Dynamic Content Research Articles

Embedding based learning for collection selection in federated search

PurposeThere have been many challenges in crawling deep web by search engines due to their proprietary nature or dynamic content. Distributed Information Retrieval (DIR) tries to solve these problems by providing a unified searchable interface to these databases. Since a DIR must search across many databases, selecting a specific database to search against the user query is challenging. The challenge can be solved if the past queries of the users are considered in selecting collections to search in combination with word embedding techniques. Combining these would aid the best performing collection selection method to speed up retrieval performance of DIR solutions.Design/methodology/approachThe authors propose a collection selection model based on word embedding using Word2Vec approach that learns the similarity between the current and past queries. They used the cosine and transformed cosine similarity models in computing the similarities among queries. The experiment is conducted using three standard TREC testbeds created for federated search.FindingsThe results show significant improvements over the baseline models.Originality/valueAlthough the lexical matching models for collection selection using similarity based on past queries exist, to the best our knowledge, the proposed work is the first of its kind that uses word embedding for collection selection by learning from past queries.

Smart Dissemination by Using Natural Language Processing Technology

Background: Recently, WhatsApp has become the world's most popular text and voice messaging application with 1.5 billion users. A lot of WhatsApp Application Programming Interface (API) has also been established to be connected to other applications. On the other hand, the development of natural language processing (NLP) for WhatsApp messages has snowballed. There are extensive studies on the dissemination information using WhatsApp but the study on NLP involving data from WhatsApp is lacking.Objective: This study aims to implement NLP in smart dissemination applications by using WhatsApp API.Methods: We build a framework that embeds an intelligent system based on the NLP in WhatsApp API to disseminate a dynamic message. Some of the sentences are used to evaluate the accuracy of this application.Results: Smart dissemination consists of dynamic filter and dynamic content. Dynamic filter was conducted by using the POS tagger and clause statement. Meanwhile, dynamic content was built by using the replace MySQL function. There are twofold limitation: the application could not transform a message that matches rule <3> with conjunction “dan”; has the same attribute before and after <CC> tag; and the maximum of the logical operator is one type for coordinating conjunction (AND/OR) in one sentence.Conclusion: Our framework can be used for dynamic dissemination of messages using dynamic message content and dynamic message recipient with an accuracy of 95% from twenty sample messages.

HSR: A History of New Beginnings and a Tribute to Founding Editor Nándor F. Dreisziger

<i>HSR</i>: A History of New Beginnings and a Tribute to Founding Editor Nándor F. Dreisziger

Mechanically robust, intrinsically self-healing crosslinked polymer enabled by dynamic urea bond exchange reaction

Designing a crosslinked polymer with self-healing, recyclable, and mechanical properties is a significant challenge in the field of self-healing chemistry. Herein, robust, intrinsically self-healing, crosslinked polyureas (C-PUs) are prepared via a facile one-pot photo-induced copolymerization to address the aforementioned limitation. The key strategy involves the introduction of a synthetic monomer with reversible urea bonds into the polymer network as a versatile dynamic crosslinker. The resultant C-PUs are a class of resource-saving materials with a combination of excellent intrinsic self-healing capability with outstanding mechanical robustness. Notably, the properties of the materials can be easily tuned by simply adjusting the dynamic crosslinker content. In addition, an environmentally friendly polymer reprocessing is achieved and the potential of the materials in the smart anticorrosion application is investigated. These desirable properties are attributed to the underlying topological network rearrangement enabled by the dynamic urea bond exchange reaction, which is confirmed by stress relaxation tests. Therefore, the resulting self-healing C-PUs can serve as models to extend the scope of applications in smart protective materials or ocean engineering.

