Profile Creation Research Articles

“I take hot showers so I can practice burning in hell” A corpus analytical study of Tinder female profiles in the UK

The focus of this article is the linguistic choices made by women-seeking-men (WSM) and women-seeking-women (WSW) on Tinder profiles in the UK, which builds on prior corpus-analytical research on dating profiles (Baker 2018; Collins 2019). Data was collected through TinderBotz, a scraping tool which gathered the information publicly displayed on Tinder profiles. Then, to ensure an ethical research praxis, it assigned each profile random identification numbers that guaranteed the anonymity of the users. The corpus consists of two subcorpora (WSM.C and WSW.C) each containing 405 profiles (average number of words=20.15) from women (18-24 y.o.) based in the UK. Given that profile creation on Tinder consists of two parts (the first being semi-guided and the second part, aka the ‘about me’ section, being fully creative), each sub-corpora (WSM.C and WSW.C) was in turn divided into two parts. UK.WSM.T.P.C.1 and UK.WSW.T.P.C.1 include the semi-guided part of the profile while UK.WSM.T.P.C.2 and UK.WSW.T.P.C.2 include the ‘about me’ section). Sketch Engine was used to search for significant n-grams and keywords in both sub-corpora and chi-square tests were conducted to determine the significance of our findings. Our results showed that WSM had less elaborate profiles, mentioned physical attributes (e.g., height) and often redirected potential matches to other platforms (i.e., Instagram). Meanwhile, WSW had more creative ‘about me’ sections in which they described their identity in greater detail (e.g., leatherdyke). Overall, both groups showed similar profiles in terms of work and relationship status.

Pembuatan Profile Kampung Mojo RW 10 Kota Surabaya

A village profile is needed to provide an accurate, comprehensive and integral picture of the village's potential and level of development. Through the University's Real Work Lecture activities on August 17 1945, we tried to help Kalidami RW 10 Village located in Surabaya City, East Java Province in an effort to create a village profile. Based on problem identification, the profile of Mojo RW 10 Village contains some information on the number of existing UMKM. However, there is some data that is still not complete or updated. For example, data that has not been updated is about Waste Generation, Village Profile, Waste Savings, etc. Therefore, the objectives of this service activity are: 1) creating a profile of Mojo RW 10 Village which contains complete village potential data and 2) presenting profile data of Mojo RW 10 Village which can be accessed in printed and electronic versions. The methods used are secondary data collection, surveys and interviews. Evaluation of this activity is carried out directly during the profile creation activity, so that output can be produced that meets the wishes of the target audience. The output of this activity is a printed book on the profile of Mojo RW 10 Village which is one of the documents available at the RW 10 hall and a video profile of Mojo RW 10 Village which has been uploaded on social media.

CareerConnector

Abstract: CareerConnector is an innovative online platform designed to facilitate efficient job searching and recruitment processes, connecting job seekers with potential employers. Powered by a robust technology stack, with .Net serving as the backend language, CareerConnector offers a seamless user experience for both job seekers and employers. Key functionalities encompass user registration and profile creation, advanced job search and filtering, streamlined job posting and application processes, user notifications, and an array of administrative tools for platform management. Additionally, the system incorporates advanced features such as resume upload and parsing, applicant tracking, machine learning-driven job recommendations, and user feedback and review systems.CareerConnector places paramount importance on data privacy and security, adhering to pertinent data protection laws while implementing stringent encryption measures. Scalability and performance optimization are integral to ensuring a smooth and responsive user experience, even as the platform expands. By embracing.Net as the backend language, CareerConnector benefits from its reputation for reliability, scalability, and a wealth of community support. .Net , including React.js, provide the essential infrastructure for handling HTTP requests and database interactions. The ultimate aim of CareerConnector is to create a dynamic, user-centric job portal that effectively bridges the gap between job seekers and employers. By streamlining the job search and recruitment process, it contributes to a more efficient and vibrant

TRAVEL TECH VISIONARY

The Travel Tech Visionary is the project help in making hotels rooms and resort available for the tourist and the owner himself can upload the availability of there rooms on the Travel Tech website. This website is not limited for the rooms only but it provide the facility of booking the flight and trains ticket. The website is build using the HTML, CSS, JavaScript and MERN stack. This website helps in fasten the booking process in the metropolitan cities and this website is ease to use and provide the customer friendly experience. The website is completely trustworthy and help in connecting the customer and user together. The system incorporates essential features, including user registration, detailed Customer service listings, advanced search and filtering options, automated booking and scheduling, comprehensive Customer registration with profile creation, a robust ratings and reviews system, and an intuitive admin dashboard for system monitoring and management.

