OpenRaiser/Intern-Atlas huggingface dataset:
A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem
Venue: arXiv cs.AI 2020
Year: 2020

A benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms. An extended Team Orienteering Problem is modeled for a kind of multi-UAV task assignment problem. Three intelligent algorithms, i.e., Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization are implemented to solve the problem. A series of experiments with different settings are conducted to evaluate three algorithms. The modeled problem and the evaluation results constitute a benchmark, which can be used to evaluate other algorithms used for multi-UAV task assignment problems.
Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we introduce and formalize a rank-one partitioning learning paradigm that unifies partitioning methods that proceed by summarizing a data set using a single vector that is further used to derive the final clustering partition. Using this unification as a starting point, we propose a novel algorithmic solution for the partitioning problem based on rank-one matrix factorization and denoising of piecewise constant signals. Finally, we propose an empirical demonstration of our findings and demonstrate the robustness of the proposed denoising step. We believe that our work provides a new point of view for several unsupervised learning techniques that helps to gain a deeper understanding about the general mechanisms of data partitioning.
Structural balance of alliance and rivalry networks in international relations
Venue: arXiv cs.SI 2020
Year: 2020

Does the enemy of my enemy become my friend? A growing literature on structural analysis of interstate relationships has tackled this old question from the network perspective. However, the mechanism of long-term change in the structure of cooperation and enmity has yet to be fully understood. In search for a general explanation for the long-term evolution of interstate structure, we empirically examine the structural balance theory which predicts that a signed network evolves toward a more "balanced" structure where in many triangular relationships (i.e., triads) two states tend to share a common enemy or three states cooperate with each other. We investigate the network of alliances (positive edges) and rivalries (negative edges) between sovereign states and examine whether its evolution from 1816 to 2009 can be explained by the structural balance theory. We find the consistency with the structural balance theory drastically changes over time. The empirical pattern follows the prediction by the theory before the German unification in the nineteenth century and after World War II while inconsistent in the middle period. This result reveals the impact of the two historical events on the underlying mechanism of network evolution. Moreover, the contrast with previous studies of signed social networks that generally support the structural balance theory indicates that international alliance and rivalry networks can be a promising material to study novel mechanisms behind the time evolution of signed networks.
A Scheduling Policy for Downlink OFDMA in IEEE 802.11ax with Throughput Constraints
Venue: arXiv cs.NI 2020
Year: 2020

In order to meet the ever-increasing demand for high throughput in WiFi networks, the IEEE 802.11ax (11ax) standard introduces orthogonal frequency division multiple access (OFDMA). In this letter, we address the station-resource unit scheduling problem in downlink OFDMA of 11ax subject to minimum throughput requirements. To deal with the infeasible instances of the constrained problem, we propose a novel scheduling policy based on weighted max-min fairness, which maximizes the minimum fraction between the achievable and minimum required throughputs. Thus, the proposed policy has a well-defined behavior even when the throughput constraints cannot be fulfilled. Numerical results showcase the merits of our approach over the popular proportional fairness and constrained sum-rate maximization strategies.
Church's thesis and related axioms in Coq's type theory
Venue: arXiv cs.LO 2020
Year: 2020

"Church's thesis" ($\mathsf{CT}$) as an axiom in constructive logic states that every total function of type $\mathbb{N} \to \mathbb{N}$ is computable, i.e. definable in a model of computation. $\mathsf{CT}$ is inconsistent in both classical mathematics and in Brouwer's intuitionism since it contradicts Weak König's Lemma and the fan theorem, respectively. Recently, $\mathsf{CT}$ was proved consistent for (univalent) constructive type theory. Since neither Weak König's Lemma nor the fan theorem are a consequence of just logical axioms or just choice-like axioms assumed in constructive logic, it seems likely that $\mathsf{CT}$ is inconsistent only with a combination of classical logic and choice axioms. We study consequences of $\mathsf{CT}$ and its relation to several classes of axioms in Coq's type theory, a constructive type theory with a universe of propositions which does neither prove classical logical axioms nor strong choice axioms. We thereby provide a partial answer to the question which axioms may preserve computational intuitions inherent to type theory, and which certainly do not. The paper can also be read as a broad survey of axioms in type theory, with all results mechanised in the Coq proof assistant.
Towards Evaluating Exploratory Model Building Process with AutoML Systems
Venue: arXiv cs.HC 2020
Year: 2020

