Lazy Algorithm Research Articles

HMT: A Hardware-centric Hybrid Bonsai Merkle Tree Algorithm for High-performance Authentication

The Bonsai Merkle tree (BMT) is a widely used tree structure for authentication of metadata such as encryption counters in a secure computing system. Common BMT algorithms were designed for traditional Von Neumann architectures with a software-centric implementation in mind and as such, they are predominantly recursive and sequential in nature. However, the modern heterogeneous computing platforms employing Field-Programmable Gate Array (FPGA) devices require concurrency-focused algorithms to fully utilize the versatility and parallel nature of such systems. The recursive nature of traditional BMT algorithms makes them challenging to implement in such hardware-based setups. Our goal for this work is to introduce HMT, a hardware-friendly BMT algorithm that enables the verification and update processes to function independently and provides the benefits of relaxed update while being comparable to the eager update in terms of update complexity. The methodology of HMT contributes both novel algorithmic revisions and innovative hardware techniques to implementing BMT. We mathematically demonstrate the challenges of potentially unbounded recursions in relaxed BMT updates. To solve this problem, we use a partitioned BMT caching scheme that allocates a separate write-back cache for each BMT level—thus allowing for low and fixed upper bounds for dirty evictions compared to the traditional BMT caches. Then we introduce the aforementioned hybrid BMT algorithm that is hardware-targeted, parallel, and relaxes the update depending on BMT cache hit but makes the update conditions more flexible compared to lazy update to save additional write-backs. Deploying this new algorithm, we have designed a new BMT controller with a dataflow architecture including speculative buffers and parallel write-back engines to facilitate performance-enhancing mechanisms (like multiple concurrent authentication and independent updates) that were not possible with the conventional lazy algorithm. Our empirical performance measurements on a Xilinx U200 accelerator FPGA have demonstrated that HMT can achieve up to 7× improvement in bandwidth and 4.5× reduction in latency over lazy-update BMT baseline and up to 14% faster execution in standard benchmarks compared to a state-of-the-art, eager-update BMT solution.

A Novel Classification Performance Approach for Remotely Sensed Multispectral Image Data by Using Data Mining Techniques

In order to combine multimedia imagery and multispectral remote sensing data to analyze information, preprocessing becomes a necessary part of it. As one of the most important branches in the field of data analysis, it is widely used in many fields such as classification, regression, missing value filling, and machine learning. As a lazy algorithm, this method requires no prior statistical knowledge and no additional data to train description rules and is easy to implement. This study compares classification algorithm performances of data mining clustering algorithms for remotely sensed multispectral image data using WEKA data mining software. Clustering algorithm selection is very important for data mining classification method-based clustering. The class attribute for remotely sensed multispectral image data is obtained from six different clustering algorithms for classification. Classification algorithm performances computed depending on the data labeling of six different clustering algorithms in terms of correctly classified instances and kappa statistics for seven different classification algorithms. A strategy is developed for selecting the best unsupervised clustering algorithm, among different clustering algorithms, giving the highest supervised classification accuracy in terms of correctly classified instances and kappa statistics for semi supervised classification of remotely-sensed multispectral image data. The performances of seven semi-supervised classification methods assessed depending on six different unsupervised clustering algorithms for supervised classification of remotely sensed multispectral image data. This study determines data free clustering algorithms for classification.

