Set Of New Metrics Research Articles

Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI.

Adversarial training has attracted much attention in enhancing the visual realism of images, but its efficacy in clinical imaging has not yet been explored. This work investigated adversarial training in a clinical context, by training 206 networks on the OASIS-1 dataset for improving low-resolution and low signal-to-noise ratio (SNR) magnetic resonance images. Each network corresponded to a different combination of perceptual and adversarial loss weights and distinct learning rate values. For each perceptual loss weighting, we identified its corresponding adversarial loss weighting that minimized structural disparity. Each optimally weighted adversarial loss yielded an average SSIM reduction of 1.5%. We further introduced a set of new metrics to assess other clinically relevant image features: Gradient Error (GE) to measure structural disparities; Sharpness to compute edge clarity; and Edge-Contrast Error (ECE) to quantify any distortion of the pixel distribution around edges. Including adversarial loss increased structural enhancement in visual inspection, which correlated with statistically consistent GE reductions (p-value << 0.05). This also resulted in increased Sharpness; however, the level of statistical significance was dependent on the perceptual loss weighting. Additionally, adversarial loss yielded ECE reductions for smaller perceptual loss weightings, while showing non-significant increases (p-value >> 0.05) when these weightings were higher, demonstrating that the increased Sharpness does not adversely distort the pixel distribution around the edges in the image. These studies clearly suggest that adversarial training significantly improves the performance of an MRI enhancement pipeline, and highlights the need for systematic studies of hyperparameter optimization and investigation of alternative image quality metrics.

Toward tiny high-power-density thermoelectric harvesters

Thermoelectric (TE) generators (TEGs) have recently attracted significant attention for harvesting ubiquitous heat energy to power industrial sensor nodes or wearable and implantable electronics. Such applications require that a small, fitted heat sink be incorporated into a tiny harvester to ensure conformal deployment and ease of use. Since the small heat sink will exhibit very poor heat rejection performance, the optimal structural design of the TEG device and the proper selection of its constituent TE materials are extremely important to ensure that sufficient electric power is provided to the connected post-stage circuits. Regrettably, the traditional forward design strategies, i.e., material-to-device and device-to-harvester, are often disconnected from one another and fail to form a complete chain, thereby presenting a fundamental challenge in achieving ultrahigh-power-density harvesters. Here we present a zero-dimensional model for a tiny harvester to establish a set of new metrics (not limited to the figure of merit zT) that can be used to determine the optimal TE material and to directly deduce the optimal configuration of the TEG for guaranteeing the maximum output power at various load conditions, making backward design strategy possible. Two tiny harvesters are accordingly fabricated and exhibit areal power densities 52 and 94 times that of a harvester incorporating a commercial TEG, with identical amounts of TE material used for each.

Adoption of AI integrated partner relationship management (AI-PRM) in B2B sales channels: Exploratory study

Partner relationship management (PRM) is a set of methods, tools, strategies, and web-based capabilities that a business-to-business (B2B) firm uses to manage its relationships with partners, resellers, and other third parties. Integrating artificial intelligence (AI) into PRM helps automate processes and procedures by eliminating human error and processing data faster and more accurately. Following growing attention from scholars and practitioners to AI-PRM, this study builds on the dynamic capability view (DCV) and absorptive capacity theory to develop a conceptual model to understand the requirements for a B2B firm's adoption of AI-PRM and its impact on business value. Since AI-PRM is still relatively new in scholarly research, there are no specific scales in the existing literature that could be used to capture specific factors and preconditions for its adoption, thus we explore a set of new metrics. We test the conceptual model using structural equation modeling with data from 427 B2B firms. Our results show that firms improve operational performance when an AI-PRM system is reflected in customized partner services and partner engagement, which in turn yields business value.

Metrics for the assessment of quantity and quality of the data by Argo floats

Observing system or research initiative's foundation lies on reliable in situ data from sensors, which accurately tell about various key parameters that are being measured. Argo floats had brought huge amount of ocean observational in-situ data which is widely used from analysis to modelling. Present work describes a metrics for analyzing performance of sensors on Argo floats which can be used to assess the performance of float or set of floats as a whole. A set of new metrics like Total Data Return, Quality Data Returned and Quality Data Expected are proposed including the well-known Half-Life Period utilizing all of the Argo profile data. From the analysis, temperature and sensors performance is found to be more than 80 % and average Half-Life is found to be 1065 days. These metrics provide the overall performance of the floats, and can also be applied to other similar floats deployed by other countries as well as sensors fitted on other oceanic platforms.