The use of Automated Machine Learning (AutoML) systems are highly open-ended and exploratory. While rigorously evaluating how end-users interact with AutoML is crucial, establishing a robust evaluation methodology for such exploratory systems is challenging. First, AutoML is complex, including multiple sub-components that support a variety of sub-tasks for synthesizing ML pipelines, such as data preparation, problem specification, and model generation, making it difficult to yield insights that tell us which components were successful or not. Second, because the usage pattern of AutoML is highly exploratory, it is not possible to rely solely on widely used task efficiency and effectiveness metrics as success metrics. To tackle the challenges in evaluation, we propose an evaluation methodology that (1) guides AutoML builders to divide their AutoML system into multiple sub-system components, and (2) helps them reason about each component through visualization of end-users' behavioral patterns and attitudinal data. We conducted a study to understand when, how, why, and applying our methodology can help builders to better understand their systems and end-users. We recruited 3 teams of professional AutoML builders. The teams prepared their own systems and let 41 end-users use the systems. Using our methodology, we visualized end-users' behavioral and attitudinal data and distributed the results to the teams. We analyzed the results in two directions: what types of novel insights the AutoML builders learned from end-users, and (2) how the evaluation methodology helped the builders to understand workflows and the effectiveness of their systems. Our findings suggest new insights explaining future design opportunities in the AutoML domain as well as how using our methodology helped the builders to determine insights and let them draw concrete directions for improving their systems.
DropLeaf: a precision farming smartphone application for measuring pesticide spraying methods
Venue: arXiv cs.CV 2020
Year: 2020

Pesticide application has been heavily used in the cultivation of major crops, contributing to the increase of crop production over the past decades. However, their appropriate use and calibration of machines rely upon evaluation methodologies that can precisely estimate how well the pesticides' spraying covered the crops. A few strategies have been proposed in former works, yet their elevated costs and low portability do not permit their wide adoption. This work introduces and experimentally assesses a novel tool that functions over a smartphone-based mobile application, named DropLeaf - Spraying Meter. Tests performed using DropLeaf demonstrated that, notwithstanding its versatility, it can estimate the pesticide spraying with high precision. Our methodology is based on image analysis, and the assessment of spraying deposition measures is performed successfully over real and synthetic water-sensitive papers. The proposed tool can be extensively used by farmers and agronomists furnished with regular smartphones, improving the utilization of pesticides with well-being, ecological, and monetary advantages. DropLeaf can be easily used for spray drift assessment of different methods, including emerging UAV (Unmanned Aerial Vehicle) sprayers.
toki: A Build- and Test-Platform for Prototyping and Evaluating Operating System Concepts in Real-Time Environments
Venue: arXiv cs.OS 2020
Year: 2020

Typically, even low-level operating system concepts, such as resource sharing strategies and predictability measures, are evaluated with Linux on PC hardware. This leaves a large gap to real industrial applications. Hence, the direct transfer of the results might be difficult. As a solution, we present toki, a prototyping and evaluation platform based on FreeRTOS and several open-source libraries. toki comes with a unified build- and test-environment based on Yocto and Qemu, which makes it well suited for rapid prototyping. With its architecture chosen similar to production industrial systems, toki provides the ground work to implement early prototypes of real-time systems research results, up to technology readiness level 7, with little effort.
Graph Embedding with Data Uncertainty
Venue: arXiv cs.LG 2020
Year: 2020

spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. The main aim is to learn a meaningful low dimensional embedding of the data. However, most subspace learning methods do not take into consideration possible measurement inaccuracies or artifacts that can lead to data with high uncertainty. Thus, learning directly from raw data can be misleading and can negatively impact the accuracy. In this paper, we propose to model artifacts in training data using probability distributions; each data point is represented by a Gaussian distribution centered at the original data point and having a variance modeling its uncertainty. We reformulate the Graph Embedding framework to make it suitable for learning from distributions and we study as special cases the Linear Discriminant Analysis and the Marginal Fisher Analysis techniques. Furthermore, we propose two schemes for modeling data uncertainty based on pair-wise distances in an unsupervised and a supervised contexts.
Finding Core Members of Cooperative Games using Agent-Based Modeling
Venue: arXiv cs.MA 2020
Year: 2020

