Performance metrics

This paper presents a literature review on WSN networks, in which the capacity of network nodes is limited with respect to energy supply, restricted computational capacity and communication bandwidth. WSN is a complex set of applications, link technologies, communication protocols, traffic flows and routing algorithms. Simulation is a predominant technique used to study and analyze the performance of a wireless senor network design. To prolong the lifetime of these sensor nodes, designing efficient routing protocols are critical. Basically, the Routing protocols for wireless sensor networks are responsible for maintaining the routes in the network which ensures reliable multi-hop communication. To address this issue, in this paper, various protocols like AODV (Ad-hoc on-demand distance vector routing), DSDV (Destination-Sequenced Distance-Vector Routing) and ZRP (Zone Routing Protocol) are discussed along with various comparative parameters like Throughput, Packet Loss Ratio, End to End Delay and Dropped Packets. The performance of these protocols varies depending on the simulation environment. It will be analyzed in two ways. Firstly, by varying nodes within a margin of 10 to 100 nodes. Another way is by keeping the number of nodes constant and varying the speed of nodes from 10 m/s to 90 m/s.

The aim of this paper is to implement metrics and to define indicators to provide a unified criteria for evaluation method of the performance of autonomous mobile robots using different control algorithms. There is a first description of the mobile robot problem and the importance of a standardized process for evaluation of robot performances. There is a comparison between a simple navigation controller and an intelligent prototype based on consciousness. The architecture main features are also outlined. Test cases with and without conscious controller show that the latter performs an optimized source-to-target path.

Productivity metrics provoke mixed responses from the research community. The Open Researcher and Contributor ID (ORCID) facilitates an open and clear data infrastructure to handle publication products. ORCID is a unique identifier that accepts data interoperability for funders, industries, and publishers by exploring indicators for research aspects that they aspire to evaluate instead of using current indicators because they are readily accessible. This manuscript examines the use of ORCID for publication metrics, grant applications, management and reporting platforms. The benefits of ORCID offer a quick way to promote and organize a researcher's work as well as to foster better communication among researchers.

Data driven companies effectively use regression machine learning methods for making predictions in many sectors. Cloud-based Azure Machine Learning Studio (MLS) has a potential of expediting machine learning experiments by offering a convenient and powerful integrated development environment. The process of evaluating machine learning models in Azure MLS has certain limitations, e.g. small number of performance metrics and lack of functionality to evaluate custom built regression models with R language. This paper reports the results of an effort to build an Enhanced Evaluate Model (EEM) module which facilitates and accelerates Azure experiments development and evaluation. The EEM combines multiple performance metrics allowing for multisided evaluation of the regression models. EEM offers 4 times more metrics than built-in Azure Evaluate Model module. supports evaluation of the R language based regression models. The operational Enhanced Evaluate Model module has been published to the web at Azure MLS and openly available for experiments and extensions.

İz füzyonu, komuta kontrol sistemlerinin sahip olması gereken önemli özelliklerden biridir. Farklı sensörlerden gelen iz bilgileri bu sayede birleştirilerek daha doğru bir sonuç elde edilebilir. İz füzyonu yöntemleri geliştirilirken performansın değerlendirilmesi ve karşılaştırılabilmesi için kriterler ve ölçütlere ihtiyaç duyulmaktadır. Bu çalışma önemli bazı iz füzyonu ölçütlerini özetlemektedir.

