Data Structures and Algorithms

For an edge-weighted connected undirected graph, the minimum k-way cut problem is to find a subset of edges of minimum total weight whose removal separates the graph into k connected components. The problem is NP-hard when k is part of the input and W[1]-hard when k is taken as a parameter. A simple algorithm for approximating a minimum k-way cut

Abstract. The restricted rotation distance dR(S, T) between two binary trees S, T of n vertices is the minimum number of rotations by which S can be transformed into T, where rotations can only take place at the root of the tree, or at the right child of the root. A sharp upper bound ...

Decision support tools that employ the use of computational intelligence are natural candidates to tackle the problem of dispatching trains in a railroad, allowing for the construction of flexible algorithms that explore the solution space optimizing the result for a defined objective, besides supporting the insertion of knowledge to the solution search process. We present a solution based on discrete-event simulation and heuristics to find feasible routes for all the trains. Decisions about passings and overtakings follow a set of heuristics that adhere to the railway circulation rules. Empirical results show that this algorithm runs in polynomial time. Some possibilities for improving the results provided by the tool are discussed. The first is to collect information from one planning run to feed the next run and improve the subsequent result, this approach is called adaptive. We use a rules executing environment that process a set of facts collected from previous executions of th...

Dynamically changing background ("dynamic background") still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: that of swaying tree branches and their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new dataset suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected dataset, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in the dynamic background literature.

The emergence of Blockchain have revolutionize the decentralization in distributed architecture. The advances in the consensus mechanism techniques and the development of different variants of consensus algorithms gives a huge impact on its progress. These technologies allow to have a distributed peer-to-peer network in which each external entity can be able to interact with other entities without any trusted intermediary in a verifiable manner. The existing consensus algorithms are mostly concerned with public blockchain having focused on public ledgers in general. The consortium blockchain is least focused as compared with other variants of blockchain (public and private) showing the need to address this vacuum. In this paper, we proposed a consensus algorithm named Rift for consortium blockchain which works on the principle of trust mechanism for achieving consensus in a blockchain. The consensus is achieved by distributed nodes in a consortium blockchain which were controlled by...

This paper presents the systematic way of hashing string values using NFO and NOF collision resolution strategies. NFO and NOF are techniques used for hashing numeric keys. The same principles and techniques for hashing numeric keys are deployed in the hashing of string values but with slight modifications in the hashing process and implementations. These variants followed the standard ways of evaluating and implementing algorithms to resolve collisions in hash tables. They are very effective in resolving the problem of collisions of string keys or values in the same slot of a hash table.

Abstract. We consider the problem of assigning radii to a given set of points in the plane, such that the resulting set of circles is connected, and the sum of radii is minimized. We show that the problem is polynomially solvable if a connectivity tree is given. If the connectivity tree is unknown, the problem is NP-hard if there are upper bounds on the radii and open otherwise. We give approximation guarantees for a variety of polynomial-time algorithms, describe upper and lower bounds (which are matching in some of the cases), provide polynomial-time approximation schemes, and conclude with experimental results and open problems.

A bloom filter is a space efficient probabilistic data structure which can quickly
test for set membership.
However, since a standard bloom filter will only test for presence, it has limitations.
This project shows the feasibility of Locality Sensitive Bloom Filters (LSBF) for
single-dimensional data and points to further, theoretical, algorithms for distance
sensitive hashes that can be used efficiently for multidimensional data. This report
also contains an approximate calculation of space and time complexity.
This has applications in areas like machine learning and searching where an answer
that's close enough may be satisfactory.

An analysis An analysis and implementation of an efficient in-place bucket sort

An analysis An analysis and implementation of an efficient in-place bucket sort

An analysis An analysis and implementation of an efficient in-place bucket sort

We examine the Maximum Independent Set Problem in an undirected graph. The main result is that this problem can be considered as the solving the same problem in a subclass of the weighted normal twinorthogonal graphs. The problem is formulated which is dual to the problem above. It is shown that, for trivial twin-orthogonal graphs, any of its maximal independent set is also maximum one. 1 Statement of the problem Consider the class L of undirected graphs without loops and multiple edges with weighted vertices.

