OpenRaiser/Intern-Atlas huggingface dataset:
Picturing Bivariate Separable-Features for Univariate Vector Magnitudes in Large-Magnitude-Range Quantum Physics Data
Venue: arXiv cs.GR 2019
Year: 2019

We present study results from two experiments to empirically validate that separable bivariate pairs for univariate representations of large-magnitude-range vectors are more efficient than integral pairs. The first experiment with 20 participants compared: one integral pair, three separable pairs, and one redundant pair, which is a mix of the integral and separable features. Participants performed three local tasks requiring reading numerical values, estimating ratio, and comparing two points. The second 18-participant study compared three separable pairs using three global tasks when participants must look at the entire field to get an answer: find a specific target in 20 seconds, find the maximum magnitude in 20 seconds, and estimate the total number of vector exponents within 2 seconds. Our results also reveal the following: separable pairs led to the most accurate answers and the shortest task execution time, while integral dimensions were among the least accurate; it achieved high performance only when a pop-out separable feature (here color) was added. To reconcile this finding with the existing literature, our second experiment suggests that the higher the separability, the higher the accuracy; the reason is probably that the emergent global scene created by the separable pairs reduces the subsequent search space.
Distributed Construction of Light Networks
Venue: arXiv cs.DS 2019
Year: 2019

A $t$-{\em spanner} $H$ of a weighted graph $G=(V,E,w)$ is a subgraph that approximates all pairwise distances up to a factor of $t$. The {\em lightness} of $H$ is defined as the ratio between the weight of $H$ to that of the minimum spanning tree. An $(α,β)$-{\em Shallow Light Tree} (SLT) is a tree of lightness $β$, that approximates all distances from a designated root vertex up to a factor of $α$. A long line of works resulted in efficient algorithms that produce (nearly) optimal light spanners and SLTs. Some of the most notable algorithmic applications of light spanners and SLTs are in distributed settings. Surprisingly, so far there are no known efficient distributed algorithms for constructing these objects in general graphs. In this paper we devise efficient distributed algorithms in the CONGEST model for constructing light spanners and SLTs, with near optimal parameters. Specifically, for any $k\ge 1$ and $0<ε<1$, we show a $(2k-1)\cdot(1+ε)$-spanner with lightness $O(k\cdot n^{1/k})$ can be built in $\tilde{O}\left(n^{\frac12+\frac{1}{4k+2}}+D\right)$ rounds (where $n=|V|$ and $D$ is the hop-diameter of $G$). In addition, for any $α>1$ we provide an $(α,1+\frac{O(1)}{α-1})$-SLT in $(\sqrt{n}+D)\cdot n^{o(1)}$ rounds. The running time of our algorithms cannot be substantially improved. We also consider spanners for the family of doubling graphs, and devise a $(\sqrt{n}+D)\cdot n^{o(1)}$ rounds algorithm in the CONGEST model that computes a $(1+ε)$-spanner with lightness $(\log n)/ε^{O(1)}$. As a stepping stone, which is interesting in its own right, we first develop a distributed algorithm for constructing nets (for arbitrary weighted graphs), generalizing previous algorithms that worked only for unweighted graphs.
Explainable Software Bot Contributions: Case Study of Automated Bug Fixes
Venue: arXiv cs.SE 2019
Year: 2019

In a software project, esp. in open-source, a contribution is a valuable piece of work made to the project: writing code, reporting bugs, translating, improving documentation, creating graphics, etc. We are now at the beginning of an exciting era where software bots will make contributions that are of similar nature than those by humans. Dry contributions, with no explanation, are often ignored or rejected, because the contribution is not understandable per se, because they are not put into a larger context, because they are not grounded on idioms shared by the core community of developers. We have been operating a program repair bot called Repairnator for 2 years and noticed the problem of "dry patches": a patch that does not say which bug it fixes, or that does not explain the effects of the patch on the system. We envision program repair systems that produce an "explainable bug fix": an integrated package of at least 1) a patch, 2) its explanation in natural or controlled language, and 3) a highlight of the behavioral difference with examples. In this paper, we generalize and suggest that software bot contributions must explainable, that they must be put into the context of the global software development conversation.
PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms
Venue: arXiv cs.SI 2019
Year: 2019

