Mobile applications represent a digital environment where users can interact with heterogeneous content and advertising stimuli. In this study, we focused on the impact on the user of the content category, considered in three macro-themes (current news, health, and environment), and the features of animation and interactivity in the ad stimulus. Eye-tracking (number-of-fixations, time-to-first fixation) and autonomic parameters were considered to assess attentional processes, cognitive workload, and arousal. Subjects (n = 18) used an app programmed by the research team, exposing themselves to different content categories while randomised ad stimuli were occurring. Ad stimuli were 4, obtained by considering animation and interactivity. Results showed higher visual attention, in terms of number-of-fixations, towards animated stimulus, confirming the effectiveness of animation. Conversely, animated-interactive stimuli seemed to have elicited a visual-avoidance behavior. Furthermore, higher SCR was observed in health and current news content. Insights regarding user experience and communication efficacy are derived and discussed.
Although social networks have grown in popularity and usage in recent years, little is known about users' willingness to pay (WTP) for the premium version. We investigate how consumers' perceived value is associated with their intention to use freemium services and to purchase premium content. The data was collected through an online survey among users of freemium social media networks such as LinkedIn, YouTube, Reddit, and Flickr and was modelled using structural equations (SEM). The results reveal that users who value the security component in freemium social networks will have a higher intention to use the service and that the price value of freemium services is shown to have a negative association with the intention to purchase premium content. The perceived value of the service, improved security, or increased frequency of use can impact future profitability through increased retention on the one hand and reduced monetisation on the other.
The COVID-19 pandemic has created an infodemic, flooding various media channels. While much research has focused on detecting false information or assessing the severity of the problem, little attention has been given to the role of message and source characteristics in information dissemination. To address this gap, we developed a research model based on the Undeutsch hypothesis, four-factor theory, and source credibility theory. We analysed a pre-defined dataset involving fake and true tweets from Twitter. We examined their message and source characteristics through descriptive statistics, negative binomial regression, and multi-group analyses. Our findings revealed significant differences in the dissemination of false and true tweets. By understanding the impact of message and source characteristics on the spread of misinformation, we can work towards creating a more informed and trustworthy information ecosystem during times of crisis. These results have crucial implications for practitioners, providing insight into developing effective strategies to combat COVID-19 misinformation.
The proliferation of smartphones and the increase in consumption of multimedia content have spurred the growth of video production. However, despite this, online collaboration for video production still faces many unresolved problems in facilitating accurate communication among team members. Among these problems, video editing software can be time-consuming to save, and storing videos in dynamic environments can lead to quality degradation. This paper proposes a new method for addressing these problems by allowing team members to add and share information within videos at specific times and locations. The proposed method makes it easy to identify added markers without the need for special encoding and is designed to allow multiple people to add markers simultaneously. Furthermore, it is designed to enhance collaboration efficiency by enabling team members to exchange feedback without modifying the original video. Additionally, various effects are utilised to facilitate accurate tagging of videos, and an efficient communication means is provided during online video production, enabling precise tagging of multimedia content.
Upsurge of the social media and the ubiquity of fake news have been the common phenomena, which negatively affects the spirit and ethics of journalism profession. The policies of the social media giants are inadequate to challenge this menace. Also, the existing legal and regulatory bodies are barely equipped to counter the vicious circle. There are frail and inadequate public policies to monitor this progressive dysfunction of media. The paper examines the origin of fake news, its intricacies and the role of social media in circulating fake contents. Further, it highlights the inadequacies of the existing policies to regulate the flow of fake content. The study administers the qualitative method, involving focus group discussions (FGD) with academicians and millennials, and in-depth interviews with industry professionals. Finally, it attempts to examine the purpose of 'misinformation' circulation and enumerates certain guiding principles to frame policies to regulate its exorbitant rise online.
The proliferation of social media has transformed the interface between technology and interpersonal communication. In the rural context, there is scarce work done to understand the use of social media by farmers with the objective of community building. To address this gap, the current study endeavoured to gain insights into the WhatsApp community in the rural context formed to facilitate farming among the community members in developing country like India. In this study, an attempt has been made to understand how WhatsApp, as a platform for content generation and sharing, is used by a group of farmers located in remote villages of Gujarat, India. From the study, it is evident that the WhatsApp platform helped to share locally relevant, mainly, the farming related information. WhatsApp group has enabled the members to create their content, and therefore has given rise to a new information and communication ecosystem.
