Item Taxonomy Research Articles

PPP project price mode typologies: a China-based comparative case study

PurposePublic–Private Partnerships (PPPs) are crucial to the procurement of global infrastructure projects. Moreover, a price mode based on a cluster of core concessionary items is key to the delivery of value-for-money and successful project outcomes. However, existing research has yet to fully identify PPP concessionary items, nor yet described the range of practical price modes. This study provides taxonomy of core concessionary items impacting PPP projects, systematically classifies price modes, and assesses the applicability and risk impacts of those price modes on PPP projects.Design/methodology/approachThis study adopts a comparative case study method in analyzing core concessionary items and alternative price modes. China is taken as the context, as it is one of the world’s largest PPP markets. In ensuring research validity and reliability, diverse data sources are utilized, with a graphic content analysis tool developed to capture the structure of price modes.FindingsEight PPP price modes are identified. These are: (1) UP (Unit Price) mode, (2) ALS (Annual Lump Sum) mode, (3) IRR (Internal Rate of Return) mode, (4) RP (Return for Investing Capital (RIC) - Profit Rate of O&M (PROM)) mode, (5) RFP (RIC - Financing Interest Rate (FR) - PROM) mode, (6) RFPL (RIC - FR - PROM - Lower Limit of User Charge (LLoUC)) mode, (7) RFL (RIC - FR - Lump Sum/Fixed Unit Price O&M Contract (LSOM/FUP)) mode, and (8) RFLL (RIC - FR - LSOM/FUP - LLoUC) mode. Other main findings are as follows: (1) Five risk allocation configurations can be achieved via these price modes. Yet while different price modes enable the allocation of specific risks, these do not always align with contracting parties’ original intentions. (2) IRR and RP modes may be less applicable in general because of their vulnerability in allocating critical risks and capacity for spurring opportunistic behavior.Originality/valueBy depicting the paths by which concessionary items in price modes affect cash flow, a systematic analysis of price modes was conducted exposing structural characteristics, along with risk allocation choice implications. The study is unique in: (1) Providing a systematic classification of PPP price modes used in PPP projects, (2) Presenting a comprehensive identification and streamlining of concessionary items in PPP practice, and (3) Analyzing the risk effects of different price modes. Together, these outcomes offer a hitherto unavailable perspective on PPP project risk management. The value of the study lies in the following: (1) Existing studies employ diverse concessionary items, but their applicability varies. This study offers an overarching framework facilitating decision-making in selecting appropriate PPP price modes and in determining concessionary items. (2) This study adds to the understanding of PPP price modes in significant ways that will aid local governments and potential sponsors in crafting and administrating more workable contract designs.

A Nature-Inspired Method to Mine Top-k Multi-Level High-Utility Itemsets

High-Utility Itemset Mining (HUIM) is designed to discover sets of itemsets that can bring high profits from the database. However, HUIM encounters several challenges in picking a suitable minimum utility threshold for each database. A class of algorithms that select the top- k itemsets based on their utility has been proposed to address this issue. Although traditional top-k HUI mining algorithms do not require a specific threshold, they tend to be very time-consuming and memory-intensive when dealing with large datasets. To tackle the combinational complexity involved in HUIM algorithms, nature-inspired methods have been suggested and adopted. Nonetheless, these algorithms have traditionally focused on handling conventional, often overlooking critical data structures like product hierarchies. Consequently, they fail to extract crucial insight from this novel database format. Thus, our research introduces a heuristic-based algorithm designed to leverage top-k itemsets from databases enriched with item taxonomy data. We propose a technique involving the early pruning of unpromising items to enhance mining efficiency. Experimental evaluations are conducted on several datasets to assess the method’s performance, both with and without adopting this strategy, demonstrating its effectiveness.

