Candidate Matches Research Articles

Predictive Value of Neurosurgery Applicant Metrics on Resident Academic Productivity

BACKGROUND AND OBJECTIVES: Scholarship has been critical to neurosurgery. As grades and board examinations become pass-fail, finding metrics to distinguish applicants coupled with an emphasis on research has led to growth of reported academic output among neurosurgery applicants. We aimed to evaluate applicant factors that associate with an academically productive neurosurgery resident. METHODS: Applicant characteristics were extracted from Electronic Residency Application Service archives from 2 geographically distinct neurosurgical programs for the 2014 to 2015 match cycle. Publications during residency were quantified, and residency careers were examined. Factors associated with residency publications were examined using univariate and multivariate regressions. RESULTS: A total of 228 United States (US) applicants to neurosurgery were assessed (89% of US neurosurgery applicants), with 173 matching across 93 programs. The average publication number of matched applicants was higher at 6.6 (median: 4, range: 0-43) that of than unmatched applicants (mean: 2.9, median: 1, range: 0-51). A total of 93.1% of publications were substantiated on PubMed review. Matched candidates published 19.3 manuscripts (median: 13, range: 0-120) on average during residency. On univariate analysis, factors associated with higher residency publications included taking a non–degree-granting extra year for research in medical school, consistently high clerkship grades, depth of preresidency research involvement, number of coresidents, program R25 status, and academic output of neurosurgery department leadership. After multivariate correction, the training environment played an outsized role in predicting resident academic output, with program R25 status significantly associated with resident academic output (odds ratio: 1.25, P = .012). Taking an extra research year in medical school approached but was not significant (odds ratio: 1.19, P = .099). Twelve matched international medical school graduates (IMGs) were also assessed (75% of matched IMG neurosurgery applicants). IMGs exhibited higher total publications and conference abstracts than US matched applicants and also published more during residency. CONCLUSION: Cultivating an environment that promotes research endeavors is critical for neurosurgical resident academic growth. Preresidency publication number does not predict publication potential during residency.

Internal fellowship candidate match rates in obstetrics and gynecology before and after the widespread adoption of virtual interviews post-COVID

Internal fellowship candidate match rates in obstetrics and gynecology before and after the widespread adoption of virtual interviews post-COVID

A robust fingerprint identification approach using a fuzzy system and novel rotation method

Forensic science has developed significantly in the last few decades. Its key role is to provide crime investigators with processed data obtained from the crime scene to achieve more accurate results presented in court. Biometrics has proved its robustness against various critical crimes encountered by forensics experts. Fingerprints are the most important biometric used until now due to their uniqueness and production low cost. The automated fingerprint identification system (AFIS) came into existence in the early 1960s through the cooperation of the countries: USA, UK, France, and Japan. Ever since it started to develop gradually because of the challenges found at the crime scenes such as fingerprints distortions and partial cuts which in turn can severely affect the final calculations made by experts. The vagueness of the results was the main motivation to build a robust fingerprint identification system that introduces new and enhanced methods in its stages to help experts make more accurate decisions. The proposed fingerprint identification system uses Fourier domain analysis for image enhancement, then the system cuts the image around the core point after applying the rotation and core point detection methods. After that, it calculates the similarity based on the distance between fingerprint histograms extracted using the Local Binary Pattern (LBP). The system's last step is to translate the results into a sensible form where it utilizes fuzziness to provide more possibilities for the answer. The proposed identification system showed high efficiency on FVC 2002 and FVC 2000 databases. For instance, the results of applying our system on FVC 2002 provided a set of three ordered matching candidates such that 97.5 % of the results provided the correct candidate as the first order, and the rest of 2.5 % provided the correct candidate as the second order.

