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Article
Publication date: 7 August 2017

Eun-Suk Yang, Jong Dae Kim, Chan-Young Park, Hye-Jeong Song and Yu-Seop Kim

In this paper, the problem of a nonlinear model – specifically the hidden unit conditional random fields (HUCRFs) model, which has binary stochastic hidden units between…

Abstract

Purpose

In this paper, the problem of a nonlinear model – specifically the hidden unit conditional random fields (HUCRFs) model, which has binary stochastic hidden units between the data and the labels – exhibiting unstable performance depending on the hyperparameter under consideration.

Design/methodology/approach

There are three main optimization search methods for hyperparameter tuning: manual search, grid search and random search. This study shows that HUCRFs’ unstable performance depends on the hyperparameter values used and its performance is based on tuning that draws on grid and random searches. All experiments conducted used the n-gram features – specifically, unigram, bigram, and trigram.

Findings

Naturally, selecting a list of hyperparameter values based on a researchers’ experience to find a set in which the best performance is exhibited is better than finding it from a probability distribution. Realistically, however, it is impossible to calculate using the parameters in all combinations. The present research indicates that the random search method has a better performance compared with the grid search method while requiring shorter computation time and a reduced cost.

Originality/value

In this paper, the issues affecting the performance of HUCRF, a nonlinear model with performance that varies depending on the hyperparameters, but performs better than CRF, has been examined.

Details

Engineering Computations, vol. 34 no. 6
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 3 June 2020

Christopher Enyioma Alozie

This paper assessed accuracy level in accounting for government funds in Nigeria's federal treasury and their faithful presentation in government financial reporting. It…

Abstract

Purpose

This paper assessed accuracy level in accounting for government funds in Nigeria's federal treasury and their faithful presentation in government financial reporting. It aimed to determine whether the reported annual balances in Nigeria's financial reporting were reliable or otherwise. Data used in analysis were obtained from secondary sources from federal treasury.

Design/methodology/approach

Ex-post “facto” analysis method was adopted in the study involving the use of statistical techniques of absolute or aggregate mean percentage error derived from differences between recomputed and published fund balances and was employed. This was augmented with interactive review meetings of the initial case research report with the management of Nigeria's audit agency.

Findings

Results distilled from the consolidated revenue fund (CRF), development fund and public debt show that recomputed values were greater than the fund balances in the gazetted financial statements. Results for contingency fund (CTF), federation account fund (FAF), special trust fund (STF) and sundry deposit fund yield equal figures and accurate. The paper concludes that there were serial understatements of the core public fund balances in the financial statements over the years. This trend of reporting incorrect in three core public funds in financial statements rendered Nigeria's financial position unreliable in the affected years for decisions. It also facilitated frauds, mismanagement of funds and corrupt practices.

Research limitations/implications

The scope of the research is restricted to assessment of degree of accuracy in fund accounting, faithful representation of the respective fund balance in the liabilities side of FGN balance sheet and the reliability of the financial position. But, it did not consider or cover the implementation of International Public Sector Accounting Standards (IPSASs) in federal treasury since FGN had not issued any full IPSAS–oriented financial statements as on 2015.

Practical implications

Identification of deficiencies in fund account balances, structural defects in fund accounting and acts of understatement of carrying balances in CRF and capital development fund (CDF) implies that the aggregate core fund liabilities reported in financial statement of government entities without corresponding assets do not actually reflect a true and fair financial position in some countries. It reveals remarkable degree of financial information asymmetry in government financial reporting. Illusionary fund accounting has direct linkage to poor fiscal governance in many sovereign with associated sub-optimal delivery of public goods and service level distress syndrome in many economies; lead to poverty, unemployment, crisis and macroeconomic disturbances.

Social implications

The study contributes to the development of fund accounting system; strengthening government financial reporting architecture and practices. It provides framework for tracking financial information asymmetry in government financial reporting and mismanagement of public funds. It provides platform to effect necessary adjustment (correction) during the “first time 3-year adoption” adjustment window in Nigeria. Flowing from the findings, it advocates for institutionalization of government fund accounting standards and provides evidence for migration to accrual accounting system in countries that have not already implemented it. Evaluation system developed herein will improve fund management in federal treasury and contribute to efficient public financial management, good governance and enhance development of public accounting practice.

