OpenRaiser/Intern-Atlas huggingface dataset:
Explaining Autonomous Decisions in Swarms of Human-on-the-Loop Small Unmanned Aerial Systems
Venue: arXiv cs.HC 2021
Year: 2021

Rapid advancements in Artificial Intelligence have shifted the focus from traditional human-directed robots to fully autonomous ones that do not require explicit human control. These are commonly referred to as Human-on-the-Loop (HotL) systems. Transparency of HotL systems necessitates clear explanations of autonomous behavior so that humans are aware of what is happening in the environment and can understand why robots behave in a certain way. However, in complex multi-robot environments, especially those in which the robots are autonomous, mobile, and require intermittent interventions, humans may struggle to maintain situational awareness. Presenting humans with rich explanations of autonomous behavior tends to overload them with too much information and negatively affect their understanding of the situation. Therefore, explaining the autonomous behavior or autonomy of multiple robots creates a design tension that demands careful investigation. This paper examines the User Interface (UI) design trade-offs associated with providing timely and detailed explanations of autonomous behavior for swarms of small Unmanned Aerial Systems (sUAS) or drones. We analyze the impact of UI design choices on human awareness of the situation. We conducted multiple user studies with both inexperienced and expert sUAS operators to present our design solution and provide initial guidelines for designing the HotL multi-sUAS interface.
Linear complexity over ${\mathbb{F}_{q}}$ and 2-adic complexity of a class of binary generalized cyclotomic sequences with low-value autocorrelation
Venue: arXiv cs.IT 2021
Year: 2021

A class of binary sequences with period $2p$ is constructed using generalized cyclotomic classes, and their linear complexity, minimal polynomial over ${\mathbb{F}_{q}}$ as well as 2-adic complexity are determined using Gauss period and group ring theory. The results show that the linear complexity of these sequences attains the maximum when $p\equiv \pm 1(\bmod~8)$ and is equal to {$p$+1} when $p\equiv \pm 3(\bmod~8)$ over extension field. Moreover, the 2-adic complexity of these sequences is maximum. According to Berlekamp-Massey(B-M) algorithm and the rational approximation algorithm(RAA), these sequences have quite good cryptographyic properties in the aspect of linear complexity and 2-adic complexity.
VARGAN: Variance Enforcing Network Enhanced GAN
Venue: arXiv cs.LG 2021
Year: 2021

Generative adversarial networks (GANs) are one of the most widely used generative models. GANs can learn complex multi-modal distributions, and generate real-like samples. Despite the major success of GANs in generating synthetic data, they might suffer from unstable training process, and mode collapse. In this paper, we introduce a new GAN architecture called variance enforcing GAN (VARGAN), which incorporates a third network to introduce diversity in the generated samples. The third network measures the diversity of the generated samples, which is used to penalize the generator's loss for low diversity samples. The network is trained on the available training data and undesired distributions with limited modality. On a set of synthetic and real-world image data, VARGAN generates a more diverse set of samples compared to the recent state-of-the-art models. High diversity and low computational complexity, as well as fast convergence, make VARGAN a promising model to alleviate mode collapse.
Linking disjoint axis-parallel segments into a simple polygon is hard too
Venue: arXiv cs.CG 2021
Year: 2021

Deciding whether a family of disjoint axis-parallel line segments in the plane can be linked into a simple polygon (or a simple polygonal chain) by adding segments between their endpoints is NP-hard.
Modular Framework for Visuomotor Language Grounding
Venue: arXiv cs.AI 2021
Year: 2021

Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research. However, data collection for these tasks is expensive and end-to-end approaches suffer from data inefficiency. We propose the structuring of language, acting, and visual tasks into separate modules that can be trained independently. Using a Language, Action, and Vision (LAV) framework removes the dependence of action and vision modules on instruction following datasets, making them more efficient to train. We also present a preliminary evaluation of LAV on the ALFRED task for visual and interactive instruction following.
Supervised DKRC with Images for Offline System Identification
Venue: arXiv cs.LG 2021
Year: 2021

