Candidate Attributes Research Articles

Graph-based dynamic attribute clipping for conversational recommendation

Conversational recommender systems (CRS) enable traditional recommender systems to interact with users by asking questions about their preferences and recommending items. Conversation recommendation has made significant progress, however, studies on attribute-aware conversational recommendation have overlooked the problem that after obtaining the candidate attribute set, the relevance of the attributes in the candidate attribute set to the user’s current preferred attributes is not further considered, resulting in the existence of many user-uninterested attributes in the candidate attribute set. This seriously affects the dialogue quality and reduces the user’s patience, decreasing recommendation accuracy. To address this problem, this paper proposes a new framework called Attribute Clipping based on Dynamic Graph (ACDG). In ACDG, firstly, the reasoning module uses the restriction property of graph structure to obtain a large set of candidate attributes, and then the attribute clipping module filters out the attributes with high relevance to the current user’s preferred attributes. Thus, ACDG can obtain a better-quality set of candidate attributes. Through extensive experiments on four benchmark CRS datasets, we validate the effectiveness of the method.

Improving the quality of hires via the use of machine learning and an expansion of the person–environment fit theory

PurposeOur proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.Design/methodology/approachWithin the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.FindingsThe K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.Research limitations/implicationsEmployee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.Originality/valueThis study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

The Role of Social Media Platforms in Forecasting Elections: A Comparison of Twitter and Facebook

Prior literature shows that social media could be used to forecast political elections. Most studies have focused on a single social media platform, and few studies have explored the use of social media data across multiple platforms to make election predictions. Though candidates’ personal attributes have also been suggested as critical factors affecting election results, there has been little research into the interacting effect of social media and candidate attributes in predicting elections. To address the research gap, this paper investigates the role of two different social media platforms, Twitter and Facebook, in forecasting the 2019 Finnish parliamentary elections, and how candidates’ political experience moderates the role of the two platforms in predicting elections. The findings show that both the number of Facebook likes and the number of Twitter followers are associated with the election outcome positively. Political experience of candidates moderates the association between the number of Facebook likes and election outcomes as well as the association between the number of Twitter followers and election outcomes. This research adds to the discussion on how social media can predict election results. It considers both different social media platforms and the roles of the candidate attributes.

A composite entropy-based uncertainty measure guided attribute reduction for imbalanced mixed-type data

In real-world scenarios, datasets generally exhibit containing mixed-type of attributes and imbalanced classes distribution, and the minority classes in the data are the primary research focus. Attribute reduction is a key step in the data preprocessing process, but traditional attribute reduction methods commonly overlook the significance of minority class samples, causing the critical information possessed in minority class samples to damage and decrease the performance of classification. In order to address this issue, we develop an attribute reduction algorithm based on a composite entropy-based uncertainty measure to handle imbalanced mixed-type data. To begin with, we design a novel oversampling method based on the three-way decisions boundary region to synthesize the samples of minority class, for the boundary region to contain more high-quality samples. Then, we propose an attribute measure to select candidate attributes, which considers the boundary entropy, degree of dependency and weight of classes. On this basis, a composite entropy-based uncertainty measure guided attribute reduction algorithm is developed to select the attribute subset for the imbalanced mixed-type data. Experimental on UCI imbalanced datasets, as well as the results indicate that the developed attribute reduction algorithm is significantly outperforms compared to other attribute reduction algorithms, especially in total AUC, F1-Score and G-Mean.

A Novel Spark-Based Attribute Reduction and Neighborhood Classification for Rough Evidence.

Neighborhood classification (NEC) algorithms have been widely used to solve classification problems. Most traditional NEC algorithms employ the majority voting mechanism as the basis for final decision making. However, this mechanism hardly considers the spatial difference and label uncertainty of the neighborhood samples, which may increase the possibility of the misclassification. In addition, the traditional NEC algorithms need to load the entire data into memory at once, which is computationally inefficient when the size of the dataset is large. To address these problems, we propose a novel Spark-based attribute reduction and NEC for rough evidence in this article. Specifically, we first construct a multigranular sample space using the parallel undersampling method. Then, we evaluate the significance of attribute by neighborhood rough evidence decision error rate and remove the redundant attribute on different samples subspaces. Based on this attribute reduction algorithm, we design a parallel attribute reduction algorithm which is able to compute equivalence classes in parallel and parallelize the process of searching for candidate attributes. Finally, we introduce the rough evidence into the classification decision of traditional NEC algorithms and parallelize the classification decision process. Furthermore, the proposed algorithms are conducted in the Spark parallel computing framework. Experimental results on both small and large-scale datasets show that the proposed algorithms outperform the benchmarking algorithms in the classification accuracy and the computational efficiency.

