OpenRaiser/Intern-Atlas huggingface dataset:
Charging Techniques for UAV-assisted Data Collection: Is Laser Power Beaming the Answer?
Venue: arXiv cs.NI 2021
Year: 2021

As Covid-19 has increased the need for connectivity around the world, researchers are targeting new technologies that could improve coverage and connect the unconnected in order to make progress toward the United Nations Sustainable Development Goals. In this context, drones are seen as one of the key features of 6G wireless networks that could extend the coverage of previous wireless network generations. That said, limited on-board energy seems to be the main drawback that hinders the use of drones for wireless coverage. Therefore, different wireless and wired charging techniques, such as laser beaming, charging stations, and tether stations are proposed. In this paper, we analyze and compare these different charging techniques by performing extensive simulations for the scenario of drone-assisted data collection from ground-based Internet of Things (IoT) devices. We analyze the strengths and weaknesses of each charging technique, and finally show that laser-powered drones strongly compete with, and outperform in some scenarios other charging techniques.
Towards Grassroots Peering at the Edge
Venue: arXiv cs.OH 2021
Year: 2021

Fog Computing allows applications to address their latency and privacy requirements while coping with bandwidth limitations of Internet service providers (ISPs). Existing research on fog systems has so far mostly taken a very high-level view on the actual fog infrastructure. In this position paper, we identify and discuss the problem of having multiple ISPs in edge-to-edge communication. As a possible solution we propose that edge operators create direct edge-to-edge links in a grassroots fashion and discuss different implementation options. Based on this, we highlight some important open research challenges that result from this.
Protocolo de Revisão Sistemática da Literatura para Wireless Mesh Networks com mecanismos de incentivo financeiro
Venue: arXiv cs.NI 2021
Year: 2021

The objective of this systematic review is to assess the state of the art on routing protocols aimed at WMNs, when associated with mechanisms for financial incentives that aim to ensure the self-sustainability of the network, seeking to identify the improvements and associated risks as well as the efficiency of the mechanisms adopted within networks, thus ensuring increased reliability in the use of such protocols for routing encouraged WMNs.
A Synthetic Prediction Market for Estimating Confidence in Published Work
Venue: arXiv cs.CY 2021
Year: 2021

Explainably estimating confidence in published scholarly work offers opportunity for faster and more robust scientific progress. We develop a synthetic prediction market to assess the credibility of published claims in the social and behavioral sciences literature. We demonstrate our system and detail our findings using a collection of known replication projects. We suggest that this work lays the foundation for a research agenda that creatively uses AI for peer review.
Exploring Spreadsheet Use and Practices in a Technologically Constrained Setting
Venue: arXiv cs.SE 2021
Year: 2021

This paper explores the impacts of spreadsheets on business operations in a water utility parastatal in Malawi, Sub-Saharan Africa. The organisation is a typical example of a semi-government body operating in a technologically underdeveloped country. The study focused on spreadsheet scope of use and life cycle as well as organisational policy and governance. The results will help define future spreadsheet usage by influencing new approaches for managing potential risks associated with spreadsheets in the organization. Generally, findings indicate that the proliferation of spreadsheets in the organization has provided an enabling environment for business automation. The paper also highlights management, technological and human factor issues contributing to high risks associated with the pervasive spreadsheet use. The conclusions drawn from the research confirms that there is ample room for improvement in many areas such as implementation of comprehensive policies and regulations governing spreadsheet development processes and adoption.
Selecting the suitable resampling strategy for imbalanced data classification regarding dataset properties
Venue: arXiv cs.LG 2021
Year: 2021

In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class. This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples. Thus, the prediction model is unreliable although the overall model accuracy can be acceptable. Oversampling and undersampling techniques are well-known strategies to deal with this problem by balancing the number of examples of each class. However, their effectiveness depends on several factors mainly related to data intrinsic characteristics, such as imbalance ratio, dataset size and dimensionality, overlapping between classes or borderline examples. In this work, the impact of these factors is analyzed through a comprehensive comparative study involving 40 datasets from different application areas. The objective is to obtain models for automatic selection of the best resampling strategy for any dataset based on its characteristics. These models allow us to check several factors simultaneously considering a wide range of values since they are induced from very varied datasets that cover a broad spectrum of conditions. This differs from most studies that focus on the individual analysis of the characteristics or cover a small range of values. In addition, the study encompasses both basic and advanced resampling strategies that are evaluated by means of eight different performance metrics, including new measures specifically designed for imbalanced data classification. The general nature of the proposal allows the choice of the most appropriate method regardless of the domain, avoiding the search for special purpose techniques that could be valid for the target data.
Dynamic Channel Access via Meta-Reinforcement Learning
Venue: arXiv cs.NI 2021
Year: 2021

