Ranking Scheme Research Articles

KGroot: A knowledge graph-enhanced method for root cause analysis

Fault localization in online microservices is a challenging task due to the vast amount of monitoring data, diversity of types and events, and complex interdependencies among services and components. Fault events in services are propagative and can trigger a cascade of faults in a short period of time. In the industry, fault localization is typically conducted manually by experienced personnel. This reliance on experience is unreliable and lacks automation. Different modules present information barriers during manual localization, making it difficult to quickly align during urgent faults. This inefficiency lags stability assurance to minimize fault detection and repair time. Although actionable methods aimed to automate the process, the accuracy and efficiency are less than satisfactory. The precision of fault localization results is of paramount importance as it underpins engineers’ trust in the diagnostic conclusions, which are derived from multiple perspectives and offer comprehensive insights. Therefore, a more reliable method is required to automatically identify the associative relationships among fault events and propagation paths. To achieve this, a knowledge graph-enhanced root cause analysis (KGroot) method is designed for efficient and effective diagnosis of recurring failures in complex microservices environments. As the first event-driven knowledge graph method, KGroot uses event knowledge and the correlation between events to perform root cause reasoning for Root Cause Analysis (RCA). A Fault Event Knowledge Graph (FEKG) is built based on historical data, an online graph is constructed in real-time when a failure event occurs, and the similarity between each event knowledge graph and online graph is compared using GCNs to pinpoint the fault type through a ranking strategy. Comprehensive experiments demonstrate that KGroot can locate the root cause with an accuracy of 93.5% top 3 potential causes in second-level. This performance matches the level of real-time fault diagnosis in the industrial environment and significantly surpasses state-of-the-art baselines in RCA in terms of effectiveness and efficiency. (KGroot is available at https://github.com/daixixiwang/KGroot).

Optimizing the decarbonization of a geographically dispersed building portfolio

Many organizations are actively pursuing decarbonization goals to help mitigate the effects of climate change. This situation often requires capital replacement of building components with different fuel or higher efficiency variants to reduce emissions. However, this problem is complicated when an organization operates across a wide geographic area due to regional differences in weather, utility costs, and electrical grid emissions. This paper develops a framework for this problem by implementing energy, emissions, cost, and decision models that can scale across large organizations. The resulting decisions show the ability to reach decarbonization goals with lower costs and better rates of return. Compared to condition or unit-capacity ranking strategies, case studies show that the developed model reduces emissions by as much as 68.99 % or costs by as much as 47.08 %, depending on organizational goals.

Generative learning facilitated discovery of high-entropy ceramic dielectrics for capacitive energy storage

Dielectric capacitors offer great potential for advanced electronics due to their high power densities, but their energy density still needs to be further improved. High-entropy strategy has emerged as an effective method for improving energy storage performance, however, discovering new high-entropy systems within a high-dimensional composition space is a daunting challenge for traditional trial-and-error experiments. Here, based on phase-field simulations and limited experimental data, we propose a generative learning approach to accelerate the discovery of high-entropy dielectrics in a practically infinite exploration space of over 1011 combinations. By encoding-decoding latent space regularities to facilitate data sampling and forward inference, we employ inverse design to screen out the most promising combinations via a ranking strategy. Through only 5 sets of targeted experiments, we successfully obtain a Bi(Mg0.5Ti0.5)O3-based high-entropy dielectric film with a significantly improved energy density of 156 J cm−3 at an electric field of 5104 kV cm−1, surpassing the pristine film by more than eight-fold. This work introduces an effective and innovative avenue for designing high-entropy dielectrics with drastically reduced experimental cycles, which could be also extended to expedite the design of other multicomponent material systems with desired properties.

Підходи до комерціалізації результатів інноваційної діяльності спільних підприємств

Various approaches to the commercialization of the results of innovative activities of joint enterprises are considered. According to the results of the review of the results of the evaluation of the innovative activity of the countries of the world, a ranking scheme of the leading countries in terms of the directions of innovative activity was generated. The process of commercialization of the results of innovative activities of enterprises is described. Systematized methods of evaluating the potential of commercialization of innovative activity, among which the method of technological audit, the method of fuzzy logic and the four-dimensional approach are highlighted. Approaches to evaluating its effectiveness are considered. Emphasis is placed on the main criteria in approaches to the commercialization of innovations, namely on the evaluation of the potential of commercialization and the assessment of its effectiveness. The list of indicators allowing to evaluate the financial result of the commercialization of the company’s innovations is highlighted. Methods of assessing the prospects and readiness for market commercialization of innovative developments, which are most adaptable for joint ventures, are described. The priority list includes the methodology for assessing the readiness for market commercialization of scientific developments, the TRL (Technology Readiness Level) methodology, developed by the US National Aerospace Agency NASA, and the TPRL (Technology Project Readiness Level) methodology. The main centers of transformation of innovative ideas or research results into marketable products or services are highlighted. The focus is on Technology Transfer Offices (TTO), startup accelerators and incubators, public-private partnerships, direct investment in research and development, open innovation, cluster innovation projects, and market-oriented innovation. The procedure for assessing the levels of suitability for commercialization of innovative development is illustrated. Based on the results of the conducted research, a general scheme for the commercialization of innovations was built.

