Time Complexity Of Algorithm Research Articles

Fair Top-k Ranking with multiple protected groups

Ranking items or people is a fundamental operation at the basis of several processes and services, not all of them happening online. Ranking is required for different tasks, including search, personalization, recommendation, and filtering. While traditionally ranking has been aimed solely at maximizing some global utility function, recently the awareness of potential discrimination for some of the elements to rank, has captured the attention of researchers, which have thus started devising ranking systems which are non-discriminatory or fair for the items being ranked. So far, researchers have mostly focused on group fairness, which is usually expressed in the form of constraints on the fraction of elements from some protected groups that should be included in the top-k positions, for any relevant k. These constraints are needed in order to correct implicit societal biases existing in the input data and reflected in the relevance or fitness score computed.In this article, we tackle the problem of selecting a subset of k individuals from a pool of n≫k candidates, maximizing global utility (i.e., selecting the “best” candidates) while respecting given group-fairness criteria. In particular, to tackle this Fair Top-kRanking problem, we adopt a ranked group-fairness definition which extends the standard notion of group fairness based on protected groups, by ensuring that the proportion of protected candidates in every prefix of the top-k ranking remains statistically above, or indistinguishable from, a given minimum threshold. Our notion of utility requires, intuitively, that every individual included in the top-k should be more qualified than every candidate not included; and that for every pair of candidates in the top-k, the more qualified candidate should be ranked above.The main contribution of this paper is an algorithm for producing a fair top-k ranking that can be used when more than one protected group is present, which means that a statistical test based on a multinomial distribution needs to be used instead of one for a binomial distribution, as the original FA*IR algorithms does. This poses important technical challenges and increases both the space and time complexity of the re-ranking algorithm. Our experimental assessment on real-world datasets shows that our approach yields small distortions with respect to rankings that maximize utility without considering our fairness criteria.

Research Challenges in Combinatorial Search

I provide a personal view of some of the major research challenges in the area of combinatorial search. These include solving and playing games with chance, hidden information, and multiple players, optimally solving larger instances of well-known single-agent toy problems, applying search techniques to more realistic problem domains, analyzing the time complexity of heuristic search algorithms, and capitalizing on advances in computing hardware, such as very large external memories and multi-core processors.

A Novel Convex Hull Coverage Algorithm for the Deployment of Electric Taxi Swap Stations Based on Urban Traffic Flow

Electric taxis have been adopted as a new energy public transportation tool as opposed to traditional taxis in modern city. Designing an efficient swapping station deployment scheme has become an important issue to improve the endurance capability of electric taxis. In this study, based on real operation trajectory data from 3997 taxis in Suzhou city, the battery swapping demand of taxis based on urban traffic flow is obtained to construct a network coverage deployment model under rule constraints, where the main optimization goal is to minimize the number of swapping stations and load balancing. According to this model, a traffic drive planning algorithm based on computational geometry is presented. The experimental results illustrate that the deployment scheme obtained by the proposed algorithm is significantly optimized in terms of the deployment cost and service load and has a lower algorithm time complexity than the typical unified deployment scheme. Therefore, the proposed method can be applied to improve the operating efficiency of the urban electric taxi system.

Interval joint robust regression method

Interval-valued data are needed to manage either the uncertainty related to measurements, or the variability inherent to the description of complex objects representing group of individuals. A number of regression methods suitable to interval variables describing variability of complex objects are already available. However, less attention has been given to methods that, simultaneously, take into account the full interval information and are resistant to interval outlier observations, even with the frequent presence of atypical observations on interval-valued data sets. This paper proposes a new robust linear regression method for interval variables, where the presence of outliers either in the center or in the radius penalize both the center and the radius regression models. Moreover, the interval observations with outliers on both center and radius are more penalized than those observations with outliers only in the center (or in the radius). Besides, this paper provides a suitable iterative algorithm to estimate the parameters of the proposed method. The algorithm estimates the parameters of the center (or of the radius) model taking into account both information of the center and the radius. The convergence and time complexity of the iterative algorithm are also presented. Finally, the performance of the new method is compared with some previous robust regression approaches and evaluated on synthetic and real interval-valued data sets.