ABSTRACTThe growth of the telecommunication industry is fast-paced with ground-breaking engineering achievements. Notwithstanding the technological advancement in the industry, it had continued to cope with the phenomenon ofresource constraint in portable mobile telecommunication devices compared to fixed and tethered devices. Portable mobile handheld devices have very low computational, storage and energy carrying capacity occasioned by the needs to satisfy portability, very small form factor, ergonomics, style and trends. Solutions such as cloudlets, cyber foraging, mobile cloud computing (MCC), and more recently but most applicable, multi-access edge computing (MEC) have been proffered with different application methodologies including computational offloading, distributed computing, thin clients, middleware, mobile environment cloning as well as representational state transfer. There is a need to satisfy requirements of new and emerging use cases, especially the deployments of 5...

In this paper we present a new data structure for double ended priority queue, called min-max fine heap, which combines the techniques used in fine heap and traditional min-max heap. The standard operations on this proposed structure are also presented, and their analysis indicates that the new structure outperforms the traditional one.

Wireless Mesh Network has more attractive features such as high-speed last-mile wireless internet access facility, instant deployability and low-cost backhaul services for large area coverage, etc. These features increase the application areas of Wireless Mesh Network (WMN) which in turn, persuade the researchers to pay more attention on this field. Routing plays a vital role in WMN for providing the Quality of Service (QoS) to the users in the network. In this paper, an EQ-AODV protocol is proposed to select a forwarding neighbor based on the factors such as node’s maximum remaining energy and minimum queue length. The above mentioned factors can be incorporated in the multi-channel and multi-interface approach of WMN to increase the capacity and throughput of the network. The EQ-AODV protocol constructs a stable and unbiased traffic route by considering the above mentioned factors. The analysis was carried out by comparing the proposed protocol EQ-AODV with the existing AODV and w...

A Compendium of over 1,500 short questions and answers This is a quick assessment/quiz book, with a vast collection of questions with answers on Data structures and Algorithms. It covers all commonly used data structures and algorithms: P-NP. It also covers a few specialized areas (string processing; polynomial operations; numerical & matrix computations; computational geometry & computer graphics). Unique features of the book are: (1) over 1,500 short (True/False and sentence completion) questions; (2) consistent, single-sentence format questions, with wide range of difficulty levels; (3) popular internship / job interview questions; (4) questions on some specialized topics. This can be of use to: (1) students: for self-assessment, and to prepare for exams and industry interviews; (2) faculty: to quickly select a few questions for a quiz; (3) interviewers/managers: to make a quick, initial assessment of candidates; (4) participants / quiz masters in quiz competitions.

Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed. In contrast to most related previous work, in this task we are constrained to using low-level, spatio-temporally local motion features only. This particular challenge arises in many semi-automatic surveillance systems which alert a human operator to potential abnormalities in the scene. Low-level, spatio-temporally local motion features are sparse (and thus require comparatively little storage space) and sufficiently powerful in the context of video abnormality detection to reduce the need for human intervention by more than 100-fold. This paper introduces three significant contributions: (i) we describe a dense algorithm for perspective estimation which uses motion features to estimate the perspective distortion at each image locus and then polls all such local estimates to arrive at the globally best estimate, (ii) we also present an alternative coarse algorithm which subdivides the image frame into blocks, and uses motion features to derive block-specific motion characteristics and constrain the relationships between these characteristics, with the perspective estimate emerging as a result of a global optimization scheme, and (iii) we report the results of an evaluation using nine large sets acquired using existing close-circuit television (CCTV) cameras, not installed specifically for the purposes of this work. Our findings demonstrate that both of the proposed methods are successful, their accuracy matching that of human labelling using complete visual data (by the constraints of the setup unavailable to our algorithms).

