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With the advancement of multimedia and human-computer interaction, it has become increasingly crucial to perceive people's emotional states in dynamic data (e.g., video, audio, text stream) in order to effectively serve them. Emotion recognition has emerged as a prominent research area over the past decades. Traditional methods for emotion recognition heavily rely on manually crafted features and primarily focus on uni-modality. However, these approaches encounter challenges in extracting sufficient discriminative information for complex emotion recognition tasks. To tackle this issue, deep neural model-based methods have gained significant popularity in emotion recognition tasks. These methods leverage deep neural models to automatically learn more discriminative emotional features, thereby addressing the problem of poor discriminability associated with manually designed features. Moreover, deep neural models are also employed to integrate information across multiple modalities, thereby enhancing the extraction of discriminative information. In this paper, we provide a comprehensive review of the relevant studies on deep neural model-based emotion recognition in dynamic data using facial, speech, and textual cues published within the past five years. Specifically, we first explain discretized and continuous representations of emotions by introducing widely accepted emotion models. Subsequently, we elucidate how advanced methods integrate different neural models by scoping these methods using variant popular deep neural models (e.g. Transformer), along with corresponding preprocessing mechanisms. In addition, we present the development trend by surveying diverse datasets, metrics, and competitive performances. Finally, we have a discussion and explore significant research challenges and opportunities. Our survey bridges the gaps in the literature since existing surveys are narrow in focus, either exclusively covering single-modal methods, solely concentrating on multi-modal methods, overlooking certain aspects of face, speech, and text, or emphasizing outdated methodologies. [ABSTRACT FROM AUTHOR]

Binge watching has become the preferred way for viewing on streaming platforms. With the competition getting intense, streaming platforms are looking at way to engage the subscribers. TV plus appointment viewing has been introduced on streaming platforms. The viewing preferences of the audience have not been studied on whether the return of appointment viewing in a new form is acceptable to the audience that is habituated to binge watching. Streaming platforms have made content libraries available to subscribers to control and the menus are personalizing content using algorithms. Having given the control to the user to pick the menu, the experimentation with TV plus appointment viewing needs to be studied. This paper studies the audience preferences using a purposive sampling method. The findings of the casual survey led to some interesting findings. Binge watching continues to be the ideal method of consuming content and viewers are not ready to give up the personalized control over the menu. So, while streaming platforms may be experimenting with delivery methods, audiences want to watch shows when and how they want. Instant gratification and control over the content menu are important arguments for binge watching. Future research may have to study decreasing attention spans and ideal lengths of episodes to engage audiences. [ABSTRACT FROM AUTHOR]

Independent film is rapidly gaining prominence in today's dynamic film industry. The democratization of filmmaking techniques and the progress of digital technology have given independent filmmakers greater opportunities to showcase their distinctive stories on large screens. This study focuses on the role of streaming platforms in the production of independent films and their impact on society. the study uses qualitative approach and interview-based data is collected from 16 experts drawn using purposive sampling technique. Later on, thematic analysis was applied to draw results and findings of the study. Findings of the study suggest that technology played a crucial part in the production and distribution of independent films. Since these films have low production and distribution budget online platforms prove helpful. In terms of social impact such films have a mixed impression on society. Some aspects are appreciated while other greatly discouraged. Despite, all responses it is hard to decline that independent films have earned mass viewership and following among the viewers. [ABSTRACT FROM AUTHOR]

Over 30 years ago Fred and Nancy Poses founded Understood for All, an organization aimed at providing resources and support to those diagnosed with dyslexia. The organization's website Understood (), expands their mission by providing free materials and information to caretakers, educators, and those with dyslexia. Through their website, Understood for All assists more than 20 million people a year. I feel that this is an invaluable resource and I highly recommend it as a consumer health website for dyslexia and other reading disabilities. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic affected educational institutions and clinical organizations, with universities and clinical sites experiencing a complete lock down with unexpected changes to meeting students' and clients' needs within this new landscape. Consequently, music therapy educators had to pivot from their current teaching and practicum format to comply with university and governmental restrictions. This manuscript explores how COVID-19 impacted music therapy teaching practices and learning delivery, identifying resources and tools implemented by music therapy educators, explicating changes in assessment of student learning, compliance with required guidelines and approvals, and innovations used or discovered during this time. International music therapy educators completed an online survey about their music therapy education and teaching practices, including questions about their teaching practices during the COVID-19 pandemic. Sixty-one music therapy educators from 19 countries completed a 55-question survey, including 6 questions specifically about the impact of the COVID-19 pandemic on their teaching practices. Majority of respondents reported that COVID-19 had a significant impact on their teaching practices, sharing the various changes implemented and tools and resources integrated into their online teaching platforms. Two exemplars further illustrate detailed processes necessitated by educators from two different universities. Clearly, the pandemic impacted music therapy education and training, including integration and adaptation of new and existing tools into their teaching and assessment practices, plus changes to safety procedures in implementing guidelines. Lastly, innovative teaching practices introduced during the pandemic that educators plan to retain as a part of their teaching portfolio are explored. [ABSTRACT FROM AUTHOR]