Koopman spectral theory has provided a new perspective in the field of dynamical systems in recent years. Modern dynamical systems are becoming increasingly non-linear and complex, and there is a need for a framework to model these systems in a compact and comprehensive representation for prediction and control. The central problem in applying Koopman theory to a system of interest is that the choice of finite-dimensional basis functions is typically done apriori, using expert knowledge of the systems dynamics. Our approach learns these basis functions using a supervised learning approach where a combination of autoencoders and deep neural networks learn the basis functions for any given system. We demonstrate this approach on a simple pendulum example in which we obtain a linear representation of the non-linear system and then predict the future state trajectories given some initial conditions. We also explore how changing the input representation of the dynamic systems time series data can impact the quality of learned basis functions. This alternative representation is compared to the traditional raw time series data approach to determine which method results in lower reconstruction and prediction error of the true non-linear dynamics of the system.
Surgery Scene Restoration for Robot Assisted Minimally Invasive Surgery
Venue: arXiv cs.RO 2021
Year: 2021

Minimally invasive surgery (MIS) offers several advantages including minimum tissue injury and blood loss, and quick recovery time, however, it imposes some limitations on surgeons ability. Among others such as lack of tactile or haptic feedback, poor visualization of the surgical site is one of the most acknowledged factors that exhibits several surgical drawbacks including unintentional tissue damage. To the context of robot assisted surgery, lack of frame contextual details makes vision task challenging when it comes to tracking tissue and tools, segmenting scene, and estimating pose and depth. In MIS the acquired frames are compromised by different noises and get blurred caused by motions from different sources. Moreover, when underwater environment is considered for instance knee arthroscopy, mostly visible noises and blur effects are originated from the environment, poor control on illuminations and imaging conditions. Additionally, in MIS, procedure like automatic white balancing and transformation between the raw color information to its standard RGB color space are often absent due to the hardware miniaturization. There is a high demand of an online preprocessing framework that can circumvent these drawbacks. Our proposed method is able to restore a latent clean and sharp image in standard RGB color space from its noisy, blur and raw observation in a single preprocessing stage.
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations
Venue: arXiv cs.CL 2021
Year: 2021

The rapid growth in published clinical trials makes it difficult to maintain up-to-date systematic reviews, which requires finding all relevant trials. This leads to policy and practice decisions based on out-of-date, incomplete, and biased subsets of available clinical evidence. Extracting and then normalising Population, Intervention, Comparator, and Outcome (PICO) information from clinical trial articles may be an effective way to automatically assign trials to systematic reviews and avoid searching and screening - the two most time-consuming systematic review processes. We propose and test a novel approach to PICO span detection. The major difference between our proposed method and previous approaches comes from detecting spans without needing annotated span data and using only crowdsourced sentence-level annotations. Experiments on two datasets show that PICO span detection results achieve much higher results for recall when compared to fully supervised methods with PICO sentence detection at least as good as human annotations. By removing the reliance on expert annotations for span detection, this work could be used in human-machine pipeline for turning low-quality crowdsourced, and sentence-level PICO annotations into structured information that can be used to quickly assign trials to relevant systematic reviews.
Does Melania Trump have a body double from the perspective of automatic face recognition?
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we explore whether automatic face recognition can help in verifying widespread misinformation on social media, particularly conspiracy theories that are based on the existence of body doubles. The conspiracy theory addressed in this paper is the case of the Melania Trump body double. We employed four different state-of-the-art descriptors for face recognition to verify the integrity of the claim of the studied conspiracy theory. In addition, we assessed the impact of different image quality metrics on the variation of face recognition results. Two sets of image quality metrics were considered: acquisition-related metrics and subject-related metrics.
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser
Venue: arXiv cs.CL 2021
Year: 2021

Considering the importance of building a good Visual Dialog (VD) Questioner, many researchers study the topic under a Q-Bot-A-Bot image-guessing game setting, where the Questioner needs to raise a series of questions to collect information of an undisclosed image. Despite progress has been made in Supervised Learning (SL) and Reinforcement Learning (RL), issues still exist. Firstly, previous methods do not provide explicit and effective guidance for Questioner to generate visually related and informative questions. Secondly, the effect of RL is hampered by an incompetent component, i.e., the Guesser, who makes image predictions based on the generated dialogs and assigns rewards accordingly. To enhance VD Questioner: 1) we propose a Related entity enhanced Questioner (ReeQ) that generates questions under the guidance of related entities and learns entity-based questioning strategy from human dialogs; 2) we propose an Augmented Guesser (AugG) that is strong and is optimized for the VD setting especially. Experimental results on the VisDial v1.0 dataset show that our approach achieves state-of-theart performance on both image-guessing task and question diversity. Human study further proves that our model generates more visually related, informative and coherent questions.
A Survey on Resilience in the IoT: Taxonomy, Classification and Discussion of Resilience Mechanisms
Venue: arXiv cs.DC 2021
Year: 2021