Agent-based modeling (ABM) is a powerful paradigm to gain insight into social phenomena. One area that ABM has rarely been applied is coalition formation. Traditionally, coalition formation is modeled using cooperative game theory. In this paper, a heuristic algorithm is developed that can be embedded into an ABM to allow the agents to find coalition. The resultant coalition structures are comparable to those found by cooperative game theory solution approaches, specifically, the core. A heuristic approach is required due to the computational complexity of finding a cooperative game theory solution which limits its application to about only a score of agents. The ABM paradigm provides a platform in which simple rules and interactions between agents can produce a macro-level effect without the large computational requirements. As such, it can be an effective means for approximating cooperative game solutions for large numbers of agents. Our heuristic algorithm combines agent-based modeling and cooperative game theory to help find agent partitions that are members of a games' core solution. The accuracy of our heuristic algorithm can be determined by comparing its outcomes to the actual core solutions. This comparison achieved by developing an experiment that uses a specific example of a cooperative game called the glove game. The glove game is a type of exchange economy game. Finding the traditional cooperative game theory solutions is computationally intensive for large numbers of players because each possible partition must be compared to each possible coalition to determine the core set; hence our experiment only considers games of up to nine players. The results indicate that our heuristic approach achieves a core solution over 90% of the time for the games considered in our experiment.
A survey on Blockchain-based applications for reforming data protection, privacy and security
Venue: arXiv cs.CR 2020
Year: 2020

The modern society, economy and industry have been changed remarkably by many cutting-edge technologies over the last years, and many more are in development and early implementation that will in turn led even wider spread of adoptions and greater alteration. Blockchain technology along with other rising ones is expected to transform virtually every aspect of global business and individuals' lifestyle in some areas. It has been spreading with multi-sector applications from financial services to healthcare, supply chain, and cybersecurity emerging every passing day. Simultaneously, in the digital world, data protection and privacy are the most enormous issues which customers, companies and policymakers also take seriously into consideration due to the recent increase of security breaches and surveillance in reported incidents. In this case, blockchain has the capability and potential to revolutionize trust, security and privacy of individual data in the online world. Hence, the purpose of this paper is to study the actual cases of Blockchain applied in the reformation of privacy and security field by discussing its impacts as well as the opportunities and challenges.
Inducing Predictive Uncertainty Estimation for Face Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Knowing when an output can be trusted is critical for reliably using face recognition systems. While there has been enormous effort in recent research on improving face verification performance, understanding when a model's predictions should or should not be trusted has received far less attention. Our goal is to assign a confidence score for a face image that reflects its quality in terms of recognizable information. To this end, we propose a method for generating image quality training data automatically from 'mated-pairs' of face images, and use the generated data to train a lightweight Predictive Confidence Network, termed as PCNet, for estimating the confidence score of a face image. We systematically evaluate the usefulness of PCNet with its error versus reject performance, and demonstrate that it can be universally paired with and improve the robustness of any verification model. We describe three use cases on the public IJB-C face verification benchmark: (i) to improve 1:1 image-based verification error rates by rejecting low-quality face images; (ii) to improve quality score based fusion performance on the 1:1 set-based verification benchmark; and (iii) its use as a quality measure for selecting high quality (unblurred, good lighting, more frontal) faces from a collection, e.g. for automatic enrolment or display.
Document Similarity from Vector Space Densities
Venue: arXiv cs.CL 2020
Year: 2020

We propose a computationally light method for estimating similarities between text documents, which we call the density similarity (DS) method. The method is based on a word embedding in a high-dimensional Euclidean space and on kernel regression, and takes into account semantic relations among words. We find that the accuracy of this method is virtually the same as that of a state-of-the-art method, while the gain in speed is very substantial. Additionally, we introduce generalized versions of the top-k accuracy metric and of the Jaccard metric of agreement between similarity models.
Distributed Cooperation Under Uncertainty in Drone-Based Wireless Networks: A Bayesian Coalitional Game
Venue: arXiv cs.NI 2020
Year: 2020

We study the resource sharing problem in a drone-based wireless network. We consider a distributed control setting under uncertainty (i.e. unavailability of full information). In particular, the drones cooperate in serving the users while pooling their spectrum and energy resources in the absence of prior knowledge about different system characteristics such as the amount of available power at the other drones. We cast the aforementioned problem as a Bayesian cooperative game in which the agents (drones) engage in a coalition formation process, where the goal is to maximize the overall transmission rate of the network. The drones update their beliefs using a novel technique that combines the maximum likelihood estimation with Kullback-Leibler divergence. We propose a decision-making strategy for repeated coalition formation that converges to a stable coalition structure. We analyze the performance of the proposed approach by both theoretical analysis and simulations.
Encodable: Configurable Grammar for Visualization Components
Venue: arXiv cs.HC 2020
Year: 2020