Aim/Purpose
The aim of this study was to analyze various performance metrics and approaches to their classification. The main goal of the study was to develop a new typology that will help to advance knowledge of metrics and facilitate their use in machine learning regression algorithms
Background
Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. A performance metric can be defined as a logical and mathematical construct designed to measure how close are the actual results from what has been expected or predicted. A vast variety of performance metrics have been described in academic literature. The most commonly mentioned metrics in research studies are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), etc. Knowledge about metrics properties needs to be systematized to simplify the design and use of the metrics.
Methodology
A qualitative study was conducted to achieve the objectives of identifying related peer-reviewed research studies, literature reviews, critical thinking and inductive reasoning.
Contribution
The main contribution of this paper is in ordering knowledge of performance metrics and enhancing understanding of their structure and properties by proposing a new typology, generic primary metrics mathematical formula and a visualization chart
Findings
Based on the analysis of the structure of numerous performance metrics, we proposed a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of de-termining point distance, method of normalization, method of aggrega-tion of point distances over a data set. For each component, implementa-tion options have been identified. The suggested new typology has been shown to cover a total of over 40 commonly used primary metrics
Recommendations for Practitioners
Presented findings can be used to facilitate teaching performance metrics to university students and expedite metrics selection and implementation processes for practitioners
Recommendations for Researchers
By using the proposed typology, researchers can streamline development of new metrics with predetermined properties
Impact on Society
The outcomes of this study could be used for improving evaluation results in machine learning regression, forecasting and prognostics with direct or indirect positive impacts on innovation and productivity in a societal sense
Future Research
Future research is needed to examine the properties of the extended metrics, composite metrics, and hybrid sets of metrics. Empirical study of the metrics is needed using R Studio or Azure Machine Learning Studio, to find associations between the properties of primary metrics and their “numerical” behavior in a wide spectrum of data characteristics and business or research requirements
Keywords
performance metrics, error measures, accuracy measures, distance, similarity, dissimilarity, properties, typology, classification, machine learning, regression, forecasting, prognostics, prediction, evaluation, estimation, modeling

Named Entity Recognition (NER) is the task in which proper nouns in a given document are discovered
and then categorized into respective classes. The various classes of proper nouns may be name of
location, name of person, Organization, River, Quantity, Time, Percentage etc. Today, there is a great
need to perform NER in the Indian Languages, since not much work has been done in the field of
Information retrieval in the Indian languages. In this paper, we have tried to explain NER, different
approaches of NER and finally some results of NER in natural languages.

Many organizations have invested substantial resources in recent years to implement a balanced scorecard of performance metrics. From a historical focus exclusively on financial metrics of performance, the emphasis has shifted recently to a consideration of a portfolio of non-financial performance metrics related to customers, business process and technology. In this paper, we investigate the best practice frontier relationship between a financial performance metric (return on assets - ROA) and three nonfinancial performance metrics (number of access lines per employee, percentage of digital access lines and percentage of business access lines) reported and used in the U.S. telecommunications industry. Analyzing detailed data from over fifty local exchange carriers for a period of five years (from 1993 to 1997), we find that managers must tradeoff contemporaneous ROA when increasing the percentage of business access lines. We also find that managers do not have to trade off ROA with...

The Autonomy Levels for Unmanned Systems (ALFUS) framework is generic and applicable to multiple unmanned system (UMS) domains. The key component of the Framework is metrics along the three established axes or aspects. This paper attempts to examine how the metrics might be applied to selected domains that include homeland security, manufacturing, and defense. In particular, the paper attempts to lay out how the critical UMS concerns, including requirements specification, performance measures, safety, and risks might be established from the Framework.

Recent decades have witnessed a significant increase in the use of visual odometry(VO) in the computer vision area. It has also been used in varieties of robotic applications, for example on the Mars Exploration Rovers. This paper, firstly, discusses two popular existing visual odometry approaches, namely LSD-SLAM and ORB-SLAM2 to improve the performance metrics of visual SLAM systems using Umeyama Method. We carefully evaluate the methods referred to above on three different well-known KITTI datasets, EuRoC MAV dataset, and TUM RGB-D dataset to obtain the best results and graphically compare the results to evaluation metrics from different visual odometry approaches. Secondly, we propose an approach running in real-time with a stereo camera, which combines an existing feature-based (indirect) method and an existing featureless (direct) method matching with accurate semi-dense direct image alignment and reconstructing an accurate 3D environment directly on pixels that have image gradient.

Transliteration may be defined as the process of mapping sounds in a text written in one language to another language. Current paper discusses about transliteration and its use in Named Entity Recognition. We have designed a code that executes Transliteration and assist in the process of Named Entity Recognition. We have presented some of the results of Named Entity Recognition (NER) using Transliteration.