An experimental study on the perceived quality of natively graded versus inverse tone mapped high dynamic range video content on television

High Dynamic Range (HDR) television promises to display higher brightness and deeper black levels and thus more vivid and realistic images. However, home video distribution and video broadcasting were historically designed for what we now call standard dynamic range screens (SDR). In order to display SDR content on an HDR screen, it is explicitly or implicitly converted, in a process called inverse tone mapping (iTMO). This paper’s goal is to assess the perceived quality of converted SDR content in comparison to natively graded HDR content. In doing so, this paper aims to enable content creators/distributors to make informed choices between creating/broadcasting HDR content or relying on conversion. To this end, a psychophysical experiment was performed to tests how viewers evaluate the difference between natively graded HDR and a set of SDR to HDR conversion options in a television setup. Results indicate that viewers prefer natively graded HDR content, followed by inverse tone mapping algorithms starting from videos with a compressed dynamic range. When comparing conversion options, users clearly prefer conversion from ‘compressed dynamic range’ SDR over ‘clipped dynamic range’ SDR. Users disliked videos that were naively stretched from standard SDR. In addition, a significant effect of type of sequence was found, with a preference for light scenes with low contrast.

UltraTrail: A Configurable Ultralow-Power TC-ResNet AI Accelerator for Efficient Keyword Spotting

Recent advances in machine learning show the superior behavior of temporal convolutional networks (TCNs) and especially their combination with residual networks (TC-ResNet) for intelligent sensor signal processing in comparison to classical CNNs and LSTMs. In this article, we propose UltraTrail, a configurable, ultralow-power TC-ResNet AI accelerator for sensor signal processing and its application to efficient keyword spotting (KWS). Following a strict hardware/model co-design approach, we have derived an optimized low-power hardware architecture for generalized TC-ResNet topologies consisting of a configurable array of processing elements and a distributed memory with dynamic content reallocation. We additionally extend the network with conditional computing to reduce the number of operations during inference and to provide the possibility for power-gating. The final accelerator implementation in Globalfoundries' 22FDX technology achieves a power consumption of 8.2 μW for the task of always-on KWS meeting the real-time requirement of 100 ms per inference with an accuracy of 93% on the Google Speech Command Dataset.

Cross validation of hygrothermal properties of historical Chinese blue bricks with isothermal sorption experiments

The historical, cultural, and social value of heritage buildings mandates special protection of these structures. Blue brick is one kind of the main construction materials of heritage buildings, which is porous material and easily subject to deterioration due to environmental factors such as humidity. Therefore, determining the dynamic moisture content rule of materials under fluctuant ambient humidity is necessary for preventative conservation. This study measured the equilibrium moisture content of eight types of Chinese blue bricks under isothermal conditions with varying humidity levels. The results show that when ambient humidity increased from 10% to 90%, the moisture sorption of historical Chinese blue bricks tripled, which is 3 times greater than that of modern Chinese blue bricks and 10 (at low humidity) to 20 times (at high humidity) greater than that of clay bricks in the international handbook. The results contribute to the improvement of the international building material database and research related to hygrothermal performance of heritage buildings in East Asia.

Enhancing the dynamic electron transfer of Au species on wormhole-like TS-1 for boosting propene epoxidation performance with H2 and O2

Engineering unique electronic structure of catalyst to boost catalytic performance is of prime scientific and industrial importance. Herein, the identification of intrinsic electronic sensitivity for direct propene epoxidation was first achieved over highly stable Au/wormhole-like TS-1 catalyst. Results show that the electron transfer of Au species can be regulated by manipulating the dynamic evolutions and contents of Au valence states, thus resulting in different catalytic performance in 100 h time-on-stream. By DFT calculations, kinetic analysis and multi-characterizations, it is found that the Au0 species with higher electronic population can easily transfer more electrons to activate surface O2 compared with Au1+ and Au3+ species. Moreover, there is a positive correlation between Au0 content and activity. Based on this correlation, a facile strategy is further proposed to boost Au0 percentage, resulting in the reported highest PO formation rate without adding promoters. This work harbors tremendous guiding significance to the design of highly efficient Au/Ti-containing catalyst for propene epoxidation with H2 and O2.

Chajkovskie i Pleske v delovoj zhizni Rossii XIX — nachala XX v.

