A probabilistic template-based approach to discovering repetitive patterns in broadcast videos

Television captions blocking visual information causes dissatisfaction among Deaf and Hard of Hearing (DHH) viewers, yet existing caption evaluation metrics do not consider occlusion. To create such a metric, DHH participants in a recent study imagined how bad it would be if captions blocked various on-screen text or visual content. To gather more ecologically valid data for creating an improved metric, we asked 24 DHH participants to give subjective judgments of caption quality after actually watching videos, and a regression analysis revealed which on-screen contents’ occlusion related to users’ judgments. For several video genres, a metric based on our new dataset out-performed the prior state-of-the-art metric for predicting the severity of captions occluding content during videos, which had been based on that prior study. We contribute empirical findings for improving DHH viewers’ experience, guiding the placement of captions to minimize occlusions, and automated evaluation of captioning quality in television broadcasts.

Background: The study of repetitive (quasi-periodic) spatio-temporal patterns in complex dynamical systems with a well defined spatial structure may be complicated if the recurrent behavior is confined to specific local regions, where it lasts for a limited time. This can decrease the efficacy of recurrence plots (RPs) in recognizing such patterns. It then becomes important to first detect whether repetitive spatio-temporal patterns are present, and if so, where they are located (both in space and time), to facilitate a focused RP analysis approach. This study proposes a novel framework for spatio-temporal detection of local recurrence of a quasi-periodic nature in complex dynamical systems. A motivating application for this framework is the analysis of atrial fibrillation to better understand the heart tissue involved. Methods: The spatio-temporal data observed from the system are decomposed by means of principal component analysis to identify the points in the spatial structure exhibiting quasi-periodic recurrent patterns. The frequency content of the principal components is used to determine if such patterns are present, and the corresponding eigenvectors are used to identify the points associated with those components. Geometric information about proximity of these points is used to cluster them into local regions. A sliding temporal window is used to detect the start and end of each pattern. Results: A first simulation shows how the proposed framework can handle multiple recurrent patterns simultaneously occurring in a spatial structure of a dynamical system. A second simulation shows how the method can handle more complicated patterns like 2D nonlinear spiraling waves, typical of many diffusion processes. The framework is then applied to real data to detect recurrent patterns in wave fronts propagating inside the heart during atrial fibrillation. This analysis can unveil regions of recurrence in the atria that were not visible with standard RP analysis. Conclusion: A novel framework for detecting spatio-temporal repetitive patterns in complex dynamical systems is introduced. It allows retrieve the correct recurrences associated with known 2D traveling waves, while the same information is not visible with standard RP analysis. This framework can be effectively used to investigate recurrence in real dynamical systems as cardiac arrhythmia.

Fifth-generation technology is not fully deployed in the world wireless communication till date. Millimeter-wave (mm-wave) band needs to be used due to plenty of available bandwidth and for achieving the goals of 5G such as greater data rate, ultra-high-speed video broadcasting, low latency services, and many more. Wideband antenna is required for 5G applications to access the high speed, low latency Internet services, and ultra-high-definition video streaming. Various bandwidth enhancement techniques have been reported by the researchers for microstrip antennas operating at microwave bands. High link losses, small wavelength, limited coverage, and environmental losses are the major challenges of mm-wave band. To mitigate these issues and satisfy 5G standard, an antenna with wide bandwidth, high gain, narrow steerable beam, high isolation, low side lobe levels, and multiband characteristics is required. Modifications in conventional antenna design techniques are required to achieve broader bandwidth along with stable radiation characteristics, improved gain, and low side lobe levels at mm-wave frequencies. This paper presents the survey of various bandwidth enhancement techniques which has been used in the 5G antennas designed by researchers. Reviews of some wideband 5G antennas with their performance comparisons are also discussed.

As the Internet, computer technology, and peripheral devices continue to grow, web services for users are increasing, including such streaming video broadcast services as YouTube and Ustream that allow users to watch the streaming in which they are interested. In the current streaming video broadcast services, users search for streaming videos by title and tag information. However, since many streaming videos have insufficient information, they are often difficult to find. In addition, since only one streaming video can be watched in one browser, simultaneously comparing two or more streaming videos is difficult. In this research, we propose Multi-Stream Viewer as a simultaneous viewing system for streaming videos with tag information added to the time axis to enable users to watch streaming videos. We also implement and evaluate our proposed system.