Mobile Ad-hoc Networks (MANETs) are a cluster of self-organizing and self-governing wireless nodes without any backbone infrastructure and centralized administration. The various nodes in MANET move randomly, and this node mobility may pose challenges on the performance of routing protocols. In this paper, an Intra and intergroup performance review of various MANET routing protocols are performed under varying speed of nodes. The routing protocols included in this study are reactive, proactive, and hybrid protocols. This performance review is done using the NS2 simulator and random waypoint model. The routing protocols performance is assessed through standard performance measure metrics including packet delivery ratio, throughput, routing overhead and end to end delivery with varying speed of nodes. The simulations result shows that there is no significant impact of varying speed of nodes on standard performance evaluation metrics. 1. INTRODUCTION With the development of cutting edge technology enabled powerful portable devices and availability of cost effective wireless communication, mobile ad-hoc networks (MANETs) are gaining popularity. MANETs are deploying for different applications ranging from military to natural disaster management. Mobile Ad Hoc Networks (MANETs) connects wireless mobile nodes which have an arbitrary motion which makes the topology unpredictable [1],[2]. It is self-organizing and self-configuring and can be deployed without any wired base stations or infrastructure support. The mobile nodes act as hosts and also as routers to send the data across the networks. A MANET is a collection of free mobile nodes such as laptops, smartphones, tablet PC, etc. It has many characteristics. In MANETs, there is no central server and nodes themselves are responsible for communicating with other nodes to carry out network operations such as on-demand routing[3]. Nodes are free to move anywhere whenever and wherever they want, and they are also free to change their speeds [4]. Therefore, the network topology changes randomly and at uncertain times. Nodes can send messages to other nodes which are not in range or nor directly connected with the help of intermediate nodes [5]. MANET has many advantages. It is scalable as it supports the addition of more mobile nodes in the network. The information can be accessed irrespective of the geographic position of nodes [5]. The use of mobile nodes results in many connection failures. The routing protocols are designed to handle these situations [6]. MANETs are fault tolerant. The main advantage is that the nodes do not need wireless routers to connect to the internet[7].Therefore, it reduces the cost of deploying a router and affordable than a

This paper introduces the concept of product identity-clustering based on new similarity metrics and new performance metrics for web-crawled products. Product identity-clustering is defined here as the clustering of identical products, e.g., for price comparison purposes. Products blindly crawled over web sources, e.g., online marketplaces, have different description formats, where the features describing the same products differ in both number and representation formats. This problem causes imperfect feature vectors, where the vectors are considered to be not uniform in length and structure, with the features of various data types (numeric, categorical), and unknown vector structures. Furthermore, the product information usually contains redundant, missing, or faulty data, which are regarded as noise here. Product identity-clustering becomes a challenge when the vectors’ metadata are previously unknown and the imperfect nature of the feature vectors is considered with the occurrence of noise. In this paper, the product identity-clustering concept is introduced as a new mining metric in e-commerce. Then novel similarity metrics are introduced to improve the product identity-clustering performance of legacy metrics. Finally, novel performance metrics are proposed to measure the performance of the identity-clustering algorithms. Using these metrics, a comparison of the legacy-based similarity metrics (Euclidian, cosine, etc.) and the proposed similarity metrics is given. The results show that legacy metrics are not successful in discriminating identical web-crawled products and the proposed metrics enable better achievement in the product identity-clustering problem.

Key words: Product clustering, similarity metrics, identity clustering, performance metrics, web mining

Ability for accurate hospital case cost modelling and prediction is critical for efficient health care financial management and budgetary planning. A variety of regression machine learning algorithms are known to be effective for health care cost predictions. The purpose of this experiment was to build an Azure Machine Learning Studio tool for rapid assessment of multiple types of regression models. The tool offers environment for comparing 14 types of regression models in a unified experiment: linear regression, Bayesian linear regression, decision forest regression, boosted decision tree regression, neural network regression, Poisson regression, Gaussian processes for regression, gradient boosted machine, nonlinear least squares regression, projection pursuit regression, random forest regression, robust regression, robust regression with mm-type estimators, support vector regression. The tool presents assessment results arranged by model accuracy in a single table using five performance metrics. Evaluation of regression machine learning models for performing hospital case cost prediction demonstrated advantage of robust regression model, boosted decision tree regression and decision forest regression. The operational tool has been published to the web and openly available for experiments and extensions.