How Effective Are Police? The Problem of Clearance Rates and Criminal Accountability

In recent years, the national conversation in criminal justice has centered on police. Are police using excessive force? Should they be monitored more closely? Do technology and artificial intelligence improve policing? The implied core question across these national debates is whether police are effective at their jobs. Yet we have not explored how effective police are or determined how best to measure police effectiveness. This Article endeavors to measure how effective police are at their principal function—solving crime. The metric most commonly used to measure police effectiveness at crime-solving is a “clearance rate:” the proportion of reported crimes for which police arrest a person and refer them for prosecution. But clearance rates are inadequate for many reasons, including the fact that they are highly manipulable. This Article therefore provides a set of new metrics that have never been used systematically to study police effectiveness—referred to as “criminal accountability” metrics. Criminal Accountability examines the full course of a crime to determine whether crime that is committed is detected and ultimately resolved by police. Taking into account the prevalence and the number of crimes solved by police, the proportion of crimes solved in America is dramatically lower than we realize. Only with a clearer conversation, rooted in accurate data about the effectiveness of the American police system, can we attempt a path toward increased criminal accountability and public safety.

The state of rock debris covering Earth’s glaciers

Rock debris can accumulate on glacier surfaces and dramatically reduce glacier melt. The structure of a debris cover is unique to each glacier and sensitive to climate. Despite this, debris cover has been omitted from global glacier models and forecasts of their response to a changing climate. Fundamental to resolving these omissions is a global map of debris cover and an estimate of its future spatial evolution. Here we use Landsat imagery and a detailed correction to the Randolph Glacier Inventory to show that 7.3% of mountain glacier area is debris covered and over half of Earth’s debris is concentrated in three regions: Alaska (38.6% of total debris-covered area), Southwest Asia (12.6%) and Greenland (12.0%). We use a set of new metrics, which include stage, the current position of a glacier on its trajectory towards reaching its spatial carrying capacity of debris cover, to quantify the state of glaciers. Debris cover is present on 44% of Earth’s glaciers and prominent (>1.0 km2) on 15%. Of Earth’s glaciers, 20% have a substantial percentage of debris cover for which the net stage is 36% and the bulk of individual glaciers have evolved beyond an optimal moraine configuration favourable for debris-cover expansion. Use of this dataset in global-scale models will enable improved estimates of melt over 10.6% of the global glacier domain. A global map of rock-debris cover on mountain glaciers shows its spatial distribution and evolution.

The four dimensions of social network analysis: An overview of research methods, applications, and software tools

Social network based applications have experienced exponential growth in recent years. One of the reasons for this rise is that this application domain offers a particularly fertile place to test and develop the most advanced computational techniques to extract valuable information from the Web. The main contribution of this work is three-fold: (1) we provide an up-to-date literature review of the state of the art on social network analysis (SNA); (2) we propose a set of new metrics based on four essential features (or dimensions) in SNA; (3) finally, we provide a quantitative analysis of a set of popular SNA tools and frameworks. We have also performed a scientometric study to detect the most active research areas and application domains in this area. This work proposes the definition of four different dimensions, namely Pattern & Knowledge discovery, Information Fusion & Integration, Scalability, and Visualization, which are used to define a set of new metrics (termed degrees) in order to evaluate the different software tools and frameworks of SNA (a set of 20 SNA-software tools are analyzed and ranked following previous metrics). These dimensions, together with the defined degrees, allow evaluating and measure the maturity of social network technologies, looking for both a quantitative assessment of them, as to shed light to the challenges and future trends in this active area.

A Progressive Motion-Planning Algorithm and Traffic Flow Analysis for High-Density 2D Traffic

Unmanned aircraft systems (UASs) are a promising new mode of transportation for cargo delivery. Although control, navigation, and communication technologies are becoming available on individual flight units, system-level motion management for dense UAS traffic remains an open question. This paper presents a motion-planning model based on nonlinear optimization techniques to centrally coordinate paths for all vehicles traversing a shared two-dimensional (2D) space. An exact bound is derived to characterize the discrete-time separation constraints, and a set of new metrics is proposed to measure 2D traffic flow efficiency. The grand goal is to make all vehicles that come under dispatch reach their respective destinations quickly and efficiently while maintaining a safe intervehicle separation at all times. This infinite-horizon operational problem is formulated as a nonlinear nonconvex optimization model that must be solved in a progressive, receding-horizon fashion. To ensure feasibility and overcome path deadlocks attributed to local optima, a series of heuristic measures are developed. By pivoting on artfully coined intermediate feasibility, the algorithm is able to circumvent imminent deadlocks in a predictive manner and progressively construct the solution with guaranteed feasibility. Simulation experiments are performed at various traffic density levels to generate useful insights for airspace regulators and traffic managers.