Originality/value

This exploratory empirical research is the one to ever evaluate accuracy level of fund accounting in sovereign entities and faithful representation in government's financial position prior to implementation of accrual accounting and financial reporting. The study established substantial level of illusionary accounting for public funds and information asymmetry in published government's financial reporting. It is necessary to rectify these discrepancies in fund accounting and financial reporting prior to and or during the first three years of the IPSAS transition implementation programme. These research deliverables provide adopters with relevant data for adjustment accounting during the transition period in strengthening public financial reporting in order to realize the benefit of full IPSAS accrual accounting.

Details

Journal of Public Budgeting, Accounting & Financial Management, vol. 32 no. 3
Type: Research Article
ISSN: 1096-3367

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Article
Publication date: 3 November 2020

Jagroop Kaur and Jaswinder Singh

Normalization is an important step in all the natural language processing applications that are handling social media text. The text from social media poses a different…

Abstract

Purpose

Normalization is an important step in all the natural language processing applications that are handling social media text. The text from social media poses a different kind of problems that are not present in regular text. Recently, a considerable amount of work has been done in this direction, but mostly in the English language. People who do not speak English code mixed the text with their native language and posted text on social media using the Roman script. This kind of text further aggravates the problem of normalizing. This paper aims to discuss the concept of normalization with respect to code-mixed social media text, and a model has been proposed to normalize such text.

Design/methodology/approach

The system is divided into two phases – candidate generation and most probable sentence selection. Candidate generation task is treated as machine translation task where the Roman text is treated as source language and Gurmukhi text is treated as the target language. Character-based translation system has been proposed to generate candidate tokens. Once candidates are generated, the second phase uses the beam search method for selecting the most probable sentence based on hidden Markov model.

Findings

Character error rate (CER) and bilingual evaluation understudy (BLEU) score are reported. The proposed system has been compared with Akhar software and RB\_R2G system, which are also capable of transliterating Roman text to Gurmukhi. The performance of the system outperforms Akhar software. The CER and BLEU scores are 0.268121 and 0.6807939, respectively, for ill-formed text.

Research limitations/implications

It was observed that the system produces dialectical variations of a word or the word with minor errors like diacritic missing. Spell checker can improve the output of the system by correcting these minor errors. Extensive experimentation is needed for optimizing language identifier, which will further help in improving the output. The language model also seeks further exploration. Inclusion of wider context, particularly from social media text, is an important area that deserves further investigation.

Practical implications

The practical implications of this study are: (1) development of parallel dataset containing Roman and Gurmukhi text; (2) development of dataset annotated with language tag; (3) development of the normalizing system, which is first of its kind and proposes translation based solution for normalizing noisy social media text from Roman to Gurmukhi. It can be extended for any pair of scripts. (4) The proposed system can be used for better analysis of social media text. Theoretically, our study helps in better understanding of text normalization in social media context and opens the doors for further research in multilingual social media text normalization.

Originality/value

Existing research work focus on normalizing monolingual text. This study contributes towards the development of a normalization system for multilingual text.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 4
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 30 April 2021

Shaofei Wang and Depeng Dang

Previous knowledge base question answering (KBQA) models only consider the monolingual scenario and cannot be directly extended to the cross-lingual scenario, in which the…

Abstract

Purpose

Previous knowledge base question answering (KBQA) models only consider the monolingual scenario and cannot be directly extended to the cross-lingual scenario, in which the language of questions and that of knowledge base (KB) are different. Although a machine translation (MT) model can bridge the gap through translating questions to the language of KB, the noises of translated questions could accumulate and further sharply impair the final performance. Therefore, the authors propose a method to improve the robustness of KBQA models in the cross-lingual scenario.

Design/methodology/approach

The authors propose a knowledge distillation-based robustness enhancement (KDRE) method. Specifically, first a monolingual model (teacher) is trained by ground truth (GT) data. Then to imitate the practical noises, a noise-generating model is designed to inject two types of noise into questions: general noise and translation-aware noise. Finally, the noisy questions are input into the student model. Meanwhile, the student model is jointly trained by GT data and distilled data, which are derived from the teacher when feeding GT questions.

Findings

The experimental results demonstrate that KDRE can improve the performance of models in the cross-lingual scenario. The performance of each module in KBQA model is improved by KDRE. The knowledge distillation (KD) and noise-generating model in the method can complementarily boost the robustness of models.

Originality/value

The authors first extend KBQA models from monolingual to cross-lingual scenario. Also, the authors first implement KD for KBQA to develop robust cross-lingual models.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 1 July 2014

Wen-Feng Hsiao, Te-Min Chang and Erwin Thomas

The purpose of this paper is to propose an automatic metadata extraction and retrieval system to extract bibliographical information from digital academic documents in…

Abstract

Purpose

The purpose of this paper is to propose an automatic metadata extraction and retrieval system to extract bibliographical information from digital academic documents in portable document formats (PDFs).

