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People now frequently meet and develop relationships through online dating. Yet, due to their limited accessibility, utilizing dating services can be difficult and irritating for people with visual impairments. The significance of the research issue can be attributed to the fact that dating websites are becoming more and more common and have a significant impact on how people establish romantic connections. It can be challenging for people with visual impairments to use dating services and develop lasting relationships because many of them are not created with their requirements in mind. We can encourage people with visual impairments to participate more completely in online dating and possibly enhance the success of their romantic relationships by making dating websites more accessible. There is some existing implementation that can automatically recognize the facial expression, age, gender, presence of child(ren) and other common objects from a profile photo in a dating platform. The goal of this project is incorporate additional features (presence of any common pets, indoor vs. outdoor image) to further enhance the capability of existing system and come up with test viable solut

Online dating is frequently used by individuals looking for potential relationships and intimate connections. Central to dating apps is the creation and refinement of a dating profile, which represents the way individuals desire to present themselves to potential mates, while hiding information they do not care to share. To investigate the way frequent users of dating apps construct their online profiles and perceive the effectiveness of strategies taken in making profiles, we conducted semi-structured interviews with 20 experienced users who are Chinese college-educated young adults and uncovered the processes and rationales by which they make profiles for online dating, particularly in selecting images for inclusion. We found that participants used idealized photos that exaggerated their positive personality traits, sometimes traits that they do not possess but perceive others to desire, and sometimes even traits they wish they had possessed. Users also strategically used photos that show personality and habits without showing themselves, and often hid certain identifying information to reduce privacy risks. This analysis signals potential factors that are key in building online

Radiocarbon dating poses a challenge in many archaeological contexts due to the limited precision of conventional calibration methods. In this study, we introduce a novel approach to fine-dating that is based on the repeated application of OxCal's R_Simulate function. By constructing extensive reference tables and aggregating measures of central tendency (means and medians), uncalibrated 14C measurements are directly mapped to calendar dates. The method is validated through comprehensive simulations and comparisons with dendrochronologically dated tree rings. Despite challenges in segments of the calibration curve with low gradients, the approach demonstrates that a significant improvement in dating precision is achievable. Limitations and potential avenues for further methodological optimisation are discussed.

In its early days, online dating was heralded as a great equalizer, removing biases built into the structures of heterosexuality courtship. However, as repeatedly observed, that prophecy was never fulfilled, and some biases have even become exacerbated. In this paper, we identify a general endogenous mechanism that drives the widening of the gender gap in first-contact rates of heterosexual dating. This mechanism relies on assumptions about the participants' expectations of new contacts and their time constraints. We formulate this symmetry-breaking mechanism as a system of differential equations and analyze its fixed points and their stability.

Virtual reality (VR) dating introduces novel opportunities for romantic interactions, but it also raises concerns about new harms that typically occur separately in traditional dating apps and general-purpose social VR environments. Given the subjectivity in which VR dating experiences can be considered harmful it is imperative to involve user stakeholders in anticipating harms and formulating preventative designs. Towards this goal with conducted participatory design workshops with 17 stakeholders identified as women and/or LGBTQIA+; demographics that are at elevated risk of harm in online dating and social VR. Findings reveal that participants are concerned with two categories of harm in VR dating: those that occur through the transition of interaction across virtual and physical modalities, and harms stemming from expectations of sexual interaction in VR.

The study of Greek papyri from ancient Egypt is fundamental for understanding Graeco-Roman Antiquity, offering insights into various aspects of ancient culture and textual production. Palaeography, traditionally used for dating these manuscripts, relies on identifying chronologically relevant features in handwriting styles yet lacks a unified methodology, resulting in subjective interpretations and inconsistencies among experts. Recent advances in digital palaeography, which leverage artificial intelligence (AI) algorithms, have introduced new avenues for dating ancient documents. This paper presents a comparative analysis between an AI-based computational dating model and human expert palaeographers, using a novel dataset named Hell-Date comprising securely fine-grained dated Greek papyri from the Hellenistic period. The methodology involves training a convolutional neural network on visual inputs from Hell-Date to predict precise dates of papyri. In addition, experts provide palaeographic dating for comparison. To compare, we developed a new framework for error analysis that reflects the inherent imprecision of the palaeographic dating method. The results indicate that the comput

Online dating has become a popular way for individuals to connect with potential romantic partners. Many dating apps use personal profiles that include a headshot and self-description, allowing users to present themselves and search for compatible matches. However, this traditional model often has limitations. In this study, we explore a non-traditional voice-based dating app called "Soul". Unlike traditional platforms that rely heavily on profile information, Soul facilitates user interactions through voice-based communication. We conducted semi-structured interviews with 18 dedicated Soul users to investigate how they engage with the platform and perceive themselves and others in this unique dating environment. Our findings indicate that the role of voice as a moderator influences impression management and shapes perceptions between the sender and the receiver of the voice. Additionally, the synchronous voice-based and community-based dating model offers benefits to users in the Chinese cultural context. Our study contributes to understanding the affordances introduced by voice-based interactions in online dating in China.

