OpenRaiser/Intern-Atlas huggingface dataset:
Security Threats and Research Challenges of IoT-A Review
Venue: arXiv cs.CR 2020
Year: 2020

Internet of things (IoT) is the epitome of sustainable development. It has facilitated the development of smart systems, industrialization, and the state-of-the-art quality of life. IoT architecture is one of the essential baselines of understanding the widespread adoption. Security issues are very crucial for any technical infrastructure. Since IoT comprises heterogeneous devices, its security issues are diverse too. Various security attacks can be responsible for compromising confidentiality, integrity, and availability. In this paper, at first, the IoT architecture is described briefly. After that, the components of IoT are explained with perspective to various IoT based applications and services. Finally, various security issues, including recommended solutions, are elaborately described and the potential research challenges and future research directions.
Restyling Images with the Bangladeshi Paintings Using Neural Style Transfer: A Comprehensive Experiment, Evaluation, and Human Perspective
Venue: arXiv cs.CV 2020
Year: 2020

In today's world, Neural Style Transfer (NST) has become a trendsetting term. NST combines two pictures, a content picture and a reference image in style (such as the work of a renowned painter) in a way that makes the output image look like an image of the material, but rendered with the form of a reference picture. However, there is no study using the artwork or painting of Bangladeshi painters. Bangladeshi painting has a long history of more than two thousand years and is still being practiced by Bangladeshi painters. This study generates NST stylized image on Bangladeshi paintings and analyzes the human point of view regarding the aesthetic preference of NST on Bangladeshi paintings. To assure our study's acceptance, we performed qualitative human evaluations on generated stylized images by 60 individual humans of different age and gender groups. We have explained how NST works for Bangladeshi paintings and assess NST algorithms, both qualitatively \& quantitatively. Our study acts as a pre-requisite for the impact of NST stylized image using Bangladeshi paintings on mobile UI/GUI and material translation from the human perspective. We hope that this study will encourage new collaborations to create more NST related studies and expand the use of Bangladeshi artworks.
Deep Learning Approach Combining Lightweight CNN Architecture with Transfer Learning: An Automatic Approach for the Detection and Recognition of Bangladeshi Banknotes
Venue: arXiv cs.CV 2020
Year: 2020

Automatic detection and recognition of banknotes can be a very useful technology for people with visual difficulties and also for the banks itself by providing efficient management for handling different paper currencies. Lightweight models can easily be integrated into any handy IoT based gadgets/devices. This article presents our experiments on several state-of-the-art deep learning methods based on Lightweight Convolutional Neural Network architectures combining with transfer learning. ResNet152v2, MobileNet, and NASNetMobile were used as the base models with two different datasets containing Bangladeshi banknote images. The Bangla Currency dataset has 8000 Bangladeshi banknote images where the Bangla Money dataset consists of 1970 images. The performances of the models were measured using both the datasets and the combination of the two datasets. In order to achieve maximum efficiency, we used various augmentations, hyperparameter tuning, and optimizations techniques. We have achieved maximum test accuracy of 98.88\% on 8000 images dataset using MobileNet, 100\% on the 1970 images dataset using NASNetMobile, and 97.77\% on the combined dataset (9970 images) using MobileNet.
LAIF: AI, Deep Learning for Germany Suetterlin Letter Recognition and Generation
Venue: arXiv cs.CV 2020
Year: 2020

One of the successful early implementation of deep learning AI technology was on letter recognition. With the recent breakthrough of artificial intelligence (AI) brings more solid technology for complex problems like handwritten letter recognition and even automatic generation of them. In this research, we proposed deep learning framework called Ludwig AI Framework(LAIF) for Germany Suetterlin letter recognition and generation. To recognize Suetterlin letter, we proposed deep convolutional neural network. Since lack of big amount of data to train for the deep models and huge cost to label existing hard copy of handwritten letters, we also introduce the methodology with deep generative adversarial network to generate handwritten letters as synthetic data. Main source code is in https://github.com/enkhtogtokh/LAIF repository.
Turkish Text Classification: From Lexicon Analysis to Bidirectional Transformer
Venue: arXiv cs.CL 2020
Year: 2020