Design/methodology/approach

The authors use PDFBox to extract text and font size information, a rule-based method to identify titles, and an Hidden Markov Model (HMM) to extract the titles and authors. Finally, the extracted titles and authors (possibly incorrect or incomplete) are sent as query strings to digital libraries (e.g. ACM, IEEE, CiteSeerX, SDOS, and Google Scholar) to retrieve the rest of metadata.

Findings

Four experiments are conducted to examine the feasibility of the proposed system. The first experiment compares two different HMM models: multi-state model and one state model (the proposed model). The result shows that one state model can have a comparable performance with multi-state model, but is more suitable to deal with real-world unknown states. The second experiment shows that our proposed model (without the aid of online query) can achieve as good performance as other researcher's model on Cora paper header dataset. In the third experiment the paper examines the performance of our system on a small dataset of 43 real PDF research papers. The result shows that our proposed system (with online query) can perform pretty well on bibliographical data extraction and even outperform the free citation management tool Zotero 3.0. Finally, the paper conducts the fourth experiment with a larger dataset of 103 papers to compare our system with Zotero 4.0. The result shows that our system significantly outperforms Zotero 4.0. The feasibility of the proposed model is thus justified.

Research limitations/implications

For academic implication, the system is unique in two folds: first, the system only uses Cora header set for HMM training, without using other tagged datasets or gazetteers resources, which means the system is light and scalable. Second, the system is workable and can be applied to extracting metadata of real-world PDF files. The extracted bibliographical data can then be imported into citation software such as endnote or refworks to increase researchers’ productivity.

Practical implications

For practical implication, the system can outperform the existing tool, Zotero v4.0. This provides practitioners good chances to develop similar products in real applications; though it might require some knowledge about HMM implementation.

Originality/value

The HMM implementation is not novel. What is innovative is that it actually combines two HMM models. The main model is adapted from Freitag and Mccallum (1999) and the authors add word features of the Nymble HMM (Bikel et al, 1997) to it. The system is workable even without manually tagging the datasets before training the model (the authors just use cora dataset to train and test on real-world PDF papers), as this is significantly different from what other works have done so far. The experimental results have shown sufficient evidence about the feasibility of our proposed method in this aspect.

Details

Program, vol. 48 no. 3
Type: Research Article
ISSN: 0033-0337

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Article
Publication date: 29 July 2019

Peyman Maghsoudi, Sadegh Sadeghi, Qingang Xiong and Saiied Mostafa Aminossadati

Because of the appreciable application of heat recovery systems for the increment of overall efficiency of micro gas turbines, promising evaluation and optimization are…

Abstract

Purpose

Because of the appreciable application of heat recovery systems for the increment of overall efficiency of micro gas turbines, promising evaluation and optimization are crucial. This paper aims to propose a multi-factor theoretical methodology for analysis, optimization and comparison of potential plate-fin recuperators incorporated into micro gas turbines. Energetic, exergetic, economic and environmental factors are covered.

Design/methodology/approach

To demonstrate applicability and reliability of the methodology, detailed thermo-hydraulic analysis, sensitivity analysis and optimization are conducted on the recuperators with louver and offset-strip fins using a genetic algorithm. To assess the relationship between investment cost and profit for the recuperated systems, payback period (PBP), which incorporates all the factors is used as the universal objective function. To compare the performance of the recuperated and non-recuperated systems, exergy efficiency, exergy destruction and corresponding cost rate, fuel consumption and environmental damage cost rates, capital and operational cost rates and acquired profit rates are determined.

Findings

Based on the results, optimal PBP of the louvered-fin recuperator (147 days) is slightly lower than that with offset-strip fins (153 days). The highest profit rate is acquired by reduction of exergy destruction cost rate and corresponding decrements for louver and offset-strip fins are 2.3 and 3.9 times compared to simple cycle, respectively.

Originality/value

This mathematical study, for the first time, focuses on introducing a reliable methodology, which covers energetic, exergetic, economic and environmental points of view beneficial for design and selection of efficient plate-fin recuperators for micro gas turbine applications.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 30 no. 5
Type: Research Article
ISSN: 0961-5539

Keywords

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Article
Publication date: 9 March 2015

Mohamed Khalifa, Faisal Khan and Joseph Thorp

– The purpose of this paper is to propose a quantitative model for risk-based maintenance and remaining life assessment for gas turbines.