Metrics for characterizing network structure and node importance in Spatial Social Networks

ABSTRACTSocial Network Analysis offers powerful tools to analyze the structure of relationships between a set of people. However, the addition of spatial information poses new challenges, as nodes are embedded simultaneously in network space and Euclidean space. While nearby nodes may not form social ties, ties may exist at a distance, a configuration ill-suited for traditional spatial metrics that assume adjacent objects are related. As such, there are relatively few metrics to describe these nuanced situations. We advance the burgeoning field of spatial social network analysis by introducing a set of new metrics. Specifically, we introduce the spatial social network schema, tuning parameter and the flattening ratio, each of which leverages the notion of ‘distance’ to augment insights obtained by relying on topology alone. These methods are used to answer the questions: What is the social and spatial structure of the network? Who are the key individuals at different spatial scales? We use two synthetic networks with properties mimicking the ones reported in the literature as validation datasets and a case study of employer–employee network. The methods characterize the employer–employee as spatially loose with predominantly local connections and identify key individuals responsible for keeping the network connected at different spatial scales.

New metrics for assessing the performance of multi-microgrid systems in stand-alone mode

This paper proposes a set of new metrics for quantitatively assessing the performance of multi-microgrid systems which are operated in stand-alone mode. These metrics include generation penetration indices for indicating the configuration of distributed generations and loads, power exchange indices for representing energy interaction among microgrids and with the main grid, as well as reliability and economic indices for quantifying the reliable and economic operation of muti-microgrid systems. A multi-step performance assessment method for multi-microgrid systems is proposed, which considers different reliability requirements and economic characters of critical and non-critical loads as well as the energy exchange among microgrids and with the main grid for effectively calculating the proposed metrics. Case studies with practical data show the effectiveness of the proposed metrics and multi-step performance assessment method for assessing the performance of multi-microgrid systems.

Evaluating Life Expectancy Evaluations

The quality of life expectancy estimates is one key consideration for an investor in life settlements. The predominant metric for assessing this quality is the so-called A-to-E ratio, which relies on a comparison of the actual to the predicted number of deaths. In this article, we explain key issues with this metric: In the short run, it is subject to estimation uncertainty for small and moderately sized portfolios; and, more critically, in the long run, it converges to 100% even if the underwriting is systematically biased. As an alternative, we propose and discuss a set of new metrics based on the difference in (temporary) life expectancies. We examine the underwriting quality of a leading U.S. life expectancy provider based on this new methodology.

Benchmark for Approximate Query Answering Systems

The standard benchmark for Decision Support Systems is TPC-H, which is composed of a database, a workload, and a set of metrics for the performance evaluation. However, TPC-H does not include a methodology for the benchmark of Approximate Query Answering Systems, or the software tools used to obtain fast answers to analytical queries in the decision making process. In the paper, the authors present a methodology to evaluate and compare Approximate Query Answering Systems. To this aim, a methodology that extends the standard TPC-H and a set of new metrics that take into account the specific features of these systems are proposed. Experimental results show the application of these metrics to two systems based on the data analytic approximation by orthonormal series.

An empirical analysis of package-modularization metrics: Implications for software fault-proneness

ContextIn a large object-oriented software system, packages play the role of modules which group related classes together to provide well-identified services to the rest of the system. In this context, it is widely believed that modularization has a large influence on the quality of packages. Recently, Sarkar, Kak, and Rama proposed a set of new metrics to characterize the modularization quality of packages from important perspectives such as inter-module call traffic, state access violations, fragile base-class design, programming to interface, and plugin pollution. These package-modularization metrics are quite different from traditional package-level metrics, which measure software quality mainly from size, extensibility, responsibility, independence, abstractness, and instability perspectives. As such, it is expected that these package-modularization metrics should be useful predictors for fault-proneness. However, little is currently known on their actual usefulness for fault-proneness prediction, especially compared with traditional package-level metrics. ObjectiveIn this paper, we examine the role of these new package-modularization metrics for determining software fault-proneness in object-oriented systems. MethodWe first use principal component analysis to analyze whether these new package-modularization metrics capture additional information compared with traditional package-level metrics. Second, we employ univariate prediction models to investigate how these new package-modularization metrics are related to fault-proneness. Finally, we build multivariate prediction models to examine the ability of these new package-modularization metrics for predicting fault-prone packages. ResultsOur results, based on six open-source object-oriented software systems, show that: (1) these new package-modularization metrics provide new and complementary views of software complexity compared with traditional package-level metrics; (2) most of these new package-modularization metrics have a significant association with fault-proneness in an expected direction; and (3) these new package-modularization metrics can substantially improve the effectiveness of fault-proneness prediction when used with traditional package-level metrics together. ConclusionsThe package-modularization metrics proposed by Sarkar, Kak, and Rama are useful for practitioners to develop quality software systems.