Machine learning and deep learning for the diagnosis and treatment of ankylosing spondylitis- a scoping review

Background and objectivesMachine Learning (ML) and Deep Learning (DL) are novel technologies that can facilitate early diagnosis of Ankylosing Spondylitis (AS) and predict better patient-specific treatments. We aim to provide the current update on their use at different stages of AS diagnosis and treatment, describe different types of techniques used, dataset descriptions, contributions and limitations of existing work and ed to identify gaps in current knowledge for future works. MethodsWe curated the data of this review from the PubMed database. We searched the full-text articles related to the use of ML/DL in the diagnosis and treatment of AS, for the period 2013–2023. Each article was manually scrutinized to be included or excluded for this review as per its relevance. ResultsThis review revealed that ML/DL technology is useful to assist and promote early diagnosis through AS patient characteristic profile creation, and identification of new AS-related biomarkers. They can help in forecasting the progression of AS and predict treatment responses to aid patient-specific treatment planning. However, there was a lack of sufficient-sized datasets sourced from multi-centres containing different types of diagnostic parameters. Also, there is less research on ML/DL-based AS treatment as compared to ML/DL-based AS diagnosis. ConclusionML/DL can facilitate an early diagnosis and patient-tailored treatment for effective handling of AS. Benefits are especially higher in places with a lack of diagnostic resources and human experts. The use of ML/DL-trained models for AS diagnosis and treatment can provide the necessary support to the otherwise overwhelming healthcare systems in a cost-effective and timely way.

Enhancing Entrepreneurship Resource Competence through Business Incubator Consultant Certification

This paper presents the results of a certification program aimed at enhancing the competency of entrepreneurship resources through the certification of business incubator consultants. The program, conducted by the Indonesia Management Forum and HR24/7 Communication Hub in Bali, involved participants from various regions across Indonesia. The training sessions covered topics such as advocacy for technology business incubator development, product-market fit analysis, business idea validation, feasibility study preparation, legal and institutional management, company profile creation, access to financing, marketing assistance, business valuation, and financial report preparation. The program culminated in a competency assessment on the third day. The results indicated a high level of satisfaction among participants, highlighting the program's effectiveness in improving their understanding and skills in technology business incubator management. The implications of this program include the potential enhancement of Indonesia's entrepreneurship ecosystem and contributions to the achievement of Sustainable Development Goals.

CareerConnector

Abstract: Career Connector is an innovative online platform designed to facilitate efficient job searching and recruitment processes, connecting job seekers with potential employers. Powered by a robust technology stack, with .Net serving as the backend language, CareerConnector offers a seamless user experience for both job seekers and employers. Key functionalities encompass user registration and profile creation, advanced job search and filtering, streamlined job posting and application processes, user notifications, and an array of administrative tools for platform management. Additionally, the system incorporates advanced features such as resume upload and parsing, applicant tracking, machine learning-driven job recommendations, and user feedback and review systems. CareerConnector places paramount importance on data privacy and security, adhering to pertinent data protection laws while implementing stringent encryption measures. Scalability and performance optimization are integral to ensuring a smooth and responsive user experience, even as the platform expands. By embracing. Net as the backend language, CareerConnector benefits from its reputation for reliability, scalability, and a wealth of community support. .Net , including React.js, provide the essential infrastructure for handling HTTP requests and database interactions. The ultimate aim of CareerConnector is to create a dynamic, user-centric job portal that effectively bridges the gap between job seekers and employers. By streamlining the job search and recruitment process, it contributes to a more efficient and vibrant

STRATEGI PENGEMBANGAN PARIWISATA PADA MUSEUM RAJA ALI HAJI

The results showed that applicable strategies include collection diversification, improvement of infrastructure, promotion through social media, and development of educational tour packages. In conclusion, the implementation of these strategies is expected to increase the number of visitors and advance tourism at the Raja Ali Haji Museum. Good museum management and adaptation to current events can have a positive effect on photos and tourist visits. The Raja Ali Haji Museum has many historical collections and has great historical value. Therefore, a development plan is needed to deal with the increase in tourist visits. This research seeks to create a development plan for the Raja Ali Haji Museum by increasing tourist visits based on the characteristics of the museum. This research uses a descriptive method by organizing the results of observation data, interviews, documentary studies and previous literature. The evaluation was carried out through direct observation of many things, including the tourist attraction of the Raja Ali Haji Museum and other heritage sites, the location of the museum and the management of the museum. The Raja Ali Haji Museum development plan may include image creation, museum profile creation, beautiful merchandise and special souvenirs, promotions on websites and social media, collaboration with travel groups and collaboration with educational institutions. By implementing this development strategy, it is hoped that the Raja Ali Haji Batam Museum will become a center of education, inspiration and pride for the local community and visitors from various backgrounds, making this museum a developing and sustainable cultural destination.