Parallel approaches to extract multi-level high utility itemsets from hierarchical transaction databases

In the field of data mining, high utility itemset mining (HUIM) is a relevant mining task, with the aim of analyzing customer transaction databases. HUIM consists of exploiting the set of items that are often purchased together and yield high profit value. In real-world applications, transaction databases often come with item categorization, stored in a taxonomy. Items in these databases can be clustered into specific categories at higher levels of abstraction. Extracting and analyzing itemsets discovered from different levels of abstraction can provide more useful insights into customer behaviors. However, considering item taxonomy increases the problem’s complexity, hence prolonging the execution time needed to explore the search space. Parallelism is thus employed to address this drawback, but previous approaches are not efficient as they only adopt simple scheduling strategies or do not utilize the full capabilities of a multi-core processor. This work introduces three new efficient strategies to significantly boost the performance of the multi-level high utility itemset mining task using multi-core processing. Two new algorithms, called MCML+ and MCML++, are also proposed by adopting the suggested strategies. Extensive experiments on several large databases show that our proposed algorithms have better performance compared to previous approaches in terms of running time and scalability, up to 4.0 times better than the previous parallelized algorithm, the MCML-Miner algorithm; and over 9.0 times faster than the original sequential algorithm, the MLHUI-Miner algorithm.

A first look at bug report templates on GitHub

Bug reports which are written by different people have a variety of styles, and such various styles lead to difficulty in understanding bug reports. To enhance the comprehensibility of bug reports, GitHub has proposed a template mechanism to guide how to report the bugs. However, there is no study on the use of bug report templates on GitHub. In this paper, we conduct an empirical study on the bug report templates on GitHub, including the popularity, benefits, and content of the templates. Our empirical study finds that: (1) For popularity, more and more open source projects and bug reports are applying templates over time. (2) For benefits, bug reports written using templates will be resolved quicker and have a higher comment coverage. (3) For content, the most common items for templates are expected behavior, describe the bug and to reproduce etc. Additionally, we summarize a taxonomy of items for bug report templates. Finally, we propose an automatic templating approach for templating an un-templated bug report. Our approach achieves an accuracy of 0.718 and an F1-score of 0.717 on average, which shows that our approach can effectively templatize an un-templated bug report.

DualTaxoVec: Web user embedding and taxonomy generation

Learning web user embedding based on interaction data in the context of taxonomy is a way of studying the correlation between two web users. Such user embedding is important for further user analysis. Interaction data is made up of users and the items they interact within a domain, which is a group of entities with a basic common property. Usually a taxonomy of these items that users interact with is a hierarchical category structure for a domain. However, the taxonomy is not totally suitable for a particular task. To solve this problem, we propose a dual-way method DualTaxoVec, which learns the user embedding based on the taxonomy of the user interaction items. Meanwhile, it automatically constructs the taxonomy for the items that adapts the domain of users. It is composed of user–item and item–user tracks to construct the taxonomy and embed users in a dual-way. According to the experimental results, the validity and effectiveness of the DualTaxoVec has been demonstrated.

Quantifying mammalian diets

We propose a quantitative approach for categorising mammalian diets based on the taxonomy of food items and parts consumed (the form of food). Our analysis covers 82% of the mammalian species alive today. The diet information comes from different data sources—textbooks, datasets and peer-reviewed literature and includes a transformation of narrative quantitative data into qualitative data. We link a database on nutrient composition of diet items of the quantitative diet data and analyse the distribution of macronutrients of diets across taxonomic groups and map them to the dental morphology of the eaters. The results show associations between dental complexity and the concentrations of some nutrients. Our analysis highlights omnivory as a multi-faceted concept—there are many kinds of omnivores within the dietary space we report. The developed dataset and the proposed approach relating the chemical composition of diets offers a basis for future comparative studies of living and fossil mammals. With this study, we make the accompanying large-scale dietary data publicly available online (https://www.mammalbase.net).