Bronze inscriptions from early China are fragmentary and difficult to date. We introduce BIRD(Bronze Inscription Restoration and Dating), a fully encoded dataset grounded in standard scholarly transcriptions and chronological labels. We further propose an allograph-aware masked language modeling framework that integrates domain- and task-adaptive pretraining with a Glyph Net (GN), which links graphemes and allographs. Experiments show that GN improves restoration, while glyph-biased sampling yields gains in dating.

Sexually transmitted diseases (STDs) are a group of pathogens infecting new hosts through sexual interactions. Due to its social and economic burden, multiple models have been proposed to study the spreading of pathogens. In parallel, in the ever-evolving landscape of digital social interactions, the pervasive utilization of dating apps has become a prominent facet of modern society. Despite the surge in popularity and the profound impact on relationship formation, a crucial gap in the literature persists regarding the potential ramifications of dating apps usage on the dynamics of STDs. In this paper, we address this gap by presenting a novel mathematical framework - an extended Susceptible-Infected-Susceptible (SIS) epidemiological model to elucidate the intricate interplay between dating apps engagement and the propagation of STDs. Namely, as dating apps are designed to make users revisit them and have mainly casual sexual interactions with other users, they increase the number of causal partners, which increases the overall spread of STDS. Using extensive simulation, based on real-world data, explore the effect of dating apps adoption and control on the STD spread. We show that

The rise of online dating apps has transformed how individuals connect and seek companionship, with an increase in usage among older adults. While these platforms offer opportunities for emotional support and social connection, they also present significant challenges, including a concerning trend of online dating scams targeting this demographic. To address these issues, we conducted a semi-structured interview focused on the online dating experiences of older adults (65+). Initially, we conducted a pre-screening survey, followed by focused semi-structured interviews with 11 of the selected older adults. Through this study, we investigate older adults' security and privacy concerns, the significance of design elements and accessibility, and identify areas needing improvement. Our findings reveal challenges such as deceptive practices, including catfishing and fraud, concerns over disclosing sensitive information, non-inclusive app design features, and the need for more informative visualization of match requests. We offer recommendations for enhanced identity verification, inclusive privacy controls by app developers, and increased digital literacy efforts to enable older adults t

In 2023, the Netherlands Institute for Human Rights, the Dutch non-discrimination authority, decided that Breeze, a Dutch dating app, was justified in suspecting that their algorithm discriminated against dark-skinned users. Consequently, the Institute decided that Breeze must prevent this discrimination based on ethnicity. This paper analyses the decision and explores three questions.What are the main points of the Breeze decision? Is the discrimination based on ethnicity in Breeze's matching algorithm illegal? We also explore a more general question: how can dating apps mitigate or stop discrimination in their matching algorithms? We illustrate the legal and technical difficulties dating apps face in tackling discrimination and highlight some promising solutions. We analyse the Breeze decision in-depth, combining insights from computer science and law. We discuss the implications of this judgment for scholarship and practice in the field of fair and non-discriminatory machine learning.

The archaeological dating of bronze dings has played a critical role in the study of ancient Chinese history. Current archaeology depends on trained experts to carry out bronze dating, which is time-consuming and labor-intensive. For such dating, in this study, we propose a learning-based approach to integrate advanced deep learning techniques and archaeological knowledge. To achieve this, we first collect a large-scale image dataset of bronze dings, which contains richer attribute information than other existing fine-grained datasets. Second, we introduce a multihead classifier and a knowledge-guided relation graph to mine the relationship between attributes and the ding era. Third, we conduct comparison experiments with various existing methods, the results of which show that our dating method achieves a state-of-the-art performance. We hope that our data and applied networks will enrich fine-grained classification research relevant to other interdisciplinary areas of expertise. The dataset and source code used are included in our supplementary materials, and will be open after submission owing to the anonymity policy. Source codes and data are available at: https://github.com/zh

Dating apps such as Tinder have announced plans for a dating metaverse: the incorporation of XR technologies into the online dating process to augment interactions between potential sexual partners across virtual and physical worlds. While the dating metaverse is still in conceptual stages we can forecast significant harms that it may expose daters to given prior research into the frequency and severity of sexual harms facilitated by dating apps as well as harms within social VR environments. In this workshop paper we envision how XR could enrich virtual-to-physical interaction between potential sexual partners and outline harms that it will likely perpetuate as well. We then introduce our ongoing research to preempt such harms: a participatory design study with sexual violence experts and demographics at disproportionate risk of sexual violence to produce mitigative solutions to sexual violence perpetuated by XR-enabled dating apps.