Emergence Profile Creation with CAD Technology on Vertical Edgeless Preparation (VEP)

This article proposes a digital procedure to create the emergence profile of prosthetic crowns for teeth prepared with the Vertical Edgeless Preparation (VEP) technique. This technique extends the tooth preparation below the gingival margin on the root surface of teeth with reduced periodontal attachment. The tooth prepared according to this technique does not present a defined prosthetic margin or the relative finishing line. Still, it shows an edgeless axial wall that emerges from the gingival sulcus. The digital procedure for creating a prosthetic emergence profile, although representing a novelty in absolute terms, is based on traditional concepts, as they have been known and described in the literature for a long time. This article will explain and review the various clinical and laboratory steps necessary to produce, through an entirely digital procedure, a fixed partial denture composed of three zirconia elements, which are going to be subsequently veneered in a traditional way on the vestibular side to improve the esthetic appearance.

Towards automated occupant profile creation in smart buildings: A machine learning-enabled approach for user persona generation

Towards automated occupant profile creation in smart buildings: A machine learning-enabled approach for user persona generation

Predicting user types with symbolic images: An empirical validation based on two card-sorting studies

In gamification, personalization is considered an important field of research since it can improve user engagement and motivation. Particularly, effective gamified learning needs to strike a balance between gameful design that promotes engagement and does not negatively affect content comprehension and absorption. The existing traditional user-type questionnaires approaches are time-consuming and intrusive, hampering the user’s focus. Previous research in gamification demonstrated how to automate player profile creation through log file analysis of gameful systems instead of traditional approaches, however, psychological archetypes and motivational groups can simplify the process of collecting data to achieve a balanced level of personalization for gamified systems, which are grounded on the user’s profile and preferences which can help with their immersion. To attend to the needs of a wide range of fields that introduce gamification to systems that are not classified as games, a new ludic approach was created to define user types based on user interaction using symbolic images. In this exploratory research, we tested this approach in two card-sorting studies (N=35 and N=19). Our results show that the images can be used to predict the users’ archetypes and motivational groups accurately. Thus, we contribute with validating an image-based user type classification method that is easier to deploy in systems that do not aim to behave as a game but are looking to reap the benefits of personalizing a user’s content and gameful experience where relevant. Our second contribution is delivering guidelines to replicate this validation method to other existing approaches. We expect that with this guideline, we facilitate personalization with other user typologies without disrupting the gameful experience.

RINX: A system for information and knowledge extraction from resumes

A resume is a detailed source of information about the candidate which summarizes the personal details, education, career history, project experience, certifications, trainings, awards, and any other achievements. For large organizations or job portals which receive thousands of resumes for recruitment or profile creation, it is not possible to manually go through each resume and identify the important information. Hence, there is a need for a system that automatically extracts the information of interest from the resumes. Such automatic extraction of information from resumes is very challenging because resumes are unstructured documents with a wide range of variations in terms of format, style, and contents. In this paper, we describe RINX (Resume INformation eXtraction) which is an end-to-end system for automatic extraction of information from resumes. RINX heavily utilizes traditional approaches like linguistic patterns and gazettes for information extraction. RINX also complements these traditional approaches with state-of-the-art machine learning and deep learning based techniques. We further describe a few knowledge extraction techniques as well as several real-life use-cases based on the information extracted from a large repository of resumes.

Teaching professional skills during the pandemic: Does delivery mode matter?

This study explores how the transition to online course delivery during the pandemic impacted a professional skills course that incorporates in-class and out of class experiential learning activities. We compare student performance on a resume writing assignment, a LinkedIn profile creation assignment, a mock interview with members of local businesses, and overall class scores during Fall 2019 when the professional skills course was delivered face-to-face with student performance during Fall 2020 when the course was delivered online. We explore whether the effect of delivery mode varied as a function of student sex, race and first-generation college status. Our results indicate students performed better on the resume and LinkedIn assignments when the course was delivered online than when it was offered face-to-face, and there was no difference in mock interview or overall class performance. Despite concerns that women, racial minorities and first-generation students could suffer disproportionately in the online version of the course, the relationship between class delivery mode and student performance did not vary as a function of these student characteristics. This is encouraging for institutions interested in offering these courses, and underscores the importance of future research that expands our understanding of when different delivery modes are appropriate.