Evaluation of Side Information Effectiveness in Distributed Video Coding

The rate-distortion performance of a distributed video coding system strongly depends on the characteristics of the side information. One could naïvely think that the best side information is the one with the largest PSNR with respect to the original corresponding image. However, previous works have shown that this is not always the case and a reduction of the side information MSE does not always translate into better rate-distortion performance for the complete system. The scope of this paper is to explore a set of metrics other than the PSNR and explicitly designed to classify the side information with respect to its impact on the end-to-end compression performance. A first contribution is to define an experimental framework that can be used to meaningfully compare different metrics for side information evaluation. As a second contribution, our analysis allows to understand why in some cases PSNR-based metrics provide a fairly reliable estimation of the side information quality, while in other cases they do not. This analysis also allows us to introduce a set of new metrics that are better adapted for side information effectiveness evaluation, and that are based on a suitable power of the absolute difference between side information and the original image, or on the Hamming distance between the respective transform coefficients. Besides their theoretical interest, these new metrics can also improve the rate-distortion performance of some distributed video coding systems such as the hash-based ones. We observe improvement up to 74% rate reduction in a simple study case.

Analysis of Reusability of Object-Oriented System using CK Metrics

In the object-oriented environment, one of the most important aspects having strong influence on the quality of resulting software system is the design complexity. The OO model offers the technology to create components that can be used for general programming. Design complexity has been imagining to play a strong role in the quality of the resulting software system in OO development environments. This paper gives the design of CK suit of metrics and evaluation to these metrics so that these metrics should reflect accurate and precise results for object oriented based systems. Moreover, a set of new metrics are proposed that can find the impact on reusability of a class by using the combination of one CK metric with another metric.

A Metric-Based Approach to Assess Risk for “On Cloud” Federated Identity Management

The cloud computing paradigm is set to become the next explosive revolution on the Internet, but its adoption is still hindered by security problems. One of the fundamental issues is the need for better access control and identity management systems. In this context, Federated Identity Management (FIM) is identified by researchers and experts as an important security enabler, since it will play a vital role in allowing the global scalability that is required for the successful implantation of cloud technologies. However, current FIM frameworks are limited by the complexity of the underlying trust models that need to be put in place before inter-domain cooperation. Thus, the establishment of dynamic federations between the different cloud actors is still a major research challenge that remains unsolved. Here we show that risk evaluation must be considered as a key enabler in evidence-based trust management to foster collaboration between cloud providers that belong to unknown administrative domains in a secure manner. In this paper, we analyze the Federated Identity Management process and propose a taxonomy that helps in the classification of the involved risks in order to mitigate vulnerabilities and threats when decisions about collaboration are made. Moreover, a set of new metrics is defined to allow a novel form of risk quantification in these environments. Other contributions of the paper include the definition of a generic hierarchical risk aggregation system, and a descriptive use-case where the risk computation framework is applied to enhance cloud-based service provisioning.

New method for positional quality control in cartography based on lines. A comparative study of methodologies

This article describes a set of new metrics for evaluating the positional accuracy of lines to apply a positional control to cartographic databases. In the same way as traditional points-based studies, control based on lines compares the positions between the lines (one from a given database to be controlled and the other from a more accurate independent control database). The proposed method is based on vertex displacements and their influence on adjacent segments. The new methodology which applies these metrics is called ‘vertex influence method’. It also includes an analysis for detection of systematic displacements and a variability value of displacements based on the accuracy of the lines. All the proposed metrics are applied to a real case made up of more than 180 km of roads from the two databases. By means of this study we also compare the results obtained from the main traditional methodologies. This study has revealed the viability of the use of this method to obtain an accuracy value of positional cartographic products.

On the quality assessment of advanced e‐learning services

Distance learning has been widely researched the past few years, nevertheless the focus has been more on its technological dimension. Designing, developing and supporting a large scale e‐learning application for Higher Education is still a challenging task in many ways. E‐learning is data‐intensive, user‐driven, and has increasing needs for multiculturalism, efficiency, adaptivity and competiveness. Although the complexity of such systems has increased exponentially, the design process still lacks a systematic quality control procedure. In this work we address the increasing need for new methods that maximize usability, and thus end‐user satisfaction. We analyse the technological, managerial and economic factors that affect the design and deployment of a large e‐learning platform with advanced services and propose a set of new metrics for assessing its quality. The metrics are based on the four external quality characteristics (functionality, usability, efficiency and reliability) of the ISO9126 standard for software systems.

On equivalence of two metrics in classical thermodynamics

The concepts of distance between states in equilibrium thermodynamics introduced by Weinhold and Ruppeiner were based on the stability conditions and theory of fluctuations, respectively. It has been shown that both these Riemannian matrices are equivalent up to a multiplicative function. They are connected by a contact transformation acting on the thermodynamic phase space. The family of contact transformation produces a set of new metrics from some reference metric. Such transformations can be also considered as a practical method for evaluating partial derivatives of thermodynamic functions.