In this paper, we address the channel access problem in a dynamic wireless environment via meta-reinforcement learning. Spectrum is a scarce resource in wireless communications, especially with the dramatic increase in the number of devices in networks. Recently, inspired by the success of deep reinforcement learning (DRL), extensive studies have been conducted in addressing wireless resource allocation problems via DRL. However, training DRL algorithms usually requires a massive amount of data collected from the environment for each specific task and the well-trained model may fail if there is a small variation in the environment. In this work, in order to address these challenges, we propose a meta-DRL framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). In the proposed framework, we train a common initialization for similar channel selection tasks. From the initialization, we show that only a few gradient descents are required for adapting to different tasks drawn from the same distribution. We demonstrate the performance improvements via simulation results.
Learning Based Task Offloading in Digital Twin Empowered Internet of Vehicles
Venue: arXiv cs.NI 2021
Year: 2021

Mobile edge computing has become an effective and fundamental paradigm for futuristic autonomous vehicles to offload computing tasks. However, due to the high mobility of vehicles, the dynamics of the wireless conditions, and the uncertainty of the arrival computing tasks, it is difficult for a single vehicle to determine the optimal offloading strategy. In this paper, we propose a Digital Twin (DT) empowered task offloading framework for Internet of Vehicles. As a software agent residing in the cloud, a DT can obtain both global network information by using communications among DTs, and historical information of a vehicle by using the communications within the twin. The global network information and historical vehicular information can significantly facilitate the offloading. In specific, to preserve the precious computing resource at different levels for most appropriate computing tasks, we integrate a learning scheme based on the prediction of futuristic computing tasks in DT. Accordingly, we model the offloading scheduling process as a Markov Decision Process (MDP) to minimize the long-term cost in terms of a trade off between task latency, energy consumption, and renting cost of clouds. Simulation results demonstrate that our algorithm can effectively find the optimal offloading strategy, as well as achieve the fast convergence speed and high performance, compared with other existing approaches.
On the Distribution of the Weighted Sum of Chi-Squared Variables
Venue: arXiv cs.IT 2021
Year: 2021

This paper presents the probability distribution function (p.d.f.) and cumulative distribution function (c.d.f.) of the weighted sum of central independent chi-squared random variables with non-zero weighs based on a method using moment generating functions. To obtain the p.d.f. and c.d.f. of such a function, we first derive the moment generating function of this weighted sum using the partial fractions decomposition or the residue method. The results cover the sum of two and three weighted chi-squared random variables, which can easily be adapted to more general cases.
Kalman Filters and Homography: Utilizing the Matrix $A$
Venue: arXiv cs.CV 2022
Year: 2022

Many problems in Computer Vision can be reduced to either working around a known transform, or given a model for the transform computing the inverse problem of the transform itself. We will look at two ways of working with the matrix $A$ and see how transforms are at the root of image processing and vision problems.
The Černy conjecture
Venue: arXiv cs.DM 2022
Year: 2022

A word $w$ of letters on edges of underlying graph $Γ$ of deterministic finite automaton (DFA) is called synchronizing if $w$ sends all states of the automaton to a unique state. J. Černy discovered in 1964 a sequence of $n$-state complete DFA possessing a minimal synchronizing word of length $(n-1)^2$. The hypothesis, well known today as the Černy conjecture, claims that it is also precise upper bound on the length of such a word for a complete DFA. The hypothesis was formulated in 1966 by Starke. The problem has motivated great and constantly growing number of investigations and generalizations. To prove the conjecture, we use algebra w on a special class of row monomial matrices (one unit and rest zeros in every row), induced by words in the alphabet of labels on edges. These matrices generate a space with respect to the mentioned operation. The proof is based on connection between length of words $u$ and dimension of the space generated by solutions $L_x$ of matrix equation $M_uL_x=M_s$ for synchronizing word $s$, as well as on the relation between ranks of $M_u$ and $L_x$.
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
Venue: arXiv cs.AI 2022
Year: 2022