What are heads of department looking for in new general surgeons in Aotearoa New Zealand?

Training pathways vary significantly after completion of the general surgery surgical education and training (SET) program due to increasing sub-specialization. Aotearoa New Zealand requires a diverse range of general surgeons. Appointment of new consultant surgeons can be an opaque process; trainees are often uncertain how to tailor their training to that required by potential employers. Heads of departments (HODs) are influential in new appointments, and their opinions on desirable candidate attributes are valuable. An online survey was conducted in March 2023. All public hospital general surgery HODs were invited to participate. The survey sought opinions on the importance of attributes, skills and experience when appointing a new consultant general surgeon. The response rate was 70% (14/20) including 6 of 7 HODs from tertiary hospitals and 8 of 13 from secondary hospitals. The top three desirable factors were all personal attributes (being a team player, having a strong work ethic, and good interpersonal skills). 10 of 14 respondents disagreed that SET completion alone is sufficient without the need for further training. Most respondents preferred at least 2 years of fellowship training, except for trauma and endocrine surgery, where 1 year was frequently considered sufficient. Only one respondent agreed formal research training is highly valued. Trainees would be wise to obtain training desired by the majority of HODs while building an individualized profile of attributes, skills and experience tailored to hospitals they may wish to work in. The findings should be considered by organizations responsible for general surgical training and workforce planning.

DeepCPR: Deep Path Reasoning Using Sequence of User-Preferred Attributes for Conversational Recommendation

Conversational recommender systems (CRS) have garnered significant attention in academia and industry because of their ability to capture user preferences via system questions and user responses. Typically, in a CRS, reinforcement learning (RL) is utilized to determine the optimal timing for requesting attribute information or suggesting items. However, existing methods consider user-preferred attributes independently and ignore that attributes may be of different importance to the same user, in the attribute and item selection phases, which limits the accuracy and interpretability of CRS. Inspired by this, we propose deep conversational path reasoning (DeepCPR), which involves constructing a reasoning path on a graph with a series of user-favored attributes. It utilizes the attention mechanism to thoroughly examine the connections between these attributes and provide improved explanations for which attributes to inquire about or which items to recommend. In DeepCPR, two deep-learning-based modules are proposed to realize attribute and item selection. In the first module, the sequence of attributes confirmed by the user in conversation is encoded with a gated graph neural network to obtain the user’s long-term preference using a self-attention mechanism for the selection of candidate attributes. In the second module, a self-attention approach with more appropriate strategies is developed to dynamically select candidate items. In addition, to achieve fine-grained user preference modeling, a recurrent neural network is employed to aggregate the sequence of attributes that interact with the users. Numerous experimental evaluations conducted on four real CRS datasets show that the proposed method significantly outperforms existing advanced methods in terms of conversational recommendations.

Automatic Chinese knowledge-based question answering by the MGBA-LSTM-CNN model

The purpose of knowledge-based question answering (KBQA) is to accurately answer the questions raised by users through knowledge triples. Traditional Chinese KBQA methods rely heavily on artificial features, resulting in unsatisfactory QA results. To solve the above problems, this paper divides Chinese KBQA into two parts: entity extraction and attribute mapping. In the entity extraction stage, the improved Bi-LSTM-CNN-CRF model is used to identify the entity of questions and the Levenshtein distance method is used to resolve the entity link error. In the attribute mapping stage, according to the characteristics of questions and candidate attributes, the MGBA-LSTM-CNN model is proposed to encode questions and candidate attributes from the semantic level and word level, respectively, and splice them into new semantic vectors. Finally, the cosine distance is used to measure the similarity of the two vectors to find candidate attributes most similar to questions. The experimental results show that the system achieves good results in the Chinese question and answer data set.

'Selection into training will always be an inexact process': A survey of Directors of Physician Education on selection into Basic Physician Training in Australia and New Zealand.