Enhanced stochastic soft decoding algorithm for Reed-Solomon codes

The Chase algorithm based on stochastic computing can significantly reduce the complexity of soft decoding while ensuring the decoding performance, so that the softdecoding algorithm of Reed-Solomon codes is practically applied. However, the time complexity of the stochastic Chase algorithm will increase as the number of test test-vectors increases. In addition, under high-order modulation, the search range of generating test-vectors in the symbol-level stochastic Chase algorithm (SSCA) will increase exponentially by the order of modulation, thus increasing the hardware storage consumption. In order to reduce the time complexity, this paper proposes an early output algorithm, which does not require all test-vectors to achieve successful decoding.Simulation results show that the proposed algorithm can approach the original stochastic decoding performance while reducing the time complexity to nearly $1/\\tau$ ($\\tau$ is the number of test-vectors).In view of the storage issue in the SSCA, this paper reduces the search range by search-radius-determination assisted selection method. Simulation results indicate that the proposed $3\\sigma$-SSCA can reduce the search range of a single symbol of a test-vector from $q$ ($q$ is the order of modulation) to less than ten at the cost of a small performance loss.

HD-NSA: A real-valued negative selection algorithm based on hierarchy division

The negative selection algorithm (NSA) is an important algorithm for generating immune detectors in artificial immune systems. However, the original NSA randomly generates candidate detectors that produce a large number of redundant detectors, and it is difficult to cover the entire antibody space. Moreover, the randomly generated candidate detectors have to be compared with all the self-sets; therefore, the inefficient generation of the detector seriously influences the application of NSA. To overcome these defects, a real-valued NSA based on hierarchy division (HD-NSA) is proposed. First, the feature space is divided into self and non-self subgrids, and the center point of the non-self subgrid is specified as the candidate detector, and the specified candidate detector is compared with the self-antigens located in adjacent subgrids rather than with all the self-sets. Theoretical analysis demonstrated that the HD-NSA can effectively reduce the time complexity of the NSA algorithm. Furthermore, experiments on the Abalone data set show that the detector training time of HD-NSA decreased by 97.9%, 71.2%, 56.9% and 90.1%, respectively, compared with the classical RNSA, V-Detector, GF-RNSA and BIORV-NSA, whereas the detector detection rate increased by 50%, 25.8%, 13.8% and 10.5%, respectively.

The comparison of the effectiveness of the lowest supply lowest cost (LSLC) algorithm and the exponential approach algorithm in transportation problems

Transportation problems are one of the particular forms that often appear in linear programs, one of which is the distribution of goods. A transportation method is needed to determine the optimal result, namely, the minimum cost from source to destination with all demand and supply fulfilled. There are several methods, one of which is the Lowest Supply Lowest Cost Method (LSLC) and the Exponential Approach Method (PE). Both methods are made in a MATLAB program, generating a script that calculates the algorithm's time complexity. Using the function notation, the Big-O Algorithm complexity of the Lowest Supply Lowest Cost method is more efficient than the Exponential Approach Method algorithm. At the same time, the optimal result for the minimum cost between the two methods is obtained by using the Exponential Approach Method.

Optimal dispersion on an anonymous ring in the presence of weak Byzantine robots

The problem of dispersion of mobile robots on a graph asks that n robots initially placed arbitrarily on the nodes of an n-node anonymous graph, autonomously move to reach a final configuration where each node has at most one robot on it. This problem is of significant interest due to its relationship to other fundamental robot coordination problems, such as exploration, scattering, load balancing, relocation of self-driving electric cars to recharge stations, etc. The robots have unique IDs, typically in the range [1,poly(n)] and limited memory, whereas the graph is anonymous, i.e., the nodes do not have identifiers. The objective is to simultaneously minimize two performance metrics: (i) time to achieve dispersion and (ii) memory requirement at each robot. This problem has been relatively well-studied when robots are non-faulty.In this paper, we introduce the notion of Byzantine faults to this problem, i.e., we formalize the problem of dispersion in the presence of up to f Byzantine robots. We then study the problem on a ring while simultaneously optimizing the time complexity of algorithms and the memory requirement per robot. Specifically, we design deterministic algorithms that attempt to match the time lower bound (Ω(n) rounds) and memory lower bound (Ω(log⁡n) bits per robot).Our main result is a deterministic algorithm that is both time and memory optimal, i.e., O(n) rounds and O(log⁡n) bits of memory required per robot, subject to certain constraints. We subsequently provide results that require less assumptions but are either only time or memory optimal but not both. We also provide a primitive, utilized often, that takes robots initially gathered at a node of the ring and disperses them in a time and memory optimal manner without additional assumptions required.

