The mediation of matchmaking: a comparative study of gender and generational preference in online dating websites and offline blind date markets in Chengdu

Online dating has modernized traditional partner search methods, allowing individuals to seek a partner that aligns with their preferences for attributes such as age, height, location, or education. Yet traditional forms of partner selection still exist, with continued parental involvement in the matching process. In this paper, we exploit different matchmaking methods with varying degrees of youth autonomy versus parental involvement. We use a unique dataset collected in Chengdu, China, where profiles from the blind date market (n = 158) capture parental preferences and profiles from an online dating website (n = 500) capture individual preferences. Regarding gender, we find that men generally display a desire for women younger, shorter, and less educated than themselves, while women desire older and taller men of the same education as themselves. With regards to parental influences, we find parents specify a narrower range of accepted partner attributes. Further, we find an interaction effect between gender and generational influences: the preferences of parents advertising their daughters on the blind date market show a greater discrepancy in attribute preferences to the online daters than parents advertising their sons.

Object Relocation Visual Tracking Based On Histogram Filter And Siamese Network

Siamese network based trackers formulate the visual tracking mission as an image matching process by regression and classification branches, which simplifies the network structure and improves tracking accuracy. However, there remain many problems as described below. 1) The lightweight neural networks decreases feature representation ability. The tracker is easy to fail under the disturbing distractors (e.g., deformation and similar objects) or large changes in viewing angle. 2) The tracker cannot adapt to variations of the object. 3) The tracker cannot reposition the object that has failed to track. To address these issues, we first propose a novel match filter arbiter based on the Euclidean distance histogram between the centers of multiple candidate objects to automatically determine whether the tracker fails. Secondly, Hopcroft-Karp algorithm is introduced to select the winners from the dynamic template set through the backtracking process, and object relocation is achieved by comparing the Gradient Magnitude Similarity Deviation between the template and the winners. The experiments show that our method obtains better performance on several tracking benchmarks, i.e., OTB100, VOT2018, GOT-10k and LaSOT, compared with state-of-the-art methods.

A comparative study of formal coaching and mentoring programmes in higher education

PurposeThe purpose of this paper is to evaluate two coaching and mentoring programmes focused on the ever-increasingly important aim of enhancing the chances of professional level employment for undergraduate students, at two UK universities. In addition, to offer recommendations to enhance coaching and mentoring success within higher education (HE).Design/methodology/approachTwo similar programmes are compared; the first study is a coaching programme delivered in two phases involving over 1,500 students within the business school. The second study is a mentoring programme involving over 250 students over a ten-year period within the business school at a different institution.FindingsThe two programmes have been compared against the key success criteria from the literature, endorsed by coaching and mentoring experts. The results highlight the importance of integrating with other initiatives, senior management commitment, budget, an application process, clear matching process, trained coaches and mentors, induction for both parties, supportive material, ongoing supervision and robust evaluation and record keeping.Research limitations/implicationsThe research focuses on two similar institutions, with comparable student demographics. It would have been useful to dig deeper into the effect of the diverse characteristics of coach/mentor and coachee/mentee on the effectiveness of their relationships. In addition, to test the assumptions and recommendations beyond these two institutions, and to validate the reach and application of these best practice recommendations further afield.Practical implicationsThe results identify a number of best practice recommendations to guide HE institutions when offering coaching and mentoring interventions to support career progression of their students.Originality/valueThere are limited comparison studies between universities with undergraduate career-related coaching and mentoring programmes and limited research offering best practice recommendations for coaching and mentoring programmes in HE. The top ten factors offered here to take away will add value to those thinking of running similar programmes within HE.