A Comparative Study on the Use of English Songs and Poems to Teach Vocabulary for English as a Second Language Learners

Vocabulary is an important aspect of teaching and learning a language, especially in teaching and learning a second language or a foreign language. Lexical Approach by Lewis (1993) contains a taxonomy of lexical items which shows what kind of vocabulary the learner should develop, and Cameron (2001) states that in learning vocabulary the aspects of pronunciation, meaning, spelling and the grammatical usage of the specific words should be taught. In that case, there have been various strategies to teach and learn vocabulary in the English as a Second Language (ESL) context. This study investigates whether the usage of English songs and poems in English as a Second Language (ESL) classroom enhance the knowledge in the vocabulary of the secondary level learners. This study provides an answer to the research question “How does the usage of English songs and poems enhance the ESL students’ knowledge in vocabulary at the secondary level?” and claims two hypotheses, a) The use of English songs is more effective than using English poems in teaching vocabulary. b) There is a significant and positive impact after the usage of songs and poems in grade 10 with compared to grade 6. Twenty-five students from each class from grade 6 and grade 10 at H/ Weeraketiya Rajapaksha Central College participated in the study. The data was collected using the pretests and posttests. The data was analyzed using SPSS version 22. The findings indicated that both songs and poems have a positive impact on the enhancement of vocabulary knowledge at the secondary level. In both grades, songs were more effective in teaching vocabulary than poems. The second hypothesis of this study was partially confirmed as Grade 10 group that exposed to songs had an improvement whereas Grade 10 group that exposed to poems had shown a reduction in their marks, compared to Grade 6. Overall, the findings indicate the effectiveness of usage of English songs and poems in teaching and learning vocabulary in the secondary level ESL classroom. Though teachers do not pay much attention to the usage of songs and poems, they would be very much productive in enhancing the ESL learners’ knowledge in vocabulary.

Fiscal policies-growth connection: which role for the budget composition?

ABSTRACT We assess the growth friendliness, both for the short and long term, of the budget composition over the last 16 years (2001 − 2017) for EU countries based on the OECD taxonomy of budget items – growth connection. Budget composition is still far from its optimum, sizable gaps exist for almost all EU countries.

Mobile Phone Apps in Australia for Improving Pregnancy Outcomes: Systematic Search on App Stores.

BackgroundWomen are increasingly turning to mobile health platforms to receive health information and support in pregnancy, yet the content of these platforms vary. Although there is great potential to influence health behaviors, little research has assessed the quality of these platforms or their ability to change behavior. In recent years, validated tools to assess app quality have become available.ObjectiveTo identify and assess the quality and ongoing popularity of the top 10 freely available pregnancy apps in Australia using validated tools.MethodsA systematic search on app stores to identify apps was performed. A Google Play search used subject terms pregnancy, parenting, and childbirth; the iTunes search used alternative categories medical and health and fitness. The top 250 apps from each store were cross-referenced, and the top 100 found in both Google Play and iTunes were screened for eligibility. Apps that provided health information or advice for pregnancy were included. Excluded apps focused on nonhealth information (eg, baby names). The top 10 pregnancy apps were assessed using the Mobile App Rating Scale (MARS). A comparative analysis was conducted at 2 time points over 2 years to assess the ongoing popularity of the apps. The MARS score was compared to the download and star rating data collected from iTunes and Google Play in 2017 and 2019. Health behaviors including breastfeeding, healthy pregnancy weight, and maternal awareness of fetal movements were reviewed for apparent impact on the user’s knowledge, attitudes, and behavior change intentions using the MARS perceived impact section and the Coventry, Aberdeen, and London—Refined (CALO-RE) taxonomy.ResultsA total of 2052 free apps were screened for eligibility, 1397 were excluded, and 655 were reviewed and scored. The top 10 apps were selected using download numbers and star ratings. All 10 apps were suboptimal in quality, practicality, and functionality. It was not possible to identify a primary purpose for all apps, and there was overlap in purpose for many. The mean overall MARS app quality score across all 10 apps was 3.01 (range 1.97-4.40) in 2017 and 3.40 (range 2.27-4.44) in 2019. A minority of apps scored well for perceived impact on health behavior using the MARS tool. Using the CALO-RE 40 item taxonomy, the number of behavior change techniques used was low. The mean number of behavior change techniques for breastfeeding was 5 (range 2-11), for pregnancy weight was 4 (range 2-12), and for maternal awareness of fetal movements was 5 (range 2-8).ConclusionsThis review provides valuable information to clinicians and consumers about the quality of apps currently available for pregnancy in Australia. Consideration is needed regarding the regulation of information and the potential opportunity to incorporate behavior change techniques to improve maternal and fetal outcomes.