A Novel Hash Chain-Based Data Availability Monitoring Method for Off-site Disaster Recovery Architecture

With the rapid development of mobile Internet, there is a higher demand for the real-time, reliability and availability of information systems and to prevent the possible systemic risks of information systems, various business consistency standards and regulatory guidelines have been published, such as Recovery Time Object (RTO) and Recovery Point Object (RPO). Some of the current related researches focus on the standards, methods, management tools and technical frameworks of business consistency, while others study the data consistency algorithms in the cases of large data, cloud computing and distributed storage. However, few researchers have studied on how to monitor the data consistency and RPO of production-disaster recovery, and what architecture and technology should be applied in the monitoring. Moreover, in some information systems, due to the complex structures and distributions of data, it is difficult for traditional methods to quickly detect and accurately locate the first error data. Besides, due to the separation of production data center (PDC) and disaster recovery data center (DRDC), it is difficult to calculate the data difference and RPO between the two centers. This paper first discusses the architecture of remote distributed DRDCs. The architecture can make the disaster recovery (DR) system always online and the data always readable, and support the real-time monitoring of data availability, consistency as well as other related indicators, in this way to make DRDC out-of-the-box in disasters. Second, inspired by blockchain, this paper proposes a method to realize real-time monitoring of data consistency and RTO by building hash chains for PDC and DRDC. Third, this paper evaluates the hash chain operations from the algorithm time complexity, the data consistency, and the validity of RPO monitoring algorithms and since DR system is actually a kind of distributed system, the proposed approach can also be applied to the data consistency detection and data difference monitoring in other distributed systems.

Volume-Adaptive Big Data Model for Relational Databases

Big data is traditionally associated with distributed systems and this is understandable given that the volume dimension of Big Data appears to be best accommodated by the continuous addition of resources over a distributed network rather than the continuous upgrade of a central storage resource. Based on this implementation context, non- distributed relational database models are considered volume-inefficient and a departure from their usage contemplated by the database community. Distributed systems depend on data partitioning to determine chunks of related data and where in storage they can be accommodated. In existing Database Management Systems (DBMS), data partitioning is automated which in the opinion of this paper does not give the best results since partitioning is an NP-hard problem in terms of algorithmic time complexity. The NP-hardness is shown to be reduced by a partitioning strategy that relies on the discretion of the programmer which is more effective and flexible though requires extra coding effort. NP-hard problems are solved more effectively by a combination of discretion rather than full automation. In this paper, the partitioning process is reviewed and a programmer-based partitioning strategy implemented for an application with a relational DBMS backend. By doing this, the relational DBMS is made adaptive in the volume dimension of big data. The ACID properties (atomicity, consistency, isolation, and durability) of the relational database model which constitutes a major attraction especially for applications that process transactions is thus harnessed. On a more general note, the results of this research suggest that databases can be made adaptive in the areas of their weaknesses as a one-size-fits- all database management system may no longer be feasible.

A multi-objective scheduling method for operational coordination time using improved triangular fuzzy number representation.

In modern warfare, the comprehensiveness of combat domain and the complexity of tasks pose great challenges to operational coordination.To address this challenge, we use the improved triangular fuzzy number to express the combat mission time, first present a new multi-objective operational cooperative time scheduling model that takes the fluctuation of combat coordinative time and the time flexibility between each task into account. The resulting model is essentially a large-scale multi-objective combinatorial optimization problem, intractably complicated to solve optimally. We next propose multi-objective improved Bat algorithm based on angle decomposition (MOIBA/AD) to quickly identify high-quality solutions to the model. Our proposed algorithm improves the decomposition strategy by replacing the planar space with the angle space, which helps greatly reduce the difficulty of processing evolutionary individuals and hence the time complexity of the multi-objective evolutionary algorithm based on decomposition (MOEA/D). Moreover, the population replacement strategy is enhanced utilizing the improved bat algorithm, which helps evolutionary individuals avoid getting trapped in local optima. Computational experiments on multi-objective operational cooperative time scheduling (MOOCTS) problems of different scales demonstrate the superiority of our proposed method over four state-of-the-art multi-objective evolutionary algorithms (MOEAs), including multi-objective bat Algorithm (MOBA), MOEA/D, non-dominated sorting genetic algorithm version II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO). Our proposed method performs better in terms of four performance criteria, producing solutions of higher quality while keeping a better distribution of the Pareto solution set.

ECC Data Hide – Using Steganoraphy and Deep Neural Network

In the field of computer networks, cryptography and steganography are the well-known features for best security purpose. The main idea is to transmit the data securely. So, providing acceptable level of security is essential for data transmission. Also it should reduce the time complexity of the security algorithm. Here we have employed the “Elliptic Curve Cryptography” scheme to encrypt the data and image. A “Least Significant Bit” steganography algorithm is used to insert the encrypted data to be hidden inside the image in order to send the data securely. The encrypted data from the image is then decrypted by the decryption algorithm. Finally the hidden data is taken from the decrypted data. Then the image is compressed before sending through the internet. MATLAB is utilized to mimic outcomes which show that it has great inserting limit and security.

