scholarly journals Competitive Caching with Machine Learned Advice

2021 ◽  
Vol 68 (4) ◽  
pp. 1-25
Author(s):  
Thodoris Lykouris ◽  
Sergei Vassilvitskii
Keyword(s):  
Online Algorithms ◽  
Empirical Evaluation ◽  
Optimal Solution ◽  
Poor Performance ◽  
Machine Learning Algorithms ◽  
Average Error ◽  
Generalization Error ◽  
Worst Case ◽  
Future Events ◽  
Real World Datasets

Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon unconditional worst-case lower bounds when the predictor has low error. Our approach treats the predictor as a complete black box and is not dependent on its inner workings or the exact distribution of its errors. We apply this framework to the traditional caching problem—creating an eviction strategy for a cache of size k . We demonstrate that naively following the oracle’s recommendations may lead to very poor performance, even when the average error is quite low. Instead, we show how to modify the Marker algorithm to take into account the predictions and prove that this combined approach achieves a competitive ratio that both (i) decreases as the predictor’s error decreases and (ii) is always capped by O (log k ), which can be achieved without any assistance from the predictor. We complement our results with an empirical evaluation of our algorithm on real-world datasets and show that it performs well empirically even when using simple off-the-shelf predictions.

Heterogeneous Influence Maximization Through Community Detection in Social Networks

2021 ◽  
Vol 12 (4) ◽  
pp. 118-131
Author(s):  
Jaya Krishna Raguru ◽  
Devi Prasad Sharma
Keyword(s):  
Community Detection ◽  
Greedy Algorithms ◽  
Computational Cost ◽  
Optimal Solution ◽  
Influence Maximization ◽  
Centrality Measures ◽  
Influence Spread ◽  
Real World Datasets ◽  
Initial Seed ◽  
High Computational Cost

The problem of identifying a seed set composed of K nodes that increase influence spread over a social network is known as influence maximization (IM). Past works showed this problem to be NP-hard and an optimal solution to this problem using greedy algorithms achieved only 63% of spread. However, this approach is expensive and suffered from performance issues like high computational cost. Furthermore, in a network with communities, IM spread is not always certain. In this paper, heterogeneous influence maximization through community detection (HIMCD) algorithm is proposed. This approach addresses initial seed nodes selection in communities using various centrality measures, and these seed nodes act as sources for influence spread. A parallel influence maximization is applied with the aid of seed node set contained in each group. In this approach, graph is partitioned and IM computations are done in a distributed manner. Extensive experiments with two real-world datasets reveals that HCDIM achieves substantial performance improvement over state-of-the-art techniques.

scholarly journals A personalized FEM model for reproducible measurement of anti-inflammatory drugs in transdermal administration to knee

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Pasquale Arpaia ◽  
Federica Crauso ◽  
Mirco Frosolone ◽  
Massimo Mariconda ◽  
Simone Minucci ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Reconstruction Error ◽  
Average Error ◽  
Element Analysis ◽  
Transdermal Administration ◽  
Worst Case ◽  
Human Knee ◽  
Inflammatory Drugs ◽  
Anti Inflammatory

AbstractA personalized model of the human knee for enhancing the inter-individual reproducibility of a measurement method for monitoring Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) after transdermal delivery is proposed. The model is based on the solution of Maxwell Equations in the electric-quasi-stationary limit via Finite Element Analysis. The dimensions of the custom geometry are estimated on the basis of knee circumference at the patella, body mass index, and sex of each individual. An optimization algorithm allows to find out the electrical parameters of each subject by experimental impedance spectroscopy data. Muscular tissues were characterized anisotropically, by extracting Cole–Cole equation parameters from experimental data acquired with twofold excitation, both transversal and parallel to tissue fibers. A sensitivity and optimization analysis aiming at reducing computational burden in model customization achieved a worst-case reconstruction error lower than 5%. The personalized knee model and the optimization algorithm were validated in vivo by an experimental campaign on thirty volunteers, 67% healthy and 33% affected by knee osteoarthritis (Kellgren–Lawrence grade ranging in [1,4]), with an average error of 3%.