During the various catastrophic events of recent years, the use of social media to communicate timely information in crisis periods has become a common practice, allowing affected population to quickly publish a considerable amount of disaster information which can help managers making correct and quick decisions. In this paper, we propose a new real-time alert model for the management of natural or anthropogenic disasters. This model is based on a semi-supervised inductive technique to use unlabeled multi-source data, which are often abundant during a crisis event, with less data previously labeled than previous event. We use two sets of real-world crisis data from Facebook and Twitter manually tagged to launch streaming retrieval of relevant content: it is used for evaluating our proposed approach. Preliminary results are satisfactory.
The internet protocol television took the advantage of the convergence of internet services to provide seamless interactivity, time shifting, video on demand and pay per view services. However, zapping delay is a critical problem that deters the switching intention of terrestrial subscribers and widespread of internet protocol television services. This article presents the design of an effective two-list group program driven algorithm that minimises the channel seek distance and channel seek time in order to reduce zapping delay in internet protocol television. The algorithm groups similar channel programs in related service categories such as news, movies, sports, music and documentary to make it easier for channels with similarly aired programs to be identified rapidly among several channels that an internet protocol television offers. The simulated results of the proposed algorithm show that the average seek distance is significantly reduced by 58% when benchmarked with the state of the art numerical ordering, frequency circular ordering and frequency interleaved ordering.
Predicting brain activity associated with concrete concepts has been attracted wide attention in brain imaging studies. The main task is to construct a computational model for the link between the stimuli and the brain image. However, the ordinary regression model cannot make the desired selection among the semantic features due to the small sample size problem. In this paper, we propose a sparsity constrained model to automatically choose the relevant semantic features. Specifically, we explicitly constrain the number of semantic features associated with the individual voxels. The motivation is based on the fact that the responses of a voxel to the stimuli can only be explained by a limited number of neuron activity bases. The experimental results on predicting brain images show the effectiveness of the proposed approach, as well as meaningful representation of the concepts.
By repackaging a malicious code into reverse compiled legitimate mobile code, malware authors can bypass detection step on existing mobile vaccine software using inserting AES-encrypted root exploits to loading some payload from a malicious remote server dynamically. In this case, malicious codes are constantly changing to evade detection steps by continuing its evolution by operating a metamorphic code by adding new propagation vectors, functionality, and stealth techniques to hide its presence and evade the detection of antivirus software. Those metamorphic features are aimed at changing the form of each instance of the malware by using encryption or appended/pre-pended dummy code into internal code of mobile apps. Therefore, we propose a new system to determine and detect metamorphic malicious mobile code by extracting dynamic features activated from Android platform using extended dynamic analysis technique.
Prior to the introduction of smartphones, South Korea's mobile market had already recognised the early construction of 3.5G technology HSDPA, WiBro, commercial networks, and advanced networks as an important strategy to gain competitive advantage over the market. With the emergence of a smart ecosystem, however, the competition over 4G technical standards is considered to be vital for securing the mobile operating systems and collaborations with participants within the ecosystem, such as the alliances between the devices and content providers. Thus, departing from the viewpoint that a mobile carrier-led network is required to make constant investments and improvements to help create a competitive advantage over the market. Hence, this study targets South Korea's mobile market, to analyse the different 4G mobile standard technologies that were simultaneously commercialised for the first time in the world, to examine the mobile carriers' strategies to dominate the 4G technical standards with the introduction of smartphones, and to provide implications derived from the study.
The standard dynamic adaptive streaming over HTTP (DASH) has been proposed to guarantee a quality of video under varying bandwidth. DASH enables adaptation of the media bit-rate to varying throughput conditions by offering multiple representations of the same content. However, the Moving Picture Experts Group (MPEG) DASH specification only defines the media presentation description (MPD) and the segment formats. In order to take advantages of DASH, we propose a fairness architecture, called dynamic adaptive streaming over HTTP distributed multimedia systems (DASH-DMS). DASH-DMS combines both 2-tiers and 3-tiers architectures in order to guarantee a quality of service (QoS) under changing conditions. Moreover, we propose replication algorithms to enable load balancing video servers and improve the global QoS. Furthermore, in order to improve the availability of our system, we use a fault tolerance policy. To this purpose, we have conduct simulations throughout the paper using the simulator developed by our team.