Bibliotherapy, a relatively underutilized lifestyle intervention in psychotherapy, is a unique cost-effective avenue of treatment that empowers patients by integrating therapeutic reading into their treatment plan. This approach strategically uses empirically validated cognitive-behavioral and self-help literature to facilitate the application of therapeutic topics outside of psychotherapy sessions. Bibliotherapy's range of administration styles highlights its potential as an adjunct to medication, psychotherapy, and/or healthy lifestyle interventions in a comprehensive treatment plan. Most meta-analyses conducted so far regarding bibliotherapy in a clinical setting consist of studies that use minimal patient interaction via short phone calls or emails. Despite this, meta-analyses show medium-to-large effect sizes that are comparable to traditional psychotherapy modalities for common disorders, most notably depression. This article explores the practical implementation of bibliotherapy research via an example psychotherapy session with a patient who has a diagnosis of major depressive disorder (MDD). An evidence-based reading list is proposed alongside a decision tree and actionable clinical insights for the effective implementation of bibliotherapy. [ABSTRACT FROM AUTHOR]

This resource review collated some of the materials available that could be helpful for the speech pathologist in the hospital setting. We looked at a variety of resources including podcasts, textbooks and artificial intelligence. The aim of this review was to highlight the more relevant, and easily accessible resources that could have clinical and professional applications for speech pathologists of varying levels of experience. [ABSTRACT FROM AUTHOR]

OTT (over-the-top) platforms that stream media directly to viewers via the Internet, bypassing cable, broadcast, and satellite television platforms, are one of the fastestgrowing platforms in India. This study investigates the variables influencing continuance intention to subscribe to video-on-demand streaming media services. Utilizing the expectation confirmation model (ECM) and adding habit and content availability, the moderating effects of those variables are examined. SmartPLS was used for Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the results of the measurement and structural models. The findings show that perceived usefulness and expectation had a significant impact on satisfaction and that the effect of satisfaction was significant on continuance intention to subscribe. Research establishes that habit and content availability moderate the association between satisfaction and continuance intention. The study provides valuable insights for service providers, marketers, and practitioners to strengthen continuance intentions. [ABSTRACT FROM AUTHOR]

This study centers on the problem of transposition of historical reality into the artistic form. Using the example of HBO Europe's Czech six-part drama Bez vědomí (The Sleepers, 2019), I would like to discuss how the transnational co-production, the effort to attract international audiences, and the selected genre influenced the portrayal of the past. In the context of Czech audiovisual production, the series represents a rather exceptional example. First, it reflects on the period around the Velvet Revolution, which has not been the focus of many Czech films or TV series. It is set in late 1980s Prague, and captures the time of political and societal changes that resulted in the collapse of the communist regime. Secondly, the series uses the spy genre, which is also unique in local production. Drawing from film and TV studies and from memory studies, I will explore the concept of transnational memory in relation to HBO's original production. In particular, however, I would like to focus on the formative role of the genre and argue that the choice of the genre in itself contributes to challenging the prevailing narratives and already established interpretations of the past. [ABSTRACT FROM AUTHOR]

Emotion recognition systems are typically trained to classify a given psychophysiological state into emotion categories. Current platforms for emotion ground-truth collection show limitations for real-world scenarios of long-duration content (e.g. $> $ > 10 minutes), namely: 1) Real-time annotation tools are distracting and become exhausting; 2) Perform retrospective annotation of the whole content in bulk (providing highly coarse annotations); or 3) Are used by external experts (depending on the number of annotators and their subjective experience). We explore a novel approach, the EmotiphAI Annotator, that allows undisturbed content visualisation and simplifies the annotation process by using segmentation algorithms that select brief clips for emotional annotation retrospectively. We compare three methods for content segmentation based on physiological data (Electrodermal Activity (EDA), emotion-based), scene (time-based), and random (control) selection. The EmotiphAI Annotator attained a B+ System Usability Scale score and low-average mental workload as per the NASA Task Load Index (40%). The reliability of the self-report was analysed by the inter-rater agreement (STD $< $ < 0.75), coherence across time segmentation methods (STD $< $ < 0.17), comparison against the state-of-the-art ground truth (STD $< $ < 0.7), and correlation to EDA ($> $ > 0.3 to 0.8), where the EDA-based method obtained the overall best performance. [ABSTRACT FROM AUTHOR]