Internet-of-Things (IoT) ecosystems tend to grow both in scale and complexity as they consist of a variety of heterogeneous devices, which span over multiple architectural IoT layers (e.g., cloud, edge, sensors). Further, IoT systems increasingly demand the resilient operability of services as they become part of critical infrastructures. This leads to a broad variety of research works that aim to increase the resilience of these systems. In this paper, we create a systematization of knowledge about existing scientific efforts of making IoT systems resilient. In particular, we first discuss the taxonomy and classification of resilience and resilience mechanisms and subsequently survey state-of-the-art resilience mechanisms that have been proposed by research work and are applicable to IoT. As part of the survey, we also discuss questions that focus on the practical aspects of resilience, e.g., which constraints resilience mechanisms impose on developers when designing resilient systems by incorporating a specific mechanism into IoT systems.
Characterizing Crisp Simulations and Crisp Directed Simulations between Fuzzy Labeled Transition Systems by Using Fuzzy Modal Logics
Venue: arXiv cs.LO 2021
Year: 2021

We formulate and prove logical characterizations of crisp simulations and crisp directed simulations between fuzzy labeled transition systems with respect to fuzzy modal logics that use a general t-norm-based semantics. The considered logics are fragments of the fuzzy propositional dynamic logic with the Baaz projection operator. The logical characterizations concern preservation of positive existential (respectively, positive) modal formulas under crisp simulations (respectively, crisp directed simulations), as well as the Hennessy-Milner property of such simulations.
Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning
Venue: arXiv cs.AI 2021
Year: 2021

Researchers are increasingly focusing on intelligent games as a hot research area.The article proposes an algorithm that combines the multi-attribute management and reinforcement learning methods, and that combined their effect on wargaming, it solves the problem of the agent's low rate of winning against specific rules and its inability to quickly converge during intelligent wargame training.At the same time, this paper studied a multi-attribute decision making and reinforcement learning algorithm in a wargame simulation environment, and obtained data on red and blue conflict.Calculate the weight of each attribute based on the intuitionistic fuzzy number weight calculations. Then determine the threat posed by each opponent's chess pieces.Using the red side reinforcement learning reward function, the AC framework is trained on the reward function, and an algorithm combining multi-attribute decision-making with reinforcement learning is obtained. A simulation experiment confirms that the algorithm of multi-attribute decision-making combined with reinforcement learning presented in this paper is significantly more intelligent than the pure reinforcement learning algorithm.By resolving the shortcomings of the agent's neural network, coupled with sparse rewards in large-map combat games, this robust algorithm effectively reduces the difficulties of convergence. It is also the first time in this field that an algorithm design for intelligent wargaming combines multi-attribute decision making with reinforcement learning.Attempt interdisciplinary cross-innovation in the academic field, like designing intelligent wargames and improving reinforcement learning algorithms.
A Dynamic Topic Identification and Labeling Approach of COVID-19 Tweets
Venue: arXiv cs.IR 2021
Year: 2021

This paper formulates the problem of dynamically identifying key topics with proper labels from COVID-19 Tweets to provide an overview of wider public opinion. Nowadays, social media is one of the best ways to connect people through Internet technology, which is also considered an essential part of our daily lives. In late December 2019, an outbreak of the novel coronavirus, COVID-19 was reported, and the World Health Organization declared an emergency due to its rapid spread all over the world. The COVID-19 epidemic has affected the use of social media by many people across the globe. Twitter is one of the most influential social media services, which has seen a dramatic increase in its use from the epidemic. Thus dynamic extraction of specific topics with labels from tweets of COVID-19 is a challenging issue for highlighting conversation instead of manual topic labeling approach. In this paper, we propose a framework that automatically identifies the key topics with labels from the tweets using the top Unigram feature of aspect terms cluster from Latent Dirichlet Allocation (LDA) generated topics. Our experiment result shows that this dynamic topic identification and labeling approach is effective having the accuracy of 85.48\% with respect to the manual static approach.
Binary Code based Hash Embedding for Web-scale Applications
Venue: arXiv cs.IR 2021
Year: 2021