Mobile phones provide a powerful sensing platform that researchers may adopt to understand proximity interactions among people and the diffusion, through these interactions, of diseases, behaviors, and opinions. However, it remains a challenge to track the proximity-based interactions of a whole community and then model the social diffusion of diseases and behaviors starting from the observations of a small fraction of the volunteer population. In this paper, we propose a novel approach that tries to connect together these sparse observations using a model of how individuals interact with each other and how social interactions happen in terms of a sequence of proximity interactions. We apply our approach to track the spreading of flu in the spatial-proximity network of a 3000-people university campus by mobilizing 300 volunteers from this population to monitor nearby mobile phones through Bluetooth scanning and to daily report flu symptoms about and around them. Our aim is to predict the likelihood for an individual to get flu based on how often her/his daily routine intersects with those of the volunteers. Thus, we use the daily routines of the volunteers to build a model of the volunteers as well as of the non-volunteers. Our results show that we can predict flu infection two weeks ahead of time with an average precision from 0.24 to 0.35 depending on the amount of information. This precision is six to nine times higher than with a random guess model. At the population level, we can predict infectious population in a two-week window with an r-squared value of 0.95 (a random-guess model obtains an r-squared value of 0.2). These results point to an innovative approach for tracking individuals who have interacted with people showing symptoms, allowing us to warn those in danger of infection and to inform health researchers about the progression of contact-induced diseases.
Reduction of Monitoring Register on Software Defined Networks
Venue: arXiv cs.NI 2019
Year: 2019

Characterization of data network monitoring registers allows for reductions in the number of data, which is essential when the information flow is high, and implementation of processes with short response times, such as interchange of control information between devices and anomaly detection is required. The present investigation applied wavelet transforms, so as to characterize the statistic monitoring register of a software-defined network. Its main contribution lies in the obtention of a record that, although reduced, retains detailed, essential information for the correct application of anomaly detectors.
Continual and Multi-task Reinforcement Learning With Shared Episodic Memory
Venue: arXiv cs.NE 2019
Year: 2019

Episodic memory plays an important role in the behavior of animals and humans. It allows the accumulation of information about current state of the environment in a task-agnostic way. This episodic representation can be later accessed by down-stream tasks in order to make their execution more efficient. In this work, we introduce the neural architecture with shared episodic memory (SEM) for learning and the sequential execution of multiple tasks. We explicitly split the encoding of episodic memory and task-specific memory into separate recurrent sub-networks. An agent augmented with SEM was able to effectively reuse episodic knowledge collected during other tasks to improve its policy on a current task in the Taxi problem. Repeated use of episodic representation in continual learning experiments facilitated acquisition of novel skills in the same environment.
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
Venue: arXiv cs.RO 2019
Year: 2019

Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. This paper introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. The method is based on the AlphaGo Zero algorithm, which is extended to a domain with a continuous state space where self-play cannot be used. The framework is applied to two different highway driving cases in a simulated environment and it is shown to perform better than a commonly used baseline method. The strength of combining planning and learning is also illustrated by a comparison to using the Monte Carlo tree search or the neural network policy separately.
Autonomous Open-Ended Learning of Interdependent Tasks
Venue: arXiv cs.AI 2019
Year: 2019

Autonomy is fundamental for artificial agents acting in complex real-world scenarios. The acquisition of many different skills is pivotal to foster versatile autonomous behaviour and thus a main objective for robotics and machine learning. Intrinsic motivations have proven to properly generate a task-agnostic signal to drive the autonomous acquisition of multiple policies in settings requiring the learning of multiple tasks. However, in real world scenarios tasks may be interdependent so that some of them may constitute the precondition for learning other ones. Despite different strategies have been used to tackle the acquisition of interdependent/hierarchical tasks, fully autonomous open-ended learning in these scenarios is still an open question. Building on previous research within the framework of intrinsically-motivated open-ended learning, we propose an architecture for robot control that tackles this problem from the point of view of decision making, i.e. treating the selection of tasks as a Markov Decision Process where the system selects the policies to be trained in order to maximise its competence over all the tasks. The system is then tested with a humanoid robot solving interdependent multiple reaching tasks.
Context-Aware Automatic Occlusion Removal
Venue: arXiv cs.CV 2019
Year: 2019

Occlusion removal is an interesting application of image enhancement, for which, existing work suggests manually-annotated or domain-specific occlusion removal. No work tries to address automatic occlusion detection and removal as a context-aware generic problem. In this paper, we present a novel methodology to identify objects that do not relate to the image context as occlusions and remove them, reconstructing the space occupied coherently. The proposed system detects occlusions by considering the relation between foreground and background object classes represented as vector embeddings, and removes them through inpainting. We test our system on COCO-Stuff dataset and conduct a user study to establish a baseline in context-aware automatic occlusion removal.
Online and Offline Greedy Algorithms for Routing with Switching Costs
Venue: arXiv cs.DS 2019
Year: 2019