Determining diets for fishes (Actinopterygii) from a small interior British Columbia, Canada stream: a comparison of morphological and molecular approaches

AbstractAnalysis of food webs is important for defining functional components of ecosystems, but dietary data are often difficult to obtain and coarsely characterised. We compared three methods of rainbow trout (Oncorhynchus mykiss(Walbaum); Salmoniformes: Salmonidae) and prickly sculpin (Cottus asperRichardson; Scorpaeniformes: Cottidae) gut content analysis: traditional morphological taxonomy of prey items, genetic sequencing of individual prey items, and next-generation sequencing of homogenised gut contents. Prey analysis of invertebrates by morphological identification allowed order-level classifications and produced ecologically important count and mass data. Sequencing individual specimens provided greater taxonomic resolution, while next-generation sequencing of stomach contents revealed more prey diversity in the diets of both fish species as it was possible to detect prey that were degraded beyond visual recognition. Both fish species exhibited generalist feeding characteristics; however, terrestrial Insecta were a large diet component for rainbow trout. This study demonstrates an efficient approach for prey analysis using molecular techniques that complement traditional taxonomy.

SHOAL

E-commerce taxonomy plays an essential role in online retail business. Existing taxonomy of e-commerce platforms organizes items into an ontology structure. However, the ontology-driven approach is subject to costly manual maintenance and often does not capture user's search intention, particularly when user searches by her personalized needs rather than a universal definition of the items. Observing that search queries can effectively express user's intention, we present a novel large-Scale Hierarchical taxOnomy via grAph based query coaLition ( SHOAL ) to bridge the gap between item taxonomy and user search intention. SHOAL organizes hundreds of millions of items into a hierarchical topic structure . Each topic that consists of a cluster of items denotes a conceptual shopping scenario, and is tagged with easy-to-interpret descriptions extracted from search queries. Furthermore, SHOAL establishes correlation between categories of ontology-driven taxonomy, and offers opportunities for explainable recommendation. The feedback from domain experts shows that SHOAL achieves a precision of 98% in terms of placing items into the right topics, and the result of an online A/B test demonstrates that SHOAL boosts the Click Through Rate (CTR) by 5%. SHOAL has been deployed in Alibaba and supports millions of searches for online shopping per day.

The development of the Schema-Action-World (SAW) taxonomy for understanding decision making in aeronautical critical incidents

The perceptual cycle model (PCM) offers a process-orientated approach to understanding decision making. This approach is distributed in nature as it considers how internally held schemata and external environmental information interact to produce actions and behaviour. This an essential component of any incident reporting system, although it is often lacking. In its current form the PCM only provides a very high-level of explanation. This research utilised data from critical decision making interviews to deconstruct the three high-level categories of the PCM into a 28 item taxonomy. In doing so, we were able to provide a more detailed description of aeronautical critical decision making (ACDM) by demonstrating the relevance of different concepts in different phases of dealing with an incident. The data were used to model the ACDM process. The taxonomy can be used for gaining a comprehensive understanding of both the contextual and cognitive components of decision making, something that existing taxonomies and models often fail to do.

A Taxionomia revisada de Bloom aplicada à avaliação: um estudo de provas escritas de Matemática

Resumo Este artigo apresenta uma análise, à luz da Taxionomia revisada de Bloom, de itens de provas escritas de Matemática elaboradas por professores do Ensino Fundamental II e do Ensino Médio de escolas públicas. Esses professores participaram de oficinas que discutiram a temática "avaliação e aprendizagem", projeto de extensão que ocorreu no ano de 2013, e foram convidados a analisar provas escritas que eles mesmos elaboraram. Os resultados apontaram uma prevalência de itens classificados nos níveis mais baixos do domínio cognitivo da Taxionomia (lembrar, entender e aplicar) e uma quase ausência de itens nos níveis mais elevados (analisar, avaliar e criar). Tais resultados corroboram a necessidade de estudos e propostas de formação continuada que oportunizem aos professores repensar: (i) os instrumentos avaliativos que utilizaram (em geral, prioritariamente a prova escrita), (ii) a elaboração das tarefas que propõem (em geral, restritas aos modelos presentes em livros didáticos) e (iii) a sua própria prática avaliativa.