Intuitive global sourcing – a study of supplier selection decisions by apparel SMEs

PurposeThe purpose of this paper is to create knowledge on the intuitive global sourcing process applied by small and medium-sized enterprise (SME) managers.Design/methodology/approachThis study reports on qualitative inquiries with experienced sourcing managers from 10 SMEs in the textile industry. The study follows a three-step semi-structured interviewing process, allowing us to gradually unveil the detailed nature of the intuitive supplier selection process.FindingsNine of the 10 SMEs rely on a highly intuitive supplier selections process, where one supplier at a time is gradually taken into the exchange while testing the supplier’s behavior. The process consists of an early heuristics sub-process, which gradually switches over to a more advanced intuiting behavioral pattern-matching process.Practical implicationsMost OM/SCM research has treated global sourcing and supplier selection as a highly rational, analytical and deliberate optimization problem. This study uncovers a completely different, and frequently successful, intuitive process, which could inspire managers in companies of all sizes, faced with high uncertainty about global supplier selection decisions.Originality/valueIntuition has recently been adopted in the global sourcing literature. However, this study is the first to offer detailed insights into a predominantly intuitive global sourcing process, specifically as it is managed by SMEs.

Neighborhood Rough Set Approach with Biometric Application

This paper provides a new approach for human identification based on Neighborhood Rough Set (NRS) algorithm with biometric application of ear recognition. The traditional rough set model can just be used to evaluate categorical features. The neighborhood model is used to evaluate both numerical and categorical features by assigning different thresholds for different classes of features. The feature vectors are obtained from ear image and ear matching process is performed. Actually, matching is a process of ear identification. The extracted features are matched with classes of ear images enrolled in the database. NRS algorithm is developed in this work for feature matching. A set of 20 persons are used for experimental analysis and each person is having six images. The experimental result illustrates the high accuracy of NRS approach when compared to other existing techniques.

Sibship Size, Height and Cohort Selection: A Methodological Approach

This article deals with the historical relationship between the number of siblings in a family or household and height, a proxy for biological living standards. Ideally, this relationship is better assessed when we have evidence on the exact number of siblings in a family from its constitution onwards. However, this generally requires applying family reconstitution techniques, which, unfortunately, is not always possible. In this latter case, scholars must generally settle for considering only particular benchmark years using population censuses, from which family and household structures are derived. These data are then linked to the height data for the young males of the family or household. Height data are generally obtained from military records. In this matching process, several decisions have to be taken, which, in turn, are determined by source availability and the number of available observations. Using data from late 19th-century Catalonia, we explore whether the methodology used in matching population censuses and military records as described above might affect the relationship between sibship size and biological living standards and, if so, to what extent. We conclude that, while contextual factors cannot be neglected, the methodological decisions made in the initial steps of research also play a role in assessing this relationship.

Word Spotting as a Service: An Unsupervised and Segmentation-Free Framework for Handwritten Documents

Word spotting strategies employed in historical handwritten documents face many challenges due to variation in the writing style and intense degradation. In this paper, a new method that permits efficient and effective word spotting in handwritten documents is presented that relies upon document-oriented local features that take into account information around representative keypoints and a matching process that incorporates a spatial context in a local proximity search without using any training data. The method relies on a document-oriented keypoint and feature extraction, along with a fast feature matching method. This enables the corresponding methodological pipeline to be both effectively and efficiently employed in the cloud so that word spotting can be realised as a service in modern mobile devices. The effectiveness and efficiency of the proposed method in terms of its matching accuracy, along with its fast retrieval time, respectively, are shown after a consistent evaluation of several historical handwritten datasets.

Successful Case Study of Machine Learning Application to Streamline and Improve History Matching Process for Complex Gas-Condensate Reservoirs in Hai Thach Field, Offshore Vietnam