Motivated by the use of high speed circuit switches in large scale data centers, we consider the problem of circuit switch scheduling. In this problem we are given demands between pairs of servers and the goal is to schedule at every time step a matching between the servers while maximizing the total satisfied demand over time. The crux of this scheduling problem is that once one shifts from one matching to a different one a fixed delay $δ$ is incurred during which no data can be transmitted. For the offline version of the problem we present a $(1-\frac{1}{e}-ε)$ approximation ratio (for any constant $ε>0$). Since the natural linear programming relaxation for the problem has an unbounded integrality gap, we adopt a hybrid approach that combines the combinatorial greedy with randomized rounding of a different suitable linear program. For the online version of the problem we present a (bi-criteria) $ ((e-1)/(2e-1)-ε)$-competitive ratio (for any constant $ε>0$ ) that exceeds time by an additive factor of $O(\fracδε)$. We note that no uni-criteria online algorithm is possible. Surprisingly, we obtain the result by reducing the online version to the offline one.
Atomic Commitment Across Blockchains
Venue: arXiv cs.DB 2019
Year: 2019

The recent adoption of blockchain technologies and open permissionless networks suggest the importance of peer-to-peer atomic cross-chain transaction protocols. Users should be able to atomically exchange tokens and assets without depending on centralized intermediaries such as exchanges. Recent peer-to-peer atomic cross-chain swap protocols use hashlocks and timelocks to ensure that participants comply to the protocol. However, an expired timelock could lead to a violation of the all-or-nothing atomicity property. An honest participant who fails to execute a smart contract on time due to a crash failure or network delays at her site might end up losing her assets. Although a crashed participant is the only participant who ends up worse off, current proposals are unsuitable for atomic cross-chain transactions in asynchronous environments where crash failures and network delays are the norm. In this paper, we present AC3WN, the first decentralized all-or-nothing atomic cross-chain commitment protocol. The redeem and refund events of the smart contracts that exchange assets are modeled as conflicting events. An open permissionless network of witnesses is used to guarantee that conflicting events could never simultaneously occur and either all smart contracts in an atomic cross-chain transaction are redeemed or all of them are refunded.
Pilot evaluation of Collection API with PID Kernel Information
Venue: arXiv cs.DL 2019
Year: 2019

As digital data become increasingly available for research, there is a growing awareness of the value of domain agnostic Persistent Identifiers (PIDs) for data. A PID is a globally unique reference to a digital object, which in our case is data. In an ecosystem of connected digital objects, a PID will reference a digital object, and the digital object will be a simple entity, a collection of homogeneous objects, or a set of heterogeneous objects. In this paper, we study two recent recommendations from the Research Data Alliance (RDA) that both address pieces of an ecosystem of connected digital objects. The recommendations address Persistent ID records and representations of collections of data. We evaluate different approaches in where to locate key information about a data collection between these two component solutions.
Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
Venue: arXiv cs.IT 2019
Year: 2019

This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. Fast channel variations in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In response, we model the resource sharing as a multi-agent reinforcement learning problem, which is then solved using a fingerprint-based deep Q-network method that is amenable to a distributed implementation. The V2V links, each acting as an agent, collectively interact with the communication environment, receive distinctive observations yet a common reward, and learn to improve spectrum and power allocation through updating Q-networks using the gained experiences. We demonstrate that with a proper reward design and training mechanism, the multiple V2V agents successfully learn to cooperate in a distributed way to simultaneously improve the sum capacity of V2I links and payload delivery rate of V2V links.
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances
Venue: arXiv cs.CL 2019
Year: 2019

Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data in platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration) and more. Additionally, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user's emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a strenuous problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on the recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC.
A Two-Stage Beam Alignment Framework for Hybrid MmWave Distributed Antenna Systems
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we investigate the beam alignment problem in millimeter-wave (mmWave) distributed antenna systems where a home base station communicates with multiple users through a number of distributed remote radio units (RRUs). Specifically, a two-stage schedule-and-align (TSSA) scheme is proposed to facilitate efficient communications. In the first stage, a coarse beam scanning over the entire angular space is performed while beam indices and the corresponding peak-to-background ratios of the received power-angle-spectrum are obtained from users' feedback. Then, by exploiting the user feedback, an efficient user scheduling algorithm is developed to improve the system spectral efficiency and to reduce the system misalignment probability. Next, the second stage of beam search is performed by each RRU with reconfigured search angles, search steps, and power levels to obtain a refined beam alignment. Simulation results show that the proposed TSSA scheme can significantly outperform the conventional one-stage method in both centralized and distributed mmWave systems in terms of beam alignment accuracy and spectral efficiency.
SAdam: A Variant of Adam for Strongly Convex Functions
Venue: arXiv cs.LG 2019
Year: 2019

The Adam algorithm has become extremely popular for large-scale machine learning. Under convexity condition, it has been proved to enjoy a data-dependant $O(\sqrt{T})$ regret bound where $T$ is the time horizon. However, whether strong convexity can be utilized to further improve the performance remains an open problem. In this paper, we give an affirmative answer by developing a variant of Adam (referred to as SAdam) which achieves a data-dependant $O(\log T)$ regret bound for strongly convex functions. The essential idea is to maintain a faster decaying yet under controlled step size for exploiting strong convexity. In addition, under a special configuration of hyperparameters, our SAdam reduces to SC-RMSprop, a recently proposed variant of RMSprop for strongly convex functions, for which we provide the first data-dependent logarithmic regret bound. Empirical results on optimizing strongly convex functions and training deep networks demonstrate the effectiveness of our method.
On the Feasibility of Automated Detection of Allusive Text Reuse
Venue: arXiv cs.CL 2019
Year: 2019

The detection of allusive text reuse is particularly challenging due to the sparse evidence on which allusive references rely---commonly based on none or very few shared words. Arguably, lexical semantics can be resorted to since uncovering semantic relations between words has the potential to increase the support underlying the allusion and alleviate the lexical sparsity. A further obstacle is the lack of evaluation benchmark corpora, largely due to the highly interpretative character of the annotation process. In the present paper, we aim to elucidate the feasibility of automated allusion detection. We approach the matter from an Information Retrieval perspective in which referencing texts act as queries and referenced texts as relevant documents to be retrieved, and estimate the difficulty of benchmark corpus compilation by a novel inter-annotator agreement study on query segmentation. Furthermore, we investigate to what extent the integration of lexical semantic information derived from distributional models and ontologies can aid retrieving cases of allusive reuse. The results show that (i) despite low agreement scores, using manual queries considerably improves retrieval performance with respect to a windowing approach, and that (ii) retrieval performance can be moderately boosted with distributional semantics.
Automatic Video Colorization using 3D Conditional Generative Adversarial Networks
Venue: arXiv cs.CV 2019
Year: 2019

In this work, we present a method for automatic colorization of grayscale videos. The core of the method is a Generative Adversarial Network that is trained and tested on sequences of frames in a sliding window manner. Network convolutional and deconvolutional layers are three-dimensional, with frame height, width and time as the dimensions taken into account. Multiple chrominance estimates per frame are aggregated and combined with available luminance information to recreate a colored sequence. Colorization trials are run succesfully on a dataset of old black-and-white films. The usefulness of our method is also validated with numerical results, computed with a newly proposed metric that measures colorization consistency over a frame sequence.
Handshake between Fibonacci series and pure preferential attachment mechanism on a graph-model
Venue: arXiv cs.SI 2019
Year: 2019

In order to better understand dynamical functions on amounts of natural and man-made complex systems, lots of researchers from a wide range of disciplines, covering statistic physics, mathematics, theoretical computer science, and so on, have spent much time in doing this intriguing study. In this paper, the discussed popularly topic, how to construct reasonable graph-model and then to explain many features of realistic networks using previously presented theoretical models, is still our main work. Compared with many pre-existing deterministic graph-model in single evolution way, our new graph-model can be constructed using three types of growth ways to meet preferential attachment mechanism. Meanwhile several typical indices associated with network research will be reported. In addition, some interesting findings will be shown, including the first handshake between Fibonacci series and "pure" preferential attachment mechanism, an obvious relationship connecting two well-known rules, power-law and Zipf-law, and a common but useful equation on the basis of both spanning trees number and the number of spanning trees with maximum leaves. Based on these foregoing discussions, we can demonstrate that our graph-model obeys power-law and small-world property. For the future research directions, we present some unknown problems to be studied at the end of this paper.