Text classification has seen an increased use in both academic and industry settings. Though rule based methods have been fairly successful, supervised machine learning has been shown to be most successful for most languages, where most research was done on English. In this article, the success of lexicon analysis, support vector machines, and extreme gradient boosting for the task of text classification and sentiment analysis are evaluated in Turkish and a pretrained transformer based classifier is proposed, outperforming previous methods for Turkish text classification. In the context of text classification, all machine learning models proposed in the article are domain-independent and do not require any task-specific modifications.
Incremental Medians via Online Bidding
Venue: arXiv cs.DS 2020
Year: 2020

In the k-median problem we are given sets of facilities and customers, and distances between them. For a given set F of facilities, the cost of serving a customer u is the minimum distance between u and a facility in F. The goal is to find a set F of k facilities that minimizes the sum, over all customers, of their service costs. Following Mettu and Plaxton, we study the incremental medians problem, where k is not known in advance, and the algorithm produces a nested sequence of facility sets where the kth set has size k. The algorithm is c-cost-competitive if the cost of each set is at most c times the cost of the optimum set of size k. We give improved incremental algorithms for the metric version: an 8-cost-competitive deterministic algorithm, a 2e ~ 5.44-cost-competitive randomized algorithm, a (24+epsilon)-cost-competitive, poly-time deterministic algorithm, and a (6e+epsilon ~ .31)-cost-competitive, poly-time randomized algorithm. The algorithm is s-size-competitive if the cost of the kth set is at most the minimum cost of any set of size k, and has size at most s k. The optimal size-competitive ratios for this problem are 4 (deterministic) and e (randomized). We present the first poly-time O(log m)-size-approximation algorithm for the offline problem and first poly-time O(log m)-size-competitive algorithm for the incremental problem. Our proofs reduce incremental medians to the following online bidding problem: faced with an unknown threshold T, an algorithm submits "bids" until it submits a bid that is at least the threshold. It pays the sum of all its bids. We prove that folklore algorithms for online bidding are optimally competitive.
Orthogonal multifilters image processing of astronomical images from scanned photographic plates
Venue: arXiv cs.CV 2021
Year: 2021

In this paper orthogonal multifilters for astronomical image processing are presented. We obtained new orthogonal multifilters based on the orthogonal wavelet of Haar and Daubechies. Recently, multiwavelets have been introduced as a more powerful multiscale analysis tool. It adds several degrees of freedom in multifilter design and makes it possible to have several useful properties such as symmetry, orthogonality, short support, and a higher number of vanishing moments simultaneously. Multifilter decomposition of scanned photographic plates with astronomical images is made.
Multiplierless Modules for Forward and Backward Integer Wavelet Transform
Venue: arXiv cs.AR 2021
Year: 2021

This article is about the architecture of a lossless wavelet filter bank with reprogrammable logic. It is based on second generation of wavelets with a reduced of number of operations. A new basic structure for parallel architecture and modules to forward and backward integer discrete wavelet transform is proposed.
Errata list for "Error Control Coding" by Lin and Costello
Venue: arXiv cs.IT 2021
Year: 2021

This document lists some errors found in the second edition of Error Control Coding by Shu Lin and Daniel J. Costello, Jr.
Discovering new technique for mapping relational database based on semantic web technology
Venue: arXiv cs.DB 2021
Year: 2021

Most of data on the Web are still stored in relational databases. Therefore, it is more important to make the correspondence between relational databases (RDB) and ontologies for storing the Web data. In this paper, we present an new approach to map the data stored in relational databases into the Semantic Web, we exploit simple mappings based on some specifications of the database schema, and we explain how relational databases can be used to define a mapping mechanism between relational database and OWL ontology. A framework has been developed, which migrates successfully RDB into OWL document. The experimental results were very important, demonstrating that the proposed method is feasible and efficient.
Numerical Computations For Operator Axioms
Venue: arXiv cs.NA 2021
Year: 2021

