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Images and caption descriptions serve as important visual stimuli that influence consumer preferences; therefore, the current study focuses on property images and captions illustrated on the home pages of accommodation-sharing platforms. Specifically, this study investigates the relative importance of hue, brightness and saturation of a property image and caption description styles on potential consumers’ preferences.

A mixed-method approach was used, and a total of 293 valid responses were collected through a discrete choice experiment approach. Interviews were conducted for additional analyses to explore the detailed explanations.

The utility model demonstrated that the image’s saturation was the most critical attribute perceived by the respondents, followed by caption description style, hue and brightness.

This is one of the first studies to investigate the display of attributes on a digital accommodation platform by exploring potential customers’ stated preferences. This study focuses explicitly on images and captions illustrated on the home page of an accommodation booking platform. Detailed image investigation is also a new research area in sharing economy-related research.

The purpose of this paper is to illustrate one library’s role in the development of a new collaborative captioning service to provide accessible media for classroom use. This pilot project created a new workflow for on-demand captioning of videos.

Through collaboration and iteration, university and library personnel addressed the challenges involved in building new and improved services related to accessible media resources. Circulation and acquisition data were collected via Alma Analytics.

Working collaboratively enabled the expansion and improvement of specialized services to the deaf and hard of hearing community on campus.

While many libraries provide captioning for their communities, little is found in the literature to help others establish a similar service. This study demonstrates how collaborative planning can efficiently and effectively use resources and expertise to create a sustainable service, and it may provide an example that could be followed by other institutions with limited budgets.

With the start of a new decade in 1980, the public witnessed the arrival of a significant new technology, closed‐captioned television. The culmination of nearly a decade of research and development, closed‐captioned television opened up a new world for the hearing‐impaired. Closed captioning provides a line of on‐screen, written messages co‐ordinated with the sound of the television program. These captions are “closed” in that they are visible only to viewers who have specially designed adapters, known as decoders, to make the words appear on the screen. More than just subtitles, captioning transcribes narration and sound effects as well as dialog. At last, over sixteen million hearing‐impaired individuals in the United States can read what they cannot hear on television.

The purpose of this paper is to explore the issues on how to achieve crowdsourced real-time captioning of sign language by deaf and hard-of-hearing (DHH) people, such that how a system structure should be designed, how a continuous task of sign language captioning should be divided into microtasks and how many DHH people are required to maintain a high-quality real-time captioning.

The authors first propose a system structure, including the new design of worker roles, task division and task assignment. Then, based on an implemented prototype, the authors analyze the necessary setting for achieving a crowdsourced real-time captioning of sign language, test the feasibility of the proposed system and explore its robustness and improvability through four experiments.

The results of Experiment 1 have revealed the optimal method for task division, the necessary minimum number of groups and the necessary minimum number of workers in a group. The results of Experiment 2 have verified the feasibility of the crowdsourced real-time captioning of sign language by DHH people. The results of Experiment 3 and Experiment 4 have shown the robustness and improvability of the captioning system.

Although some crowdsourcing-based systems have been developed for the captioning of voice to text, the authors intend to resolve the issues on the captioning of sign language to text, for which the existing approaches do not work well due to the unique properties of sign language. Moreover, DHH people are generally considered as the ones who receive support from others, but our proposal helps them become the ones who offer support to others.

To explore the historical construction of the US broadcast television closed‐captioning system as a case study of debates over “public service broadcasting” during the late twentieth century.

Historical.

Neither the corporate voluntarism promoted by the FCC in the 1970s nor the “public‐private partnership” of the National Captioning Institute (NCI) in the 1980s proved able to sustain a closed‐captioning system; instead, a progressive round of re‐regulation on both the demand side (universal decoder distribution) and the supply side (mandatory program captioning) was necessary to bring the promise of broadcast equality to all deaf and hard‐of‐hearing (D/HOH) citizens.

The decades‐long legal, technological, and institutional battle to define the “public interest” responsibilities of broadcasters toward non‐hearing viewers was fraught with contradiction and compromise.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

This paper aims to review existing literature on distance library services for individuals with disabilities with a specific focus on deaf and hard of hearing (D/HH) users and provide strategies for creating an online library that is accessible to this community.

