearby mobile devices in addition to MEC server embedded access point (AP). In this paper, we propose a non-orthogonal multiple-access (NOMA)-aided cooperative computing scheme in a basic three-node MEC system consisting of a user, a helper, and an AP. In particular, we assume that the user can simultaneously offload data to the helper and the AP using NOMA, while the helper can locally compute data and offload data to the AP at the same time. We study two optimization problems, energy consumption minimization and offloading data maximization, by joint communication and computation resource allocation of the user and helper. We find the optimal solutions for the two non-convex problems by some proper mathematical methods. Simulation results are presented to demonstrate the effectiveness of the proposed schemes. Some useful insights are provided for practical designs.
Bas-relief Generation from Point Clouds Based on Normal Space Compression with Real-time Adjustment on CPU
Venue: arXiv cs.GR 2019
Year: 2019

Bas-relief generation based on 3d models is a hot topic in computer graphics. State-of-the-art algorithms take a mesh surface as input, but real-time interaction via CPU cannot be realized. In this paper, a bas-relief generation algorithm that takes a scattered point cloud as input is proposed. The algorithm takes normal vectors as the operation object and the variation of the local surface as the compression criterion. By constructing and solving linear equations of bas-relief vertices, the closed-form solution can be obtained. Since there is no need to compute discrete gradients on a point cloud lacking topology information, it is easier to implement and more intuitive than gradient domain methods. The algorithm provides parameters to adjust the bas-relief height, saturation and detail richness. At the same time, through the solution strategy based on the subspace, it realizes the real-time adjustment of the bas-relief effect based on the computing power of a consumer CPU. In addition, an iterative solution to generate a bas-relief model of a specified height is presented to meet specific application requirements. Experiments show that our algorithm provides a unified solution for various types of bas-relief creation and can generate bas-reliefs with good saturation and rich details.
C. H. Robinson Uses Heuristics to Solve Rich Vehicle Routing Problems
Venue: arXiv cs.AI 2019
Year: 2019

We consider a wide family of vehicle routing problem variants with many complex and practical constraints, known as rich vehicle routing problems, which are faced on a daily basis by C.H. Robinson (CHR). Since CHR has many customers, each with distinct requirements, various routing problems with different objectives and constraints should be solved. We propose a set partitioning framework with a number of route generation algorithms, which have shown to be effective in solving a variety of different problems. The proposed algorithms have outperformed the existing technologies at CHR on 10 benchmark instances and since, have been embedded into the company's transportation planning and execution technology platform.
Diversity Transfer Network for Few-Shot Learning
Venue: arXiv cs.CV 2019
Year: 2019

Few-shot learning is a challenging task that aims at training a classifier for unseen classes with only a few training examples. The main difficulty of few-shot learning lies in the lack of intra-class diversity within insufficient training samples. To alleviate this problem, we propose a novel generative framework, Diversity Transfer Network (DTN), that learns to transfer latent diversities from known categories and composite them with support features to generate diverse samples for novel categories in feature space. The learning problem of the sample generation (i.e., diversity transfer) is solved via minimizing an effective meta-classification loss in a single-stage network, instead of the generative loss in previous works. Besides, an organized auxiliary task co-training over known categories is proposed to stabilize the meta-training process of DTN. We perform extensive experiments and ablation studies on three datasets, i.e., \emph{mini}ImageNet, CIFAR100 and CUB. The results show that DTN, with single-stage training and faster convergence speed, obtains the state-of-the-art results among the feature generation based few-shot learning methods. Code and supplementary material are available at: \texttt{https://github.com/Yuxin-CV/DTN}
Definitions and Semantic Simulations Based on Object-Oriented Analysis and Modeling
Venue: arXiv cs.AI 2019
Year: 2019

We have proposed going beyond traditional ontologies to use rich semantics implemented in programming languages for modeling. In this paper, we discuss the application of executable semantic models to two examples, first a structured definition of a waterfall and second the cardiopulmonary system. We examine the components of these models and the way those components interact. Ultimately, such models should provide the basis for direct representation.
Medusa: A C++ Library for solving PDEs using Strong Form Mesh-Free methods
Venue: arXiv cs.MS 2019
Year: 2019

Medusa, a novel library for implementation of strong form mesh-free methods, is described. We identify and present common parts and patterns among many such methods reported in the literature, such as node positioning, stencil selection and stencil weight computation. Many different algorithms exist for each part and the possible combinations offer a plethora of possibilities for improvements of solution procedures that are far from fully understood. As a consequence there are still many unanswered questions in mesh-free community resulting in vivid ongoing research in the field. Medusa implements the core mesh-free elements as independent blocks, which offers users great flexibility in experimenting with the method they are developing, as well as easily comparing it with other existing methods. The paper describes the chosen abstractions and their usage, illustrates aspects of the philosophy and design, offers some executions time benchmarks and demonstrates the application of the library on cases from linear elasticity and fluid flow in irregular 2D and 3D domains.
3D Sensing of a Moving Object with a Nodding 2D LIDAR and Reconfigurable Mirrors
Venue: arXiv cs.CV 2019
Year: 2019

