Evaluation Of Proposed Scheme Research Articles

Insight Into Voting in DAOs: Conceptual Analysis and a Proposal for Evaluation Framework

Driven by the development of blockchain infrastructures and the promotion of Web 3, more than 4000 Decentralized Autonomous Organizations (DAOs) have been developed as online organizations jointly owned and managed by their members who work for the same interests. Voting mechanisms as the democratic administration of DAOs without the involvement of central authority, are crucial to both the development of the DAO community and the protection of individual interests. This paper is one of the first analyses of the critical role of voting mechanisms in DAOs' operation. In the absence of systematic studies of voting mechanisms in DAOs, we propose five tiers of decentralization in DAO voting which marks the critical difference between DAO voting and conventional voting. We also define four dimensions to comprehensively evaluate the performance of DAO voting mechanisms, which identify the demands and characteristics of DAO voting and put forward clear design guidelines for voting mechanisms in DAOs. Finally, we take seven typical voting mechanisms as examples and analyze their performance in our proposed evaluation schemes.

A Comprehensive Evaluation Scheme of Students' Classroom Learning Status Based on Analytic Hierarchy Process

The state of students' classroom learning refers to whether students are concentrating and listening in the class, whether they are interested in the content of the lecture, and whether they understand and master the content of the teacher’s psychological state. The research on the state of students’ classroom learning is of great significance in teaching activities. Usually, measuring students’ classroom learning status is a subjective evaluation, and it is difficult to have a quantitative description. With the development of artificial intelligence (AI)technology, a large number of modern information technologies have been applied in education and teaching. People use video images, eye tracking technology, orfacial expression recognition technology in deep learning to describe students' attention and interest in classroom learning. However, with the complexity of students' learning process, no single information technology can accurately describe students' learning status and learning performance. Therefore, when evaluating students' learning status, it is necessary to consider the students' learning status, learning process, and learning performance. To this end, we used the Analytic Hierarchy Process (AHP)to establish a mathematical model in the four dimensions of interest, pleasure, concentration, and classroom performance and homomorphically adjusted the corresponding weights of the four dimensions in evaluating students' learning status. The proposed evaluation scheme of students' classroom learning status evaluated students' classroom learning status in real-time, providing modern information technology support for improving classroom learning efficiency and the quality of education and teaching.

Building Wireless Performance Evaluation of Channel Hardening for Massive MIMO System

This letter proposes the first building wireless performance evaluation scheme considering channel hardening for an indoor massive multiple-input multiple-output (MIMO) system. We first define a metric, channel hardening obstruction (CHO), to describe the expectation value change of channel hardening ratio that a building under design (BUD) brings about. Then, the closed-form expressions are derived for the defined metric. Finally, we carry out Monte-Carlo simulations to validate the proposed scheme under various indoor scenarios, indicating the correlation between the CHO and BUD’s room amount. The proposed evaluation scheme can be used to design new or retrofit existing buildings with adequate massive MIMO performance.

Streaming egocentric action anticipation: An evaluation scheme and approach

Egocentric action anticipation aims to predict the future actions the camera wearer will perform from the observation of the past. While predictions about the future should be available before the predicted events take place, most approaches do not pay attention to the computational time required to make such predictions. As a result, current evaluation schemes assume that predictions are available right after the input video is observed, i.e., presuming a negligible runtime, which may lead to overly optimistic evaluations. We propose a streaming egocentric action evaluation scheme which assumes that predictions are performed online and made available only after the model has processed the current input segment, which depends on its runtime. To evaluate all models considering the same prediction horizon, we hence propose that slower models should base their predictions on temporal segments sampled ahead of time. Based on the observation that model runtime can affect performance in the considered streaming evaluation scenario, we further propose a lightweight action anticipation model based on feed-forward 3D CNNs which is optimized using knowledge distillation techniques with a novel past-to-future distillation loss. Experiments on the three popular datasets EPIC-KITCHENS-55, EPIC-KITCHENS-100 and EGTEA Gaze+ show that (i) the proposed evaluation scheme induces a different ranking on state-of-the-art methods as compared to classic evaluations, (ii) lightweight approaches tend to outmatch more computationally expensive ones, and (iii) the proposed model based on feed-forward 3D CNNs and knowledge distillation outperforms current art in the streaming egocentric action anticipation scenario.