The Operator axioms have produced new real numbers with new operators. New operators naturally produce new equations and thus extend the traditional mathematical models which are selected to describe various scientific rules. So new operators help to describe complex scientific rules which are difficult described by traditional equations and have an enormous application potential. As to the equations including new operators, engineering computation often need the approximate solutions reflecting an intuitive order relation and equivalence relation. However, the order relation and equivalence relation of real numbers are not as intuitive as those of base-b expansions. Thus, this paper introduces numerical computations to approximate all real numbers with base-b expansions.
Decoding billions of integers per second through vectorization
Venue: arXiv cs.IR 2021
Year: 2021

In many important applications -- such as search engines and relational database systems -- data is stored in the form of arrays of integers. Encoding and, most importantly, decoding of these arrays consumes considerable CPU time. Therefore, substantial effort has been made to reduce costs associated with compression and decompression. In particular, researchers have exploited the superscalar nature of modern processors and SIMD instructions. Nevertheless, we introduce a novel vectorized scheme called SIMD-BP128 that improves over previously proposed vectorized approaches. It is nearly twice as fast as the previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the same time, SIMD-BP128 saves up to 2 bits per integer. For even better compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while being two times faster during decoding.
Pigouvian Tolls and Welfare Optimality with Parallel Servers and Heterogeneous Customers
Venue: arXiv cs.GT 2021
Year: 2021

Congestion externalities are a well-known phenomenon in transportation and communication networks, healthcare etc. Optimization by self-interested agents in such settings typically results in equilibria which are sub-optimal for social welfare. Pigouvian taxes or tolls, which impose a user charge equal to the negative externality caused by the marginal user to other users, are a mechanism for combating this problem. In this paper, we study a non-atomic congestion game in which heterogeneous agents choose amongst a finite set of heterogeneous servers. The delay at a server is an increasing function of its load. Agents differ in their sensitivity to delay. We show that, while selfish optimisation by agents is sub-optimal for social welfare, imposing admission charges at the servers equal to the Pigouvian tax causes the user equilibrium to maximize social welfare. In addition, we characterize the structure of welfare optimal and of equilibrium allocations.
Choosing by means of approval-preferential voting. The path-revised approval choice
Venue: arXiv cs.GT 2021
Year: 2021

Approval-preferential voting is problematical since it combines two different kinds of information that could by themselves lead to different choices. This article analyses the problem and studies a new proposal to deal with it. The existing methods are overviewed with special attention to several desirable properties that are not always met. Particular emphasis is made on certain rather unknown views of Condorcet about this subject. The proposed procedure definitely aims for a choice in the spirit of approval voting. However, it still makes use of the preferential information so as to improve the approval one. This is done by means of the path scores, in common to Schulze's method for preferential voting. The resulting method, that we call path-revised approval choice, is shown to enjoy several good properties. In particular, it fulfils in a well-defined sense Condorcet's latest view that a surely good option should prevail over a doubtfully best one.
Riposte: An Anonymous Messaging System Handling Millions of Users
Venue: arXiv cs.CR 2021
Year: 2021

This paper presents Riposte, a new system for anonymous broadcast messaging. Riposte is the first such system, to our knowledge, that simultaneously protects against traffic-analysis attacks, prevents anonymous denial-of-service by malicious clients, and scales to million-user anonymity sets. To achieve these properties, Riposte makes novel use of techniques used in systems for private information retrieval and secure multi-party computation. For latency-tolerant workloads with many more readers than writers (e.g. Twitter, Wikileaks), we demonstrate that a three-server Riposte cluster can build an anonymity set of 2,895,216 users in 32 hours.
Incremental Computation with Names
Venue: arXiv cs.PL 2021
Year: 2021