There has been a great deal of emphasis
on using data analytics to close achievement
gaps in terms of persistence, graduation
rates, and time-to-degree among varying and
intersecting identity groups. For some institutions,
applying just-in-time academic and student support
initiatives predicted as necessary by data analytics has been
fruitful. For other institutions, this approach may be less
welcomed, as it may not account for institutional leaders’
desire to understand individual students’ needs or to critically
examine how well the institution is avoiding a deficit
mindset. Indeed, both of these may occur simultaneously.
Using predictive analytics that are in essence based on
historical institutional processes without an understanding of
how those analytics intersect with students’ attainment of desired
career readiness skills could potentially increase rather
than decrease achievement gaps. With increasing emphasis
on preparing career readiness competencies, such as social
emotional intelligence, self-awareness, global citizenship,
compassion, pro-social behavior, and lifelong learning skills
and abilities, this article seeks to offer an additional lens
through which to collect data in order to close achievement
gaps while also ensuring optimal career readiness preparation.

Differentiated Services is the most advanced method for traffic management which provides QoS. QoS is essential for today's internet. QoS represents set of techniques necessary to manage throughput, packet loss and delay. Traffic Conditioners or Policers are the most important components of differentiated services. In this research, different Traffic Conditioners or Policers are compared on the basis of QoS parameters (Performance metrics) which are throughput, packet loss and delay to provide QoS on internet. Traffic classification and traffic conditioning are important functions of differentiated services also known as admission control which are governed by service level agreement (SLA). Traffic classification and traffic conditioning is done by traffic conditioners at edge routers. Goal of this research is to choose the best Traffic Conditioner to apply from source to destination pair because only one traffic conditioner can be applied from source to destination so that QoS can be guaranteed.

Ability for accurate hospital case cost modelling and prediction is critical for efficient health care financial management and budgetary planning. A variety of regression machine learning algorithms are known to be effective for health care cost predictions. The purpose of this experiment was to build an Azure Machine Learning Studio tool for rapid assessment of multiple types of regression models. The tool offers environment for comparing 14 types of regression models in a unified experiment: linear regression, Bayesian linear regression, decision forest regression, boosted decision tree regression, neural network regression, Poisson regression, Gaussian processes for regression, gradient boosted machine, nonlinear least squares regression, projection pursuit regression, random forest regression, robust regression, robust regression with mm-type estimators, support vector regression. The tool presents assessment results arranged by model accuracy in a single table using five perfo...

Aim/Purpose: The aim of this study was to analyze various performance metrics and approaches to their classification. The main goal of the study was to develop a new typology that will help to advance knowledge of metrics and facilitate their use in machine learning regression algorithms Background: Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. A performance metric can be defined as a logical and mathematical construct designed to measure how close are the actual results from what has been expected or predicted. A vast variety of performance metrics have been described in academic literature. The most commonly mentioned metrics in research studies are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), etc. Knowledge about metrics properties needs to be systematized to simplify the design and use of the metrics. Methodology: A qualitative study was conducted to achieve the objectives of identifying related peer-revie...

Transliteration may be defined as the alteration of text from one language to another. In this paper, we have discussed how transliteration is useful in handling unknown words in Named Entity Recognition (NER). We have shown some of our results on unknown words handling in NER using transliteration.

Anaphora Resolution is a method of determining what a particular noun phrase or pronoun at a given
instance refers to. In this paper, we have discussed how Anaphora Resolution is useful in performing
computation linguistic task in various Natural languages including the Indian languages. Also, we have
discussed how Anaphora Resolution is conducive in handling unknown words in Named Entity Recognition.