Previous research on subscriptions has not focused on the characteristics of content where multiple content is consumed over a certain period, and evaluations of that are not easily associated. Therefore, considering these characteristics, this study theoretically and empirically examines the effects and interactions of conditions such as the ratio of good content, the content selection criterion, and the evaluation level of original content on consumer satisfaction and intention to extend. The results show that 1) the ratio of good content, i.e., the proportion of highly rated content, is the most influential factor for satisfaction and extension intention; 2) when the main criterion of content selection is the consumers themselves rather than recommendations by the streaming service, the difference in satisfaction based on the ratio of good content is significant; 3) the influence of original content exclusively planned and produced by the streaming service is relatively lower on satisfaction compared to the influence of the ratio of highly rated content, but similar on extension intention; and 4) the influence of original content on satisfaction is as high as that of the ratio of highly rated content, only when the content selection is done based on consumer preferences. [ABSTRACT FROM AUTHOR]

The rise of online social networks has fundamentally transformed the traditional way of social interaction and information dissemination, leading to a growing interest in precise community detection and in-depth network structure analysis. However, the complexity of network structures and potential issues like singularity and subjectivity in information extraction affect the accuracy of community detection. To overcome these challenges, we propose a new community detection algorithm, known as the Hierarchical Louvain (H-Louvain) algorithm. It enhances the performance of community detection through a multi-level processing and information fusion strategy. Specifically, the algorithm integrates graph compression techniques with the Hyperlink-Induced Topic Search (HITS) algorithm for initial network hierarchical partitioning, simultaneously filtering out low-quality posts and users while retaining critical information. Furthermore, the proposed method enhances semantic representation by automatically determining an appropriate number of attribute vector dimensions and obtaining attribute weight information through the calculation of self-authority values and the "minimum distance" attribute of posts. Lastly, the method creates an initial user training set through network re-partitioning in hierarchical layers and improves the Louvain algorithm for community partitioning by estimating the comprehensive influence of nodes. Extensive experimentation has demonstrated that the H-Louvain algorithm outperforms state-of-the-art comparative algorithms in terms of accuracy and stability in community detection based on real-world Twitter datasets. [ABSTRACT FROM AUTHOR]

India employs voting machines for its democratic processes, incurring significant costs and manual efforts. The proposed online system allows citizens to vote globally through a government assigned IP address. Users register on the website, providing necessary details, and the Election Commission collects fingerprints and face images for database storage. On election day, received images are compared with the database for secure voting. The system utilizes facial recognition and fingerprints similar to smartphone authentication. Unlike the current physical presence requirement, this online system is more convenient and time-efficient. The address of face and fingerprint images aids in reducing fake votes, with constant eye and eyebrow distance ensuring security. Ten print images are used to accurately identify the voter's name. Key terms include Online Voting, Face Recognition using Haar cascade, Fingerprint Pre-processing, CNN for Fingerprint Matching. [ABSTRACT FROM AUTHOR]

The article presents the discussion on editorial leadership at Arthritis & Rheumatology, highlighting the challenges and developments encountered, particularly during the COVID-19 pandemic. Topics include increase in manuscript submissions related to COVID-19, the importance of expert opinions in delivering quality care, and the introduction of new article types such as Expert Perspectives on Clinical Challenges and Notes From the Field.