This study aims to determine how Russian business conditions combine with the goals and interests of family business owners background to engender to two patterns of interaction: succession foundation of a new ventures. Through a holistic analysis of historical indicators drawn from the activities of representatives of two groups of relatives (Tchaikovskies and Pleske), it is noted that entrepreneurial legacies feature both stable and variability elements, and that forward-looking areas in institutional, economic and organizational interaction affect this dynamic content.

Improving Network Throughput by Hardware Realization of a Dynamic Content Caching Scheme for Information-Centric Networking (ICN)

Caching popular content at storage enabled routers or small-cell base stations (SBSs) is a key technology for future communication systems. Primarily limited to wired backbone systems, content-caching techniques are useful to offload mobile data traffic too. Several protocols have been developed towards realization of content-caching, namely, information-centric networking (ICN), content-centric networking, and named data networking. Studies have revealed that incorporating ICN caching schemes at SBSs could significantly enhance the user level performance, i.e., the lesser download delay and the higher throughput. However, smaller coverage area of a SBS would make the communication prone to high handover probability. Therefore, the implementation of ICN caching schemes at SBS requires extremely low processing delay, i.e., delay to search if the requested content is available at the cache, and accordingly make decision to serve the content request or forward it to the content server. In this article, a configurable hardware add-on has been developed to incorporate content caching features in a traditional SBS that can serve the user with extremely low processing delay while optimizing a combination of various network and hardware metrics. Using extensive results being generated from simulations, the usefulness of content caching has been demonstrated, and the effect of several hardware parameters over the latency performance has been studied too. In addition, the caching hardware accelerator has the ability to adapt dynamically to the optimal caching policy required by the prevailing network conditions as well as hardware requirements. An improvement upto 53% in network delay and 15% in handover probability due to the support of hardware-based caching is observed that incurs a negligible $$0.07\,{\hbox {mm}}^2$$ and up to only 11.1 mW energy overhead while operating in the range of 690–910 MHz.

Visual SLAM in dynamic environments based on object detection

A great number of visual simultaneous localization and mapping (VSLAM) systems need to assume static features in the environment. However, moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption. To cope with this challenging topic, a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed. To reduce the influence of dynamic content, we incorporate the deep-learning-based object detection method in the visual odometry, then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system. Experiment with both on the TUM and KITTI benchmark dataset, as well as in a real-world environment, the results clarify that our method can significantly reduce the tracking error or drift, enhance the robustness, accuracy and stability of the VSLAM system in dynamic scenes.

Automatic generation of checklists from business process model and notation (BPMN) models for surgical assist systems

Abstract Checklists are a valuable tool to ensure process quality and quality of care. To ensure proper integration in clinical processes, it would be desirable to generate checklists directly from formal process descriptions. Those checklists could also be used for user interaction in context-aware surgical assist systems. We built a tool to automatically convert Business Process Model and Notation (BPMN) process models to checklists displayed as HTML websites. Gateways representing decisions are mapped to checklist items that trigger dynamic content loading based on the placed checkmark. The usability of the resulting system was positively evaluated regarding comprehensibility and end-user friendliness.

On the Use of Deep Active Semi-Supervised Learning for Fast Rendering in Global Illumination

Convolution neural networks usually require large labeled data-sets to construct accurate models. However, in many real-world scenarios, such as global illumination, labeling data are a time-consuming and costly human intelligent task. Semi-supervised learning methods leverage this issue by making use of a small labeled data-set and a larger set of unlabeled data. In this paper, our contributions focus on the development of a robust algorithm that combines active and deep semi-supervised convolution neural network to reduce labeling workload and to accelerate convergence in case of real-time global illumination. While the theoretical concepts of photo-realistic rendering are well understood, the increased need for the delivery of highly dynamic interactive content in vast virtual environments has increased recently. Particularly, the quality measure of computer-generated images is of great importance. The experiments are conducted on global illumination scenes which contain diverse distortions. Compared with human psycho-visual thresholds, the good consistency between these thresholds and the learning models quality measures can been seen. A comparison has also been made with SVM and other state-of-the-art deep learning models. We do transfer learning by running the convolution base of these models over our image set. Then, we use the output features of the convolution base as input to retrain the parameters of the fully connected layer. The obtained results show that our proposed method provides promising efficiency in terms of precision, time complexity, and optimal architecture.

