Search Results (34)

This study presents a system for automatic cookie collection using bots that simulate user browsing behavior. Five bots were deployed, one for each of the most commonly used university browsers, enabling comprehensive data collection across multiple platforms. The infrastructure included an Ubuntu server with PiHole and Tshark services, facilitating cookie classification and association with third-party advertising and tracking networks. The BotSoul algorithm automated navigation, analyzing 440,000 URLs over 10.9 days with uninterrupted bot operation. The collected data established relationships between visited domains, generated cookies, and captured traffic, providing a solid foundation for security and privacy analysis. Machine learning models were developed to classify suspicious web domains and predict their vulnerability to XSS attacks. Additionally, clustering algorithms enabled user segmentation based on cookie data, identification of behavioral patterns, enhanced personalized web recommendations, and browsing experience optimization. The results highlight the system’s effectiveness in detecting security threats and improving navigation through adaptive recommendations. This research marks a significant advancement in web security and privacy, laying the groundwork for future improvements in protecting user information. Full article

Cisgender sexual minority women (SMW, e.g., lesbian, queer) are at greater risk for poor mental and physical health compared to heterosexual women and face challenges when accessing health care. Previous research has largely focused on general sexual and gender minority barriers to health care, but more research is needed on the experiences of specific subgroups, including cisgender SMW. The current study qualitatively explored barriers and facilitators for cisgender SMW seeking health care. Twenty cisgender SMW aged 18–40 recruited using Meta advertisements and past participant lists completed 45 min semi-structured interviews and a brief survey. Thematic analysis conducted by two coders revealed a barrier theme with six subthemes, and a facilitator theme with seven subthemes. The barrier subthemes included discrimination, dominant culture centric, unsupportive socio-political environment, lack of patient-centered care, avoidance/concealment of sexual identity, and socio-economic challenges. The facilitator subthemes included supportive socio-political environment, advance identification of LGBTQ-affirming HCPs, patient-centered care, HCP identity similar to patient, social support, re-engagement with care after bad experiences, and socio-economic advantages. This study provides insight into the lived experiences of cisgender SMW that can help improve knowledge about health care disparities and inform health care interventions for this population. Full article

This paper addresses the issue of distinguishing commercially played songs from non-music audio in radio broadcasts, where automatic song identification systems are commonly employed for reporting purposes. Service call costs increase because these systems need to remain continuously active, even when music is not being broadcast. Our solution serves as a preliminary filter to determine whether an audio segment constitutes “music” and thus warrants a subsequent service call to an identifier. We collected 139 h of non-consecutive 5 s audio samples from various radio broadcasts, labeling segments from talk shows or advertisements as “non-music”. We implemented multiple data augmentation strategies, including FM-like pre-processing, trained a custom Convolutional Neural Network, and then built a live inference platform capable of continuously monitoring web radio streams. This platform was validated using 1360 newly collected audio samples, evaluating performance on both 5 s chunks and 15 s buffers. The system demonstrated consistently high performance on previously unseen stations, achieving an average accuracy of 96% and a maximum of 98.23%. The intensive pre-processing contributed to these performances with the benefit of making the system inherently suitable for FM radio. This solution has been incorporated into a commercial product currently utilized by Italian clients for royalty calculation and reporting purposes. Full article

This paper aims to analyze the influence of digital marketing in Kosovo’s tourism industry from three key perspectives: consumers, businesses, and industry experts (IEs). The research explores how digital marketing influences the identification of tourist destinations, the promotion of tourism businesses, and sustainable tourism development. The study used semi-structured interviews to collect primary data from twenty-five participants, selected through non-probability and purposive heterogeneous sampling techniques. The data were analyzed qualitatively using a thematic analysis approach, encompassing a multi-step coding process involving data categorization, reduction, and display techniques. The findings reveal that digital marketing significantly impacts Kosovo’s tourism industry. A qualitative analysis of the interviews confirms that digital media is crucial for consumers in identifying tourist destinations. Tourism businesses utilize digital marketing channels to advertise destinations, while IEs recognize digital marketing’s pivotal role in fostering tourism sustainable growth. This study sheds light on how digital marketing not only supports the promotion and identification of tourist destinations but also contributes to long-term tourism development. The study offers practical implications, providing valuable insights for tourists seeking destination information, for businesses in enhancing digital engagement with tourists, and for policymakers aiming to develop targeted, sustainable tourism strategies that leverage digital marketing trends. Full article