scholarly journals Chronic Kidney Disease Prediction using Machine Learning Models

2019 ◽  
Vol 9 (1) ◽  
pp. 6364-6367
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Failure ◽  
Bone Diseases ◽  
Machine Learning Algorithms ◽  
Knowledge Generation ◽  
Good Prediction ◽  
Worst Case ◽  
Wide Range

The field of biosciences have advanced to a larger extent and have generated large amounts of information from Electronic Health Records. This have given rise to the acute need of knowledge generation from this enormous amount of data. Data mining methods and machine learning play a major role in this aspect of biosciences. Chronic Kidney Disease(CKD) is a condition in which the kidneys are damaged and cannot filter blood as they always do. A family history of kidney diseases or failure, high blood pressure, type 2 diabetes may lead to CKD. This is a lasting damage to the kidney and chances of getting worser by time is high. The very common complications that results due to a kidney failure are heart diseases, anemia, bone diseases, high potasium and calcium. The worst case situation leads to complete kidney failure and necessitates kidney transplant to live. An early detection of CKD can improve the quality of life to a greater extent. This calls for good prediction algorithm to predict CKD at an earlier stage . Literature shows a wide range of machine learning algorithms employed for the prediction of CKD. This paper uses data preprocessing,data transformation and various classifiers to predict CKD and also proposes best Prediction framework for CKD. The results of the framework show promising results of better prediction at an early stage of CKD

scholarly journals Using Global Terrorism Database (GTD) and Machine Learning Algorithms to Predict Terrorism and Threat

2019 ◽  
Vol 9 (1) ◽  
pp. 5995-6000 ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Intelligent System ◽  
Machine Learning Algorithms ◽  
Internet Technology ◽  
Social Media Platforms ◽  
Future Events ◽  
Global Terrorism ◽  
Terrorist Event ◽  
Global Terrorism Database

It is evident that there has been enormous growth in terrorist attacks in recent years. The idea of online terrorism has also been growing its roots in the internet world. These types of activities have been growing along with the growth in internet technology. These types of events include social media threats such as hate speeches and comments provoking terror on social media platforms such as twitter, Facebook, etc. These activities must be prevented before it makes an impact. In this paper, we will make various classifiers that will group and predict various terrorism activities using k-NN algorithm and random forest algorithm. The purpose of this project is to use Global Terrorism Database as a dataset to detect terrorism. We will be using GTD which stands for Global Terrorism Database which is a publicly available database which contains information on terrorist event far and wide from 1970 through 2017 to train a machine learning-based intelligent system to predict any future events that could bring threat to the society.

scholarly journals A Data-Driven Model to Identify Fatigue Level Based on the Motion Data from a Smartphone

2019 ◽  
Author(s):  
Swapnali Karvekar ◽  
Masoud Abdollahi ◽  
Ehsan Rashedi
Keyword(s):  
Perceived Exertion ◽  
Poor Performance ◽  
Machine Learning Algorithms ◽  
Motion Sensors ◽  
Motion Data ◽  
Work Related ◽  
Proactive Approach ◽  
Fatiguing Exercise ◽  
Four Levels ◽  
Work Related Musculoskeletal Disorder