Characterization of Plasma Process-Induced Low-Density Defect Creation by Lateral Junction Leakage

In the design of ultralow leakage devices such as image sensors, it is necessary to understand the influence of low-density defects during plasma processing-plasma-induced physical damage (PPD)-on device performance. Defects created by plasma exposure act as carrier conduction sites and induce an increase in leakage (e.g., dark) current. This study proposes a PPD evaluation scheme for low-density defect assessments, specifically defects created in the lateral direction due to lateral stochastic straggling (lateral PPD). Two test structures were designed: a single device with a leakage current (Ileak) and a complementary metal-oxide-semiconductor image sensor (CIS) circuit with a dark current (Idark). The energy level and density of defects distributed in the lateral direction were estimated using the Shockley-Read-Hall (SRH) model. The energy level (Et) was derived using the SRH model from the temperature dependence of Ileak and Idark defined as the activation energy Ea. The trap density (Nt) was also determined. A comprehensive comparison of these parameters was conducted. Both Ileak and Idark are dependent on the contact opening diameter after plasma exposure, which implies the presence of defects in the lateral direction via lateral PPD. From the analysis of the temperature dependence of Ileak and Idark, the lateral PPD influenced the mean value of Ea. Moreover, we confirmed that an increase in Idark indicates an increase in the number of trap sites, and more specifically, an increase in defects at shallow levels. The derived trap site density in the CIS circuit was consistent with that of a single device. The proposed evaluation scheme is useful for PPD evaluation in the presence of low-density defects, and is critical for the design of future low-leakage devices.

Heatmap Assisted Accuracy Score Evaluation Method for Machine-Centric Explainable Deep Neural Networks

There have existed many studies about the explainable artificial intelligence (XAI) that explains the logic behind the complex deep neural network called a black box. At the same time, researchers have tried to evaluate the explainability performance of various XAIs. However, most previous evaluation methods are human-centric, that is, subjective, where they rely on how much the results of explanation are similar to what people&#x2019;s decision is based on rather than what features actually affect the decision in the model. Their XAI selections are also dependent of datasets. Furthermore, they are focusing only on the output variation of a target class. On the other hand, this paper proposes a robust heatmap assisted accuracy score (HAAS) scheme over datasets that helps selecting machine-centric explanation algorithms to show what actually leads to the decision of a given classification network. The proposed method modifies the input image with the heatmap scores obtained by a given explanation algorithm and then puts the resultant heatmap assisted (HA) images into the network to estimate the accuracy change. The resultant metric (<i>HAAS</i>) is computed as a ratio of accuracies of the given network over HA and original images. The proposed evaluation scheme is verified in the image classification models of LeNet-5 for MNIST and VGG-16 for CIFAR-10, STL-10, and ILSVRC2012 over totally 11 XAI algorithms of saliency map, deconvolution, and 9 layer-wise relevance propagation (LRP) configurations. Consequently, for LRP1 and LRP3, MINST showed largest <i>HAAS</i> values of 1.0088 and 1.0079, CIFAR-10 achieved 1.1160 and 1.1254, STL-10 had 1.0906 and 1.0918, and ILSVRC2012 got 1.3207 and 1.3469. While LRP1 consists of &#x03F5;-rules for input, convolutional, and fully-connected layers, LRP3 adopts a bounded-rule for an input layer and the same &#x03F5;-rules for other layers as LRP1. The consistency of evaluation results of HAAS and AOPC has been compared by means of Kullback-Leibler divergence, ensuring that HAAS is the more robust evaluation method than AOPC independently of datasets since HAAS has much lower average divergence of 0.0251 than AOPC of 0.3048. In addition, the validity of the proposed HAAS scheme is further investigated through the inverted HA test that employs inverted HA images made up with inverted heatmap scores and estimates the accuracy degradation caused by applying them to the network. The XAI algorithms with largest <i>HAAS</i> results experience biggest accuracy degradation in the inverted HA test.