Nowadays, deep learning models are widely adopted in web-scale applications such as recommender systems, and online advertising. In these applications, embedding learning of categorical features is crucial to the success of deep learning models. In these models, a standard method is that each categorical feature value is assigned a unique embedding vector which can be learned and optimized. Although this method can well capture the characteristics of the categorical features and promise good performance, it can incur a huge memory cost to store the embedding table, especially for those web-scale applications. Such a huge memory cost significantly holds back the effectiveness and usability of EDRMs. In this paper, we propose a binary code based hash embedding method which allows the size of the embedding table to be reduced in arbitrary scale without compromising too much performance. Experimental evaluation results show that one can still achieve 99\% performance even if the embedding table size is reduced 1000$\times$ smaller than the original one with our proposed method.
Audio-based Musical Version Identification: Elements and Challenges
Venue: arXiv cs.SD 2021
Year: 2021

In this article, we aim to provide a review of the key ideas and approaches proposed in 20 years of scientific literature around musical version identification (VI) research and connect them to current practice. For more than a decade, VI systems suffered from the accuracy-scalability trade-off, with attempts to increase accuracy that typically resulted in cumbersome, non-scalable systems. Recent years, however, have witnessed the rise of deep learning-based approaches that take a step toward bridging the accuracy-scalability gap, yielding systems that can realistically be deployed in industrial applications. Although this trend positively influences the number of researchers and institutions working on VI, it may also result in obscuring the literature before the deep learning era. To appreciate two decades of novel ideas in VI research and to facilitate building better systems, we now review some of the successful concepts and applications proposed in the literature and study their evolution throughout the years.
Recommendation System Simulations: A Discussion of Two Key Challenges
Venue: arXiv cs.IR 2021
Year: 2021

As recommendation systems become increasingly standard for online platforms, simulations provide an avenue for understanding the impacts of these systems on individuals and society. When constructing a recommendation system simulation, there are two key challenges: first, defining a model for users selecting or engaging with recommended items and second, defining a mechanism for users encountering items that are not recommended to the user directly by the platform, such as by a friend sharing specific content. This paper will delve into both of these challenges, reviewing simulation assumptions from existing research and proposing alternative assumptions. We also include a broader discussion of the limitations of simulations and outline of open questions in this area.
An Axiomatic Perspective on the Performance Effects of End-Host Path Selection
Venue: arXiv cs.NI 2021
Year: 2021

In various contexts of networking research, end-host path selection has recently regained momentum as a design principle. While such path selection has the potential to increase performance and security of networks, there is a prominent concern that it could also lead to network instability (i.e., flow-volume oscillation) if paths are selected in a greedy, load-adaptive fashion. However, the extent and the impact vectors of instability caused by path selection are rarely concretized or quantified, which is essential to discuss the merits and drawbacks of end-host path selection. In this work, we investigate the effect of end-host path selection on various metrics of networks both qualitatively and quantitatively. To achieve general and fundamental insights, we leverage the recently introduced axiomatic perspective on congestion control and adapt it to accommodate joint algorithms for path selection and congestion control, i.e., multi-path congestion-control protocols. Using this approach, we identify equilibria of the multi-path congestion-control dynamics and analytically characterize these equilibria with respect to important metrics of interest in networks (the 'axioms') such as efficiency, fairness, and loss avoidance. Moreover, we analyze how these axiomatic ratings for a general network change compared to a scenario without path selection, thereby obtaining an interpretable and quantititative formalization of the performance impact of end-host path-selection. Finally, we show that there is a fundamental trade-off in multi-path congestion-control protocol design between efficiency, stability, and loss avoidance on one side and fairness and responsiveness on the other side.
Parsimonious Edge Computing to Reduce Microservice Resource Usage
Venue: arXiv cs.NI 2021
Year: 2021

Cloud Computing (CC) is the most prevalent paradigm under which services are provided over the Internet. The most relevant feature for its success is its capability to promptly scale service based on user demand. When scaling, the main objective is to maximize as much as possible service performance. Moreover, resources in the Cloud are usually so abundant, that they can be assumed infinite from the service point of view: an application provider can have as many servers it wills, as long it pays for it. This model has some limitations. First, energy efficiency is not among the first criteria for scaling decisions, which has raised concerns about the environmental effects of today's wild computations in the Cloud. Moreover, it is not viable for Edge Computing (EC), a paradigm in which computational resources are distributed up to the very edge of the network, i.e., co-located with base stations or access points. In edge nodes, resources are limited, which imposes different parsimonious scaling strategies to be adopted. In this work, we design a scaling strategy aimed to instantiate, parsimoniously, a number of microservices sufficient to guarantee a certain Quality of Service (QoS) target. We implement such a strategy in a Kubernetes/Docker environment. The strategy is based on a simple Proportional-Integrative-Derivative (PID) controller. In this paper we describe the system design and a preliminary performance evaluation.