AbstractThe fatigue due to repetitive and physically challenging jobs may result in workers’ poor performance and Work-related Musculoskeletal Disorder (WMSD). Thus, it is imperative to frequently monitor fatigue and take necessary recovery actions. Our purpose was to develop a methodology to objectively classify subjects’ fatigue level in the workplace utilizing the motion sensors embedded in the smartphones. An experiment consisting of twenty-four participants (12 M, 12 F) with a smartphone attached to their right shank was conducted using a fatiguing exercise (squatting), targeted mainly the lower extremity musculature. After each set of an exercise (2-min squatting), participants were asked about their ratings of perceived exertion (RPE), then a reference gait data were collected during a straight walk of 20-32 steps. This process was continued until they reported strong fatigue (≥17). Using the RPE to label the gait data, we have developed machine learning algorithms (i.e., binary and multi-class SVM models) to classify the individuals’ gait into two (no-vs. strong-fatigue) and four levels (no-, low-, medium-, and strong-fatigue). The models reached the accuracies of 91% and 61% for two and four-level classification, respectively. The outcomes of this study may facilitate the implementation of a proactive approach in continuous monitoring of operators’ fatigue level, which may subsequently increase the workers’ performance and reduce the risk of WMSDs.

Individual Prediction Reliability Estimates in Classification and Regression

2012 ◽  
pp. 35-56
Author(s):  
Darko Pevec ◽  
Zoran Bosnic ◽  
Igor Kononenko
Keyword(s):  
Empirical Evaluation ◽  
Cancer Recurrence ◽  
Machine Learning Algorithms ◽  
Reliability Estimation ◽  
Local Error ◽  
Research Areas ◽  
Reliability Estimates ◽  
Benchmark Datasets ◽  
Classification And Regression ◽  
Prediction Reliability

Current machine learning algorithms perform well in many problem domains, but in risk-sensitive decision making – for example, in medicine and finance – experts do not rely on common evaluation methods that provide overall assessments of models because such techniques do not provide any information about single predictions. This chapter summarizes the research areas that have motivated the development of various approaches to individual prediction reliability. Based on these motivations, the authors describe six approaches to reliability estimation: inverse transduction, local sensitivity analysis, bagging variance, local cross-validation, local error modelling, and density-based estimation. Empirical evaluation of the benchmark datasets provides promising results, especially for use with decision and regression trees. The testing results also reveal that the reliability estimators exhibit different performance levels when used with different models and in different domains. The authors show the usefulness of individual prediction reliability estimates in attempts to predict breast cancer recurrence. In this context, estimating prediction reliability for individual predictions is of crucial importance for physicians seeking to validate predictions derived using classification and regression models.

scholarly journals Visualising project management dimensions

2012 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Luis Copertari
Keyword(s):  
Project Management ◽  
Best Practice ◽  
Optimal Solution ◽  
Computer Interface ◽  
Average Error ◽  
Human Computer Interface ◽  
Optimisation Model ◽  
Mathematical Optimisation

It is easier to decide upon which projects to include in a portfolio if a comprehensive human–computer interface is provided. Two alternative displays were designed according to best practice from the literature and tested according to a simple mathematical optimisation model and the average error from testing subjects and the “optimal” solution to decide which one performs better.

scholarly journals An Experimental Strategy for Characterizing Inductive Electromagnetic Energy Harvesters

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 647
Author(s):  
Pedro Martín Sánchez ◽  
Fco. Javier Rodríguez Sánchez ◽  
Enrique Santiso Gómez
Keyword(s):  
High Voltage ◽  
Energy Harvester ◽  
Optimal Solution ◽  
Distribution Networks ◽  
Magnetic Core ◽  
Electromagnetic Energy ◽  
Power Conditioning ◽  
Case Scenario ◽  
Worst Case ◽  
Voltage Range

Condition monitoring of high voltage power lines through self-powered sensor systems has become a priority for utilities with the aim of detecting potential problems, enhancing reliability of the power transmission and distribution networks and mitigating the adverse impact of faults. Energy harvesting from the magnetic field generated by the alternating current flowing through high voltage lines can supply the monitoring systems with the required power to operate without relying on hard-wiring or battery-based approaches. However, developing an energy harvester, which scavenges the power from such a limited source of energy, requires detailed design considerations, which may not result in a technically and economically optimal solution. This paper presents an innovative simulation-based strategy to characterize an inductive electromagnetic energy harvester and the power conditioning system. Performance requirements in terms of the harvested power and output voltage range, or level of magnetic core saturation can be imposed. Different harvester configurations, which satisfy the requirements, have been produced by the simulation models. The accuracy and efficiency of this approach is verified with an experimental setup based on an energy harvester, which consists of a Si-steel magnetic core and a power conditioning unit. For the worst-case scenario with a primary current of 5 A, the maximum power extracted by the harvester can be as close as 165 mW, resulting in a power density of 2.79 mW/cm3.