Optimizing resources allocation in a heterogeneous cloud radio access network using machine learning

AbstractIn this article, we propose an iterative algorithm based on lazy learning. The proposed scheme considers two consecutive algorithms for optimal resource allocation in heterogeneous cloud radio access networks (H‐CRAN). In the conventional approach, resources allocation is usually presented as a mathematical optimization problem and solved online using a real‐time scenario. Due to the non‐convex nature of resource allocation problems, finding optimal solutions in real‐time is a difficult challenge and has high computational complexity. To overcome these challenges in this paper, the problem is first formulated as a mixed‐integer nonlinear programming (MINLP) to optimize the system capacity and assign the underlay users to the subchannels, which is NP‐hard. To obtain the solution, in the first algorithm, the proposed problem is first reformulated as a convex problem using a matching theory and coalition formation method. Then, in the second algorithm, the lazy learning method is employed to find the optimal solution to the resources allocation problem. The lazy algorithm uses the output results of the first algorithm as training data, which are known as historical data. Similarities between current scenario data and historical scenario data, as well as the solution of resources allocation in historical scenarios, can be exploited to improve the resource allocation of the current scenario. Simulation results confirm that compared with the conventional approach, the suggested technique can significantly improve the system performance in terms of the total users assigned, overall throughput, the pace of resource allocation, and user fairness.

Multispectral Remote Sensing Data Analysis Based on KNNLC Algorithm and Multimedia Image

In order to combine multimedia imagery and multispectral remote sensing data to analyze information, preprocessing becomes a necessary part of it. It is found that the KNN algorithm is one of the classic algorithms of data mining. As one of the most important branches in the field of data analysis, it is widely used in many fields such as classification, regression, missing value filling, and machine learning. As a lazy algorithm, this method requires no prior statistical knowledge and no additional data to train description rules and is easy to implement. However, the algorithm inevitably has many problems, such as how to determine the appropriate K value, the unsatisfactory effect of data processing for some special distributions, and the unacceptable computational complexity of high-dimensional data. In order to solve these shortcomings, the researchers proposed the KNNLC algorithm. Then, taking the classification experiment as an example, through the comparison of the experimental results on different data sets, it is proved that the average level of the classification performance of the KNNLC algorithm is better than the classic KNN classification algorithm. The KNNLC algorithm shows better performance in most cases, with an accuracy rate of 2 to 5 percentage points higher. An improved algorithm is proposed for the nearest neighbor selection strategy of the traditional KNN algorithm. First, in theory, combined with the theory of sparse coding and locally constrained linear coding, the classical KNN algorithm is improved, and the KNNLC algorithm is proposed. The comparison of the experimental results on the data set proves that the average level of the KNNLC algorithm is better than the classical KNN classification algorithm in terms of classification performance.

Three-Dimension Measurement of Mechanical Parts Based on Structure from Motion(SfM) Algorithm

Background: As an important branch of computer vision, visual measurement is a fast developing cutting-edge technology, which has been widely used in the manufacturing field. In recent years, the visual measurement of feature size of probes through small IC probes has aroused wide concern. Objective: This study aims to take small shaft parts as the research object in order to provide a full set of novel and reliable technical means for the three-dimension measurement of mechanical parts. Methods: Firstly, the trinocular vision measurement system based on the curved cantilever mechanism was designed and constructed. Secondly, the measurement system was used to collect the part images from different angles, and the images derived from the four categories of segmentation algorithms such as threshold-based, region-based segmentation algorithm were compared and analyzed. Lazy Snapping image segmentation algorithm was used to extract the foreground parts of each image. After comparing and analyzing SfM-based algorithm and Visual Hull-based algorithm, the SfM-based algorithm was adopted to reconstruct the 3D morphology of the parts. The measurement of the relevant dimensions was performed. Results: The results shows that Lazy Snapping's human-computer interaction brush function improves the accuracy and stability of image segmentation of different algorithms, such as threshold value method, regional method, Grab Cut, and Dense Cut. The SfM-based 3D reconstruction algorithm is of high robustness and fast speed. Conclusion: This study provides an effective method for measuring small mechanical parts, which will shorten the measurement cycle, improve the measurement speed, and reduce the measurement cost.