Over the past thirty years, there has been significant progress in developing general-purpose, language-based approaches to incremental computation, which aims to efficiently update the result of a computation when an input is changed. A key design challenge in such approaches is how to provide efficient incremental support for a broad range of programs. In this paper, we argue that first-class names are a critical linguistic feature for efficient incremental computation. Names identify computations to be reused across differing runs of a program, and making them first class gives programmers a high level of control over reuse. We demonstrate the benefits of names by presenting NOMINAL ADAPTON, an ML-like language for incremental computation with names. We describe how to use NOMINAL ADAPTON to efficiently incrementalize several standard programming patterns -- including maps, folds, and unfolds -- and show how to build efficient, incremental probabilistic trees and tries. Since NOMINAL ADAPTON's implementation is subtle, we formalize it as a core calculus and prove it is from-scratch consistent, meaning it always produces the same answer as simply re-running the computation. Finally, we demonstrate that NOMINAL ADAPTON can provide large speedups over both from-scratch computation and ADAPTON, a previous state-of-the-art incremental computation system.
Utility Optimal Thread Assignment and Resource Allocation in Multi-Server Systems
Venue: arXiv cs.DC 2021
Year: 2021

Achieving high performance in many multi-server systems requires finding a good assignment of worker threads to servers and also effectively allocating each server's resources to its assigned threads. The assignment and allocation components of this problem have been studied extensively but largely separately in the literature. In this paper, we introduce the assign and allocate (AA) problem, which seeks to simultaneously find an assignment and allocation that maximizes the total utility of the threads. Assigning and allocating the threads together can result in substantially better overall utility than performing the steps separately, as is traditionally done. We model each thread by a utility function giving its performance as a function of its assigned resources. We first prove that the AA problem is NP-hard. We then present a $2 (\sqrt{2}-1) > 0.828$ factor approximation algorithm for concave utility functions, which runs in $O(mn^2 + n (\log mC)^2)$ time for $n$ threads and $m$ servers with $C$ amount of resources each. We also give a faster algorithm with the same approximation ratio and $O(n (\log mC)^2)$ time complexity. We then extend the problem to two more general settings. First, we consider threads with nonconcave utility functions, and give a 1/2 factor approximation algorithm. Next, we give an algorithm for threads using multiple types of resources, and show the algorithm achieves good empirical performance. We conduct extensive experiments to test the performance of our algorithms on threads with both synthetic and realistic utility functions, and find that they achieve over 92\% of the optimal utility on average. We also compare our algorithms with a number of practical heuristics, and find that our algorithms achieve up to 9 times higher total utility.
Reversible Quantum Process Algebra with Guards
Venue: arXiv cs.LO 2021
Year: 2021

Truly concurrent process algebras are generalizations to the traditional process algebras for true concurrency, CTC to CCS, APTC to ACP, $π_{tc}$ to $π$ calculus, APPTC to probabilistic process algebra. And we also did some work on reversible process algebra and probabilistic truly concurrent process algebra. In this book, we utilize reversible truly concurrent process algebras APRTC and probabilistic process algebra APPTC to model quantum computing and unify quantum and classical computing.
FPT Approximation Schemes for Maximizing Submodular Functions
Venue: arXiv cs.DS 2021
Year: 2021

We investigate the existence of approximation algorithms for maximization of submodular functions, that run in fixed parameter tractable (FPT) time. Given a non-decreasing submodular set function $v: 2^X \to \mathbb{R}$ the goal is to select a subset $S$ of $K$ elements from $X$ such that $v(S)$ is maximized. We identify three properties of set functions, referred to as $p$-separability properties, and we argue that many real-life problems can be expressed as maximization of submodular, $p$-separable functions, with low values of the parameter $p$. We present FPT approximation schemes for the minimization and maximization variants of the problem, for several parameters that depend on characteristics of the optimized set function, such as $p$ and $K$. We confirm that our algorithms are applicable to a broad class of problems, in particular to problems from computational social choice, such as item selection or winner determination under several multiwinner election systems.
Distributed Bare-Bones Communication in Wireless Networks
Venue: arXiv cs.DC 2021
Year: 2021