Utilizing passage‐level relevance and kernel pooling for enhancing BERT‐based document reranking

AbstractThe pre‐trained language model (PLM) based on the Transformer encoder, namely BERT, has achieved state‐of‐the‐art results in the field of Information Retrieval. Existing BERT‐based ranking models divide documents into passages and aggregate passage‐level relevance to rank the document list. However, these common score aggregation strategies cannot capture important semantic information such as document structure and have not been extensively studied. In this article, we propose a novel kernel‐based score pooling system to capture document‐level relevance by aggregating passage‐level relevance. In particular, we propose and study several representative kernel pooling functions and several different document ranking strategies based on passage‐level relevance. Our proposed framework KnBERT naturally incorporates kernel functions from the passage level into the BERT‐based re‐ranking method, which provides a promising avenue for building universal retrieval‐then‐rerank information retrieval systems. Experiments conducted on two widely used TREC Robust04 and GOV2 test datasets show that the KnBERT has made significant improvements over other BERT‐based ranking approaches in terms of MAP, P@20, and NDCG@20 indicators with no extra or even less computations.

Identification of Construction Project Risks in the Indian Context: A Case Study on Building Construction

Abstract: This paper presents a comprehensive investigation into the risk factors prevalent in building construction projects within the Indian context, focusing on their identification, ranking, and management strategies. Through data collected from 206 construction professionals via an online questionnaire, the study employs robust reliability analysis, yielding a high Cronbach's Alpha coefficient of 0.898, affirming the questionnaire's internal consistency and reliability. Utilizing the Relative Importance Index (RII) and Safety Risk Score (SRS), the research ranks critical risk factors, including poor stakeholder communication, inaccurate cost estimation, funding delays, and market demand fluctuations, underlining the necessity of addressing these challenges to mitigate potential impacts on project outcomes. The paper emphasizes the significance of proactive risk management strategies to ensure project success and resilience amidst evolving circumstances, providing valuable insights into construction project risks specific to the Indian landscape and offering recommendations for enhancing risk management practices in the field.

Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling.

Passive mobile sensing provides opportunities for measuring and monitoring health status in the wild and outside of clinics. However, longitudinal, multimodal mobile sensor data can be small, noisy, and incomplete. This makes processing, modeling, and prediction of these data challenging. The small size of the data set restricts it from being modeled using complex deep learning networks. The current state of the art (SOTA) tackles small sensor data sets following a singular modeling paradigm based on traditional machine learning (ML) algorithms. These opt for either a user-agnostic modeling approach, making the model susceptible to a larger degree of noise, or a personalized approach, where training on individual data alludes to a more limited data set, giving rise to overfitting, therefore, ultimately, having to seek a trade-off by choosing 1 of the 2 modeling approaches to reach predictions. The objective of this study was to filter, rank, and output the best predictions for small, multimodal, longitudinal sensor data using a framework that is designed to tackle data sets that are limited in size (particularly targeting health studies that use passive multimodal sensors) and that combines both user agnostic and personalized approaches, along with a combination of ranking strategies to filter predictions. In this paper, we introduced a novel ranking framework for longitudinal multimodal sensors (FLMS) to address challenges encountered in health studies involving passive multimodal sensors. Using the FLMS, we (1) built a tensor-based aggregation and ranking strategy for final interpretation, (2) processed various combinations of sensor fusions, and (3) balanced user-agnostic and personalized modeling approaches with appropriate cross-validation strategies. The performance of the FLMS was validated with the help of a real data set of adolescents diagnosed with major depressive disorder for the prediction of change in depression in the adolescent participants. Predictions output by the proposed FLMS achieved a 7% increase in accuracy and a 13% increase in recall for the real data set. Experiments with existing SOTA ML algorithms showed an 11% increase in accuracy for the depression data set and how overfitting and sparsity were handled. The FLMS aims to fill the gap that currently exists when modeling passive sensor data with a small number of data points. It achieves this through leveraging both user-agnostic and personalized modeling techniques in tandem with an effective ranking strategy to filter predictions.