The authors reviewed articles covering distance library services for D/HH users and then identified specific parts of the Web Content Accessibility Guidelines (WCAG) 2.0 that are applicable to the D/HH community. By using the literature, strategies for developing and purchasing accessible electronic library resources are presented.

While there is a breadth of literature focused on creating accessible resources for online libraries, there is a gap when it comes to D/HH users. Libraries can cater to this community by providing text-based alternatives for all library instructional materials and working closely with vendors to ensure that library databases are accessible.

The authors present strategies for creating and converting electronic resources and services that are accessible to D/HH users.

This paper fills a gap in literature by addressing fully online library services for users with disabilities with a particular focus on meeting the needs of D/HH users in a distance-learning environment.

The paper aims to examine how employees influence their employer’s brand by applying Taylor’s (1999) six segment message strategy wheel in an employee influencer context.

The research uses a content analysis of employees’ public social media posts – including captions and images – to analyze the message strategies employees use to promote their employers.

While ego and social were popular message strategies in both the images and captions, the findings evidence the varying message strategies employees use in text-based versus image-based messages. Four “imagined audiences” of employee influencers are identified: current customers, prospective customers, current employees and prospective employees.

The research provides insight into how employees act as influencers in building their employer brand on social media.

A unique measurement tool is developed that can be used by companies and future researchers to decode employees’ online communications.

This research contributes to theory and practice in the following important ways. First, the research provides a modernization of an existing framework from an offline setting to an applied industry context in an online setting. Second, this research focuses on a subtype of social media influencer, the employee influencer, which is an underdeveloped area of research. Third, a unique measurement tool to analyze text-based and image-based social media data is developed that can be used by companies and future researchers to decode employees’ online communications.

The purpose of this study is twofold: to investigate whether it is meaningful to use the Engineering Index (Ei) classification scheme for browsing, and then, if proven useful, to investigate the performance of an automated classification algorithm based on the Ei classification scheme.

A user study was conducted in which users solved four controlled searching tasks. The users browsed the Ei classification scheme in order to examine the suitability of the classification systems for browsing. The classification algorithm was evaluated by the users who judged the correctness of the automatically assigned classes.

The study showed that the Ei classification scheme is suited for browsing. Automatically assigned classes were on average partly correct, with some classes working better than others. Success of browsing showed to be correlated and dependent on classification correctness.

Further research should address problems of disparate evaluations of one and the same web page. Additional reasons behind browsing failures in the Ei classification scheme also need further investigation.

Improvements for browsing were identified: describing class captions and/or listing their subclasses from start; allowing for searching for words from class captions with synonym search (easily provided for Ei since the classes are mapped to thesauri terms); when searching for class captions, returning the hierarchical tree expanded around the class in which caption the search term is found. The need for improvements of classification schemes was also indicated.

A user‐based evaluation of automated subject classification in the context of browsing has not been conducted before; hence the study also presents new findings concerning methodology.

Lectures can be digitally recorded and replayed to provide multimedia revision material for students who attended the class and a substitute learning experience for students unable to attend. Deaf and hard of hearing people can find it difficult to follow speech through hearing alone or to take notes while they are lip‐reading or watching a sign‐language interpreter. Notetakers can only summarise what is being said while qualified sign language interpreters with a good understanding of the relevant higher education subject content are in very scarce supply. Synchronising the speech with text captions can ensure deaf students are not disadvantaged and assist all learners to search for relevant specific parts of the multimedia recording by means of the synchronised text. Real time stenography transcription is not normally available in UK higher education because of the shortage of stenographers wishing to work in universities. Captions are time consuming and expensive to create by hand and while Automatic Speech Recognition can be used to provide real time captioning directly from lecturers’ speech in classrooms it has proved difficult to obtain accuracy comparable to stenography. This paper describes the development of a system that enables editors to correct errors in the captions as they are created by Automatic Speech Recognition.