This paper reports a successful case study of applying machine learning to improve the history matching process, making it easier, less time-consuming, and more accurate, by determining whether Local Grid Refinement (LGR) with transmissibility multiplier is needed to history match gas-condensate wells producing from geologically complex reservoirs as well as determining the required LGR setup to history match those gas-condensate producers. History matching Hai Thach gas-condensate production wells is extremely challenging due to the combined effect of condensate banking, sub-seismic fault network, complex reservoir distribution and connectivity, uncertain HIIP, and lack of PVT data for most reservoirs. In fact, for some wells, many trial simulation runs were conducted before it became clear that LGR with transmissibility multiplier was required to obtain good history matching. In order to minimize this time-consuming trial-and-error process, machine learning was applied in this study to analyze production data using synthetic samples generated by a very large number of compositional sector models so that the need for LGR could be identified before the history matching process begins. Furthermore, machine learning application could also determine the required LGR setup. The method helped provide better models in a much shorter time, and greatly improved the efficiency and reliability of the dynamic modeling process. More than 500 synthetic samples were generated using compositional sector models and divided into separate training and test sets. Multiple classification algorithms such as logistic regression, Gaussian Naive Bayes, Bernoulli Naive Bayes, multinomial Naive Bayes, linear discriminant analysis, support vector machine, K-nearest neighbors, and Decision Tree as well as artificial neural networks were applied to predict whether LGR was used in the sector models. The best algorithm was found to be the Decision Tree classifier, with 100% accuracy on the training set and 99% accuracy on the test set. The LGR setup (size of LGR area and range of transmissibility multiplier) was also predicted best by the Decision Tree classifier with 91% accuracy on the training set and 88% accuracy on the test set. The machine learning model was validated using actual production data and the dynamic models of history-matched wells. Finally, using the machine learning prediction on wells with poor history matching results, their dynamic models were updated and significantly improved.

Full-Field History-Matching of Commingling Stacked Reservoirs: A Case Study of an Oman Southern Asset

This paper presents an innovative and practical workflow framework implemented in an Oman southern asset. The asset consists of three isolated accumulations or fields or structures that differ in rock and fluid properties. Each structure has multiple stacked members of Gharif and Alkhlata formations. Oil production started in 1986, with more than 60 commingling wells. The accumulations are not only structurally and stratigraphically complicated but also dynamically complex with numerous input uncertainties. It was impossible to assist the history matching process using a modern optimization-based technique due to the structural complexities of the reservoirs and magnitudes of the uncertain parameters. A structured history-matching approach, Stratigraphic Method (SM), was adopted and guided by suitable subsurface physics by adjusting multi-uncertain parameters simultaneously within the uncertainty envelope to mimic the model response. An essential step in this method is the preliminary analysis, which involved integrating various geological and engineering data to understand the reservoir behavior and the physics controlling the reservoir dynamics. The first step in history-matching these models was to adjust the critical water saturation to correct the numerical water production by honoring the capillary-gravity equilibrium and reservoir fluid flow dynamics. The significance of adjusting the critical water saturation before modifying other parameters and the causes of this numerical water production is discussed. Subsequently, the other major uncertain parameters were identified and modified, while a localized adjustment was avoided except in two wells. This local change was guided by a streamlined technique to ensure minimal model modification and retain geological realism. Overall, acceptable model calibration results were achieved. The history-matching framework's novelty is how the numerical water production was controlled above the transition zone and how the reservoir dynamics were understood from the limited data.

A Peer Intervention Facilitates Trust and Improves Psychosocial Well-Being in Diverse, Low-Income Older Adults

We evaluate a peer outreach intervention to improve the psychosocial well-being of diverse, low-income older adults. Participants (N=74, Age 58-96 years) were recruited from an urban senior center and matched with peers who were >55 years old, received mental health training, and connected participants with health or social activities. We conducted surveys at baseline and 6-month follow-up for 2 years with validated measures of loneliness, social interaction, barriers to socializing, and depression, and thematically analyzed qualitative, semi-structured interviews conducted among a subset of participants (n=15) and peers (n=6). Participants were 58% male, 18% African-American, 19% Latinx, and 8% Asian. Over 2 years, participants experienced sustained reductions in loneliness (p=0.015), depression (p<0.001), and barriers to socializing (p<0.001). Qualitative interviews detailed the role of longitudinal relationships, program flexibility, and the matching process in facilitating trust, motivation, and improved mood. Results can inform larger efficacy studies and implementation of peer-driven community programs.