LAZY R-tree: The R-tree with lazy splitting algorithm

The spatial index is a data structure formed according to the position and shape of the spatial object or the relationship between the spatial objects according to certain rules, and the spatial data is managed by an effective spatial data structure. The quality of a spatial index directly affects the performance of spatial queries. The R-tree index structure is a highly efficient spatial index. According to the R-tree query rule, when performing spatial query, most data that is not related to the query condition can be filtered out, and finally, a few leaf nodes can be accessed to query the data satisfying the condition. Its query performance is affected by factors such as non-leaf node overlap and node space utilisation. This article proposes a lazy splitting method to improve the R-tree construction process. The scheme works as follows: (1) When a node overflows, it creates an overflow node for that node and all overflow nodes are saved in a hash table. (2) If the node continues to insert data, the data are added to its overflow node. (3) When an overflow node is saturated, the node and its overflow node are split into two saturated nodes. We use both simulated and actual data to perform experiments. The experimental results show that an R-tree constructed by the lazy algorithm is superior to an R-tree constructed using the original R-tree PM algorithm or the corner-based splitting (CBS) algorithm based on the number of splits created, the node space used and the efficiency of region queries and k-nearest neighbour (kNN) queries.

Multi-label active learning based on submodular functions

Abstract In the data collection task, it is more expensive to annotate the instance in multi-label learning problem, since each instance is associated with multiple labels. Therefore it is more important to adopt active learning method in multi-label learning to reduce the labeling cost. Recent researches indicate submodular function optimization works well on subset selection problem and provides theoretical performance guarantees while simultaneously retaining extremely fast optimization. In this paper, we propose a query strategy by constructing a submodular function for the selected instance-label pairs, which can measure and combine the informativeness and representativeness. Thus the active learning problem can be formulated as a submodular function maximization problem, which can be solved efficiently and effectively by a simple greedy lazy algorithm. Experimental results show that the proposed approach outperforms several state-of-the-art multi-label active learning methods.

Mining Location Influence for Location Promotion in Location-Based Social Networks

With the extensive application and in-depth study of location-based social networks (LBSNs), more and more businesses are utilizing the new social platform to promote their products and services. In this paper, we explore an important technique that can help business to promote their locations by distributing items with business information or the leaflets of the business at some locations. In order to maximize the benefit of location promotion, this paper formally defines it as a location influence scope maximization problem on an location-based social network, i.e., given a target business and an LBSN, the problem is to find a set of K locations, where the business distributes small promotional gifts or leaflets on, such that people who visited these locations can successfully propagate the advertisement information to most other locations for attracting most people to visit the target location. The existing related researches neglect location influences studies on location promotion (outdoor marketing). And it is more challenging to derive the influence between locations and the spatial influence scope of each location, since location influence depends on users mobility and the target location. In this paper, we provide a new approach (called as LoP) to exploit influence between locations for location promotion. First, LoP incrementally mines correlations between locations to instruct the location-location graph (LLG). Then, LoP predicts each location's influence on others and its influence scope based on betweenness centrality. Finally, we provide the lazy algorithm to efficiently find a top-K set of influential locations for the business. Extensive experiments based on two real LBSN datasets have verified the superior effectiveness of our proposed method in term of location promotion.

An efficient instance selection algorithm for k nearest neighbor regression

The k-Nearest Neighbor algorithm(kNN) is an algorithm that is very simple to understand for classification or regression. It is also a lazy algorithm that does not use the training data points to do any generalization, in other words, it keeps all the training data during the testing phase. Thus, the population size becomes a major concern for kNN, since large population size may result in slow execution speed and large memory requirements. To solve this problem, many efforts have been devoted, but mainly focused on kNN classification. And now we propose an algorithm to decrease the size of the training set for kNN regression(DISKR). In this algorithm, we firstly remove the outlier instances that impact the performance of regressor, and then sorts the left instances by the difference on output among instances and their nearest neighbors. Finally, the left instances with little contribution measured by the training error are successively deleted following the rule. The proposed algorithm is compared with five state-of-the-art algorithms on 19 datasets, and experiment results show it could get the similar prediction ability but have the lowest instance storage ratio.