A Secure Live Signature Verification with Aho-Corasick Histogram Algorithm for Mobile Smart Pad

There is a long history of using handwritten signatures to verify or authenticate a “signer” of the signed document. With the development of Internet technology, many tasks can be accomplished through the document management system, such as the applications of digital contracts or important documents, and more secure signature verification is demanded. Thus, the live handwriting signatures are attracting more interest for biological human identification. In this paper, we propose a handwriting signature verification algorithm by using four live waveform elements as the verification features. A new Aho-Corasick Histogram mechanism is proposed to perform this live signature verification. The benefit of the ACH algorithm is mainly its ability to convert time-series waveforms into time-series short patterns and then perform a statistical counting on the AC machine to measure the similarity. Since AC is a linearly time complexity algorithm, our ACH method can own a deterministic processing time. According to our experiment result, the proposed algorithm has satisfying performance in terms of speed and accuracy with an average of 91% accuracy.

Peak density algorithm based on KD-tree optimization

In view of the high time complexity of the density peak algorithm, it is necessary to manually confirm the clustering center according to the decision graph. A density peaking algorithm KT-DPC based on kd-tree optimization is proposed. The algorithm defines the local density p through K-nearest neighbor and uses kd-tree to accelerate the local density p and distance 8. In addition, in the confirmation stage of clustering center, a clustering center confirmation strategy (C2BD, clustering center confirmation based on difference) is proposed. The difference between adjacent y is calculated by arranging y=p*S in ascending order, and the boundary between clustering center and non-clustering center is found according to the change of difference. This method automatically confirms the clustering center of the algorithm, avoiding the problems of strong subjectivity and insufficient clustering accuracy caused by manually confirming the clustering center. Experiments on multiple UCI public data sets show that the running time and clustering accuracy of the KT-DPC algorithm under low-dimensional data are better than traditional DPC algorithms, KNN-DPC and other improved algorithms.

A partially hidden policy CP-ABE scheme against attribute values guessing attacks with online privacy-protective decryption testing in IoT assisted cloud computing

In recent years, to address the security defect that the explicit attribute values in access policies may reveal the privacy, a new variant of ciphertext-policy attribute-based encryption(CP-ABE)——hidden policy CP-ABE (HP-CP-ABE) is proposed in some recent works. But there are two tremendous flaws in most existing HP-CP-ABE schemes. The one issue is that an attacker can launch the attribute values guessing attacks (AVGA) to detect the attribute values in access policies of many HP-CP-ABE schemes. And another issue is that, if the HP-CP-ABE schemes are using the “Linear Secret Sharing Schemes (LSSS)” as their access structures, as the rows of the LSSS matrix grows, the time complexity of the decryption testing algorithm will boost rapidly which will greatly aggravate the computing burden of the user. So in this paper, we propose a partially HP-CP-ABE (PHP-CP-ABE) scheme which can perfectly withstand the attribute values guessing attacks (AVGA). As our access structure is using the LSSS, to alleviate the computing burden of the user, we design a online privacy-protective decryption testing algorithm for the users to privately and securely outsource the decryption testing phase to the cloud server. Our online testing algorithm is privacy-protective which means during running the privacy-protective decryption testing algorithm, the cloud server has no chance to know anything about the attribute values in the access policy and the attribute values of the user. This will prevent the privacy from leaking out to the third party cloud server. Then we rigorously prove that our scheme is selectively indistinguishable secure under chosen plaintext attacks (IND-CPA). Next, by reduction to the computational q-PBDHE assumption which is firstly proposed in our paper, we prove that our HP-CP-ABE scheme is indistinguishable secure under the attribute values guessing attacks (IND-AVGA). Finally through the comparison with the state-of-art HP-CP-ABE schemes from the perspective of functionality and efficiency, it is easily to observe that our scheme has high-security and high-efficiency. In appendix, we give a straightaway analysis to some relevant works to point out the security vulnerabilities in their schemes.