Co-creation of a novel approach for improving supply chain management for SARS-CoV-2 point of care diagnostic services in Mopani District, Limpopo Province: nominal group technique.

Effective supply chain management (SCM) of point-of-care (POC) tests for diseases like severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) requires active participation from diverse stakeholders, government entities, and regulatory bodies. The responsibility for overseeing various aspects of POC tests, including procurement, quality assurance, storage, inventory management, distribution, and human resource capacity, lies with national, provincial, and local levels of government. This study aimed to collaboratively develop an innovative approach to enhance SCM for SARS-CoV-2 POC diagnostic services in resource-limited settings, using the Mopani District in Limpopo province, South Africa, as a case study. Key stakeholders were invited to participate in an online workshop using purposive sampling. The study employed the nominal group technique (NGT) for data collection, which consisted of two phases. Phase 1 focused on identifying barriers in the supply chain of COVID-19 rapid tests, while phase 2 aimed to devise strategies to overcome the priority barriers identified in phase 1. Participants used a Likert scale of 1-5 to rank barriers and strategies, and an overall ranking score was calculated for each. The participants were provided with the results of the ranking exercise for their feedback. Eleven key stakeholders from national (n = 1), provincial (n = 4), and local government (n = 2) levels, research entities (n = 3), and non-governmental organizations (n = 1) took part in the study. Participants identified significant barriers in the supply chain, such as the availability of testing kits, unknown demand, information on SCM during a pandemic, methods of controlling stock, and procurement processes. Strategies suggested by key stakeholders included monitoring stock levels and optimizing stock visibility systems to improve test availability, enhancing information visibility and consistent data updates to address unknown demand and improve SCM during a pandemic, employing data capturing and digitization for effective stock control, and implementing demand planning and standardized procurement processes at the national level to enhance stock procurement. The successful collaboration with key stakeholders, facilitated by the NGT, resulted in the co-creation of a novel approach to enhance SCM for COVID-19 diagnostic services in resource-limited settings. This study holds the potential to support the provision of COVID-19 diagnostic services in such settings. A recommended follow-up study would assess the feasibility of implementing this approach.

EPSMR: An efficient privacy-preserving semantic-aware multi-keyword ranked search scheme in cloud

Traditional privacy-preserving search schemes have commonly used the TF-IDF model, which relies on keyword frequency statistics but neglects the semantic relationships between and documents. In recent years, a few secure multi-keyword ranked search schemes over encrypted cloud data have considered the data semantics. However, the search efficiency of these schemes can still be improved. This paper presents an efficient privacy-preserving semantic-aware multi-keyword ranked search scheme (EPSMR) over encrypted cloud data. EPSMR utilizes the LDA topic model to discover hidden semantic information in documents and convert documents into vector representations. vector encryption by using the secure inner product is employed to implement the privacy-preserving semantic relevance score computation between search keywords and documents. To achieve efficient multi-keyword searches, the bisecting k-means algorithm and search tree structure are introduced to design a novel ω-way balanced search tree index which benefits finding search results quickly. to further improve the search efficiency, a filtering-threshold lookup table is presented, which can be used to prune unqualified sub-trees in advance. The experimental results show the superior performance of the proposed scheme compared to existing schemes in terms of both semantic search precision and search time cost.

Assessing the potential impact of livestock immunisation and acaricide use on controlling the spread of East Coast fever

Immunisation of livestock with high-quality vaccines and the use of acaricides are highly ranked tick control strategies worldwide. However, the effects of coupling livestock immunisation and acaricide control on mitigating the spread of East Coast Fever (ECF) is not well understood. Effective strategies to curb the disease require an understanding of the influence of control strategies on ECF dynamics. This paper presents a new mathematical model for ECF in ticks and livestock to analyze the effect of livestock immunisation and acaricide control on preventing ECF spread. Our research is focused on examining how vaccine efficacy, inoculation rate, and acaricide efficacy affect disease progression. Our finding is that acaricide control alone may not be sufficient to stop the spread of ECF, even if it has an 80% effectiveness all the time. However, by pairing acaricide control with livestock vaccination, disease transmission is significantly reduced and elimination is possible under certain circumstances. Overall, results show that it is crucial to understand the influence of combining control strategies to mitigate the spread of this devastating livestock disease and enhance decision making among policymakers and livestock keepers.