There are so many libraries of visualization components nowadays with their APIs often different from one another. Could these components be more similar, both in terms of the APIs and common functionalities? For someone who is developing a new visualization component, how should the API look like? This work drew inspiration from visualization grammar, decoupled the grammar from its rendering engine and adapted it into a configurable grammar for individual components called Encodable. Encodable helps component authors define grammar for their components, and parse encoding specifications from users into utility functions for the implementation. This paper explains the grammar design and demonstrates how to build components with it.
Visualizing information on watch faces: A survey with smartwatch users
Venue: arXiv cs.HC 2020
Year: 2020

People increasingly wear smartwatches that can track a wide variety of data. However, it is currently unknown which data people consume and how it is visualized. To better ground research on smartwatch visualization, it is important to understand the current use of these representation types on smartwatches, and to identify missed visualization opportunities. We present the findings of a survey with 237 smartwatch wearers, and assess the types of data and representations commonly displayed on watch faces. We found a predominant display of health & fitness data, with icons accompanied by text being the most frequent representation type. Combining these results with a further analysis of online searches of watch faces and the data tracked on smartwatches that are not commonly visualized, we discuss opportunities for visualization research.
Properties of f-divergences and f-GAN training
Venue: arXiv cs.LG 2020
Year: 2020

In this technical report we describe some properties of f-divergences and f-GAN training. We present an elementary derivation of the f-divergence lower bounds which form the basis of f-GAN training. We derive informative but perhaps underappreciated properties of f-divergences and f-GAN training, including a gradient matching property and the fact that all f-divergences agree up to an overall scale factor on the divergence between nearby distributions. We provide detailed expressions for computing various common f-divergences and their variational lower bounds. Finally, based on our reformulation, we slightly generalize f-GAN training in a way that may improve its stability.
A perception centred self-driving system without HD Maps
Venue: arXiv cs.CV 2020
Year: 2020

Building a fully autonomous self-driving system has been discussed for more than 20 years yet remains unsolved. Previous systems have limited ability to scale. Their localization subsystem needs labor-intensive map recording for running in a new area, and the accuracy decreases after the changes occur in the environment. In this paper, a new localization method is proposed to solve the scalability problems, with a new method for detecting and making sense of diverse traffic lines. Like the way human drives, a self-driving system should not rely on an exact position to travel in most scenarios. As a result, without HD Maps, GPS or IMU, the proposed localization subsystem relies only on detecting driving-related features around (like lane lines, stop lines, and merging lane lines). For spotting and reasoning all these features, a new line detector is proposed and tested against multiple datasets.
MESSI: In-Memory Data Series Indexing
Venue: arXiv cs.DB 2020
Year: 2020

Data series similarity search is a core operation for several data series analysis applications across many different domains. However, the state-of-the-art techniques fail to deliver the time performance required for interactive exploration, or analysis of large data series collections. In this work, we propose MESSI, the first data series index designed for in-memory operation on modern hardware. Our index takes advantage of the modern hardware parallelization opportunities (i.e., SIMD instructions, multi-core and multi-socket architectures), in order to accelerate both index construction and similarity search processing times. Moreover, it benefits from a careful design in the setup and coordination of the parallel workers and data structures, so that it maximizes its performance for in-memory operations. Our experiments with synthetic and real datasets demonstrate that overall MESSI is up to 4x faster at index construction, and up to 11x faster at query answering than the state-of-the-art parallel approach. MESSI is the first to answer exact similarity search queries on 100GB datasets in _50msec (30-75msec across diverse datasets), which enables real-time, interactive data exploration on very large data series collections.
Embedded Development Boards for Edge-AI: A Comprehensive Report
Venue: arXiv cs.CY 2020
Year: 2020

The use of Deep Learning and Machine Learning is becoming pervasive day by day which is opening doors to new opportunities in every aspect of technology. Its application Ranges from Health-care to Self-driving Cars, Home Automation to Smart-agriculture, and Industry 4.0. Traditionally the majority of the processing for IoT applications is being done on a central cloud but that has its issues; which include latency, security, bandwidth, and privacy, etc. It is estimated that there will be around 20 Million IoT devices by 2020 which will increase problems with sending data to the cloud and doing the processing there. A new trend of processing the data on the edge of the network is emerging. The idea is to do processing as near the point of data production as possible. Doing processing on the nodes generating the data is called Edge Computing and doing processing on a layer between the cloud and the point of data production is called Fog computing. There are no standard definitions for any of these, hence they are usually used interchangeably. In this paper, we have reviewed the development boards available for running Artificial Intelligence algorithms on the Edge