Evaluation of Sexually Transmitted Disease Surveillance System Electronic Laboratory Processing in Florida: Automating Case Creation, Reporting, and Closure of Chlamydia and Gonorrhea Cases.

Each year, Florida Department of Health staff process hundreds of thousands of electronically received laboratory results for chlamydia (CT) and gonorrhea (GC). These processing steps are currently performed manually in Florida's surveillance system and divert from other sexually transmitted disease prevention efforts. We developed processes that would automate these procedures and evaluated the impact on potential programmatic time savings. We evaluated 575,952 electronic CT/GC laboratory results from January 2019 to December 2021. Laboratory results were processed through the newly automated procedures and algorithms. Expected time savings were projected using conservative estimates of 1 minute saved every time an automated process replaced a current manual procedure: profile matching, profile creation, event record creation, case review, and case reporting. Exceptions to automatic case reporting applied to certain higher-priority populations needing intervention. During this period, 297,348 electronic CT/GC laboratory results were received for people with no previous recorded history of sexually transmitted diseases and required profile creation. In total, 386,763 new surveillance infection records were created for reporting. Of reported cases, 127,345 were from higher-priority groups. The proposed automations would have saved an estimated 33,121 hours of staff time, about 11,040 hours or the work of 5.3 full-time staff annually. Automating current CT/GC laboratory processing would save thousands of personnel hours that could be redirected to higher-priority activities. Flexibility in prioritization criteria for automated case reporting allows programs to adjust automation to disease prevention priorities and resources. Similar automation procedures could be developed by other jurisdictions or health programs.

Towards an Adaptive e-Learning System Based on Deep Learner Profile, Machine Learning Approach, and Reinforcement Learning

Now-a-days, the great challenge of adaptive e-learning systems is to recommend an individualized learning scenario according to the specific needs of learners. Therefore, the perfect adaptive e-learning system is the one that is based on a deep learner profile to recommend the most appropriate learning objects for that learner. Yet, the majority of existing adaptive e-learning systems do not give high importance to the adequacy of the real learner profile and its update with the one taken into account in the learning path recommendation. In this paper, we proposed an intelligent adaptive e-learning system, based on machine learning and reinforcement learning. The objectives of this system are the creation of a deep profile of a given learner, via the implementation of K-means and linear regression, and the recommendation of adaptive learning paths according to this deep profile, by implementing the Q-learning algorithm. The proposed system is decomposed into three principal modules, data preprocessing module, learner deep profile creation module, and learning path recommendation module. These three modules interact with each other to provide a personalized adaptation according to the learner's deep profile. The results obtained indicate that taking into account the learner's deep profile improves the quality of learning for learners.

Ocean depth–temperature profiles for operational oceanography from a shark-borne transmitter

BackgroundMany regions of the ocean are under-sampled in terms of their biology and physical structure. Increasingly sophisticated animal-borne electronic tags are capable of measuring and transmitting in situ environmental data such as ocean temperature–depth profiles. This has the potential to significantly augment the volume of data acquired from under-sampled regions of the ocean. These data would enhance interpretation of animal behavior and distribution and could be used to inform oceanographic and meteorological models. Building on results obtained from marine mammals and turtles, we present a case study of depth–temperature profiles obtained from a tagged tiger shark.ResultsDuring a 102-day deployment, 1350 geolocations were obtained from a shark from waters around Oahu, Hawaii. Of these, 520 were associated with depth–temperature profiles—some of which were from depths exceeding 500 m. Delay between profile creation and transmission to satellite or land-based receiver averaged 8.9 h (range: 35 s–43 h, median 6.32 h). The profiles were in close agreement with profiles extracted from nearby locations in an operational ROMS model. Land-based receivers played a significant role in augmenting data throughput obtained via satellites.ConclusionsShark-borne transmitters offer a viable option for collecting ocean profiles with reporting latencies that make them suitable for operational oceanography. They can significantly increase sampling frequency (especially subsurface) and sample geographic areas that are otherwise difficult to monitor with Lagrangian methods such as Argo floats. They sample locations and depths that are important to the animal and which in some cases may be biological hotspots. The resolution of the data is comparable with that derived from traditional platforms. By targeting appropriate species of shark, different areas of the ocean could be monitored at significantly higher rates than is currently the case.