We examine a probabilistic model for the diagnosis of multiple diseases. In the model, diseases and findings are represented as binary variables. Also, diseases are marginally independent, features are conditionally independent given disease instances, and diseases interact to produce findings via a noisy OR-gate. An algorithm for computing the posterior probability of each disease, given a set of observed findings, called quickscore, is presented. The time complexity of the algorithm is O(nm-2m+), where n is the number of diseases, m+ is the number of positive findings and m- is the number of negative findings. Although the time complexity of quickscore i5 exponential in the number of positive findings, the algorithm is useful in practice because the number of observed positive findings is usually far less than the number of diseases under consideration. Performance results for quickscore applied to a probabilistic version of Quick Medical Reference (QMR) are provided.
Infinite Probabilistic Secret Sharing
Venue: arXiv cs.CR 2022
Year: 2022

A probabilistic secret sharing scheme is a joint probability distribution of the shares and the secret together with a collection of secret recovery functions. The study of schemes using arbitrary probability spaces and unbounded number of participants allows us to investigate their abstract properties, to connect the topic to other branches of mathematics, and to discover new design paradigms. A scheme is perfect if unqualified subsets have no information on the secret, that is, their total share is independent of the secret. By relaxing this security requirement, three other scheme types are defined. Our first result is that every (infinite) access structure can be realized by a perfect scheme where the recovery functions are non-measurable. The construction is based on a paradoxical pair of independent random variables which determine each other. Restricting the recovery functions to be measurable ones, we give a complete characterization of access structures realizable by each type of the schemes. In addition, either a vector-space or a Hilbert-space based scheme is constructed realizing the access structure. While the former one uses the traditional uniform distributions, the latter one uses Gaussian distributions, leading to a new design paradigm.
On the Rise and Fall of Online Social Networks
Venue: arXiv cs.SI 2022
Year: 2022

The rise and fall of online social networks recently generated an enormous amount of interest among people, both inside and outside of academia. Gillette [Businessweek magazine, 2011] did a detailed analysis of MySpace, which started losing its popularity since 2008. Cannarella and Spechler [ArXiv, 2014] used a model of disease spread to explain the rise and fall of MySpace. In this paper, we present a graph theoretical model that may be able to provide an alternative explanation for the rise and fall of online social networks. Our model is motivated by the well-known Barabasi-Albert model of generating random scale-free networks using preferential attachment or `rich-gets-richer' phenomenon. As shown by our empirical analysis, we conjecture that such an online social network growth model is inherently flawed as it fails to maintain the stability of such networks while ensuring their growth. In the process, we also conjecture that our model of preferential attachment also exhibits scale-free phenomenon.
A Benchmark Suite for Template Detection and Content Extraction
Venue: arXiv cs.IR 2022
Year: 2022

Template detection and content extraction are two of the main areas of information retrieval applied to the Web. They perform different analyses over the structure and content of webpages to extract some part of the document. However, their objective is different. While template detection identifies the template of a webpage (usually comparing with other webpages of the same website), content extraction identifies the main content of the webpage discarding the other part. Therefore, they are somehow complementary, because the main content is not part of the template. It has been measured that templates represent between 40% and 50% of data on the Web. Therefore, identifying templates is essential for indexing tasks because templates usually contain irrelevant information such as advertisements, menus and banners. Processing and storing this information is likely to lead to a waste of resources (storage space, bandwidth, etc.). Similarly, identifying the main content is essential for many information retrieval tasks. In this paper, we present a benchmark suite to test different approaches for template detection and content extraction. The suite is public, and it contains real heterogeneous webpages that have been labelled so that different techniques can be suitable (and automatically) compared.
Evolutionary algorithms
Venue: arXiv cs.NE 2022
Year: 2022

This manuscript contains an outline of lectures course "Evolutionary Algorithms" read by the author. The course covers Canonic Genetic Algorithm and various other genetic algorithms as well as evolutionary strategies, genetic programming, tabu search and the class of evolutionary algorithms in general. Some facts, such as the Rotation Property of crossover, the Schemata Theorem, GA performance as a local search and "almost surely" convergence of evolutionary algorithms are given with complete proofs. The text is in Russian.
On Computing the Minkowski Difference of Zonotopes
Venue: arXiv cs.CG 2022
Year: 2022

Zonotopes are becoming an increasingly popular set representation for formal verification techniques. This is mainly due to their efficient representation and their favorable computational complexity of important operations in high-dimensional spaces. In particular, zonotopes are closed under Minkowski addition and linear maps, which can be very efficiently implemented. Unfortunately, zonotopes are not closed under Minkowski difference for dimensions greater than two. However, we present an algorithm that efficiently computes a halfspace representation of the Minkowski difference of two zonotopes. In addition, we present an efficient algorithm that computes an approximation of the Minkowski difference in generator representation. The efficiency of the proposed solution is demonstrated by numerical experiments. These experiments show a reduced computation time in comparison to that when first the halfspace representation of zonotopes is obtained and the Minkowski difference is performed subsequently.
Profit-Driven Team Grouping in Social Networks
Venue: arXiv cs.SI 2022
Year: 2022