Despite being one of the largest medical specialty training programmes in Australasia, there is no standardised method for selection into Basic Physician Training (BPT), and limited data exist regarding current practices. To address existing knowledge gaps, we aimed to create a 'snapshot' of current BPT selection practices and explore the perspectives of Directors of Physician Education (DPEs) regarding trainee selection. An electronic survey of DPEs from adult and paediatric medicine BPT sites in Australia and New Zealand was undertaken in January-February 2022. A combination of free text, multiple-choice and yes/no answers were analysed using descriptive statistics and qualitative content analysis. A total of 70 responses were received, achieving a response rate of 35% (70/198). Selection practices were found to be heterogenous across BPT sites. Respondents had varying opinions regarding the utility of selection tools and desirable candidate attributes. A heavy reliance upon interviews and the reported use of subjective assessments raise concerns for selection process bias. BPT sites should critically evaluate their selection methods, and more research in this field is needed to establish best practice.

The Institutionalization of Public Innovation: Evidence from Indonesia

This study examines critical factors contributing to institutionalizing creative ideas into a formal innovation and their difference in the regulatory-pillar output of innovation, which public sector innovation academics (PSI) rarely explore. Using multiple case study methods, this study interviewed 23 informants involved in four innovation cases in two local governments in Indonesia. This study highlighted nine critical factors in institutionalizing public innovation categorized into four dimensions: leadership, intraorganizational, innovation candidate attributes, and external environment. This study’s novel contribution lies in identifying critical factors shaping and the outputs of institutionalization of public innovation.

From Non-Optimal Routing Protocols to Routing on Multiple Optimality Criteria

At a suitable level of abstraction, all that standard routing protocols do is iterate extension and election operations on path attributes. An extension operation composes the attribute of a path from those of a link and another path, while an election operation produces the most preferred attribute of a set of candidate attributes, given a total order on them. These protocols are guaranteed to compute optimal paths only if the extension operation and the total order are entwined by the algebraic property of isotonicity, which states that the relative preference between two attributes is not inverted when both are extended by a third attribute. We solve the problem of computing and routing on optimal paths with generality by recognizing that every total order contains a partial order for which isotonicity holds. Then, we design a partial-order vectoring protocol where every election operation produces a subset of attributes from a set of candidate attributes, rather than a single attribute as is the case with a standard vectoring protocol; the election operation is derived from the partial order, ensuring that no attribute of the set of candidate attributes is preferred to an attribute of the elected subset. Moreover, we show how partial-order vectoring protocols can be designed to allow routing on optimal paths concurrently for diverse optimality criteria. Our evaluation over publicly available network topologies and attributes, covering both intra- and inter-AS routing, evince that the sizes of elected subsets of attributes are surprisingly small and that the partial-order vectoring protocol converges fast, sometimes faster than a standard vectoring protocol operating in the absence of isotonicity.

Hispanics’ Perceptions of Candidates & Voting in the 2018 Texas Senate Election

ABSTRACT This study examines Hispanic voters’ use of campaign information to form beliefs about candidates, evaluate them, and make voting decisions. Accordingly, we test a model of Hispanic voting using survey data (N = 424) collected during the 2018 Texas senate election and featuring Anglo Democrat, Beto O’Rourke, and Hispanic Republican, Ted Cruz. Although pathways leading to voter support differed for the two candidates, results suggest that exposure to political communication guides voter perception of candidate attributes, such as the candidate’s ethnic identity, which in turn can influence vote choice. Taken together, findings highlight the contribution of campaign communication to Hispanic voters’ candidate preference and voting behaviour.

Enhanced Fuzzy Random Forest by Using Doubly Randomness and Copying From Dynamic Dictionary Attributes

While fuzzy random forest (FRF) as a fuzzy implementation of random forest has earned its strong ambiguity/uncertainty handling capability on a rich variety of considerably low dimensional datasets, this article revisits FRF and attempts to enhance its generalization capability and computational speed on high dimensional datasets. For the first issue, in addition to the original use of randomness in FRF, a doubly randomness is newly introduced into the generation of both the candidate attributes and the best splitting attributes in FRF. For the computational speed issue, while the proposed new fuzzy information gain (NFG) measure does not apply to all candidate attributes, the remaining NFG values can be quickly retrieved by copying from the dynamically generated dictionary. As a result, a new method called enhanced fuzzy random forest (E-FRF) is proposed and justified theoretically from the consistency perspective. Our extensive experimental results indicate that the proposed method E-FRF has at least comparable performance to the comparative methods, and is an advantageous alternative to FRF in terms of both testing accuracy and running speed in most of the adopted high dimensional datasets.