A Study on General Staffing Services and Manpower Providing Services of QUESS CORP LTD. in Ahmedabad

This research paper presents a comprehensive study of general staffing services and manpower providing services offered by Quess Corp in Ahmedabad. The study aims to explore the operational frameworks, client engagement strategies, and service delivery mechanisms that make Quess Corp a significant player in the staffing industry. Through a combination of qualitative and quantitative research methods, including interviews with company executives, surveys of client companies, and analysis of industry reports, the paper delves into the challenges and opportunities faced by the company in the rapidly evolving employment landscape of Ahmedabad. Key findings indicate that Quess Corp's emphasis on technology- driven solutions, robust training programs, and strategic partnerships has significantly enhanced its service efficiency and client satisfaction levels. Additionally, the study highlights the critical role of adaptive workforce solutions in addressing the dynamic needs of businesses in Ahmedabad. The paper concludes with recommendations for leveraging technological advancements and optimizing human resource management practices to further strengthen Quess Corp's market position. Quess Corp’s general staffing services encompass temporary staffing, contract staffing, and permanent staffing solutions. The company’s comprehensive approach ensures that it can meet the diverse staffing needs of its clients, ranging from short- term project-based requirements to long-term strategic hires. The study explores how Quess Corp tailors its services to suit the specific demands of various sectors, including IT, manufacturing, retail, and healthcare. The company utilizes advanced technology platforms to manage its talent pool, ensuring quick and accurate matching of candidates to job requirements. This technological integration is a significant factor in Quess Corp’s ability to deliver high- quality staffing solutions efficiently

ShapeBench: A new approach to benchmarking local 3D shape descriptors

The ShapeBench evaluation methodology is proposed as an extension to the popular Area Under Precision-Recall Curve (PRC/AUC) for measuring the matching performance of local 3D shape descriptors. It is observed that the PRC inadequately accounts for other similar surfaces in the same or different objects when determining whether a candidate match is a true positive. The novel Descriptor Distance Index (DDI) metric is introduced to address this limitation. In contrast to previous evaluation methodologies, which identify entire objects in a given scene, the DDI metric measures descriptor performance by analysing point-to-point distances. The ShapeBench methodology is also more scalable than previous approaches, by using procedural generation. The benchmark is used to evaluate both old and new descriptors. The results produced by the implementation of the benchmark are fully replicable, and are made publicly available.

AI Revolution in Banking Recruitment: Enhancing Efficiency and Objectivity

This paper examines the profound impact of Artificial Intelligence (AI) on recruitment and selection processes within the banking sector. Through the integration of AI technologies, traditional practices are undergoing a paradigm shift, resulting in heightened efficiency, objectivity, and effectiveness in talent acquisition. The utilization of AI facilitates automated resume screening, precise candidate matching, predictive analytics, and bias reduction, while also incorporating innovative tools such as chatbots for initial engagement. Furthermore, AI enables comprehensive analyses of employee retention and skills gap, empowering organizations to optimize workforce management strategies. This abstract highlight the transformative role of AI in reshaping recruitment practices within the dynamic landscape of the banking sector.

A STUDY ON IMPACT OF ARTIFICIAL INTELLIGENCE ON HR RECRUITMENT PROCESS IN LEADTEQZ

The emergence of generative AI as a revolutionary force is destined to reshape the landscape of human resource management. This transformative technology is poised to automate mundane tasks, drive operational efficiency, and elevate the overall employee experience within HR recruitment processes. Delving into specific use cases of generative AI within HR contexts, this research aims to spotlight how its integration transcends traditional HR practices, facilitating data-driven decision-making and amplifying the recruitment experience. By automating resume screening, personalizing candidate interactions, and curating tailored training materials, generative AI exemplifies an organizational commitment to leverage transformative technologies, cultivating a sophisticated, data-driven, and candidate-centric recruitment ecosystem. While the integration of generative AI in HR recruitment processes promises a paradigm shift towards efficiency and individualized candidate engagement, it also necessitates addressing potential challenges and ethical considerations. Rigorous validation, monitoring, and transparent communication are essential to ensure a fair, inclusive, and compliant recruitment process. As a comprehensive exploration, this research paper provides a comprehensive blueprint for organizations seeking to harness the transformative potential of generative By embracing AI tools for automation, data analysis, and candidate matching, Leadteqz has experienced efficiency gains, reduced time-to-hire, and improved recruitment decision-making processes. This evolution is crucial for organizations to adapt to future trends, enhance recruitment outcomes, and maintain a competitive edge in the dynamic landscape of talent acquisition.

Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process

In the evolving realities of recruitment, the precision of job–candidate matching is crucial. This study explores the application of Zero-Shot Recommendation AI Models to enhance this matching process. Utilizing advanced pretrained models such as all-MiniLM-L6-v2 and applying similarity metrics like dot product and cosine similarity, we assessed their effectiveness in aligning job descriptions with candidate profiles. Our evaluations, based on Top-K Accuracy across various rankings, revealed a notable enhancement in matching accuracy compared to conventional methods. Specifically, the all-MiniLM-L6-v2 model with a chunk length of 768 exhibited outstanding performance, achieving a remarkable Top-1 accuracy of 3.35%, 55.45% for Top-100, and an impressive 81.11% for Top-500, establishing it as a highly effective tool for recruitment processes. This paper presents an in-depth analysis of these models, providing insights into their potential applications in real-world recruitment scenarios. Our findings highlight the capability of Zero-Shot Learning to address the dynamic requirements of the job market, offering a scalable, efficient, and adaptable solution for job–candidate matching and setting new benchmarks in recruitment efficiency.

REVOLUTIONIZING RECRUITMENT: THE ROLE OF ARTIFICIAL INTELLIGENCE IN TALENT ACQUISITION

The rapid advancements in Artificial Intelligence (AI) are transforming various sectors, and talent acquisition is no exception. This research paper exploring how AI-driven tools are reshaping the way organizations attract, evaluate, and hire talent. With the global competition for top talent intensifying, the need for efficient, data-driven hiring strategies is critical. AI technologies, including machine learning, natural language processing, and predictive analytics, have become pivotal in streamlining tasks such as resume screening, candidate matching, and interview scheduling, significantly reducing time-to-hire and improving candidate experiences. This paper conducts a thorough analysis of AI's integration into talent acquisition, AI’s benefits, challenges, and ethical considerations. Key findings show that AI reduces hiring time by up to 50%, enhances candidate engagement through automation, and allows for more informed hiring decisions. Despite its potential, organizations must carefully manage the ethical challenges AI poses to ensure fair and transparent recruitment practices. In conclusion, AI has the potential to revolutionize talent acquisition, but its success depends on thoughtful implementation and continuous evaluation to mitigate risks and maximize benefits.

VIEW GRAPH CONSTRUCTION FOR LARGE-SCALE UAV IMAGES: AN EVALUATION OF STATE-OF-THE-ART METHODS

Abstract. Structure from Motion (SfM) is a 3D reconstruction framework that has achieved great success on large-scale Unmanned Aerial Vehicle (UAV) images. Due to the high time consumption of feature matching, a matching candidate subset is obtained by image retrieval to improve efficiency. Bag of Word (BoW) based image retrieval has been widely used in SfM systems, but the large number of local features and the high dimension of the BoW vector cause the retrieval method time-consuming. Vector of Locally Aggregated Descriptors (VLAD) and learning-based NetVLAD perform well in image retrieval, and these vector representation methods are evaluated in this study. After images are transformed into vectors, Nearest Neighbour (NN) searching methods like Brute-force and KD-Tree are used to find similar images. But as the number of images and the vector dimension increase, Approximate Nearest Neighbour (ANN) searching methods like Hierarchical Navigable Small World (HNSW) and Locality-Sensitive Hashing (LSH) are considered to replace NN searching to avoid efficiency degradation. These vector searching methods are also evaluated in this study. The test results demonstrate that the optimal method VLAD with HNSW can speed up about 100 times in finding matching candidate subset. A view graph that guides scene partition and sub-scene reconstruction in parallel SfM can be created by the optimal method. With this view graph construction method, the efficiency of SfM is significantly improved.