Aspect-level item recommendation based on user reviews with variational autoencoders

In this paper we propose an aspect-based recommendation model based on variational autoencoders, that provides not only coarse predictions about what items users may like, but also finer-grained predictions of specific aspects users may be interested in of the recommended items. The proposed model first employs convolution operations to learn probability distributions of aspect-level embeddings for users and items from user aspect-level sentiments on seen items. Then it samples from these distributions and feeds the samples into transpose convolution operations to ‘reconstruct’ missing aspect-level sentiments for unseen items, thereby serving as signals for generating recommendations. To prevent overfitting, we impose a prior on the representation distributions and penalize the model if the learned distributions diverge from the prior. Based on the output of the proposed model, we propose a two-stage ranking scheme that combines aspect-level and overall sentiment signals to rank items. Experiment results show that the proposed model outperforms state-of-the-art aspect-based recommendation models, and the two-stage ranking scheme improves the traditional ranking by overall sentiment predictions.

An integrated multi-attribute decision framework for sustainability assessment of renewable diesel fuel production pathways

Ascertaining the sustainability of a fuel production technology in the presence of conflicting factors out of different existing production pathways is a typical and time-consuming exercise. Thus, it is necessary to follow a resourceful and systematic methodology to decide the best fuel production method for any application. The present work proposes a systematic decision framework that utilizes various MCDM schemes for identifying suitable renewable diesel production technology to replace conventional diesel fuel. Sustainability assessment for a total of five production methods was conducted, based on fifteen criteria. The MOORA, VIKOR, and COPRAS techniques are utilized to compute ranking schemes for different alternatives according to integrated criteria weights based on AHP and CRITIC methods. Qualitative assessment in terms of rank reversal test and sensitivity analysis employed to compare and check the robustness of applied methodologies. The results demonstrated that FT diesel is the most suitable alternative followed by green diesel-I and feedstock price as the most persuasive criteria followed by PM2.5.The qualitative assessment revealed that the integrated weight-based MOORA technique is the most robust framework for the present decision problem, followed by VIKOR and COPRAS strategies respectively. Further, the proposed MCDM techniques can be suggested independently to encounter similar types of decision-making problems.

The best at the top? Candidate ranking strategies under closed list proportional representation

Abstract Under closed-list proportional representation, a party's electoral list determines the order in which legislative seats are allocated to candidates. When candidates differ in their ability, parties face a trade-off between competence and incentives. Ranking candidates in decreasing order of competence ensures that elected politicians are most competent. Yet, party lists create incentives for candidates that may lead parties not to place their best candidates at the top of the list. We examine this trade-off in a game-theoretical model in which parties rank their candidates on a list, candidates choose their campaign effort, and the election is a team contest for multiple prizes. We analyze how the candidates’ objectives, voters’ attention and media coverage, incumbency, the number of parties competing in the election, and the electoral environment influence how parties rank candidates.

Principal Support and Teacher Self-Efficacy as Predictors of Collective Teacher Efficacy

<p style="text-align: justify;">This study investigated the extent to which demographic variables, principal support and teacher self-efficacy contribute to the collective teacher efficacy. In the study, a non-probability purposive sample was used, which included 761 teachers working in compulsory primary schools in all parts of the Republic of Croatia. The first part of the administered online questionnaire provided data on sociodemographic characteristics of the respondents, while in the second part, the following scales were used: The Principal Support Scale, The Teacher Sense of Teacher Efficacy Scale and The Collective Teacher Efficacy Scale. The results of the performed regression analyses show that: 1) the demographic variables - workplace, promotion to a higher rank and principal support have a predictive value in explaining teachers’ self-efficacy; 2) an average number of students per class, promotion to a higher rank, principal support, teacher engagement and teaching strategies have a predictive value in explaining collective teacher efficacy. The findings of this study will be discussed in terms of their potential impact and future research.</p>

Multiple Keyword Search in Cloud Data

Cloud computing is often regarded as a great innovation due to the increasing demand for endless storage space and reliable retrieval services. Several works have been developed on ranked multi-keyword search over cloud data with numerous data owners concerned about privacy. However, most of these methods can be broken by a clever attacker using a combination of a keyword guess and an equivalency test. Also, when searching across data from many data owners, they can't reliably return the top k results to the users. The unreliability of search results and the potential for the exposure of critical keyword information are two obvious drawbacks. To address this issue, we first propose a novel efficient ranked multi-keyword retrieval scheme with keyword privacy for multiple data owners, allowing the cloud server to perform multikeyword search over the cloud data and then return the top-k ranked search results to data users without leaking any keyword and trapdoor information. Furthermore, we demonstrate through stringent security analysis that our method is safe from both internal and external threats. Finally, the results of the performance evaluation show that our scheme outperforms other ranked multi-keyword search systems. Keywords: cloud computing, multi-keyword retrieval, multiple data owner model, privacy sensitive, ranked.