Research on Comprehensive Evaluation of the Operation and Maintenance Status of Urban Deep Metro Station

With the rapid development of urban metro system, the operation and maintenance status of urban metro station has received increasing attention. The operation and maintenance status of urban subway stations involve various factors including structure, equipment, and environment, which are difficult to be accurately evaluated directly. On the other hand, as the burial depth of metro station increases, disaster-triggering factors become increasingly complex and diverse, which enhances the difficulty of evaluating the operation and maintenance status. A new method is proposed herein to realize the comprehensive evaluation of the operation and maintenance status of urban deep metro stations. Firstly, the evaluation indexes needed for evaluation are selected by referring to a large number of literatures, combined with many relevant specifications and standards, and based on some existing index selection principles. According to the selected indexes, an evaluation index system of operation and maintenance status of underground metro station is established. Then, based on the established evaluation index system, a comprehensive evaluation method for the operation and maintenance status of urban deep metro station is developed by combining the analytic hierarchy process (AHP) and relevant information aggregation methods. This method is characterized by considering the functions with prominent defects with the use of weighted geometric average (WGA) and variable weight methods. Finally, the proposed evaluation scheme is applied to guide the maintenance of China’s deepest metro station. The proposed comprehensive evaluation method will promote the in-time management and maintenance of urban deep metro station.

On Performance of Ultra-Dense Neighborhood Small Cell Networks in Urban Scenarios

The neighborhood small cell (NSC) network, which employs indoor small base stations (SBSs) to serve both indoor and outdoor users, is considered a cost-effective solution to address the increasing wireless traffic demand. In this letter, a novel evaluation scheme is proposed to predict the performance of a pure NSC network. A network model composed of indoor ultra-dense small cells (UDS) with coordinated multi-point (CoMP) transmissions is employed to address the maximum achievable performance gain in a given neighborhood. A closed-form analytical model is proposed to approximately compute the signal strength received by a specific outdoor user from the SBSs in a complex-shaped building and verified by the Monte Carlo simulations. The performance prediction of the ultra-dense NSC network is demonstrated in two practical urban scenarios. The proposed evaluation scheme facilitates the tractability of NSC network performance predictions in practical scenarios, which can be employed in future NSC embedded neighborhoods design.

On Evaluation of Indoor to Outdoor Communications Using Neighbourhood Small Cells

A novel concept, the neighbourhood small cell (NSC) network, has been brought up to provide high-capacity wireless communications in a cost-effective manner. The indoor small cells not only cover indoor users but are also considered open access to serve outdoor users through indoor to outdoor (I2O) propagations. In this correspondence, we facilitate the analytical performance evaluation for a pure NSC network system by deriving the distribution of the signal to interference ratio (SIR) and the outage probability in closed-form expressions. In the proposed system evaluation scheme, both the indoor penetration loss and the Nakagami- $m$ fading are taken into account. Numerical results obtained in a typical urban scenario show that the system performance of a pure NSC network depends on the propagation environment, the indoor building structures and the layout of the neighbourhood significantly.

Piezocone/cone penetration test-based pile capacity analysis: calibration, evaluation, and implication of geological conditions

ABSTRACT Several studies have proposed evaluation schemes to assess the performance of cone/piezocone penetration test (CPT/PCPT)-based pile analyses. However, approaches that calibrate, evaluate, and consider the locality of these methods are not fully recognized, yet. In this study, a calibration analysis using a least square method and subsequent evaluation of CPT/PCPT-based methods was conducted based on a pile and CPT/PCPT database. The evaluation scheme comprised different statistical criteria and a ranking index (RI) was used to evaluate the methods. After the evaluation, it turned out that methods by De Ruiter and Beringen, Tumay and Fakhrooand Clisby et al. showed a fair performance for H-piles with overall RI of 8, 9, and 13, respectively. Methods by De Ruiter and Beringen , Eslami and Fellenius , and Bustamante and Gianeselli showed a fair performance for closed-ended pipe and prestressed concrete piles with overall RI 9, 14, and 15, respectively. In addition, consideration of the geological conditions for the better performing techniques showed that they were commonly developed for soils mostly derived from marine deposit during Mesozoic era and melting of thick ice sheet during Cenozoic era. Consideration of prevailing geological condition for the test sites, therefore, is recommended in predicting pile bearing capacity based on CPT/PCPT-based.