The paper aims to examine the relationships between behavioural biases (such as overconfidence and herding) and the rational behaviour of Australian female consumers when making financial decisions. In doing so, the paper showcases the financial illiteracy of Australian female consumers when confronted with irregularities within the Australian financial markets. From a theoretical standpoint, the study adopts the notions of the adaptive market hypothesis (AMH) to understand the reasoning behind the relationships between behavioural biases (such as overconfidence and herding) and the rational behaviour of Australian female consumers when making decisions rationally. Using a quantitative approach, a structural equation modelling (SEM) was conducted on the proposed theoretical framework with a cleaned dataset of 357 Australian female consumers, which revealed that behavioural biases significantly influence each stage of rational decision-making when making financial decisions. More precisely, the structural equation modelling (SEM) showcases that herding behaviour has a significant positive relationship with the information search and evaluation of alternative stages when making financial decisions. However, overconfidence behaviour has a significant negative relationship with demand identification and evaluation of alternative stages when making financial decisions. Moreover, the findings also showcase that the proposed theoretical model closely fits with the data utilised, indicating that Australian female consumers do follow rational decision-making when making financial decisions. Additionally, the findings revealed that the education and income levels of Australian female consumers positively influence the stages of rational decision-making. The findings also contend that Australian female consumers have a risk-averse attitude (i.e., within three key hypothetical scenarios) towards financial decisions due to the presence of financial illiteracy. Hence, it is strongly suggested that financial institutions highlight the calculative benefits and returns from financial product purchases in advertising and promotions in a way that appeals to female consumer segments. Full article

Disabled students are systematically disadvantaged compared to their non-disabled peers and Disability Services can provide important access to accommodations and support. Such services are not, however, without issues. The present study investigates student experiences with University Disability Services in order to identify shared barriers to inclusion and recommendations for practice. Individual semi-structured online interviews were conducted with twelve female students. Each student discussed their engagement with Disability Services as an undergraduate or postgraduate student, and each student disclosed a long-term, nonvisible condition. A thematic analysis was used to identify three themes. These were (1) Identity and Legitimacy (Identification as Disabled, Perceived Legitimacy, The Importance of Evidence), (2) Knowledge and Understanding (Knowledge of Specific Conditions, Knowledge of Disability Services, Disability Services Staff Knowledge and Understanding, Peer Knowledge and Understanding), and (3) Independence and Support (Desire for Autonomy, The Importance of Self-Advocacy, Additional Support). The findings highlight shared barriers to support experienced by students with different diagnoses who engage with University Disability Services. A range of recommendations are provided to improve Disability Services provision (e.g., universities are advised to review the language used to advertise Disability Services). Full article

The motion picture industry is subject to extensive business and management research conducted on a wide range of topics. Due to high research productivity, it is challenging to keep track of the abundance of publications. Against this background, we employ a bibliographic coupling analysis to gain a comprehensive understanding of current research topics. The following themes were defined: Key factors for success, word of mouth and social media, organizational and pedagogical dimensions, advertising—product placement and online marketing, tourism, the influence of data, the influence of culture, revenue maximization and purchase decisions, and the perception and identification of audiences. Based on the cluster analysis, we suggest the following future research opportunities: Exploring technological innovations, especially the influence of social media and streaming platforms in the film industry; the in-depth analysis of the use of artificial intelligence in film production, both in terms of its creative potential and ethical and legal challenges; the exploration of the representation of wokeness and minorities in films and their cultural and economic significance; and, finally, a detailed examination of the long-term effects of the COVID-19 pandemic and other crises on the film industry, especially in terms of changed consumption habits and structural adjustments. Full article

An emerging topic that has the potential to enhance user experience, reduce crime, and target advertising is human emotion recognition, utilizing DeepFace and Artificial Intelligence (AI). The same feeling may be expressed differently by many individuals. Accurately identifying emotions can be challenging, in light of this. It helps to understand an emotion’s significance by looking at the context in which it is presented. Depending on the application, one must decide which AI technology to employ for detecting human emotions. Because of things like lighting and occlusion, using it in real-world situations can be difficult. Not every human emotion can be accurately detected by technology. Human–machine interaction technology is becoming more popular, and machines must comprehend human movements and expressions. When a machine recognizes human emotions, it gains a greater understanding of human behavior and increases the effectiveness of work. Text, audio, linguistic, and facial movements may all convey emotions. Facial expressions are important in determining a person’s emotions. There has been little research undertaken on the topic of real-time emotion identification, utilizing face photos and emotions. Using an Artificial Intelligence-based DeepFace approach, the proposed method recognizes real-time feelings from facial images and live emotions of persons. The proposed module extracts the facial features from an active shape DeepFace model by identifying 26 facial points to recognize human emotions. This approach recognizes the emotions of frustration, dissatisfaction, happiness, neutrality, and wonder. The proposed technology is unique, in that it implements emotion identification in real-time, with an average accuracy of 94% acquired from actual human emotions. Full article