A true video-on-demand(TVOD) system lets users view any video program, at any time, and perform any VCR functions, but its per-user video delivery cost is too expensive. A near video-ondemand( NVOD) is a more scalable approach by batching multiple clients to a shared stream or broadcasting videos. Staggered video broadcasting, one of NVOD techniques, broadcasts multiple streams of the same video at staggered times, with one stream serving multiple clients. In order to provide subscribers with a high-quality VOD service, it is desirable to add VCR functionality such as fast forward and fast backward, but it is not easy to provide VCR functionality in NVOD, especially video broadcasting system where any dedicated or interaction channel is not available.In this paper, we analyze the conditions necessary to provide VCR functions and then propose a reception schedule which satisfies these conditions, with minimal resource requirements. Since our proposed scheme receives video frames as a unit it can keep up rapidly with a changing VCR action pattern. It is demonstrated that the scheme provide VCR functionality consistently through simulations.

Vehicular Ad Hoc Network (VANET) is emerged as an important research area that provides ubiquitous short-range connectivity among moving vehicles. This network enables efficient traffic safety and infotainment applications. One of the promising applications is video transmission in vehicle-to-vehicle or vehicle-to-infrastructure environments. But, video streaming over vehicular environment is a daunting task due to high movement of vehicles. This paper presents a survey on state-of-arts of video streaming over VANET. Furthermore, taxonomy of vehicular video transmission is highlighted in this paper with special focus on significant applications and their requirements with challenges, video content sharing, multi-source video streaming and video broadcast services. The comparative study of the paper compares the video streaming schemes based on type of error resilient technique, objective of study, summary of their study, the utilized simulator and the type of video sharing. Lastly, we discussed the open issues and research directions related to video communication over VANET.

With the explosive growth of multimedia data, video presentation has become an important technology for fast browsing of video content. In this paper, we present a novel video presentation technique called "Free-Shaped Video Collage" (FS-Collage), which is motivated from and built upon our previous work on Video Collage [3]. Video Collage is a kind of static summary which selects the most representative regions-of-interest (ROI) from video and seamlessly arranges them on a synthesized image. Unlike Video Collage in which both the shapes of ROI and final collage are fixed as rectangle, we support arbitrary shapes of ROI and a set of collage templates in FS-Collage. Furthermore, we design three ROI arrangement schemes (i.e., book, diagonal, and spiral ) for satisfying different video genres. We formulate the generation of FS-Collage as an energy minimization problem and solve the problem by designing a random sampling process. The experiment results show that our FS-Collage achieves satisfying performance.

This paper describes the development of a multilingual and multigenre manually annotated speech dataset, freely available to the research community as ground truth for the evaluation of automatic transcription systems and spoken language translation systems. The dataset includes two video genres--television broadcast news and talk-shows--and covers Flemish, English, German, and Italian, for a total of about 35 h of television speech. Besides segmentation and orthographic transcription, we added a very rich annotation on the audio signal, both at the linguistic level (e.g. filled pauses, pronunciation errors, disfluencies, speech in a foreign language) and at the acoustic level (e.g. background noise and different types of non-speech events). Furthermore, a subset of the transcriptions is translated in four directions, namely Flemish to English, German to English, German to Italian and English to Italian. The development of this dataset was organized in several phases, relying on expert transcribers as well as involving non-expert contributors through crowdsourcing. We first conducted a feasibility study to test and compare two methods for crowdsourcing speech transcription on broadcast news data. These methods are based on different transcription processes (i.e. parallel vs. iterative) and incorporate two different quality control mechanisms. With both methods, we achieved near-expert transcription quality--in terms of word error rate--for English, German and Italian data. Instead, for Flemish data we were not able to get a sufficient response from the crowd to complete the offered transcription tasks. The results obtained demonstrate that the viability of methods for crowdsourcing speech transcription significantly depends on the target language. This paper provides a detailed comparison of the results obtained with the two crowdsourcing methods tested, describes the main characteristics of the final ground truth resource created as well as the methodology adopted, and the guidelines prepared for its development.