The use of algorithmic prediction in insurance is regarded as the beginning of a new era, because it promises to personalise insurance policies and premiums on the basis of individual behaviour and level of risk. The core idea is that the price of the policy would no longer refer to the calculated uncertainty of a pool of policyholders, with the consequence that everyone would have to pay only for her real exposure to risk. For insurance, however, uncertainty is not only a problem – shared uncertainty is a resource. The availability of individual risk information could undermine the principle of risk-pooling and risk-spreading on which insurance is based. The article examines this disruptive change first by exploring the possible consequences of the use of predictive algorithms to set insurance premiums. Will it endanger the principle of mutualisation of risks, producing new forms of discrimination and exclusion from coverage? In a second step, we analyse how the relationship betwee...

For an edge-weighted connected undirected graph, the minimum k-way cut problem is to find a subset of edges of minimum total weight whose removal separates the graph into k connected components. The problem is NP-hard when k is part of the input and W[1]-hard when k is taken as a parameter. A simple algorithm for approximating a minimum k-way cut

In this talk I'll explore gaps between the theoretical study of algorithms and the use of algorithms in practice. Examples will be drawn from my own experiences in industry and academia, and will include data structures and network algorithms. Based on these examples I'll try to draw conclusions to help guide the work of theoreticians and experimentalists, in an effort to make this work more relevant to the needs of practitioners.

This textbook grew out of a collection of lecture notes that I wrote for various
algorithms classes at the University of Illinois at Urbana-Champaign, which I
have been teaching about once a year since January 1999. Spurred by changes
of our undergraduate theory curriculum, I undertook a major revision of my
notes in 2016; this book consists of a subset of my revised notes on the most
fundamental course material, mostly reflecting the algorithmic content of our
new required junior-level theory course.

This research paper will take a look at sort algorithms and their efficiency. For this investigation two types of data structures and five different sorting algorithms were selected. The selected sort algorithms are; bubble sort, insert sort, selection sort, quick sort and merge sort. These algorithms together with two selected data structures have been examined and analyzed for the primary purpose of choosing the most effective among them. Algorithms use several computation resources, and their efficiency in our case is derived by analyzing which one of the resources used is valued most. The resources which were studied in this paper are; time of execution, amount of computer memory required and amount of effort invested in implementation. The investigation was concluded and a most effective sort algorithm was identified. But the question of the best algorithm was inconclusive. We found that some algorithms are effective with small input size but not for large input size while other algorithms are not effective with small input size but instead works better with larger input size. Therefore, the best algorithm for one case would not be necessarily the best for all other cases. At the end, a sort algorithm was selected to best fit different cases depending on particular scenarios.

Comme tout processus industriel, un système photovoltaïque peut être soumis, au cours de son fonctionnement, à différents défauts et anomalies conduisant à une baisse de la performance du système et voire à son indisponibilité. Permettre de diagnostiquer finement et de faire de la détection et de localisation de défauts dans une installation photovoltaïque réduit les coûts de maintenance et surtout augmente la productivité. Dans ce travail, nous nous intéressons spécifiquement à la détection et la classification de défauts d'un générateur photovoltaïque (GPV) en utilisant l'approche neuronale. Les performances de l'approche neuronale sont analysées sur la base d'une comparaison avec la méthode de seuillage. Abstract-As every industrial process, a photovoltaic system may subject, during his operation, to various faults and abnormalities leading to a drop of its efficiency up to its unavailability. A precise diagnosis and a fault detection and isolation make it possible to reduce the maintenance costs and above all increase the yield. In this work, a focus has been made on the fault detection and classification in the photovoltaic generator using the neural approach (Artificial Neural Network: ANN). The ANN approach is compared to the thresholding method.
Mots-clés: Générateur photovoltaïque-Détection et classification des défauts-Méthode de seuillage-Réseaux de neurones.

Cancer is a disease that develops in the human body due to gene mutation. Because of various factors, cells can become cancerous and grow rapidly, destroying normal cells at the same time. Support vector machines allow for accurate classification and detection of the classes. The advantage of kernel selection is to derive global learning rates for SVMs using the Genetic Folding algorithm. The developed GF algorithm outperforms traditional SVMs in the UCI Breast Cancer Wisconsin Diagnostic (BCWD) dataset under a certain comparative analysis, which is conducted under a set of conditions that describe the behavior of the compared algorithms. The observation that relates the GF performance appears to be comparable with SVM. The statistical analysis relies on a careful analysis of the ROC curve. Moreover, the GF algorithm shows that accuracy rates are obtained adaptively, that is, without knowing the parameters resulting from the margin conditions. The experimental results show that the ...