Perception in 3D has become standard practice for a large part of robotics applications. High quality 3D perception is costly. Our previous work on a nodding 2D Lidar provides high quality 3D depth information with low cost, but the sparse data generated by this sensor poses challenges in understanding the characteristics of moving objects within an uncertain environment. This paper proposes a novel design of the nodding Lidar but provides dynamic reconfigurability in terms of limiting the field of view of the sensor using a set of optical mirrors. It not only provides denser scans, but it also achieves a three times higher scan update rate. Additionally, we propose a novel calibration mechanism for this sensor and prove its effectiveness for dynamic object detection and tracking.
Revisiting Landscape Analysis in Deep Neural Networks: Eliminating Decreasing Paths to Infinity
Venue: arXiv cs.LG 2019
Year: 2019

Traditional landscape analysis of deep neural networks aims to show that no sub-optimal local minima exist in some appropriate sense. From this, one may be tempted to conclude that descent algorithms which escape saddle points will reach a good local minimum. However, basic optimization theory tell us that it is also possible for a descent algorithm to diverge to infinity if there are paths leading to infinity, along which the loss function decreases. It is not clear whether for non-linear neural networks there exists one setting that no bad local-min and no decreasing paths to infinity can be simultaneously achieved. In this paper, we give the first positive answer to this question. More specifically, for a large class of over-parameterized deep neural networks with appropriate regularizers, the loss function has no bad local minima and no decreasing paths to infinity. The key mathematical trick is to show that the set of regularizers which may be undesirable can be viewed as the image of a Lipschitz continuous mapping from a lower-dimensional Euclidean space to a higher-dimensional Euclidean space, and thus has zero measure.
On the Difference Between the Information Bottleneck and the Deep Information Bottleneck
Venue: arXiv cs.LG 2019
Year: 2019

Combining the Information Bottleneck model with deep learning by replacing mutual information terms with deep neural nets has proved successful in areas ranging from generative modelling to interpreting deep neural networks. In this paper, we revisit the Deep Variational Information Bottleneck and the assumptions needed for its derivation. The two assumed properties of the data $X$, $Y$ and their latent representation $T$ take the form of two Markov chains $T-X-Y$ and $X-T-Y$. Requiring both to hold during the optimisation process can be limiting for the set of potential joint distributions $P(X,Y,T)$. We therefore show how to circumvent this limitation by optimising a lower bound for $I(T;Y)$ for which only the latter Markov chain has to be satisfied. The actual mutual information consists of the lower bound which is optimised in DVIB and cognate models in practice and of two terms measuring how much the former requirement $T-X-Y$ is violated. Finally, we propose to interpret the family of information bottleneck models as directed graphical models and show that in this framework the original and deep information bottlenecks are special cases of a fundamental IB model.
HMM-guided frame querying for bandwidth-constrained video search
Venue: arXiv cs.CV 2019
Year: 2019

We design an agent to search for frames of interest in video stored on a remote server, under bandwidth constraints. Using a convolutional neural network to score individual frames and a hidden Markov model to propagate predictions across frames, our agent accurately identifies temporal regions of interest based on sparse, strategically sampled frames. On a subset of the ImageNet-VID dataset, we demonstrate that using a hidden Markov model to interpolate between frame scores allows requests of 98% of frames to be omitted, without compromising frame-of-interest classification accuracy.
The Mobile AR Sensor Logger for Android and iOS Devices
Venue: arXiv cs.HC 2019
Year: 2019

In recent years, commodity mobile devices equipped with cameras and inertial measurement units (IMUs) have attracted much research and design effort for augmented reality (AR) and robotics applications. Based on such sensors, many commercial AR toolkits and public benchmark datasets have been made available to accelerate hatching and validating new ideas. To lower the difficulty and enhance the flexibility in accessing the rich raw data of typical AR sensors on mobile devices, this paper present the mobile AR sensor (MARS) logger for two of the most popular mobile operating systems, Android and iOS. The logger highlights the best possible synchronization between the camera and the IMU allowed by a mobile device, and efficient saving of images at about 30Hz, and recording the metadata relevant to AR applications. This logger has been tested on a relatively large spectrum of mobile devices, and the collected data has been used for analyzing the sensor characteristics. We see that this application will facilitate research and development related to AR and robotics, so it has been open sourced at https://github.com/OSUPCVLab/mobile-ar-sensor-logger.
Joint 3D Maneuver and Power Adaptation for Secure UAV Communication with CoMP Reception
Venue: arXiv cs.IT 2019
Year: 2019