Coherent video generation for multiple hand-held cameras with dynamic foreground

For many social events such as public performances, multiple hand-held cameras may capture the same event. This footage is often collected by amateur cinematographers who typically have little control over the scene and may not pay close attention to the camera. For these reasons, each individually captured video may fail to cover the whole time of the event, or may lose track of interesting foreground content such as a performer. We introduce a new algorithm that can synthesize a single smooth video sequence of moving foreground objects captured by multiple hand-held cameras. This allows later viewers to gain a cohesive narrative experience that can transition between different cameras, even though the input footage may be less than ideal. We first introduce a graph-based method for selecting a good transition route. This allows us to automatically select good cut points for the hand-held videos, so that smooth transitions can be created between the resulting video shots. We also propose a method to synthesize a smooth photorealistic transition video between each pair of hand-held cameras, which preserves dynamic foreground content during this transition. Our experiments demonstrate that our method outperforms previous state-of-the-art methods, which struggle to preserve dynamic foreground content.

Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning

Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users' privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks.

Cognitive load management in mobile learning systems: principles and theories

With the widespread adoption of mobile technologies, mobile-assisted learning is gaining lots of momentum. This new learning paradigm promotes education across different contexts, which is a key factor that contributes to enhancing learning irrespective of the conditions and location of the learner. Therefore, it creates an authentic learning setting whereby students can make meaningful connections to the real world while learning takes place. Previous research works in the field of mobile learning showed that improper design of learning elements is still present in mobile systems and consequently results in poor dynamic content adaptation. Some attempts to adapt learning contents with appropriate instructional design principles are conducted, but with moderate exploitation of smart technological assets in mobile learning systems and limited pedagogical reflections and cognitive factors. In this paper, a learning efficiency model chart is derived using important learning factors that can be considered to enhance mobile learning experiences. Some popular learning theories are analysed and compared with the proposed learning efficiency model chart. This investigation is considered to significantly reduce complexities that exist in mobile learning platforms and promote an enhanced mobile learning experience.

A Combination of Self-Reported Data and Social-Related Neural Measures Forecasts Viral Marketing Success on Social Media

Consumers often share product-related content (e.g., advertising), and highly shared advertising has a huge impact on consumer behavior. Despite its apparent effectiveness, prediction of whether such advertising will be highly shared is a poorly understood area of marketing. Advances in brain imaging techniques may allow researchers to forecast aggregate consumer behavior beyond subjective reports. Using neuroimaging techniques, previous research has established models showing that expectations of self-related outcomes (potential for self-enhancement) and the social impact of sharing (potential for social approval) contribute to the likelihood of users sharing non-commercial static content (i.e., text-based health news). However, whether this finding can be applied to forecasting the virality of dynamic commercial stimuli, which is more relevant to interactive marketing (i.e., video ads), remains unknown. Combining brain imaging techniques, cross-validation methods, and real-world data regarding sharing on social media, the present study investigated whether brain data can be used to forecast the viral marketing success of video ads. We used neuroimaging (functional magnetic resonance imaging: fMRI) to measure neural activity during three sets of theory-driven neural measures implicated in value, self, and social (mentalizing) processes while 40 participants viewed video ads that brands had posted on Facebook. Contrary to previous findings regarding value-related virality in non-commercial static content, our results indicate that social-related neural activity contributes significantly to forecasting the virality of dynamic marketing-related content. The model that included both social-related neural measures and subjective intentions to share forecasted viral marketing success better than the model that included only social-related neural measures. The model that included only subjective intention to share did not forecast viral marketing success. Overall, these findings provide a novel connection between neurophysiological measures and real-world dynamic commercial content. Contrary to previous neuroforecasting findings, social-related but not value-related neural measures can significantly improve our ability to predict market-level sharing of video ads.