Green and friendly media transmission algorithms for wireless body sensor networks

Video transmission is considered as a quite significant step towards health monitoring of the emergency patients during any critical incident. However, the energy hungry video transmission and slow progress in battery technologies have become a major and serious problem for the evolution of video technology in WBSNs. Therefore, the need arose to conduct research on sustainable, “Green”, i.e., energy-efficient and “Friendly”, i.e., battery-friendly technologies to cater the need of upcoming mobile and portable devices. The main challenge addressed in this research is how to increase the battery lifetime during on-demand Variable Bit Rate (VBR) video transmission from medical video server to base station in WBSNs. In order to overcome this problem, sustainable, Green and Friendly frame transmission algorithms are enunciated i.e. Lazy Algorithm (LA) and Battery-friendly Smoothing Algorithm (BSA) with analytical battery model. The proposed algorithms minimize transmission energy consumption, battery charge consumption and high current profile. These algorithms also prolongs the battery lifetime of those sensor nodes during video transmission. Experimental results demonstrates that BSA outperforms LA to minimize battery drain by improving its lifetime up to 19.8 %. However, the LA performs better than BSA in context of transmission energy saving up to 49.49 %. Furthermore, a video transmission framework of Remote Medical Education System (RMES) for elderly persons and infants is proposed to provide viable and sustainable battery solutions to serve the community.

A Unifying Formalism for Shortest Path Problems with Expensive Edge Evaluations via Lazy Best-First Search over Paths with Edge Selectors

While the shortest path problem has myriad applications, the computational efficiency of suitable algorithms depends intimately on the underlying problem domain. In this paper, we focus on domains where evaluating the edge weight function dominates algorithm running time. Inspired by approaches in robotic motion planning, we define and investigate the Lazy Shortest Path class of algorithms which is differentiated by the choice of an edge selector function. We show that several algorithms in the literature are equivalent to this lazy algorithm for appropriate choice of this selector. Further, we propose various novel selectors inspired by sampling and statistical mechanics, and find that these selectors outperform existing algorithms on a set of example problems.

Effective lazy learning algorithm based on a data gravitation model for multi-label learning

In the last decade, an increasing number of real-world problems surrounding multi-label data have appeared, and multi-label learning has become an important area of research. The data gravitation model is an approach that applies the principles of the universal law of gravitation to resolve machine learning problems. One advantage of the data gravitation model, compared with other techniques, is that it is based on simple principles with high performance levels. This paper presents a multi-label lazy algorithm based on a data gravitation model, named MLDGC. MLDGC directly handles multi-label data, and considers each instance as an atomic data particle. The proposed multi-label lazy algorithm was evaluated and compared to several state-of-the-art multi-label lazy methods on 34 datasets. The results showed that our proposal outperformed state-of-the-art lazy methods. The experimental results were validated using non-parametric statistical tests, confirming the effectiveness of this data gravitation model for multi-label lazy learning.

Lazy Multi-label Learning Algorithms Based on Mutuality Strategies

Lazy multi-label learning algorithms have become an important research topic within the multi-label community. These algorithms usually consider the set of standard k-Nearest Neighbors of a new instance to predict its labels (multi-label). The prediction is made by following a voting criteria within the multi-labels of the set of k-Nearest Neighbors of the new instance. This work proposes the use of two alternative strategies to identify the set of these examples: the Mutual and Not Mutual Nearest Neighbors rules, which have already been used by lazy single-learning algorithms. In this work, we use these strategies to extend the lazy multi-label algorithm BRkNN. An experimental evaluation carried out to compare both mutuality strategies with the original BRkNN algorithm and the well-known MLkNN lazy algorithm on 15 benchmark datasets showed that MLkNN presented the best predictive performance for the Hamming-Loss evaluation measure, although it was significantly outperformed by the mutuality strategies when F-Measure is considered. The best results of the lazy algorithms were also compared with the results obtained by the Binary Relevance approach using three different base learning algorithms.