Online Bipartite Matching with Reusable Resources

We study the classic online bipartite matching problem with a twist: off-line vertices, called resources, are reusable. In particular, when a resource is matched to an online vertex, it is unavailable for a deterministic time duration d, after which it becomes available again for a rematch. Thus, a resource can be matched to many different online vertices over a period of time. Whereas recent work on the problem has resolved the asymptotic case in which we have large starting inventory (i.e., many copies) of every resource, we consider the (more general) case of unit inventory and give the first algorithms that are provably better than the naïve greedy approach, which has a competitive ratio of (exactly) 0.5. Our first algorithm, which achieves a competitive ratio of 0.589, generalizes the classic RANKING algorithm for online bipartite matching of nonreusable resources (Karp et al. 1990) by reranking resources independently over time. Whereas reranking resources frequently has the same worst case performance as greedy, we show that reranking intermittently on a periodic schedule succeeds in addressing reusability of resources and performs significantly better than greedy in the worst case. Our second algorithm, which achieves a competitive ratio of 0.505, is a primal-dual randomized algorithm that works by suggesting up to two resources as candidate matches for every online vertex and then breaking the tie to make the final matching selection in a randomized correlated fashion over time. As a key component of our algorithm, we suitably adapt and extend the powerful technique of online correlated selection (Fahrbach et al. 2020) to reusable resources in order to induce negative correlation in our tie-breaking step and beat the competitive ratio of 0.5. Both of our results also extend to the case in which off-line vertices have weights. Funding: R. Niazadeh’s research is supported by the Asness Junior Faculty Fellowship at The University of Chicago Booth School of Business.

Global optimisation matching method for multi-representation buildings constrained by road network

Entity matching is one of the key technologies for geospatial data update and fusion. In response to the shortcomings of most spatial entity matching methods that use local optimisation strategies, a global optimisation matching method for multi-representation buildings using road network constraints is proposed. First, the road network is used for region segmentation to obtain candidate matches. Second, the spatial similarity among the candidate matching objects is calculated and the characteristic similarity weights are determined using the entropy weight method. Third, the matching of building entities is transformed into an allocation problem using integer programming ideas, and the Hungarian algorithm is solved to obtain the optimal matching combination with minimum global variance. Finally, two test areas are selected to validate the proposed method, and the precision, recall, and F-measure values of the experiments are 96.35%, 97.11%, and 96.73% versus 95.96%, 97.03%, and 96.49%, respectively. The matching accuracy is greatly improved compared with the local search strategy.

Towards Trajectory Forecasting From Detection.

Trajectory forecasting for traffic participants (e.g., vehicles) is critical for autonomous platforms to make safe plans. Currently, most trajectory forecasting methods assume that object trajectories have been extracted and directly develop trajectory predictors based on the ground truth trajectories. However, this assumption does not hold in practical situations. Trajectories obtained from object detection and tracking are inevitably noisy, which could cause serious forecasting errors to predictors built on ground truth trajectories. In this paper, we propose to predict trajectories directly based on detection results without relying on explicitly formed trajectories. Different from traditional methods which encode the motion cues of an agent based on its clearly defined trajectory, we extract the motion information only based on the affinity cues among detection results, in which an affinity-aware state update mechanism is designed to manage the state information. In addition, considering that there could be multiple plausible matching candidates, we aggregate the states of them. These designs take the uncertainty of association into account which relax the undesirable effect of noisy trajectory obtained from data association and improve the robustness of the predictor. Extensive experiments validate the effectiveness of our method and its generalization ability to different detectors or forecasting schemes.