Abstract

Purpose

The purpose of this paper is to propose a quantitative model for risk-based maintenance and remaining life assessment for gas turbines.

Design/methodology/approach

The proposed model uses historical failure and repair data from the operation of gas turbines. The time to failure of gas turbines is modeled using Weibull distribution.

Findings

The total risk is estimated considering replacement cost, repair cost, operation cost, risk of failure and turbine remaining value after a specified period of time.

Originality/value

The model is an effective tool to make optimal decisions regarding maintenance strategy (repair or replacement) and to assess the remaining life based on a comparison of the total risk. The literature review focusses on developing different models to make risk-based decisions regarding the selection of a maintenance strategy and maintenance interval, however, literature is silent regarding risk-based assessment of the equipment remaining life, which is the focus of present work. The model is tested and applied to ageing gas turbines in a cross-country pipeline.

Details

Journal of Quality in Maintenance Engineering, vol. 21 no. 1
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 2 December 2020

Yohanes Sigit Purnomo W.P., Yogan Jaya Kumar and Nur Zareen Zulkarnain

Extracting information from unstructured data becomes a challenging task for computational linguistics. Public figure’s statement attributed by journalists in a story is…

Abstract

Purpose

Extracting information from unstructured data becomes a challenging task for computational linguistics. Public figure’s statement attributed by journalists in a story is one type of information that can be processed into structured data. Therefore, having the knowledge base about this data will be very beneficial for further use, such as for opinion mining, claim detection and fact-checking. This study aims to understand statement extraction tasks and the models that have already been applied to formulate a framework for further study.

Design/methodology/approach

This paper presents a literature review from selected previous research that specifically addresses the topics of quotation extraction and quotation attribution. Research works that discuss corpus development related to quotation extraction and quotation attribution are also considered. The findings of the review will be used as a basis for proposing a framework to direct further research.

Findings

There are three findings in this study. Firstly, the extraction process still consists of two main tasks, namely, the extraction of quotations and the attribution of quotations. Secondly, most extraction algorithms rely on a rule-based algorithm or traditional machine learning. And last, the availability of corpus, which is limited in quantity and depth. Based on these findings, a statement extraction framework for Indonesian language corpus and model development is proposed.

Originality/value

The paper serves as a guideline to formulate a framework for statement extraction based on the findings from the literature study. The proposed framework includes a corpus development in the Indonesian language and a model for public figure statement extraction. Furthermore, this study could be used as a reference to produce a similar framework for other languages.

Details

Global Knowledge, Memory and Communication, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9342

Keywords

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Abstract

Details

Sustainability Disclosure: State of the Art and New Directions
Type: Book
ISBN: 978-1-78560-341-9

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Article
Publication date: 21 March 2019

K.M. Ibrahim Khalilullah, Shunsuke Ota, Toshiyuki Yasuda and Mitsuru Jindai

Wheelchair robot navigation in different weather conditions using single camera is still a challenging task. The purpose of this study is to develop an autonomous…

Abstract

Purpose

Wheelchair robot navigation in different weather conditions using single camera is still a challenging task. The purpose of this study is to develop an autonomous wheelchair robot navigation method in different weather conditions, with single camera vision to assist physically disabled people.

Design/methodology/approach

A road detection method, called dimensionality reduction deep belief neural network (DRDBNN), is proposed for drivable road detection. Due to the dimensionality reduction ability of the DRDBNN, it detects the drivable road area in a short time for controlling the robot in real-time. A feed-forward neural network is used to control the robot for the boundary following navigation using evolved neural controller (ENC). The robot detects road junction area and navigates throughout the road, except in road junction, using calibrated camera and ENC. In road junction, it takes turning decision using Google Maps data, thus reaching the final destination.

Findings

The developed method is tested on a wheelchair robot in real environments. Navigation in real environments indicates that the wheelchair robot moves safely from source to destination by following road boundary. The navigation performance in different weather conditions of the developed method has been demonstrated by the experiments.

Originality/value

The wheelchair robot can navigate in different weather conditions. The detection process is faster than that of the previous DBNN method. The proposed ENC uses only distance information from the detected road area and controls the robot for boundary following navigation. In addition, it uses Google Maps data for taking turning decision and navigation in road junctions.

Details

Industrial Robot: the international journal of robotics research and application, vol. 46 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

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