Evaluation of fault diagnosability for nonlinear uncertain systems with multiple faults occurring simultaneously

Up to present, the problem of the evaluation of fault diagnosability for nonlinear systems has been investigated by many researchers. However, no attempt has been done to evaluate the diagnosability of multiple faults occurring simultaneously for nonlinear systems. This paper proposes a method based on differential geometry theories to solve this problem. Then the evaluation of fault diagnosability for affine nonlinear systems with multiple faults occurring simultaneously is achieved. To deal with the effect of control laws on the evaluation results of fault diagnosability, a design scheme of the evaluation of fault diagnosability is proposed. Then the influence of uncertainties on the evaluation results of fault diagnosability for affine nonlinear systems with multiple faults occurring simultaneously is analyzed. The numerical simulation results are obtained to show the effectiveness of the proposed evaluation scheme of fault diagnosability.

Использование побочных потоков товарного производства нефтепродуктов

Oily wastes, along with the problem of their disposal, comprise an inevitable factor in the processing and storage of oil and oil-derived products. Contemporary disposal methods such as landfill and incineration entail secondary pollution of environmental objects. However, since oily wastes contain valuable hydrocarbons, the search for new ways to extract them for use as components of the target products of oil refineries is an urgent task. The object of the study comprised oily wastes derived from an oilwell product of the commercial production of the Angarsk Petrochemical Company. According to the existing scheme, this product is combined with other oil-containing streams and sent for rectification. Thus, in 2017, 1,200 tonnes of secondary oil product were sent to the conversion unit. The transportation and processing of such a quantity of substrates leads to the release into the environment of more than 2 tonnes of harmful substances per year. The results of qualitative and quantitative analysis showed that the high content of gasoline fractions – predominantly n-alkanes C 5 –С 10 (more than 90% by weight) – in the hydrocarbon composition suggests the possibility of using the oilwell product as a component of motor gasolines without re-processing using the rectification method. However, the quality of the oilwell product, mainly in terms of water content and colour, is rather unstable. In order to select the preliminary preparation method for the oilwell product, tests of a bottom sample having a high water content (50% vol.) were carried out. It was established that sedimentation of the sample at a temperature of 22–23 °С for 4 hours leads to the effective separation of the water component. To reduce the influence of the colour factor of the oilwell product when adding it to motor gasolines, it is proposed to dilute it with other refinery by-products. The quality of laboratory samples of AI-92 and AI-80 gasolines obtained with the involvement of the prepared oilwell product complies with the requirements of GOST 32513. In order to scale the process of preparing the substation in order to use it as a component of motor fuels, Angarsk Petrochemical Company developed a simple, affordable technological scheme, based on mixing and separation processes using available equipment. The economic effect of the implementation of this measure will be about 3.7 million rubles per year. The proposed evaluation scheme and the method of separation of water-organic by-products for further use as a component of motor fuel are generic and can be applied to similar wastes from refineries and oil depots.

A comprehensive approach for the evaluation of recommender systems using implicit feedback

Evaluation strategies are essentials in assessing the degree of satisfaction that recommender systems can provide to users. The evaluation schemes rely heavily on user feedback, however these feedbacks may be casual, biased or spam which leads to an inappropriate evaluation. In this paper, a comprehensive approach for the evaluation of recommendation system is proposed. The implicit user feedbacks are taken for the different products on the basis of the reviews provided to them. A novel sincerity check mechanism is suggested to render the biasedness and casual among the users. Further, mathematical model is presented to classify the products preference criteria. The list of the preferred products yield different ranking. Rank aggregation algorithm is used to obtain a final ranking, which is compared with the base ranking to be evaluated. Hence, with the help of suggested methodology, an evaluation strategy is suggested that avoids the risk of fake and biased feedbacks. The comparison of the proposed approach with existing schemes shows the superiority of the aforementioned approach from various parameters. It is envisaged that the proposed evaluation scheme lays a platform for users to assess the recommender systems for their ease and reliable online shopping.

A Quality Control Method of Ground-Based Weather Radar Data Based on Statistics

The detection of meteorological targets using ground-based weather radars usually suffers from ground clutter and beam blockage. These nonmeteorological or weakened signals should be identified so quality control should be implemented before weather radar data can be used. Conventional quality control methods aim at differentiating between echo structures of ground clutter and meteorological targets, and use terrain information to calculate beam blockage regions based upon standard atmospheric refraction. However, it is difficult to achieve the goal for long-term large data sets by conventional methods due to the complexity and diversity of weather radar echoes. In this paper, regions of ground clutter and beam blockage are first identified through the statistics on spatial distribution of reflectivity and fuzzy logic classification, and then they are used as masks to remove data from the scan. The new method is applied to data of the Nanjing weather radar in China. By the aid of a proposed evaluation scheme and the visual recognition, quality control results of the new method are compared with those of the conventional methods. It is found that the new method can provide better identification of ground clutter or beam blockage and thus better quality control results. The new scheme has a good prospect in operational service for its principle advantages, easy applicable conditions, and better performance compared with conventional methods.