This research works on high symbolic Bengali text and transforms it into corresponding less symbolic English complying with the transliteration method. The Huffman-based approaches serve to compress retaining the original quality of the data. On the other hand, faster encoding and decoding is the most sophisticated sphere in data compression. We propose an adjacent distance array, a novel data structure based on the Huffman principle for encoding and decoding the character of transliterated text. The encoding and decoding algorithms have been explained for the introduced modus operandi and juxtaposed with conventional Huffman-based algorithms. Our research is outdoing than any regular Huffman-based algorithms, concentrating on the speed of the encoding and decoding manner discovered after estimating all decisions.

Second Edition - Springer
This book is intended as a manual on algorithm design, providing access to combinatorial algorithm technology for both students and computer professionals. It is divided into two parts: Techniques and Resources. The former is a general guide to techniques for the design and analysis of computer algorithms. The Resources section is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations, and an extensive bibliography.

1. Abstract: With the increasing number of users growing on the internet over the years, it becomes difficult to manage this enormous data amount. Searching information from this chunk of large data amount is critical in this big data era. The searching algorithm is one of the most vital factors to optimize search time complexity. This work illustrates a preprocessing way to bring down the time complexity of the linear search. The entire work focuses on finding an optimized way to search data with the proposed data structure depending on their length. There are many ways to find value from tons of data lists. Linear search is one of the most used search algorithms among them. Linear search or sequential search is a process to locate the target value inside a list. It compares each element of the list sequentially for the target value until it finds the match, or all the list items are searched. The linear search runs in at the worst linear time and makes at most n comparisons, where n is the list's length. To improve this search technique, we will propose a data structure by storing the items based on their length and running a linear search to find the value with an unsorted data list. This proposed data structure can significantly improve the linear searching time complexity by reducing the searched value on an unsorted list. 4. Objectives: If the list is unsorted, then we cannot do better than O(n) because, in the worst case, all the elements need to be checked to decide whether an item exists in the list or not. However, with some preprocessing, we can bring down the time complexity for answering queries. The purpose of this proposed work is to: • Sorting data by their length and store in their respective list. • Takes the length of every search element.

Mother and Father, it is impossible to thank you adequately for everything you have done, from loving me unconditionally to raising me in a stable household, where your persistent efforts and traditional values taught your children to celebrate and embrace life. I could not have asked for better parents or role-models. You showed me that anything is possible with faith, hard work and determination.

ABSTRACTThe growth of the telecommunication industry is fast-paced with ground-breaking engineering achievements. Notwithstanding the technological advancement in the industry, it had continued to cope with the phenomenon ofresource constraint in portable mobile telecommunication devices compared to fixed and tethered devices. Portable mobile handheld devices have very low computational, storage and energy carrying capacity occasioned by the needs to satisfy portability, very small form factor, ergonomics, style and trends. Solutions such as cloudlets, cyber foraging, mobile cloud computing (MCC), and more recently but most applicable, multi-access edge computing (MEC) have been proffered with different application methodologies including computational offloading, distributed computing, thin clients, middleware, mobile environment cloning as well as representational state transfer. There is a need to satisfy requirements of new and emerging use cases, especially the deployments of 5...

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.

Undergraduate Topics in Computer Science (UTiCS) delivers high-quality
instructional content for undergraduates studying in all areas of computing and information science. From core foundational and theoretical material to final-year topics and applications, UTiCS books take a fresh, concise, and modern approach and are ideal for self-study or for a one- or two-semester course. The texts are all authored by established experts in their fields, reviewed by an international advisory board, and contain numerous examples and problems. Many include fully worked solutions.
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Computer graphics and animation have has become a key technology in determining future research and development activities in many academic and industrial branches. The aim of this journal is to be an international peer-reviewed open access forum for scientific and technical presentations and discus the latest advances in Computer graphics and animation.