Abstract: This paper makes the case for an approach to administrative ethics that regards it as a form of political power within contexts of organization and organizational change (reform). After articulating the logic of this perspective, it is applied to two cases of reform (No Child Left Behind in the US and National Health Service reform in the UK) where reformers have used the power of metrics and high-stakes accountability to force change--and in the process have altered the respective ethical landscapes.

Mobile Ad-hoc Networks (MANETs) are a cluster of self-organizing and self-governing wireless nodes without any backbone infrastructure and centralized administration. The various nodes in MANET move randomly, and this node mobility may pose challenges on the performance of routing protocols. In this paper, an Intra and intergroup performance review of various MANET routing protocols are performed under varying speed of nodes. The routing protocols included in this study are reactive, proactive, and hybrid protocols. This performance review is done using the NS2 simulator and random waypoint model. The routing protocols performance is assessed through standard performance measure metrics including packet delivery ratio, throughput, routing overhead and end to end delivery with varying speed of nodes. The simulations result shows that there is no significant impact of varying speed of nodes on standard performance evaluation metrics. 1. INTRODUCTION With the development of cutting edg...

Mobile Ad-hoc Networks (MANETs) are a cluster of self-organizing and self-governing wireless nodes without any backbone infrastructure and centralized administration. The various nodes in MANET move randomly, and this node mobility may pose challenges on the performance of routing protocols.  In this paper, an Intra and intergroup performance review of various MANET routing protocols are performed under varying speed of nodes. The routing protocols included in this study are reactive, proactive, and hybrid protocols. This performance review is done using the NS2 simulator and random waypoint model. The routing protocols performance is assessed through standard performance measure metrics including packet delivery ratio, throughput, routing overhead and end to end delivery with varying speed of nodes. The simulations result shows that there is no significant impact of varying speed of nodes on standard performance evaluation metrics.

The viability of Unmanned Systems as tools is increasingly recognized in many domains. As technology advances, the autonomy on board these systems also advances. In order to evaluate the systems in terms of their levels of autonomy, it is critical to have a set of standard definitions that support a set of metrics. As autonomy cannot be evaluated quantitatively without sound and thorough technical basis, the development of autonomy levels for unmanned systems must take into account many factors such as task complexity, human interaction, and environmental difficulty. An ad hoc working group assembled by government practitioners has been formed to address these issues. The ultimate objectives for the working group are: • To determine the requirements for metrics for autonomy levels of unmanned systems. • To devise methods for establishing metrics of autonomy for unmanned systems. • To develop a set of widely recognized standard definitions for the levels of autonomy for unmanned systems. This paper describes the interim results that the group has accomplished through the first four workshops that the group held. We report on the initial findings of the workshops toward developing a generic framework for the Autonomy Levels for Unmanned Systems (ALFUS).

To evaluate the performance of emerging mobile wireless networks with multihop coverage extensions, spatiotemporal network models are required. These models should include the directional characteristics of network information flows to exploit the spatial domain due to the deployment of advanced antenna systems. Furthermore, the models should be capable of handling nonstationary scenarios with dynamic evolution of mobile nodes. In an attempt to solve these two problems, which have not been fully addressed in the existing literature, and motivated by the useful analogy between classical propagation channels and wireless networks, we propose a novel network modeling framework where the relevant figure of merit (FOM) may be the received signal power, the event detection error exponent, the channel capacity, etc., depending on the network's type. We first formulate the general description methods of the framework, including the double-directional FOM impulse response, the angular spectrum, and the angular dispersion. Subsequently, we propose the ray approaches, including the geometry-based stochastic channel models (GSCMs) and the deterministic ray-tracing techniques, to support and enrich the description methods. The given analytical tools facilitate visualization of wireless network performance metrics in space-time. Due to their stochastic nature, the proposed methodology would be most useful for the design and analysis of networks featured by decentralized, randomized, and dense placement of nodes.