Fast graph clustering in large-scale systems based on spectral coarsening

Complex networks depict the individual relationship in a population, which can help to deeply mine the characteristics of complex networks and predict the potential collaboration between individuals by analyzing their interaction within different groups or clusters. However, the existing algorithms are with high complexity, which cost much computational time. In this paper, an efficient graph clustering algorithm based on spectral coarsening is proposed, to deal with the large time complexity of the traditional spectral algorithm. We first find the subset most possibly belonged to the same cluster in the original network, and merge them into a single node. The scale of the network will decrease with the network being coarsened. Then, the spectral clustering algorithm is performed on the coarsened network with the maintained advantages and the improved time efficiency. Finally, the experimental results on the multiple datasets demonstrate that the proposed algorithm, compared with the current state-of-the-art methods, has superior performance.

The Theoretical and Experimental Analysis of the Maximal Information Coefficient Approximate Algorithm

Abstract In the era of big data, correlation analysis is significant because it can quickly detect the correlation between factors. And then, it has been received much attention. Due to the good properties of generality and equitability of the maximal information coefficient (MIC), MIC is a hotspot in the research of correlation analysis. However, if the original approximate algorithm of MIC is directly applied into mining correlations in big data, the computation time is very long. Then the theoretical time complexity of the original approximate algorithm is analyzed in depth and the time complexity is n 2.4 when parameters are default. And the experiments show that the large number of candidate partitions of random relationships results in long computation time. The analysis is a good preparation for the next step work of designing new fast algorithms.

Greening Duplication-Based Dependent-Tasks Scheduling on Heterogeneous Large-Scale Computing Platforms

Low-cost and high-performance execution of nowadays computing-intensive applications will not be possible without large-scale heterogeneous computing platforms. The huge computing power of such platforms raises the problem of the electrical energy consumed by such platforms. One of the key issues to achieve high-performance in such platforms is task-scheduling. Among the heuristics-based compile-time dependent-task scheduling heuristics, duplication-based list scheduling heuristics give the earliest finish time of the application tasks. Unfortunately, due to the additional computing cost required by duplication, these heuristics consume more computing power that leads to more electrical energy consumption. Energy-efficiency and green-computing turn the attention to the need for new generations of energy-aware task-scheduling algorithms. This paper presents a duplication reduction mechanism that can be applied to any schedule produced by a duplication-based scheduling algorithm. The aims of the proposed mechanism are to keep the same finish time of the scheduled application tasks, to keep the lower-bound time-complexity of the heuristics-based dependent task scheduling algorithms, and to significantly reduce the energy consumed by task-duplication. The mechanism is called Green. Green was applied to four of the most-recent and well-known duplication-based list-scheduling algorithms. The experimental results based on computer simulation utilizing C# language for large sets of both randomly generated and three real-world applications graphs show that Green can significantly reduce the energy consumed by each algorithm.

An improved fast shapelet selection algorithm and its application to pervasive EEG

With the rapid development of pervasive devices, a great deal of time series are generated by various sensors, and many time series classification (TSC) algorithms have been proposed to deal with these data. Among them, shapelet-based algorithms have attracted great attention due to its high accuracy and strong interpretability. However, time complexity of shapelet-based algorithms is high. In this paper, we propose an improved Fast Shapelet Selection algorithm based on Clustering (FSSoC), which greatly reduces the time of shapelet selection. Firstly, time series are clustered into several groups with improved k-means, and then some time series are sampled from each cluster with a strategy based on Euclidean Distance sorting. Secondly, Important Data Points (IDPs) of the sampled time series are identified and only the subsequences between two nonadjacent IDPs are added to shapelet candidates. Therefore, the number of shapelet candidates is greatly reduced, which leads to a obviously reduction in time consumption. Thirdly, FSSoC is applied to shapelet transformation algorithm to test classification accuracy and running time, the experiments demonstrate that FSSoC is obviously faster than existing shapelet selection algorithms while keeping a high accuracy. At last, a case study on EEG time series is presented, which verifies the feasibility of FSSoC application to automatically discover representative EEG features.

Analysis of Energy Control Application Scheme Relying on Niagara Technology

In the current energy management, the energy control management system closely integrates information and physical models and proposes new analysis methods for the system, which can make the system more sensitive to changes in the external environment and analyze the power grid more humanely control. The energy control management system is similar to the traditional control method and can solve the control of the target system under the condition of satisfying the system constraints. Under this framework, the constraints will focus on computing and network performance. Constraints mainly refer to the time complexity of the system algorithm and the information flow of the network, which are reflected in the organic integration of physics and information systems in the power grid and the information flow is restricted by controlling the energy transferred by the energy flow. For example, the transmission time of the system information transmission process can be optimized. To effectively control the system, specifically to solve the excessive dependence of NCS on the communication network, lies in how to solve the local control to coordinate the overall situation, so that the system reaches the optimum, this paper will study it.