Planning the Shortest Path in Cluttered Environments: A Review and a Planar Convex Hull-Based Approach

2019 ◽  
Vol 19 (4) ◽  
Author(s):  
Nafiseh Masoudi ◽  
Georges M. Fadel ◽  
Margaret M. Wiecek
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Time Complexity ◽  
Optimal Path ◽  
Optimal Solution ◽  
Free Graph ◽  
Worst Case ◽  
Cluttered Environments ◽  
Planar Convex ◽  
Polyhedral Obstacles

Abstract Routing or path-planning is the problem of finding a collision-free and preferably shortest path in an environment usually scattered with polygonal or polyhedral obstacles. The geometric algorithms oftentimes tackle the problem by modeling the environment as a collision-free graph. Search algorithms such as Dijkstra’s can then be applied to find an optimal path on the created graph. Previously developed methods to construct the collision-free graph, without loss of generality, explore the entire workspace of the problem. For the single-source single-destination planning problems, this results in generating some unnecessary information that has little value and could increase the time complexity of the algorithm. In this paper, first a comprehensive review of the previous studies on the path-planning subject is presented. Next, an approach to address the planar problem based on the notion of convex hulls is introduced and its efficiency is tested on sample planar problems. The proposed algorithm focuses only on a portion of the workspace interacting with the straight line connecting the start and goal points. Hence, we are able to reduce the size of the roadmap while generating the exact globally optimal solution. Considering the worst case that all the obstacles in a planar workspace are intersecting, the algorithm yields a time complexity of O(n log(n/f)), with n being the total number of vertices and f being the number of obstacles. The computational complexity of the algorithm outperforms the previous attempts in reducing the size of the graph yet generates the exact solution.

THE PRICE OF NASH EQUILIBRIA IN MULTICAST TRANSMISSION GAMES

2010 ◽  
Vol 11 (03n04) ◽  
pp. 97-120 ◽  
Author(s):  
VITTORIO BILÒ
Keyword(s):  
Nash Equilibrium ◽  
Cost Sharing ◽  
Price Of Anarchy ◽  
Optimal Solution ◽  
Minimum Cost ◽  
Price Of Stability ◽  
Common Source ◽  
Cost Share ◽  
Worst Case ◽  
The Cost

We consider the problem of sharing the cost of multicast transmissions in non-cooperative undirected networks where a set of receivers R wants to be connected to a common source s. The set of choices available to each receiver r ∈ R is represented by the set of all (s, r)-paths in the network. Given the choices performed by all the receivers, a public known cost sharing method determines the cost share to be charged to each of them. Receivers are selfish agents aiming to obtain the transmission at the minimum cost share and their interactions create a non-cooperative game. Devising cost sharing methods yielding games whose price of anarchy (price of stability), defined as the worst-case (best-case) ratio between the cost of a Nash equilibrium and that of an optimal solution, is not too high is thus of fundamental importance in non-cooperative network design. Moreover, since cost sharing games naturally arise in socio-economical contests, it is convenient for a cost sharing method to meet some constraining properties. In this paper, we first define several such properties and analyze their impact on the prices of anarchy and stability. We also reconsider all the methods known so far by classifying them according to which properties they satisfy and giving the first non-trivial lower bounds on their price of stability. Finally, we propose a new method, namely the free-riders method, which admits a polynomial time algorithm for computing a pure Nash equilibrium whose cost is at most twice the optimal one. Some of the ideas characterizing our approach have been independently proposed in Ref. 10.

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