We consider wireless networks operating under the SINR model of interference. Nodes have limited individual knowledge and capabilities: they do not know their positions in a coordinate system in the plane, further they do not know their neighborhoods, nor do they know the size of the network $n$, and finally they cannot sense collisions resulting from simultaneous transmissions by at least two neighbors. Each node is equipped with a unique integer name, where $N$ as an upper bound on the a range of names. We refer as a backbone to a subnetwork induced by a diameter-preserving dominating set of nodes. Let $Δ$ denote a maximum number of nodes that can successfully receive a message transmitted by a node when no other nodes transmit concurrently. We study distributed algorithms for communication problems in three settings. In the single-node-start case, when one node starts an execution and other nodes are awoken by receiving messages from already awoken nodes, we present a randomized broadcast algorithm that wakes up all nodes in $O(n \log^2 N)$ rounds with high probability. For the synchronized-start case, when all nodes start an execution simultaneously, we give a randomized algorithm computing a backbone in $O(Δ\log^{7} N)$ rounds with high probability. In the partly-coordinated-start case, when a number of nodes start an execution together and other nodes are awoken by receiving messages from the already awoken nodes, we develop an algorithm that creates a backbone in time $O(n\log^2 N +Δ\log^{7} N)$ with high probability.
Sensor-based Proximity Detection in the Face of Active Adversaries
Venue: arXiv cs.CR 2021
Year: 2021

Contextual proximity detection (or, co-presence detection) is a promising approach to defend against relay attacks in many mobile authentication systems. We present a systematic assessment of co-presence detection in the presence of a context-manipulating attacker. First, we show that it is feasible to manipulate, consistently control and stabilize the readings of different acoustic and physical environment sensors (and even multiple sensors simultaneously) using low-cost, off-the-shelf equipment. Second, based on these capabilities, we show that an attacker who can manipulate the context gains a significant advantage in defeating context-based co-presence detection. For systems that use multiple sensors, we investigate two sensor fusion approaches based on machine learning techniques: features-fusion and decisions-fusion, and show that both are vulnerable to contextual attacks but the latter approach can be more resistant in some cases.
Power System Differential-Algebraic Equations
Venue: arXiv cs.SY 2021
Year: 2021

This document presents an introduction of two commonly used power system differential algebraic equations for studying electromechanical oscillation and transient stability. Two types of generator models are used to formulate the power system model, respectively: the second-order classical model and the fourth-order generator model. An example is provided on the IEEE 9-bus system.
Energy-Efficient Coalition Formation in Sensor Networks: a Game-Theoretic Approach
Venue: arXiv cs.NI 2021
Year: 2021

The most important challenge in Wireless Sensor Networks (WSNs) is the energy constraint. Numerous solutions have been proposed to alleviate the issue, including clustering. Game theory is an effective decision-making tool that has been shown to be effective in solving complex problems. In this paper, we employ cooperative games and propose a new clustering scheme called Coalitional Game-Theoretic Clustering (CGTC) algorithm for WSNs. The idea is to partition the entire network area into two regions, namely far and vicinity, in order to address the hotspot problem in WSNs, wherein nodes close to the base station (BS) tend to deplete their energy faster due to relaying the traffic load received from farther nodes. Then, coalitional games are utilized to group nodes as coalitions. The main factor in choosing coalition heads is the energy level of nodes so that the most powerful nodes play the role of heads. The Shapley value is adopted as the solution concept to our coalitional games. The results of simulations confirm the effectiveness of CGTC in terms of energy efficiency and improved throughput.