Ocean‐Only FAFMIP: Understanding Regional Patterns of Ocean Heat Content and Dynamic Sea Level Change

AbstractThere is large uncertainty in the future regional sea level change under anthropogenic climate change. Our study presents and uses a novel design of ocean general circulation model (OGCM) experiments to investigate the ocean's response to surface buoyancy and momentum flux perturbations without atmosphere‐ocean feedbacks (e.g., without surface restoring or bulk formulae), as part of the Flux‐Anomaly‐Forced Model Intercomparison Project (FAFMIP). In an ensemble of OGCMs forced with identical surface flux perturbations, simulated dynamic sea level (DSL) and ocean heat content (OHC) change demonstrate considerable disagreement. In the North Atlantic, the disagreement in DSL and OHC change between models is mainly due to differences in the residual (resolved and eddy) circulation change, with a large spread in the Atlantic meridional overturning circulation (AMOC) weakening (20–50%). In the western North Pacific, OHC change is similar among the OGCM ensemble, but the contributing physical processes differ. For the Southern Ocean, isopycnal and diapycnal mixing change dominate the spread in OHC change. In addition, a component of the atmosphere‐ocean feedbacks are quantified by comparing coupled, atmosphere‐ocean GCM (AOGCM) and OGCM FAFMIP experiments with consistent ocean models. We find that there is 10% more AMOC weakening in AOGCMs relative to OGCMs, since the extratropical North Atlantic SST cooling due to heat redistribution amplifies the surface heat flux perturbation. This component of the atmosphere‐ocean feedbacks enhances the pattern of North Atlantic OHC and DSL change, with relatively stronger increases and decreases in the tropics and extratropics, respectively.

Recovery of Flexible Polyurethane Foam Waste for Efficient Reuse in Industrial Formulations

Ester polyurethane (PU) foam waste was reacted at atmospheric pressure in an autoclave and using microwaves with diethylene glycol (DEG) at different PU/DEG ratios in the presence of diethanolamine as a catalyst to find the glycolysis conditions that allow for the improved recovery of the PU foam waste and enable the recycling of the whole glycolysis product in foam formulations suitable for industrial application. The recycled polyol was characterized by dynamic viscosity, hydroxyl number, water content, and density, while thermal stability was assessed using thermogravimetric analysis. In the PU foam formulation, 1% and 5% of the glycolyzed material was reused. The relationship between the reuse level of the recycled polyol and the physical properties of the foam was thoroughly investigated. It was observed that both hardness and air flow decreased with increasing recycled polyol content, particularly for the polyester type foam, while tensile strength and compression strength increased. Depending on the amount of recycled polyol and catalyst used, polyether-based foams could be obtained with a low air permeability, needed in special applications as sealed foams, or with higher air permeability desirable for comfort PU foams. The results open the way for further optimization studies of industrial polyurethane foam formulations using a glycolysis process without any separation stage.

The Impact of Field of View on Understanding of a Movie Is Reduced by Magnifying Around the Center of Interest.

PurposeMagnification is commonly used to reduce the impact of impaired central vision. However, magnification limits the field of view (FoV) which may make it difficult to follow the story. Most people with normal vision look in about the same place at about the same time, the center of interest (COI), when watching “Hollywood” movies. We hypothesized that if the FoV was centered at the COI, then this view would provide more useful information than either the original image center or an unrelated view location (the COI locations from a different video clip) as the FoV reduced.MethodsThe FoV was varied between 100% (original) and 3%. To measure video comprehension as the FoV reduced, subjects described 30-second video clips in response to two open-ended questions. A computational, natural-language approach was used to provide an information acquisition (IA) score.ResultsThe IA scores reduced as the FoV decreased. When the FoV was around the COI, subjects were better able to understand the content of the video clips (higher IA scores) as the FoV decreased than the other conditions. Thus, magnification around the COI may serve as a better video enhancement approach than simple magnification of the image center.ConclusionsThese results have implications for future image processing and scene viewing, which may help people with central vision loss view directed dynamic visual content (“Hollywood” movies).Translational RelevanceOur results are promising for the use of magnification around the COI as a vision rehabilitation aid for people with central vision loss.