In this paper, we propose a new embedded image compression scheme which uses rate-distortion optimised block coding (RDBC) of wavelet coefficients. Unlike conventional embedded compression schemes which use the set partition or block partition coding methods according to the magnitude of wavelet coefficients, the proposed scheme achieves rate-distortion optimisation by ordering wavelet coefficients or blocks according to their expected rate-distortion slopes. In addition, the proposed scheme uses a modified block partition method and new context models for the entropy coding. Experimental results demonstrate that the proposed scheme not only produces embedded bit-streams but also outperforms existing embedded compression schemes.
Along with the development of wireless communication and the rapid diffusion of handset, mobile technologies have provided individuals with unprecedented new spaces for social interactions and multiple modes of expression, and as such, they are expected to enhance individual's social capital as well as psychological well-being. This empirical study investigates the relationship between the utilisation of smartphones and social capital's formation and maintenance with an emphasis on youth uses and practices. In addition, it also explores maintained social capital by evaluating how mobile communication influences one's individual capability to keep in touch with members of a formerly inhabited community. The results from a survey of college students (N = 346) demonstrate a strong linkage between smartphone use and three different modes of social capital. The strongest relationship, revealed by Pearson correlation coefficient analysis, is between smartphone use and bridging social capital. Limitations and suggestions for future research are discussed.
These days IT organisations aspire to implement effective and efficient processes and practices for their software development. One such attempt is by trying to implement agile software development methodologies. Indeed, agile methodologies offer some useful advantages to organisations. These benefits include high team collaboration and customer satisfactions. Numerous agile software development methods such as Scrum, extreme programming (XP), Feature driven development, Kanban, dynamic system development method (DSDM) among many are often being used by various IT organisations. Several studies have shown the success and failure of adoption of agile methods or practices. Our research presents a number of additional critical success factors and obstacles by asking from agile practitioners. Moreover, this paper presents a review of some of the existing studies also. The purpose of this survey is use to present a brief guide related to success factors and obstacles of agile adoption in some companies.
This research is on the standardisation of perfusion to help increase the survival rate of hepatic insufficiency patients through the enhanced cell survival of artificial livers. Generally, artificial livers are used in the form of extracorporeal circulation with a mounted bioreactor. As artificial livers need to replace liver functions, to enhance the liver cell survival ratio in the bioreactor, the bioreactor performance needs to be improved. To this end, perfusion standardisation is required in the extracorporeal circulation and each circulation pumps should be interoperable. In this study looked at possible standardisation of perfusion methods capable of increasing liver cell survival rate in order to find out the optimal standardised bioreactor. As a result, perfusion 2 control was found to be essential in raising the cell survival ratio and perfusion 3 implementation enhanced the survival ratio.
The increased demand promotes the development of tourism industry, big data has brought new method for the tourism industry to update value chain and raise industrial development. In this paper, we clarify the definition and characteristics of big data, talking about shaping and upgrading the tourism industry value chain. The internal and external value chain of tourism industry under big data is analysed. Then we put forward the method construction and development countermeasures of tourism industry based on big data.
In the prediction of brain activity associated with concrete concepts, the main task is to construct a computational model to reveal the neural basis of the concepts. However, the ordinary regression model cannot select desired semantic features and easily over-fitting. To address these problems, in this paper, we propose a structured sparsity model to automatically choose the relevant semantic features by exploiting the sparsity of responses and the spatial relationships between the voxels. Specifically, we require the number of the non-zero responses to be sparse and the responses that two voxels are nearby in the brain to be similar. The constraints do not only regularise the model fitting but also have an interpretation in terms of brain hemodynamics. The experimental results on predicting brain images show the effectiveness of the proposed approach, as well as improved interpretability.
Many practical problems can be attributed to the clustering problem. Spectral clustering algorithm can be clustered in any shape of space, and obtain the global optimal solution. Based on the classical Ng-Jordan-Weiss (NJW) algorithm, utilising the supervision information to guide the clustering process, the result of clustering is more accurate. Meanwhile, combined the manifold learning with semi-supervised spectral clustering algorithm, and the data dimension will reduce based on locally linear embedding (LLE). Based on the heuristic thinking, calculated distance matrix, a reasonable number of nearest neighbours could be funded, thus we achieve the purpose of dimension reduction. Moreover, clustering based on reduced dimension data, the same clustering results as the original data could be obtained. Experimental results have shown that this algorithm could achieve better clustering effect on artificial datasets and real datasets.