Millisecond-scale temporal spiking patterns encode sensory information in the periphery, but their role in the neocortex remains controversial. The sense of touch provides a window into temporal coding because tactile neurons often exhibit precise, repeatable, and informative temporal spiking patterns. In the somatosensory cortex (S1), responses to skin vibrations exhibit phase locking that faithfully carries information about vibratory frequency. However, the respective roles of spike timing and rate in frequency coding are confounded because vibratory frequency shapes both the timing and rates of responses. To disentangle the contributions of these two neural features, we measured S1 responses as rhesus macaques performed frequency discrimination tasks in which differences in frequency were accompanied by orthogonal variations in amplitude. We assessed the degree to which the strength and timing of responses could account for animal performance. First, we showed that animals can discriminate frequency, but their performance is biased by amplitude variations. Second, rate-based representations of frequency are susceptible to changes in amplitude but in ways that are inconsistent with the animals' behavioral biases, calling into question a rate-based neural code for frequency. In contrast, timing-based representations are highly informative about frequency but impervious to changes in amplitude, which is also inconsistent with the animals' behavior. We account for the animals' behavior with a model wherein frequency coding relies on a temporal code, but frequency judgments are biased by perceived magnitude. We conclude that information about vibratory frequency is not encoded in S1 firing rates but primarily in temporal patterning on millisecond timescales.

Data mining is a technique to discover useful patterns or patterns of special interest as explicit knowledge from a vast quantity of data. In video editing, there are a lot of editing patterns. According to the editor's preferences, different editing patterns give the opportunity to achieve a variety of effects. Discovering the editing patterns is required, because it is useful to find each editor's skills and to use them for editing new video material. In this paper, we propose the methods of extracting editing rules from video stream by introducing data mining technique. We can edit a video material by applying the extracted rules. The edited video may produce the same quality as the video from which we extracted the patterns.

Predicting the temporal evolution of images is an interesting problem that has applications in surveillance, content recommendation and behavioral analysis. Given a single image or a stream of images with timestamps, the goal of this work is to predict possible images that could appear at different time instances in the future. We propose a data-driven Riemannian shape theoretic approach for this problem, which analyzes the space of temporal evolution patterns in training image streams, and performs statistics on this shape space to facilitate future image prediction. We consider both discriminative and generative statistical techniques on the shape space, to accommodate cases where the training and test data may or may not have an associated class/topic label, and report improved prediction results on two previously studied image prediction datasets. We also provide complimentary results on predicting images in the past, for time instances before the training data was acquired, and empirically analyze how the prediction accuracy varies over time.

High latency internet connection can impair the perception of live broadcasts, create problems in case working in video chats, trading on the exchange and streaming because of the standard way of data packet processing. The ways to reduce latency using new methods of forming and processing data packets had been considered. Comparison of latency between video chats and broadcasts with/without latency reduction technologies was made. Solutions were analyzed for implementing low latency in Internet TV. Existing low latency solutions were researched and analyzed. A streaming session was performed using some of the popular low latency solutions. The claimed delay time was compared with the actual. It was concluded that modern technologies that provide a low level of delay reduce it to a low level when streaming video over the Internet TV.