An evaluation scheme for assessing the effectiveness of intersection movement assist (IMA) on improving traffic safety

ABSTRACTObjective: Intersection movement assist (IMA) has been recognized as one of the prominent countermeasures to reduce angle crashes at intersections, which constitute 22% of total crashes in the United States. Utilizing vehicle-based sensors, vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I) communications, IMA offers extended vision to provide early warning for an imminent crash. However, most of IMA-related research implements their methods and strategies only in simulations, test tracks, or driving simulator studies that have quite a few assumptions and limitations and hence the effectiveness evaluations reported may not be transferable or comparable.Methods: This study seeks to develop a generalized evaluation scheme that can be used not only to assess the effectiveness of IMA on improving traffic safety at intersections but to facilitate comparisons across similar studies. The proposed evaluation scheme utilizes the concepts of traffic conflict in terms of time-to-collision (TTC) as a crash surrogate. This approach avoids the issue of having insufficient crash frequency data for system evaluation. To measure the effectiveness of IMA on reducing traffic conflicts, a relative risk is calculated for comparing the risk of with/without using the IMA. As a proof-of-concept study, this study applied the proposed evaluation scheme and reported the effectiveness of IMA on improving traffic safety in a field operation test (FOT). Seven test scenarios were conducted at 4 intersections, and a total of 40 participants were recruited to use the IMA for 6 months.Results: It was estimated that IMA users have 26% fewer conflicts with TTC less than 5 s and have 15% fewer conflicts with TTC less than 4 s. However, the results vary across different sites and different definitions of conflicts in terms of TTC.Conclusions: Overall, IMA is promising to effectively reduce angle crashes related to sight obstruction and has potential to reduce not only crash frequency but crash severity.

A Mass Appraisal Model Based on Market Segment Parameters

The proposed evaluation scheme is a uni-equation model to evaluate properties of Mass Appraisal (MA) in terms of widespread availability of sample data. It all allows the use of statistical models and in the opposite conditions of the absence of data of comparable properties, the functions of similar market areas are known as well as the ones near to those for which you want to estimate the function. Of course, the accuracy of the evaluation increases with the amount of available data, with other equal conditions and evaluations carried out without data (but in the presence of other market information). It requires extra-statistical appraisal procedures involving a complete knowledge of the real estate market. However, such knowledge is also required in the MA performed by quantitative models with regard to the data sampling and performance monitoring process. The model considers micro-level characteristics of the properties and macro-level parameters of the real estate market segments. The appraisal model defines the prediction function with both the statistical models and estimation procedures. For this purpose, the model considers four specific situations: the construction of a statistical model operating with a sufficiently large sample of market prices; the construction of a prediction function operating with a very few number of market prices samples; in this situation, the appraisal function of market value is defined by using a sample of market prices referred to comparable properties, and these are few for statistical use but perfectly suitable to the appraisal process; the construction of a prediction function operating with only one market price; the construction of a prediction function operating in the absence of real estate data but with similar functions of market areas with other estimated proprieties. The presented model provides a uniform method of estimating the market value of properties (and fees), through the modular functions. The model studied is able to operate also with reduced information, considering the practical circumstances, the boundary conditions, the application precautions and the significance of the results.

An evaluation scheme for product–service system models: development of evaluation criteria and case studies

A product–service system (PSS) integrates products and services to fulfill customer needs and create sustainability. PSS evaluation requires the use of diverse criteria because PSSs are complex systems with multiple stakeholders and perspectives. This paper proposes an evaluation scheme for PSS models that consists of a set of 94 evaluation criteria and an evaluation procedure. The proposed set of criteria encompasses both provider and customer perspectives, all of the 3P (profitability, planet, and people) values and various PSS lifecycle phases, whereas existing studies only partially cover these aspects of PSS. The proposed set serves as an evaluation criterion repository, and users can easily identify the criteria relevant to the evaluation targets. Using the proposed set is more efficient than starting from scratch. The proposed evaluation scheme can be used either to compare different PSS models or to evaluate a single model. Case studies show that the proposed scheme can sufficiently evaluate both existing and newly launched PSS models as well as models under development. The proposed scheme is expected to serve as an efficient and effective aid for practitioners in PSS development.