Group-Based Personalized News Recommendation with Long- and Short-Term Fine-Grained Matching

Personalized news recommendation aims to help users find news content they prefer, which has attracted increasing attention recently. There are two core issues in news recommendation: learning news representation and matching candidate news with user interests. In this context, “candidate” indicates potential for interest. Due to the superior ability to understand natural language demonstrated by Pretrained Language Models (PLMs), recent works utilize PLMs (e.g., BERT) to strengthen news modeling, obtaining more accurate user interest matching and achieving notable improvement in news recommendation. However, the existing PLM-based methods are usually incapable of fully exploring the fine-grained (i.e., word-level) relatedness between user behaviors and candidate news due to the heavy computational cost brought by PLMs. In this article, we propose a group-based personalized news recommendation method with long- and short-term matching mechanisms between users and candidate news based on PLMs to learn fine-grained matching efficiently and effectively. In our approach, we design to group user historical clicked news into chunks with quite shorter news sequences according to their clicked timestamps, which could alleviate the computation issues of PLMs. PLMs are applied in each group jointly with the candidate news to capture their word-level interaction, and global group-level matching is learned across different groups. In addition, the group-based mechanism could be naturally adapted for long- and short-term user representation learning, in which we build users’ long preferences from the representations of all groups and treat the last group as short interests, respectively. Finally, we employ a gate network to dynamically unify the group-level, long- and short-term representations, yielding comprehensive user-news matching effectively. Extensive experiments are conducted on two real-world datasets. The results show that our proposed method achieves superior performance in news recommendations.

Evaluating the representation of disaster hazards in SNOMED CT: gaps and opportunities.

Climate change, an underlying risk driver of natural disasters, threatens the environmental sustainability, planetary health, and sustainable development goals. Incorporating disaster-related health impacts into electronic health records helps to comprehend their impact on populations, clinicians, and healthcare systems. This study aims to: (1) map the United Nations Office for Disaster Risk Reduction and International Science Council (UNDRR-ISC) Hazard Information Profiles to SNOMED CT International, a clinical terminology used by clinicians, to manage patients and provide healthcare services; and (2) to determine the extent of clinical terminologies available to capture disaster-related events. Concepts related to disasters were extracted from the UNDRR-ISC's Hazard Information Profiles and mapped to a health terminology using a procedural framework for standardized clinical terminology mapping. The mapping process involved evaluating candidate matches and creating a final list of matches to determine concept coverage. A total of 226 disaster hazard concepts were identified to adversely impact human health. Chemical and biological disaster hazard concepts had better representation than meteorological, hydrological, extraterrestrial, geohazards, environmental, technical, and societal hazard concepts in SNOMED CT. Heatwave, drought, and geographically unique disaster hazards were not found in SNOMED CT. To enhance clinical reporting of disaster hazards and climate-sensitive health outcomes, the poorly represented and missing concepts in SNOMED CT must be included. Documenting the impacts of climate change on public health using standardized clinical terminology provides the necessary real time data to capture climate-sensitive outcomes. These data are crucial for building climate-resilient healthcare systems, enhanced public health disaster responses and workflows, tracking individual health outcomes, supporting disaster risk reduction modeling, and aiding in disaster preparedness, response, and recovery efforts.

Representation of Environmental Concepts Associated with Health Impacts in Computer Standardized Clinical Terminologies.

To evaluate the representation of environmental concepts associated with health impacts in standardized clinical terminologies. This study used a descriptive approach with methods informed by a procedural framework for standardized clinical terminology mapping. The United Nations Global Indicator Framework for the Sustainable Development Goals and Targets was used as the source document for concept extraction. The target terminologies were the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and the International Classification for Nursing Practice (ICNP). Manual and automated mapping methods were utilized. The lists of candidate matches were reviewed and iterated until a final mapping match list was achieved. A total of 119 concepts with 133 mapping matches were added to the final SNOMED CT list. Fifty-three (39.8%) were direct matches, 37 (27.8%) were narrower than matches, 35 (26.3%) were broader than matches, and 8 (6%) had no matches. A total of 26 concepts with 27 matches were added to the final ICNP list. Eight (29.6%) were direct matches, 4 (14.8%) were narrower than, 7 (25.9%) were broader than, and 8 (29.6%) were no matches. Following this evaluation, both strengths and gaps were identified. Gaps in terminology representation included concepts related to cost expenditures, affordability, community engagement, water, air and sanitation. The inclusion of these concepts is necessary to advance the clinical reporting of these environmental and sustainability indicators. As environmental concepts encoded in standardized terminologies expand, additional insights into data and health conditions, research, education, and policy-level decision-making will be identified.