Digitally created online celebrities (so-called virtual influencers) have appeared on various social media and video streaming platforms. While the scientific community has recently started to take an interest in this new phenomenon, it still remains mostly unclear how online audiences engage with—and relate to—these artificial digital creations. To address the identified research gap, we conducted a preregistered experiment (N = 179), comparing viewers' parasocial interactions (PSIs) with either a human or a virtual influencer. Based on natural stimuli, we find that viewers' parasocial response does not differ significantly between the two groups. However, by focusing on several theoretically relevant mediator variables, we uncover two opposing effects at play: While a significant direct effect signifies stronger PSIs with the virtual influencer, participants also attribute this persona with less mental human-likeness and similarity to themselves—which ultimately suppresses the observed advantage. Potential explanations for our results are discussed. [ABSTRACT FROM AUTHOR]

The article focuses on introducing library personnel to artificial intelligence (AI) tools through a hands-on workshop called "Try AI Day." It addresses the challenges faced by librarians in keeping up with AI technology and aims to provide them with practical experience and familiarity with common AI tools. It emphasizes the goal of increasing attendees' confidence in using AI tools and fostering discussion about their potential applications and limitations.

The evolution of data in text streams may cause feature and concept drifts. The former, while being less discussed in the literature, poses challenges for learning algorithms by changing the feature space of text representation. A common approach for handling concept drift is to maintain summarized groups of documents, known as micro-clusters. Despite the benefits, this scheme restricts document representation and poses challenges in the face of feature drift. In this paper, we propose an incremental text clustering algorithm that deals with both kinds of drifts. The algorithm uses incremental word embedding, which is rarely studied in the context of evolving data streams. We also propose a novel approach to leverage hierarchical summarized concepts instead of micro-clusters. The concepts reflect the semantic structure of the text stream and are continuously updated in the face of concept drift and evolution. The proposed method enables a customized low-dimensional and interpretable document representation, which improves the clustering quality. By employing concept modeling, in contrast with many available approaches, the proposed algorithm detaches the process of handling data evolution from document clustering. This modularization enables arbitrary variation in the granularity of document representation and allows for customized clustering when accessing the historical documents is impractical. The experimental results on several real datasets, and comparison with other incremental and non-incremental methods, show that the proposed algorithm can deal with dynamics in the feature space, and concept drift and evolution, while preserving its accuracy. [ABSTRACT FROM AUTHOR]

The Nigerian media have, over the years, moved back and forth via all manners of experience of leadership, from military to democratic, until they finally settled into the present democratic governance. Experience from previous regimes exposed them to the vagaries of power and how to cope with them. These traditional news media, newspapers/magazines, radio, and television, were then the major and authentic news sources for Nigerians. The coming of the internet with its social media handles threw the journalists of these media into confusion as the citizens have taken the reigns of news/information peddling from under them, turning them into agents of confirmation and authenticity of information. Ironically, these traditional media, television, radio, and print, have adjusted to online media streaming, thus liberating themselves from the vagaries of temporal and spatial limitedness. Deploying a survey method (opinion poll, OPL) and relying on a purposive sampling technique, the authors purposively selected WhatsApp group platforms (WGP) as the most suitable of the new/social media with access to all manners of news sources. These were used to interrogate how online media/information/news streaming has taken the audience away from traditional media by being on the 'spot' at all hours with their avalanche of "Breaking News". The findings revealed that audiences currently resort to traditional media to confirm the authenticity of news and information carried online in a sort-of-when-in-doubt-watch-the television manner or listen to radio or read the newspapers/magazines. It concludes that online news streaming has become the coveted bride of present-day information and news seekers, albeit an unreliable one. [ABSTRACT FROM AUTHOR]

Anaplastic large cell lymphoma (ALCL) and inflammatory myofibroblastic tumor (IMT) are rare cancers observed predominantly in children and young adults. ALCL accounts for 10–15% of all pediatric non-Hodgkin lymphomas and is commonly diagnosed at an advanced stage of disease. In children, 84–91% of cases of ALCL harbor an anaplastic lymphoma kinase (ALK) gene translocation. IMT is a rare mesenchymal neoplasm that also tends to occur in children and adolescents. Approximately 50–70% of IMT cases involve rearrangements in the ALK gene. A combination of chemotherapeutic drugs is typically used for children with ALK-positive ALCL, and the only known curative therapy for ALK-positive IMT is complete surgical resection. Crizotinib, a first-generation ALK inhibitor, was approved in the USA in 2021 for pediatric patients and young adults with relapsed or refractory ALK-positive ALCL; however, its safety and efficacy have not been established in older adults. In 2022, crizotinib was approved for adult and pediatric patients with unresectable, recurrent, or refractory ALK-positive IMT. This podcast provides an overview of ALK-positive ALCL and IMT. We discuss the current treatment landscape, the role of ALK tyrosine kinase inhibitors, and areas of future research. [ABSTRACT FROM AUTHOR]