Home dialysis (peritoneal dialysis and home hemodialysis) is an important renal replacement therapy modality option for patients with end-stage kidney disease. As the Internet has become a primary source for healthcare information, this study aimed to analyze the global and regional interests in home dialysis using Google Trends™ data from January 2004 to March 2024. A comprehensive analysis was conducted using Google Trends™ with the search terms "Peritoneal Dialysis" and "Home Hemodialysis." This study extracted worldwide trends and detailed regional interests within the United States. Interest levels were quantitatively assessed based on Google Trends™ indices, providing insights into temporal patterns and geographical distributions of public interest. The study found a fluctuating pattern of global interest in Peritoneal Dialysis, with peak interest in March 2022 and lowest interest in December 2008. The most recent data from March 2024 showed significant interest level of 94, indicating a new upward trend. Mexico exhibited the highest relative interest in Peritoneal Dialysis. Within the United States, Tennessee demonstrated the highest interest. For Home Hemodialysis, the peak interest was in July 2004. The most recent data from March 2024 showed a modest increase in interest. The United States led in highest relative interest for Home Hemodialysis, followed by Australia, Canada, and the United Arab Emirates. Within the United States, Mississippi demonstrated the highest interest. This study offers crucial insights into the global and regional landscape of interest in home dialysis modalities over time, highlighting the significance of leveraging online platforms to increase public awareness, education, and engagement home dialysis modalities. By understanding the temporal and geographical patterns of interest, healthcare providers, policymakers, and patient advocacy groups can develop targeted strategies to better promote the benefits of home dialysis, address knowledge gaps, and improve access to these life-sustaining treatments.

It is widely recognized that urban resilience is one of the core goals of urban development. As an important part of a city, the resilience level of urban human settlements directly affects the development trend of urban resilience. However, at present, research results on the resilience of urban human settlements are very rare, are mainly concentrated in the central region of China, and rarely take into account the economically backward northeastern region. Therefore, in order to better improve the anti-risk ability of the urban human settlement environment system in three provinces of Northeast China, fully implement the strategic goal of “Comprehensive Revitalization of Northeast China”, and achieve high-quality urban development, this paper focuses on 34 prefecture-level cities in three provinces of Northeast China and proposes an urban human settlement resilience evaluation system with 36 indicators in five dimensions, namely, the natural system, human system, housing system, supporting system, and social system. Using the entropy weight method, the Dagum Gini coefficient, and a geographical probe model, the changes in the resilience level of each city from 2005 to 2020 were measured, and the urban living environment was assessed in terms of the adaptability and resilience of the development level in each subsystem based on the temporal and spatial evolution law and its influencing factors. The following conclusions were drawn: (1) The development level of urban human settlement resilience in the three provinces in Northeast China showed an N-shaped development trend from 2005 to 2020, but the regional differences were significant, and the overall spatial pattern was “high in the south and low in the north”. (2) In terms of the overall difference, the overall difference in urban human settlement resilience in the three northeastern provinces of China was small: the inter-regional difference was the main source of the difference, and the intra-regional difference was the secondary source. The regional differences were in the order of Heilongjiang Province > Liaoning Province > Jilin Province, indicating that Jilin Province had the smallest difference and that the resilience level of urban human settlements does not show a balanced development trend. In terms of the average Gini coefficient between regions, the order of difference was Liaoning Province–Heilongjiang Province > Jilin Province–Liaoning Province > Jilin Province–Heilongjiang Province, indicating that the difference between Liaoning Province and Heilongjiang Province was the most significant. (3) The “natural system”, “human system”, “living system”, “supporting system”, and “social system” had significant spatial and temporal heterogeneity and significantly affected the resilience level of urban human settlements in the three provinces in Northeast China. Among them, the “social system” has always been the main factor affecting the resilience level of urban human settlements.

The detection and tracking of human landmarks in video streams has gained in reliability partly due to the availability of affordable RGB-D sensors. The analysis of such time-varying geometric data is playing an important role in the automatic human behavior understanding. However, suitable shape representations as well as their temporal evolution, termed trajectories, often lie to nonlinear manifolds. This puts an additional constraint (i.e., nonlinearity) in using conventional Machine Learning techniques. As a solution, this paper accommodates the well-known Sparse Coding and Dictionary Learning approach to study time-varying shapes on the Kendall shape spaces of 2D and 3D landmarks. We illustrate effective coding of 3D skeletal sequences for action recognition and 2D facial landmark sequences for macro- and micro-expression recognition. To overcome the inherent nonlinearity of the shape spaces, intrinsic and extrinsic solutions were explored. As main results, shape trajectories give rise to more discriminative time-series with suitable computational properties, including sparsity and vector space structure. Extensive experiments conducted on commonly-used datasets demonstrate the competitiveness of the proposed approaches with respect to state-of-the-art.