Market segmentation is a crucial marketing strategy that involves identifying and defining distinct groups of buyers to target a company’s marketing efforts effectively. To achieve this, the use of data to estimate consumer preferences and behavior is both appropriate and adequate. Visual elements, such as color and shape, in advertising can effectively communicate the product or service being promoted and influence consumer perceptions of its quality. Similarly, a person’s outward appearance plays a pivotal role in nonverbal communication, significantly impacting human social interactions and providing insights into individuals’ emotional states. In this study, we introduce an innovative deep learning model capable of predicting one of the styles in the seven universal styles model. By employing various advanced deep learning techniques, our models automatically extract features from full-body images, enabling the identification of style-defining traits in clothing subjects. Among the models proposed, the XCEPTION-based approach achieved an impressive top accuracy of 98.27%, highlighting its efficacy in accurately predicting styles. Furthermore, we developed a personalized ad generator that enjoyed a high acceptance rate of 80.56% among surveyed users, demonstrating the power of data-driven approaches in generating engaging and relevant content. Overall, the utilization of data to estimate consumer preferences and style traits is appropriate and effective in enhancing marketing strategies, as evidenced by the success of our deep learning models and personalized ad generator. By leveraging data-driven insights, businesses can create targeted and compelling marketing campaigns, thereby increasing their overall success in reaching and resonating with their desired audience. Full article

The proliferation of misinformation, as insidious and pervasive as water, presents an unprecedented challenge to public discourse and comprehension. Often propagated to further specific ideologies or political objectives, misinformation not only misleads the populace but also fuels online advertising revenue generation. As such, the urgent need to pinpoint and eliminate misinformation from digital platforms has never been more critical. In response to this dilemma, this paper proposes a solution built on the backbone of massive data generation in today’s digital landscape. By leveraging advanced technologies, such as AI-driven systems with deep learning models and natural language processing capabilities, we can monitor and analyze an extensive scope of social media data. This, in turn, facilitates the identification of misinformation across multiple platforms and alerts users to potential propaganda. Central to our study is the development of misinformation classifiers based on a deep bi-directional long short-term memory (Bi-LSTM) model. This model is further enhanced by employing a genetic algorithm (GA), which automates the search for an optimal neural architecture, thereby significantly impacting the training behavior of the deep learning algorithm and the performance of the model being trained. To validate our approach, we compared the efficacy of our proposed model with nine traditional machine learning algorithms and a deep learning model rooted in long short-term memory (LSTM). The results affirmed the superiority of our GA-tuned Bi-LSTM model, which outperformed all other models in detecting misinformation with remarkable accuracy. Our intention with this paper is not to present our model as a comprehensive solution to misinformation but rather as a technological tool that can aid in the process, supplementing and bolstering the existing methodologies in the field of misinformation detection. Full article

The labor market has been significantly impacted by the rapidly evolving global landscape, characterized by increased competition, globalization, demographic shifts, and digitization, leading to a demand for new skills and professions. The rapid pace of technological advancements, economic transformations, and changes in workplace practices necessitate that employees continuously adapt to new skill requirements. A quick assessment of these changes enables the identification of skill profiles and the activities of economic fields. This paper aims to utilize natural language processing technologies and data clustering methods to analyze the skill needs of Lithuanian employees, perform a cluster analysis of these skills, and create automated job profiles. The hypothesis that applying natural language processing and clustering in job profile analyzes can allow the real-time assessment of job skill demand changes was investigated. Over five hundred thousand job postings were analyzed to build job/position profiles for further decision-making. In the first stage, data were extracted from the job requirements of entire job advertisement texts. The regex procedure was found to have demonstrated the best results. Data vectorization for initial feature extraction was performed using BERT structure transformers (sentence transformers). Five dimensionality reduction methods were compared, with the UMAP technique producing the best results. The HDBSCAN method proved to be the most effective for clustering, though RCBMIDE also demonstrated a robust performance. Finally, job profile descriptions were generated using generative artificial intelligence based on the compiled job profile skills. Upon expert assessment of the created job profiles and their descriptions, it was concluded that the automated job advertisement analysis algorithm had shown successful results and could therefore be applied in practice. Full article