The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies

Purpose: The aim of this study is to contribute to the understanding of the power of artificial intelligence (AI) in recruitment and to highlight the opportunities and challenges associated with its use. Theoretical framework: This paper provides a comprehensive analytical review of current AI-based recruitment strategies, drawing on both academic research and industry reports. Design/methodology/approach: The paper critically evaluates the potential benefits and drawbacks of using AI in recruitment and assesses the effectiveness of various AI-based recruitment strategies. Findings: The results indicate that AI-based recruitment strategies such as resume screening, candidate matching, video interviewing, chatbots, predictive analytics, gamification, virtual reality assessments, and social media screening offer significant potential benefits for organizations, including improved efficiency, cost savings, and better-quality hires. However, the use of AI in recruitment also raises ethical and legal concerns, including the potential for algorithmic bias and discrimination. Research, Practical & Social implications: The study concludes by emphasizing the need for further research and development to ensure that AI-based recruitment strategies are effective, unbiased, and aligned with ethical and legal standards. Originality/value: The value of the study lies in its comprehensive exploration of AI in recruitment, synthesizing insights from academic and industry perspectives, and assessing the balance of potential benefits against ethical and legal concerns.

Context-enhanced motion coherence modeling for global outlier rejection

Feature matching is an essential step in a wide range of photogrammetry and computer vision tasks but limited by the ambiguities of local descriptors. Hence, numerous false feature matches (outliers) will inevitably be generated, especially in complex scenarios. Motion coherence can establish the statistical relationships among sparse motions to remove outliers, assuming that the true matches are coherent, and the false matches are randomly scattered. However, existing methods model motion coherence either in a local spatial context or without considering rich matching priors, thereby leading to numerous matching failures when outlier rates are high. In this study, we propose a context-enhanced motion coherence modeling (CoMo) method to distinguish consistent correct motions from erroneous matches for robust outlier rejection. The CoMo method deploys a consistency-aware motion descriptor to encode the consistency-related matching priors of feature matches as a high-dimensional representation, which can provide a rich context for differentiating heterogeneous motions. Based on this discriminative descriptor, we further introduce deformable affine transformation (DAT) as a proxy for motion and fit the coherent motions from the candidate matches with a globally smooth function under the truncated least squares estimation framework. Extensive experiments on multiple large datasets (including the Image Matching Challenge at CVPR 2020) demonstrate that the CoMo method can effectively model motion coherence from noisy candidate matches and outperform other state-of-the-art methods in outlier rejection and relative camera pose estimation. The code is available at https://github.com/geovsion/CoMo.

Colorful Matchings

Suppose a committee consisting of three members is tasked with matching candidates to different positions. However, all of the committee members disagree on the job placement for every candidate, i.e., every candidate is matched to three different positions according to three committee members. All three committee members are competitive and want to push through as many of their placements as possible. Can they find a compromise which allows each committee member to be responsible for a third of all the candidate placements? In this paper we will consider an asymptotic version of this question and several other variants of a similar problem. As an application we will consider an embedding question—which hypertrees does a large Steiner system always contain?

EDITORIAL COMMENT

Spurred on by the COVID-19 pandemic we have seen many changes in the healthcare landscape over the last few years. Those of us involved in medical education, in particular, are seeing changes in how medical students can rotate within urology and significant changes in the interview process. The authors of this study, members of the Society of Academic Urologist (SAU) Taskforce and Board of Directors, have put together important data about the perception of the virtual interview (VI) process among PGY-1 residents who were among the first groups of students to undergo such a process. A total of 116 PGY-1 residents at least partially responded to the survey, representing 32% of the matched candidates for that year. Importantly, the authors intentionally sent out the surveys late in the academic year with the hope that the respondents had rotated through their urology service by the time they completed the survey.