Selection of China's Top Leadership Cadre: The Roles of Supreme Leaders, Factional Networks, and Candidate Attributes

This article provides empirical evidence to show how the general secretaries of the Chinese Communist Party dominated provincial personnel through their factional ties. Based on panel data from 1993 to 2017, this study finds that the provincial leaders’ personal connections with the incumbent party head significantly increased their promotion chances. The positive effect of the incumbent party heads on promotion did not depend on provincial leaders’ economic performance and seniority. This study further uncovers that working experiences in the prefectural leading positions strongly increased the likelihood of promotion. However, connections with other important top leaders did not have similar effects. These findings challenge the traditional wisdom on the collective leadership and indicate the dominance of the Chinese Communist Party's heads for provincial personnel arrangements.

Otolaryngology residency programs' perspectives on virtual interviews during the COVID-19 pandemic.

ObjectiveTo evaluate perceptions of otolaryngology residency program directors (PDs) and department chairs (DCs) on virtual interviews (VIs) at the conclusion of the 2021 residency match.Study designCross‐sectional survey study.MethodsAn anonymous and voluntary survey was administered to PDs and DCs of U.S. otolaryngology residency programs from March 1, 2021 to April 11, 2021. Utilizing Likert scale ratings, the survey measured overall satisfaction with the VI format, perceived ability to assess key candidate attributes, relative importance of traditional candidate metrics, and likelihood to implement VIs in future cycles.ResultsNinety‐four surveys were completed by PDs (48.9%) and DCs (51.1%). Sixty (63.8%) respondents found the VI experience to be satisfactory or strongly satisfactory. Sixty‐six (70.2%) respondents felt confident in their ability to assess a candidate's professionalism and communication skills through VIs; however, confidence in gauging an applicant's clinical skills/knowledge (41.2%) and overall program fit (47.3%) was lower. Regarding candidate metrics, 64 (68.1%) respondents believed that personal prior knowledge of the applicant gained increased importance with VIs. Forty‐four (46.8%) and 45 (47.9%) participants believed that letters of recommendation in the specialty and perceived commitment to their program similarly attained increased significance in evaluating applicants, respectively.ConclusionMost PDs and DCs were satisfied with their VI experiences and expect the quality of interns to be unaffected by virtual assessment modalities. Majority opinion on the relative importance of traditional applicant metrics remained largely unchanged, the exception being an increase in importance of prior knowledge of the applicant.Level of Evidence: N/A.

Fault enhancement using probabilistic neural networks and Laplacian of a Gaussian filter: A case study in the Great South Basin, New Zealand

Fault identification is critical in defining the structural framework for exploration and reservoir characterization studies. Interpreters routinely use edge-sensitive attributes to accelerate the manual picking process, in which the actual choice of a particular edge-sensitive attribute varies with the seismic data quality and with the reflectivity response of the faulted geologic formations. The cyan-magenta-yellow (CMY) color blending provides an effective way to combine the information content of two or three edge-sensitive attributes when more than one attribute is sensitive to faults. We have evaluated whether combining the information content of more than three attributes using probabilistic neural networks (PNNs) provides any additional uplift. We use training data consisting of manually picked faults on a coarse grid of 3D seismic lines, and then, we use an exhaustive search PNN to identify the optimal set of attributes to create a fault probability volume for a 3D survey acquired over the Great South Basin, New Zealand. We construct a suite of candidate attributes using our understanding of the attribute response to faults seen in the data and examples extracted from the published literature to use the list as the analyzed attributes. Using a subset of picked faults as training data, we evaluate which suite of attributes and hyperparameters exhibits the highest validation on the remaining training data. When used together, we find that volume aberrancy magnitude, gray-level cooccurrence matrix (GLCM) homogeneity, GLCM entropy, Sobel filter similarity, and envelope best predict the faults for this data set. The PNN supervised classification creates a seismic image volume that exhibits fault probabilities providing a simple combination of multiple seismic attributes. We also find that applying a directional Laplacian of a Gaussian and skeletonization filters to the PNN fault volumes provides a superior result to simple CMY blending techniques.