Characterizing the Performance of Security Functions in Mobile Computing Systems

The next-generation mobile networks will be equipped with sophisticated communication security, ensuring the safety and authenticity of the transmitted information. However, many of today's prominent security measures are cryptography-based and present several operational challenges in mobile computing systems. Thus, enforcing security measures can greatly affect communication performance, particularly, when it comes to time-based guarantees such as delay and jitter. Moreover, mobile computing systems have limited energy sources, which can be depleted quickly by improperly enforcing such resource-intensive operations. Therefore, it becomes vital to understand the computational characteristics of security measures from a communication perspective. By observing these characteristics, it may be possible for existing and future mobile systems to be suited with security functions that provide the sufficient communication security while maintaining both the power-efficiency and the delay/jitter requirements. In this paper, we propose a benchmarking environment for evaluating cryptography-based security functions from a communication perspective. The paper investigates how mobile systems' design and operation characteristics have a significant impact on the computational characteristics of security functions. The paper explores the evaluation metrics that can be used in benchmarking security functions within various communication settings and proposes the use of a simple and effective delay-based metric for the benchmarking process. The computational characteristics of some selected security functions are evaluated under the proposed benchmarking environment and presented in this paper. While the main focus of the work is the widely utilized mobile communication settings, the proposed evaluation scheme can be applied for other communication settings and for noncryptographic security functions.

Assessment of PBTs in the European Union: a critical assessment of the proposed evaluation scheme with reference to plant protection products

A number of international and national programs classify substances that are persistent (P or very P), bioaccumulative (B or very B), toxic (T), or have the potential for long-range transport. The oldest of these programs is the Stockholm Convention on Persistent Organic Pollutants. More recent programs address persistent, bioaccumulative, and toxic (PBT) properties for chemicals in general (Registration, Evaluation, Authorisation and Restriction of Chemicals, REACH; EC 253/2011) and plant protection products (PPPs) (EC 1107/2009). However, these programs used different criteria for categorization. We critically assessed the criteria and process used in the categorization of PPPs and noted that EC1107/2009, in contrast to the Stockholm Convention or REACH, offers no process for carrying out a further, more refined assessment of those pesticides that are identified as having PBT properties. Thus, in contrast to REACH, few basic screening criteria are used for final-step management decisions. Guidance on the selection of data is not provided, and the criteria used are unclear. For example, no guidance is given as to how the half-lives in soil, water, and sediment should be derived and the term ‘half-life’ is not clearly defined. Large amounts of useful data on environmental and toxicological properties are available for PPPs but most of this is not used in the categorization, for example, photolysis in water, water-sediment, and on soil, important environmental degradation processes particularly relevant to pesticides. The criteria for bioaccumulation and toxicity appear to be focused only on aquatic ecosystems and do not address the terrestrial compartment which is particularly relevant for pesticides and potentially relevant for PBT considerations. The categorization process under EC 1107/2009 could be made more efficient and reduce false negatives and positives if a formal weight of evidence approach was applied to multiple lines of evidence. This paper presents these ideas and how they can be incorporated into the framework for categorization to better classify plant protection products in terms of persistence, bioaccumulation, and toxicity.

Performance Evaluation Methodology for Historical Document Image Binarization

Document image binarization is of great importance in the document image analysis and recognition pipeline since it affects further stages of the recognition process. The evaluation of a binarization method aids in studying its algorithmic behavior, as well as verifying its effectiveness, by providing qualitative and quantitative indication of its performance. This paper addresses a pixel-based binarization evaluation methodology for historical handwritten/machine-printed document images. In the proposed evaluation scheme, the recall and precision evaluation measures are properly modified using a weighting scheme that diminishes any potential evaluation bias. Additional performance metrics of the proposed evaluation scheme consist of the percentage rates of broken and missed text, false alarms, background noise, character enlargement, and merging. Several experiments conducted in comparison with other pixel-based evaluation measures demonstrate the validity of the proposed evaluation scheme.