On silent elements: a case study of grand and its silent entourage

Recent work in syntax has seen a proliferation of silent elements (SEs), e.g., van Riemsdijk (2002, 2005) and Kayne (e.g., Kayne 2005, 2006, 2012). This paper offers a feature-based taxonomy of lexical items, in which SEs are non-canonical items without phonological features. An SE and its pronounced counterpart, if any, are thus semantically equivalent, but SEs are not the result of ellipsis, which suppresses the pronunciation of pronounceable elements. Under this contextualization, the SEs in Kayne’s (2012) and Law’s (2012) accounts of the monetary expression grand are reexamined. For ten grand, Kayne (2012) proposes for its underlying source: ten THOUSAND BUCKS IN grand TOTAL, where capitalized items are SEs, while Law (2012) argues for a simpler source form: ten THOUSAND grand BUCKS, where grand remains an adjective. Yet, their starting assumptions that grand does not pluralize and is not used as a noun elsewhere are incorrect. The SE accounts also make a number of incorrect predictions syntactically and semantically and have difficulty explaining acquisition. A nominal account of grand with the same lexical status as G and nickel is far simpler and applies to all dialects. Though the feature-based taxonomy of lexical items predicts the existence of SEs, a proposed SE must still be justified syntactically and semantically. Thus, some of the SEs proposed may not be warranted upon closer scrutiny.

Color isn't Silent, Shallow isn't Deep: Two Case Studies of Evaluating Silent Elements

A notable recent trend in syntax is the proliferation of silent elements (SEs), due largely to the work by Kayne. Yet, the exact grammatical status of such SEs is not always made explicit. Consequently, few have been explicitly and empirically tested. In this paper we adopt the taxonomy of lexical items in Her and Tsai (to appear) and distinguishes two kinds of SEs in the literature: An intrinsic SE is one that does not add any meaning, while an extrinsic SE is one that does and is thus by definition illicit. We then put two of Kayne’s SE analyses to rigorous semantic and syntactic tests. One concerns the adjective shallow, as in ‘the lake is LITTLE BIT shallow’ (Kayne 2006), where capital letters indicate silence. We demonstrate that LITTLE BIT are extrinsic SEs, as the source form and the surface form are not semantically equivalent. The other SE examined is COLOR, an intrinsic SE in ‘John’s car is a bright green COLOR’ (Kayne 2005 chp.10). We demonstrate with syntactic tests that it is not free variation between COLOR/color and that color adjectives also function unmistakably as nouns. Thus, the assumption of an SE head noun COLOR for all color adjectives cannot be justified. We further demonstrate that from the perspective of language acquisition the two SE accounts are likewise problematic. This study therefore concurs with Her and Tsai (to appear) that while SEs are indispensable in language, for each SE proposed there must be sufficient semantic justification and formal motivation.

Linked taxonomies to capture usersʼ subjective assessments of items to facilitate accurate collaborative filtering

Subjective assessments (SAs), such as “elegant” and “gorgeous,” are assigned to items by users, and they are common in the reviews and tags found on many online sites. Analyzing the linked information provided by an SA assigned by a user to an item can improve the recommendation accuracy. This is because this information contains the reason why the user assigned a high or low rating value to the item. However, previous studies have failed to use SAs in an effective manner to improve the recommendation accuracy because few users rate the same items with the same SAs, which leads to the sparsity problem during collaborative filtering. To overcome this problem, we propose a novel method, called Linked Taxonomies, which links a taxonomy of items to a taxonomy of SAs to capture the userʼs interests in detail. First, our method groups the SAs assigned by users to an item into subjective classes (SCs), which are defined using a taxonomy of SAs such as those in WordNet, and they reflect the SAs/SCs assigned to an item based on their classes. Thus, our method can measure the similarity of users based on the SAs/SCs assigned to items and their classes (item classes are defined using a taxonomy of items), which overcomes the sparsity problem. Furthermore, SAs that are ineffective for accurate recommendations are excluded automatically from the taxonomy of SAs using this method. This is highly beneficial for the designers of taxonomies of SAs because it helps to ensure the production of accurate recommendations. We conducted investigations using a movie ratings/tags dataset with a taxonomy of SAs extracted from WordNet and a restaurant ratings/reviews dataset with an expert-created taxonomy of SAs, which demonstrated that our method generated more accurate recommendations than previous methods.