The diversity and distribution of the amphibians in Rwanda was critically reviewed to provide a reliable species inventory for informed conservation management. The checklist of the amphibian species of Rwanda is based on results of our own fieldwork, historical records compiled from the literature, and examination of voucher specimens in museum collections. A total of 62 species are recorded, and 22 further species listed in field guides and open-access data bases are discussed, identified as erroneous records, and consequently not included in the country list. We provide diagnostic characters of external morphology and the advertisement call for each validated species, and a short synopsis of geographic distribution, altitudinal range, occurrence in the provinces of Rwanda, and habitat preference. We provide keys to all genera, and all taxonomically described species based on morphological characters and additional keys based on features of the advertisement calls. We discuss features of amphibian communities including local community structure and delimitation of altitudinal metacommunities. Based on the IUCN red list assessment and our field surveys, we propose for the first time a national red list of amphibians in Rwanda. Full article

QR codes (short for Quick Response codes) were originally developed for use in the automotive industry to track factory inventories and logistics, but their popularity has expanded significantly in the past few years due to the widespread applications of smartphones and mobile phone cameras. QR codes can be used for a variety of purposes, including tracking inventory, advertising, electronic ticketing, and mobile payments. Although they are convenient and widely used to store and share information, their accessibility also means they might be forged easily. Digital forensics can be used to recognize direct links of printed documents, including QR codes, which is important for the investigation of forged documents and the prosecution of forgers. The process involves using optical mechanisms to identify the relationship between source printers and the duplicates. Techniques regarding computer vision and machine learning, such as convolutional neural networks (CNNs), can be implemented to study and summarize statistical features in order to improve identification accuracy. This study implemented AlexNet, DenseNet201, GoogleNet, MobileNetv2, ResNet, VGG16, and other Pretrained CNN models for evaluating their abilities to predict the source printer of QR codes with a high level of accuracy. Among them, the customized CNN model demonstrated better results in identifying printed sources of grayscale and color QR codes with less computational power and training time. Full article

There is evidence of the risk of overexposure of children on social networks by parents working as influencers. A cross-sectional study of the profiles of the sixteen most-followed Instamoms in Spain was carried out. An analysis of these profiles was performed over a full month (April 2022), three times a week, to describe the representation of influencers’ children in the posts shared by them, as well as their role in the Instamoms’ marketing. A total of 192 evaluations of the profiles were performed in the study period. The average number of children exposed by an Instamom was three, generally preschoolers and schoolchildren. The children appear in a context of the family home and accompanied by their mother. The type of advertising that accompanies the appearance of underage children is usually women or children’s clothing, but also food products, leisure, etc. Appearance of children in the posts had a statistically significant influence on followers measured by the number of likes. Results provided the identification of two Instamom clusters with differentiated behaviors in relation to appearance of children in posts. It is important to involve Social Pediatrics in the protection of the privacy and interests of children given the increase in sharenting. The authors believe that there are concerns about their explicit consent to public exposure from early childhood and about the medium and long-term effect that this may have on their future well-being. Full article

The dynamics of the financial reliability of insurers show rather unstable and often unfavorable trends, which indicate an increase in the risks of their financial insecurity and requires searching for reserves to improve their financial condition in the context of digitalization. The aim of the present research is to develop approaches for managing financial risks and profitability in Russian insurance companies in the context of digitalization. Structurally, the study consisted of a comprehensive analysis of the insurance market in the Russian Federation, as well as an identification of the components of the risk management process of insurance companies in the context of digitalization. Documents containing key features of the risk management system were selected for the study. We determined that to optimize the structure of the insurance portfolio, the insurer must regulate its portfolio by increasing the share of insurance receipts for personal insurance, which is highly profitable but occupies a meager share in the insurance portfolio. To do this, it is necessary to carry out active work to expand the insurance field, in particular, in relation to voluntary personal insurance, attracting a significant number of policyholders by conducting explanatory mass work using advertising events and agency-broker networks regarding the need and effectiveness of such insurance. Further research prospects should include proposals for replenishing the insurance portfolio with new types of personal insurance, making adjustments to the tariff policy of insurers for all types of voluntary personal insurance, and determining optimal tariffs. Full article