In this paper, we investigate the profit-driven team grouping problem in social networks. We consider a setting in which people possess different skills, and the compatibility between these individuals is captured by a social network. Moreover, there is a collection of tasks, where each task requires a specific set of skills and yields a profit upon completion. Individuals may collaborate with each other as \emph{teams} to accomplish a set of tasks. We aim to find a group of teams to maximize the total profit of the tasks that they can complete. Any feasible grouping must satisfy the following conditions: (i) each team possesses all the skills required by the task assigned to it, (ii) individuals belonging to the same team are socially compatible, and (iii) no individual is overloaded. We refer to this as the \textsc{TeamGrouping} problem. We analyze the computational complexity of this problem and then propose a linear program-based approximation algorithm to address it and its variants. Although we focus on team grouping, our results apply to a broad range of optimization problems that can be formulated as cover decomposition problems.
Refinement of the Equilibrium of Public Goods Games over Networks: Efficiency and Effort of Specialized Equilibria
Venue: arXiv cs.GT 2022
Year: 2022

Recently Bramoulle and Kranton presented a model for the provision of public goods over a network and showed the existence of a class of Nash equilibria called specialized equilibria wherein some agents exert maximum effort while other agents free ride. We examine the efficiency, effort and cost of specialized equilibria in comparison to other equilibria. Our main results show that the welfare of a particular specialized equilibrium approaches the maximum welfare amongst all equilibria as the concavity of the benefit function tends to unity. For forest networks a similar result also holds as the concavity approaches zero. Moreover, without any such concavity conditions, there exists for any network a specialized equilibrium that requires the maximum weighted effort amongst all equilibria. When the network is a forest, a specialized equilibrium also incurs the minimum total cost amongst all equilibria. For well-covered forest networks we show that all welfare maximizing equilibria are specialized and all equilibria incur the same total cost. Thus we argue that specialized equilibria may be considered as a refinement of the equilibrium of the public goods game. We show several results on the structure and efficiency of equilibria that highlight the role of dependants in the network.
Walker-Independent Features for Gait Recognition from Motion Capture Data
Venue: arXiv cs.CV 2022
Year: 2022

MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach. Yet in some applications such as video surveillance new identities can appear on the fly and labeled data for all encountered people may not always be available. This work introduces the concept of learning walker-independent gait features directly from raw joint coordinates by a modification of the Fisher Linear Discriminant Analysis with Maximum Margin Criterion. Our new approach shows not only that these features can discriminate different people than who they are learned on, but also that the number of learning identities can be much smaller than the number of walkers encountered in the real operation.
New Frameworks for Offline and Streaming Coreset Constructions
Venue: arXiv cs.DS 2022
Year: 2022

A coreset for a set of points is a small subset of weighted points that approximately preserves important properties of the original set. Specifically, if $P$ is a set of points, $Q$ is a set of queries, and $f:P\times Q\to\mathbb{R}$ is a cost function, then a set $S\subseteq P$ with weights $w:P\to[0,\infty)$ is an $ε$-coreset for some parameter $ε>0$ if $\sum_{s\in S}w(s)f(s,q)$ is a $(1+ε)$ multiplicative approximation to $\sum_{p\in P}f(p,q)$ for all $q\in Q$. Coresets are used to solve fundamental problems in machine learning under various big data models of computation. Many of the suggested coresets in the recent decade used, or could have used a general framework for constructing coresets whose size depends quadratically on what is known as total sensitivity $t$. In this paper we improve this bound from $O(t^2)$ to $O(t\log t)$. Thus our results imply more space efficient solutions to a number of problems, including projective clustering, $k$-line clustering, and subspace approximation. Moreover, we generalize the notion of sensitivity sampling for sup-sampling that supports non-multiplicative approximations, negative cost functions and more. The main technical result is a generic reduction to the sample complexity of learning a class of functions with bounded VC dimension. We show that obtaining an $(ν,α)$-sample for this class of functions with appropriate parameters $ν$ and $α$ suffices to achieve space efficient $ε$-coresets. Our result implies more efficient coreset constructions for a number of interesting problems in machine learning; we show applications to $k$-median/$k$-means, $k$-line clustering, $j$-subspace approximation, and the integer $(j,k)$-projective clustering problem.