Die dimensions impact on fibrous plant protein formation during high moisture extrusion

Plant-based meat analogues that mimic the structure and texture of meat are becoming very popular. They can be produced using high moisture extrusion (HSE). . High protein materials first undergo intense thermomechanical treatment in the extruder barrel and then are textured and simultaneously cooled in the die. The mechanisms underlying this structuring process are not sufficiently understood and depend on numerous and interconnected factors, including raw materials composition and process parameters. This study focuses on the effects of die height and die length on extruder responses (torque, pressure), textural properties and fibration of soy concentrate extrudate obtained at high moisture (60% w/w). In order to evaluation the quality of fibration, and to complete texture analysis, a visual method base on angle of fibration was developed. By comparing extrudate obtained with die of 2 mm and 5 mm height, this study shows that a minimum height is necessary for temperature gradient and velocity profile creation in the die. In order to study die length higher than the one observed in the literature, a modular die with different lengths was specially developed for this study, going from 150 to 600 mm for a extrusion barrel length of 640 mm. Results show a linear correlation between fibration and die length: the longer is the die, the more elongated is the internal flow pattern. This change in flow pattern leads to an improvement in the quality of the extrudate fibration.It confirms that die length is one of the main influencing factor for fiber formation, and that a minimum length is required to obtain extrudats which structure really mimics meat fibres.

Teaching Professional Skills During the Pandemic: Does Delivery Mode Matter?

The current study explores how the transition to online course delivery during the pandemic impacted a professional skills course which incorporates a variety of in-class and out of class experiential learning activities. In this paper, we compare student performance on a resume writing assignment, a LinkedIn profile creation assignment, a mock interview with members of local businesses and overall class scores during Fall 2019 when the professional skills course was delivered face-to-face with student performance during Fall 2020 when the course was delivered online. We explore whether the effect of delivery mode varied as a function of student sex, race and first-generation college status. Our results indicate that there were some significant differences in student performance across the two semesters. Students performed better on the resume and LinkedIn assignments when the course was delivered online. There was no difference in mock interview performance across the two class delivery modes. Students taking the online version of the course in Fall 2020 did significantly worse in the course overall than students taking the face-to-face version of the course in Fall 2019. Despite concerns that women, racial minorities and first-generation students could suffer disproportionately in the online version of the course, the relationship between class delivery mode and student performance did not vary as a function of these student characteristics. This underscores the importance of future research which can help to identify changes that can be made to online courses to assist students.

ANIMATION INTRODUCTION OF PROFILE OF SMK NEGERI 5 JAYAPURA USING MULTIMEDIA ANIMATION COMPUTACION METHOD

Advances in science and technology can help an institution to support information and promotion, such as creating school profile recognition animations. Sala one vocational secondary in Papua, SMK Negeri 5 Jayapura at this time for the process of introducing school profiles using a website in the form of text so that it would be better if using multimedia components such as animation to clarify school profiles. The purpose of this research is to design animation to help promote SMK Negeri 5 Jayapura to the community to help the school to be able to provide better promotion and service to the community. This animation was created by the multimedia development life cycle (MDLC) development model of Luther Sutopo's multimedia development model which consists of six stages namely Concept, Design, Material Collection, Manufacturing, Testing and Distribution. The software used in the animation process is Blender V 2.79b, Photoshop CS5 and Adobe Audition CS6. The results of the study in the form of animated videos are 6 minutes long and not interactive. The profile creation of SMK Negeri 5 Jayapura has been successfully built to help the school to provide information about SMK Negeri 5 Jayapura, especially the introduction of school profiles to the public.

Improving Second Language Acquisition by Extensive and Analytical Reading in a Digital Environment

ABSTRACT The premise of this research is that analytical and extensive reading in a digital environment might be used to facilitate second language acquisition at the tertiary education level. Despite the general orientation toward communicative methods in modern second language acquisition, reading methods and techniques have seen an unexpected yet very productive comeback in recent years. A regression analysis of the data gathered from three focus groups of learners (2018–2020) at an institution of higher education in Russia suggests that analytical and extensive reading (based on Adler’s method) may be associated with relatively better performance in reading tasks of all types of Cambridge Assessment certifications. The inference should be drawn that limited exposure to analytical and extensive reading of the original works of literature by three focus groups during the second and the third years of study may facilitate the preparation for the Reading and the Use of English/Reading sections of CAE, CPE, and IELTS Academic. The results suggest that reading plans and book profile creation activities, as well as extensive and analytical reading practice in a digital environment, may be used as relatively effective tools of second language acquisition.