This paper describes an experimental microprocessor-based tool, SAS (Software Analysis System), which has been developed to enable dynamic program structure acquisition and analysis to be made on digital computing machines. The system uses a universal hardware  extraction technique to obtain branch vectors which are used 10 analyze and display the structure of the software being monitored. A display, especially designed for small instrument screens, is used to present this structure. Emphasis has been directed towards development of methods with high degrees of machine independence and it is envisaged that such techniques could either be integrated into the new generation of logic analyzers or form part of a universal tool for computer programmers. Initial research has been guided towards the application of
these techniques to compiled, assembled, or machine coded systems and in this context a number of techniques are described. The motivation for this research has been provided by the present escalating software costs, in particular those in post-development which account for approximately 75% of the total software expenditure.

The process of identification of Named Entities (NEs) in a given document and then there classification into different categories of NEs is referred to as Named Entity Recognition (NER). We need to do a great effort in order to perform NER in Indian languages and achieve the same or higher accuracy as that obtained by English and the European languages. In this paper, we have presented the results that we have achieved by performing NER in Hindi, Bengali and Telugu using Hidden Markov Model (HMM) and Performance Metrics.

Transliteration may be defined as the process of mapping sounds in a text written in one language to another language. Current paper discusses about transliteration and its use in Named Entity Recognition.We have designed a code that executes Transliteration and assist in the process of Named Entity Recognition. We have presented some of the results of Named Entity Recognition (NER) using Transliteration.

Transliteration may be defined as the alteration of text from one language to another. In this paper, we have discussed how transliteration is useful in handling unknown words in Named Entity Recognition (NER). We have shown some of our results on unknown words handling in NER using transliteration.

Abstract: This paper makes the case for an approach to administrative ethics that regards it as a form of political power within contexts of organization and organizational change (reform). After articulating the logic of this perspective, it is applied to two cases of reform (No Child Left Behind in the US and National Health Service reform in the UK) where reformers have used the power of metrics and high-stakes accountability to force change--and in the process have altered the respective ethical landscapes.

Abstract. Several image mosaicing algorithms claiming to advance the state of the art have been proposed so far. Though sometimes improvements can be recognised without quantitative evidences, the importance of a principled methodology to compare different algorithms is essential as this discipline evolves. Which is the best? What means the best? How to ascertain the supremacy? To answer such questions, in this paper we propose an evaluation methodology including standard data sets, ground-truth information and performance metrics. We also compare three variants of a well-known mosaicing algorithm according to the proposed methodology.

ABSTRACT A necessary step for the recognition of scanned documents is binarization, which is essentially the segmentation of the document. In order to binarize a scanned document, we can find several algorithms in the literature. What is the best binarization result for a given document image? To answer this question, a user needs to check different binarization algorithms for suitability, since different algorithms may work better for different type of documents. Manually choosing the best from a set of binarized documents is time consuming. To automate the selection of the best segmented document, either we need to use ground-truth of the document or propose an evaluation metric. If ground-truth is available, then precision and recall can be used to choose the best binarized document. What is the case, when ground-truth is not available? Can we come up with a metric which evaluates these binarized documents? Hence, we propose a metric to evaluate binarized document images using eigen value decomposition. We have evaluated this measure on DIBCO and H-DIBCO datasets. The proposed method chooses the best binarized document that is close to the ground-truth of the document.

A significant amount of work is invested in human-machine teaming (HMT) across multiple fields. Accurately and effectively measuring system performance of an HMT is crucial for moving the design of these systems forward. Metrics are the enabling tools to devise a benchmark in any system and serve as an evaluation platform for assessing the performance, along with the verification and validation, of a system. Currently, there is no agreed-upon set of benchmark metrics for developing HMT systems. Therefore, identification and classification of common metrics are imperative to create a benchmark in the HMT field. The key focus of this review is to conduct a detailed survey aimed at identification of metrics employed in different segments of HMT and to determine the common metrics that can be used in the future to benchmark HMTs. We have organized this review as follows: identification of metrics used in HMTs until now, and classification based on functionality and measuring techniques. Additionally, we have also attempted to analyze all the identified metrics in detail while classifying them as theoretical, applied, real-time, non-real-time, measurable, and observable metrics. We conclude this review with a detailed analysis of the identified common metrics along with their usage to benchmark HMTs.