This paper studies a secrecy unmanned aerial vehicle (UAV) communication system with coordinated multi-point (CoMP) reception, in which one UAV sends confidential messages to a set of cooperative ground receivers (GRs), in the presence of several suspicious eavesdroppers. In particular, we consider two types of eavesdroppers that are non-colluding and colluding, respectively. Under this setup, we exploit the UAV's maneuver in three dimensional (3D) space together with transmit power adaptation for optimizing the secrecy communication performance. First, we consider the quasi-stationary UAV scenario, where we jointly optimize the UAV's 3D placement and transmit power control to maximize the secrecy rate. Under both non-colluding and colluding eavesdroppers, we obtain the optimal solutions to the joint 3D placement and transmit power control problems in well structures. Next, we consider the mobile UAV scenario, where we jointly optimize the UAV's 3D trajectory and transmit power allocation to maximize the average secrecy rate during the whole communication period. To deal with the difficult joint 3D trajectory and transmit power allocation problems, we present alternating-optimization-based approaches to obtain high-quality solutions. Finally, we provide numerical results to validate the performance of our proposed designs. It is shown that due to the consideration of CoMP reception, our proposed design with 3D maneuver significantly outperforms the conventional design with two dimensional (2D) (horizontal) maneuver only, by exploiting the additional degrees of freedom in altitudes. It is also shown that the non-colluding and colluding eavesdroppers lead to distinct 3D UAV maneuver behaviors.
On the Interactive Capacity of Finite-State Protocols
Venue: arXiv cs.IT 2019
Year: 2019

The interactive capacity of a noisy channel is the highest possible rate at which arbitrary interactive protocols can be simulated reliably over the channel. Determining the interactive capacity is notoriously difficult, and the best known lower bounds are far below the associated Shannon capacity, which serves as a trivial (and also generally the best known) upper bound. This paper considers the more restricted setup of simulating finite-state protocols. It is shown that all two-state protocols, as well as rich families of arbitrary finite-state protocols, can be simulated at the Shannon capacity, establishing the interactive capacity for those families of protocols.
Lightform: Procedural Effects for Projected AR
Venue: arXiv cs.GR 2019
Year: 2019

Projected augmented reality, also called projection mapping or video mapping, is a form of augmented reality that uses projected light to directly augment 3D surfaces, as opposed to using pass-through screens or headsets. The value of projected AR is its ability to add a layer of digital content directly onto physical objects or environments in a way that can be instantaneously viewed by multiple people, unencumbered by a screen or additional setup. Because projected AR typically involves projecting onto non-flat, textured objects (especially those that are conventionally not used as projection surfaces), the digital content needs to be mapped and aligned to precisely fit the physical scene to ensure a compelling experience. Current projected AR techniques require extensive calibration at the time of installation, which is not conducive to iteration or change, whether intentional (the scene is reconfigured) or not (the projector is bumped or settles). The workflows are undefined and fragmented, thus making it confusing and difficult for many to approach projected AR. For example, a digital artist may have the software expertise to create AR content, but could not complete an installation without experience in mounting, blending, and realigning projector(s); the converse is true for many A/V installation teams/professionals. Projection mapping has therefore been limited to high-end event productions, concerts, and films, because it requires expensive, complex tools, and skilled teams ($100K+ budgets). Lightform provides a technology that makes projected AR approachable, practical, intelligent, and robust through integrated hardware and computer-vision software. Lightform brings together and unites a currently fragmented workflow into a single cohesive process that provides users with an approachable and robust method to create and control projected AR experiences.
A Hybrid Framework for Topic Structure using Laughter Occurrences
Venue: arXiv cs.LG 2019
Year: 2019

Conversational discourse coherence depends on both linguistic and paralinguistic phenomena. In this work we combine both paralinguistic and linguistic knowledge into a hybrid framework through a multi-level hierarchy. Thus it outputs the discourse-level topic structures. The laughter occurrences are used as paralinguistic information from the multiparty meeting transcripts of ICSI database. A clustering-based algorithm is proposed that chose the best topic-segment cluster from two independent, optimized clusters, namely, hierarchical agglomerative clustering and $K$-medoids. Then it is iteratively hybridized with an existing lexical cohesion based Bayesian topic segmentation framework. The hybrid approach improves the performance of both of the stand-alone approaches. This leads to the brief study of interactions between topic structures with discourse relational structure. This training-free topic structuring approach can be applicable to online understanding of spoken dialogs.