Combining low-volatility and momentum: recent evidence from the Nordic equities

ABSTRACT This paper investigates the profitability of combined low-volatility and momentum investment strategies in the Nordic stock markets from January 1999 to September 2022. Confirming earlier studies, our results first indicate that both the volatility and momentum effects persist as pure-play strategies. Further, we explore combined strategies using 50/50, double screening, and ranking strategies. Among the long-only portfolios, the momentum-first strategy generates the best Sharpe ratio using the double screening method−slightly outperforming the ranking method. Additionally, all long-only combination portfolios outperform the market in terms of risk-adjusted returns. Combination long-short strategies produce significantly higher risk-adjusted returns than pure-play strategies. Surprisingly, novel evidence suggests that none of the combination long-short strategies outperforms the pure momentum strategy after risk-adjusting the returns using the Fama and French five-factor model, implying that while momentum may enhance the returns from the low-volatility strategy, the reverse is not true for the Nordic stock markets.

Fuzzy Framework for Evaluation Strategies of Climate Change Adaptation on Constructing

This study proposes a decision-making model for ranking strategies to eliminate climate change in construction. The triangular fuzzy set deals with uncertain information in the ranking process. Various criteria are used in this study, so the multi-criteria decision-making (MCDM) methodology is used to deal with these criteria. The TOPSIS method is an MCDM methodology used to rank the alternatives. The TOPSIS method is integrated with triangular fuzzy numbers. The criteria weights are computed by using the average method. The application is applied with 12 criteria and 8 options. The results show that alternative 7 is the best and alternative 3 is the worst. The results are tested with sensitivity analysis to show their stability. The sensitivity analysis was conducted with 13 cases of changing the criteria weights. Then, the TOPSIS method is applied under 13 cases in criteria weights. The results show that the ranking is stable in different cases.

Multi-Objective Evolutionary Hybrid Deep Learning for energy theft detection

Electricity theft has emerged as a notable concern for smart grids, as fraudulent users illicitly access electricity from utilities without a contractual agreement or manipulate meter readings to evade bill payments. Consequently, the importance of electricity theft detection (ETD) in preserving the cost-effectiveness of smart grids has increased. In recent years, the utilization of deep learning models in electricity theft detection has surged in popularity, owing to their capacity to capture the periodic patterns within time-series data on electricity consumption. Nonetheless, deep learning techniques for electricity theft detection encounter several challenges, such as data imbalance, higher false positive rates, and the determination of periodic cycle timings. In this paper, we present an innovative approach to tackle these challenges: the Multi-Objective Evolutionary Hybrid Deep Learning (MOE-HDL) architecture, specifically designed to identify electricity theft. In MOE-HDL techniques, first, a novel multi-objective evolutionary algorithm based on ranking scheme (RMOE) is proposed to optimize architectural model parameters and the detection window size, with precision and recall as conflicting objectives. Next, an optimized dynamic detection window is generated to effectively capture the periodic patterns in consumption data and convert the one-dimensional data into a grid structure. Finally, a hybrid deep learning model is designed with the help of optimized model parameters to identify abnormal patterns in electricity consumption data. We conducted a comprehensive set of experiments, comparing our approach with eight baseline methods for electricity theft detection. The experimental results demonstrate that the proposed MOE-HDL approach outperforms these alternatives in terms of performance.

Responsible research assessment in the area of quantitative methods research: A comment on Gärtner et al.

In this commentary, we discuss the proposed criteria in Gärtner et al. (2022) for hiring or promoting quantitative methods researchers. We argue that the criteria do not reflect aspects that are relevant to quantitative methods researchers and typical publications they produce. We introduce a new set of criteria that can be used to evaluate the performance of quantitative methods researchers in a more valid fashion. We discuss the necessity to balance scientific expertise and open science commitment in such ranking schemes.

A Refinement of Ratio Estimation in Ranked Set Sampling and Stratified Ranked Set Sampling Approaches

Estimation of population mean is a persistent subject issue in sampling surveys and many discrete efforts have been paid by various researchers to enhance the precision of the estimates by utilizing the correlated auxiliary information. In connection with this an improved version of ratio estimator are presented in this paper under the ranked set sampling scheme and stratified ranked set sampling scheme. Comparison amongst estimators is made in terms of Mean Square Errors ( ) and Percentage Relative Efficiencies ( ). The expression for of the proposed estimator is pinned-down to first order of approximations. It turns out from both simulation studies as well as real data set that the proposed estimator dominates its existing counterpart estimators.