UNEMPLOYMENT CRISIS AMONG FRESH GRADUATES

This paper attempts to explore the topic of the unemployment crisis among fresh graduates. The two main objectives of the study are to examine the influence factors of the unemployment crisis among fresh graduates in Malaysia, and the primary determinant of the unemployment has culminated in many policy implications for higher education. The unemployment rate among fresh graduates increased to 25% in 2020 than the year before, jumping from 13.8%. The null hypothesis is that employer preference, candidate attributes, and economic instability have no significant effect on the unemployment crisis. The information for the paper typically originates from a questionnaire survey method, with one hundred and thirty fresh graduates' participation in this study. The statistical approach is required to analyze numerical data using SPSS applications. Descriptive analyses such as frequency and simple percentages on demographic characteristics were used to analyze data. Inferential statistics such as linear bivariate correlation was used to test the formulated hypothesis. The finding demonstrated a significant association between employer preference, candidate attributes, and economic instability with the unemployment crisis. This finding would shed light on more preparation for fresh graduates' employability who preserve searching for a job by the education ministry. Hopefully, a revision in the academic higher education curriculum is considered to meet the job market's and stakeholders' needs for better graduate employability.

What Matters Most to Student Voters?: A Conjoint Analysis of Political Candidate Attributes

Election is an essential instrument in the development of the country. Understanding key voting preferences could resolve issues facing the country through competent elected government officials. The election of future competent officials depends on the preference of the next generation of voters. This study aims to determine how political candidate attributes affect the voting preference of students and determine which of the political candidate attributes is the most important. The study used choice-based conjoint analysis, which simulates the real-world decision-making of choosing a candidate. Students were presented with different presidential candidates, each with a unique set of demographic and political attributes to choose from. Results suggest that student voters give importance to political attributes that reflect competencies such as profession, experience, accomplishments, and priorities. Political leaders must build their image through competence and good governance among student voters.

Candidates rather than context shape campaign sentiment in French Presidential Elections (1965\u20132017)

The manuscript explores whether and how the strategic context of elections and candidate attributes affect campaign sentiment. Studying five decades of French presidential elections, it provides the first longitudinal test of campaign tone outside the USA. Thereby, the paper examines concerns of an increase in negativity due to changes in electoral competition. It takes leverage from the electoral system, to study whether the strategic environment of elections (first vs. second rounds of elections) or candidate characteristics (ideology and outsider status) determine the use of positive and negative tone. To this end, the paper applies sentiment analysis to personal manifestos (professions de foi) issued by all candidates running in presidential elections (1965–2017) and validates the French Lexicoder Sentiment Dictionary for longitudinal studies of campaign tone. Results reject worries about an increase in negativity in French elections over time. Moreover, while context matters to some extent, candidate attributes are by far more important for explaining campaign sentiment in presidential races. The findings contribute to research on the role of sentiment in electoral competition and tackle broader issues related to the impact of positive and negative political communication for elections and democracies.

Exhaustive probabilistic neural network for attribute selection and supervised seismic facies classification

Machine learning (ML) algorithms, such as principal component analysis, independent component analysis, self-organizing maps, and artificial neural networks, have been used by geoscientists to not only accelerate the interpretation of their data, but also to provide a more quantitative estimate of the likelihood that any voxel belongs to a given facies. Identifying the best combination of attributes needed to perform either supervised or unsupervised ML tasks continues to be the most-asked question by interpreters. In the past decades, stepwise regression and genetic algorithms have been used together with supervised learning algorithms to select the best number and combination of attributes. For reasons of computational efficiency, these techniques do not test all of the seismic attribute combinations, potentially leading to a suboptimal classification. In this study, we have developed an exhaustive probabilistic neural network (PNN) algorithm that exploits the PNN’s capacity in exploring nonlinear relationships to obtain the optimal attribute subset that best differentiates target seismic facies of interest. We determine the efficacy of our proposed workflow in differentiating salt from nonsalt seismic facies in a Eugene Island seismic survey, offshore Louisiana. We find that from seven input candidate attributes, the exhaustive PNN is capable of removing irrelevant attributes by selecting a smaller subset of four seismic attributes. The enhanced classification using fewer attributes also reduces the computational cost. We then use the resulting facies probability volumes to construct the 3D distribution of the salt diapir geobodies embedded in a stratigraphic matrix.