Rubah

This paper presents Rubah, the first dynamic software updating system for Java that: is portable , implemented via libraries and bytecode rewriting on top of a standard JVM; is efficient , imposing essentially no overhead on normal, steady-state execution; is flexible , allowing nearly arbitrary changes to classes between updates; and is non-disruptive , employing either a novel eager algorithm that transforms the program state with multiple threads, or a novel lazy algorithm that transforms objects as they are demanded, post-update. Requiring little programmer effort, Rubah has been used to dynamically update five long-running applications: the H2 database, the Voldemort key-value store, the Jake2 implementation of the Quake 2 shooter game, the CrossFTP server, and the JavaEmailServer.

Lazy Suffix Array: The Data Structure for Online Construction and Pattern Searching

In this paper, an idea for improvement of suffix array construction using lazy evaluation is presented. Evaluation of the suffix array is based on the searching queries; only the necessary part of the suffix array is built when unevaluated part of the suffix array is referred during the searching process. This is less time consuming than constructing complete suffix array. We propose lazy evaluation of Schurmann-Stoye algorithm. Experimental results show that lazy Schurmann-Stoye algorithm runs faster than Maniscalco, which is well-recognized as the fastest suffix sorting algorithm, under the constraint of small LCP (longest common prefix) and a limited number of searching queries.

Real-time control of a virtual hand

A myoelectric control system for prostheses was developed and evaluated on six healthy subjects. These were able to control a computer-animated hand in real-time with a 20 Hz update rate. A data glove, equipped with joint angle sensors, was used to train the system and to evaluate the continuous predictions of joint angles. A linear envelop filter was used for EMG signal pre-processing and the recognition of muscle patterns was carried out with local approximation using the lazy algorithm. Furthermore, an on-line learning was used to provide feedback to the subjects. The results show that the subjects increased their performance during the experiment and all subjects performed eight or more movements with 100% accuracy in their last recording session. The final median delay for the predicted hand joint positions, compared with the recorded, was in the range of 50 to 100 ms. Off-line evaluation has earlier been done on amputees while using a data glove on the contralateral hand. The real-time control system outlined in this paper offers an effective myoelectric prosthesis control that is suitable for a miniaturized low cost implementation.This paper is a part of a ongoing development and refinement of hand prosthesis carried out within the Artificial Hand Project. (Less)

Management of fault tolerance information for coordinated checkpointing protocol without sympathetic rollbacks

This paper presents the condition for an extended global recovery line for coordinated checkpointing protocol and a new garbage collection protocol on checkpoints and message logs in order to avoid the sympathetic rollback caused by lost messages. Since previous works assumed the communication channel does not lose the in-transit messages, those works on garbage collection in coordinated checkpointing protocols delete all the checkpoints except for the last checkpoints on each process. But coordinated checkpointing protocol based on the communication protocol with reliability (TCP) causes in-transit messages to be lost when a failure occurs, and lost messages lead to sympathetic rollbacks of faulty processes or related processes. Thus there is a need for management methods of fault tolerance information that can store and delete the coordinated checkpoint and light message log to avoid sympathetic rollback. In this paper, we define the extended global recovery line conditions for garbage collection of checkpoints and message logs for lost messages, and present the new garbage collection algorithm within the extended global recovery line. The proposed algorithm uses piggybacked process information on each message so that the additional messages for garbage collection and extended global recovery line are not needed. Since it relies on the piggybacked checkpoint information in communication message, the proposed garbage collection algorithm is called 'the lazy garbage collection algorithm'.

Sequential consistency and the lazy caching algorithm

I introduce and discuss sequential consistency, a relaxed memory model, and define what it means for a protocol to implement sequential consistency. Then, I introduce the lazy caching protocol of Afek, Brown and Merrit [ABM93] and formalize it as a labeled transition system.