Motivated by online dating platforms, we study the problem of selecting which subset of profiles to display to each user in each period. Users observe the profiles set by the platform, decide which of them to like, and a match occurs if and only if two users mutually like each other, potentially across different periods. The platform aims to maximize the expected number of matches produced over the entire time horizon, and users' behavior -- captured by their like probabilities -- may depend on their history. We develop a general theoretical model that captures the dynamic, two-sided nature of the problem and the influence of users' past experiences on their future behavior. We focus on one-lookahead policies and propose the Integral Dating Heuristic (DH-int), providing formal performance guarantees: DH-int achieves a uniform $1-1/e$ guarantee across all platform designs under reasonable assumptions. Our empirical analysis, using proprietary data from a major U.S.-based dating app, confirms that DH-int consistently outperforms other benchmarks such as Greedy, Perfect Matching and DH, and approaches the theoretical upper bound across multiple platform designs and variants of the his

We develop an AI application for archaeological dating of bronze Dings. A classification model is employed to predict the period of the input Ding, and a detection model is used to show the feature parts for making a decision of archaeological dating. To train the two deep learning models, we collected a large number of Ding images from published materials, and annotated the period and the feature parts on each image by archaeological experts. Furthermore, we design a user system and deploy our pre-trained models based on the platform of WeChat Mini Program for ease of use. Only need a smartphone installed WeChat APP, users can easily know the result of intelligent archaeological dating, the feature parts, and other reference artifacts, by taking a photo of a bronze Ding. To use our application, please scan this QR code by WeChat.

The important question of absolute dating of seismic phenomena has been the study of several researchers over the past few decades. The relevant research has concentrated on 'energy traps' of minerals, such as quartz or feldspar, which may accumulate chronological information associated with tectonic deformations. However, the produced knowledge so far, is not sufficient to allow the absolute dating of faults. Today, Luminescence and Electron Spin Resonance (ESR) dating methods could be seen as offering high potential for dating past seismic deformed features on timescales ranging from some years to even several million years. This preliminary study attempts to establish the potential of three different carbonate fault zones hosting fault mirror-like structures, to be used in luminescence and ESR dating, based on their microstructural, mineralogical and palaeo-maximum temperatures analysis. The results indicated that the collected samples can be considered datable fault-rock materials, since they contain suitable minerals (quartz) for luminescence and ESR dating, have experienced repeated cataclastic deformation and have been subject to various physical and chemical processes as we

Online dating sites have become popular platforms for people to look for potential romantic partners. It is important to understand users' dating preferences in order to make better recommendations on potential dates. The message sending and replying actions of a user are strong indicators for what he/she is looking for in a potential date and reflect the user's actual dating preferences. We study how users' online dating behaviors correlate with various user attributes using a large real-world dateset from a major online dating site in China. Many of our results on user messaging behavior align with notions in social and evolutionary psychology: males tend to look for younger females while females put more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. In addition, we observe that the geographic distance between two users and the photo count of users play an important role in their dating behaviors. Our results show that it is important to differentiate between users' true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that both mal

Online dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms i

In just a few years, online dating has become the dominant way that young people meet to date, making the deceptively error-prone task of picking good dating profile photos vital to a generation's ability to form romantic connections. Until now, artificial intelligence approaches to Dating Photo Impression Prediction (DPIP) have been very inaccurate, unadaptable to real-world application, and have only taken into account a subject's physical attractiveness. To that effect, we propose Photofeeler-D3 - the first convolutional neural network as accurate as 10 human votes for how smart, trustworthy, and attractive the subject appears in highly variable dating photos. Our "attractive" output is also applicable to Facial Beauty Prediction (FBP), making Photofeeler-D3 state-of-the-art for both DPIP and FBP. We achieve this by leveraging Photofeeler's Dating Dataset (PDD) with over 1 million images and tens of millions of votes, our novel technique of voter modeling, and cutting-edge computer vision techniques.