Web-Scale Media Recommendation Systems

Modern consumers are inundated with choices. A variety of products are offered to consumers, who have unprecedented opportunities to select products that meet their needs. The opportunity for selection also presents a time-consuming need to select. This has led to the development of recommender systems that direct consumers to products expected to satisfy them. One area in which such systems are particularly useful is that of media products, such as movies, books, television, and music. We study the details of media recommendation by focusing on a large scale music recommender system. To this end, we introduce a music rating data set that is likely to be the largest of its kind, in terms of both number of users, items, and total number raw ratings. The data were collected by Yahoo! Music over a decade. We formulate a detailed recommendation model, specifically designed to account for the data set properties, its temporal dynamics, and the provided taxonomy of items. The paper demonstrates a design process that we believe to be useful at many other recommendation setups. The process is based on gradual modeling of additive components of the model, each trying to reflect a unique characteristic of the data.

Product recommendation with temporal dynamics

In many E-commerce recommender systems, a special class of recommendation involves recommending items to users in a life cycle. For example, customers who have babies will shop on Diapers.com within a relatively long period, and purchase different products for babies within different growth stages. Traditional recommendation algorithms produce recommendation lists similar to items that the target user has accessed before (content filtering), or compute recommendation by analyzing the items purchased by the users who are similar to the target user (collaborative filtering). Such recommendation paradigms cannot effectively resolve the situation with a life cycle, i.e., the need of customers within different stages might vary significantly. In this paper, we model users’ behavior with life cycles by employing hand-crafted item taxonomies, of which the background knowledge can be tailored for the computation of personalized recommendation. In particular, our method first formalizes a user’s long-term behavior using the item taxonomy, and then identifies the exact stage of the user. By incorporating collaborative filtering into recommendation, we can easily provide a personalized item list to the user through other similar users within the same stage. An empirical evaluation conducted on a purchasing data collection obtained from Diapers.com demonstrates the efficacy of our proposed method.

Personalized recommender systems integrating tags and item taxonomy

Tags in Web 2.0 are becoming another important information source to profile users' interests and preferences to make personalized recommendations. To solve the problem of low information sharing caused by the free-style vocabulary of tags and the long tails of the distribution of tags and items, this paper proposes an approach to integrate the social tags given by users and the item taxonomy with standard vocabulary and hierarchical structure provided by experts to make personalized recommendations. The experimental results show that the proposed approach can effectively improve the information sharing and recommendation accuracy.

Updating generalized association rules with evolving fuzzy taxonomies

Mining generalized association rules with fuzzy taxonomic structures has been recognized as an important extension of generalized associations mining problem. To date most work on this problem, however, required the taxonomies to be static, ignoring the fact that the taxonomies of items cannot necessarily be kept unchanged. For instance, some items may be reclassified from one hierarchy tree to another for more suitable classification, abandoned from the taxonomies if they will no longer be produced, or added into the taxonomies as new items. Additionally, the membership degrees expressing the fuzzy classification may also need to be adjusted. Under these circumstances, effectively updating the discovered generalized association rules is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called FDiff_ET and FDiff_ET*, to update the discovered generalized frequent itemsets. Empirical evaluations show that our algorithms can maintain their performance even in high degree of taxonomy evolution, and are significantly faster than applying the contemporary fuzzy generalized association mining algorithm FGAR to the database with evolving taxonomy.