Content Retrieval Research Articles

Rapid retrieval of cadmium and lead content from urban greenbelt zones using hyperspectral characteristic bands

Greenbelts around roads are an essential part of the ecosystem that can reduce heavy metal contamination from traffic and contribute to sustainable development. However, only limited studies have examined the rapid detection of heavy metal content in urban road greenbelt zones. In the present study, soil spectral reflectance was measured using a FieldSpec-3 portable handheld ground object spectrometer to measure cadmium (Cd) and lead (Pb) content from 23 roads in the municipality of Chongqing, China. After smoothing the spectral data using Savitzy-Golay (SG), the first derivative (FD), second derivative (SD), and multiple scattering correction (MSC) procedures were applied. Pearson correlation was used to select the characteristic bands. Cd and Pb amounts were inverted using multiple linear regressions (MLR), partial least squares regressions (PLS), and the backpropagation neural network models. It was found that the Cd and Pb levels in urban road greenbelt zones were higher than the soil background values. The Nemero comprehensive pollution index and potential ecological risk index indicated modest Cd and Pb contamination on sampled roads. In the Cd content inversion models, the MLR and PLS models pretreated with SG-FD provided high prediction accuracy, with determination coefficients of >0.60. For the Pb content inversion models, the PLS model processed by SG-MSC had the best prediction accuracy, with determination coefficients of >0.66. It was shown that the MLR and PLS models processed by SG-FD could be used to forecast soil Cd concentration in urban road greenbelt zones. Comparatively, the PLS model processed by SG-MSC could be used to estimate soil Pb levels. Additionally, these results demonstrate how heavy metals in urban greenbelt zones can be inverted using hyperspectral imaging globally.

Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling

ABSTRACT The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R 2 = 0.82 and R 2 = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages.

Retrieval of Live Fuel Moisture Content Based on Multi-Source Remote Sensing Data and Ensemble Deep Learning Model

Live fuel moisture content (LFMC) is an important index used to evaluate the wildfire risk and fire spread rate. In order to further improve the retrieval accuracy, two ensemble models combining deep learning models were proposed. One is a stacking ensemble model based on LSTM, TCN and LSTM-TCN models, and the other is an Adaboost ensemble model based on the LSTM-TCN model. Measured LFMC data, MODIS, Landsat-8, Sentinel-1 remote sensing data and auxiliary data such as canopy height and land cover of the forest-fire-prone areas in the Western United States, were selected for our study, and the retrieval results of different models with different groups of remote sensing data were compared. The results show that using multi-source data can integrate the advantages of different types of remote sensing data, resulting in higher accuracy of LFMC retrieval than that of single-source remote sensing data. The ensemble models can better extract the nonlinear relationship between LFMC and remote sensing data, and the stacking ensemble model with all the MODIS, Landsat-8 and Sentinel-1 remote sensing data achieved the best LFMC retrieval results, with R2 = 0.85, RMSE = 18.88 and ubRMSE = 17.99. The proposed stacking ensemble model is more suitable for LFMC retrieval than the existing method.

Your words went straight to my heart: the role of emotional prototypicality in the recognition of emotion-label words

Emotional words differ in how they acquire their emotional charge. There is a relevant distinction between emotion-label words (those that directly name an emotion, e.g., “joy” or “sadness”) and emotion-laden words (those that do not name an emotion, but can provoke it, e.g., “party” or “death”). In this work, we focused on emotion-label words. These words vary in their emotional prototypicality, which indicates the extent to which the word refers to an emotion. We conducted two lexical decision experiments to examine the role played by emotional prototypicality in the recognition of emotion-label words. The results showed that emotional prototypicality has a facilitative effect in word recognition. Emotional prototypicality would ease conceptual access, thus facilitating the retrieval of emotional content during word recognition. In addition to the theoretical implications, the evidence gathered in this study also highlights the need to consider emotional prototypicality in the selection of emotion-label words in future studies.

A Network-Embedding-Based Approach for Scalable Network Navigability in Content-Centric Social IoT

Social Internet of Things (SIoT) boosts the Internet of Things (IoT) by integrating the concept of social networking to advance the discovery of content and service. Another common and promising solution to IoT’s improvement is adopting the emerging multiaccess edge computing (MEC) paradigm by bringing computational and storage capacity at the edge. Concerning the MEC-enabled SIoT, this article aims to propose a network-embedding-based solution for scalable network navigation via leveraging the social similarity of SIoT. Different from the traditional SIoT, we characterize the social relationship from the contents point of view since MEC enables the edge with cache storage. Then, a novel heuristic embedding algorithm termed HeurEmb is proposed to embed the SIoT into the hyperbolic space to facilitate coordinate-based navigability. HeurEmb is a decentralized approach that leverages the social similarity to compute coordinates, bypassing the heavy computation of numerically maximizing the likelihood. Moreover, HeurEmb infers the virtual coordinates on multiple-level hyperbolic disks, which enabled the efficiently vertical and horizontal search for the destination. We analyze the complexity of HeurEmb and then evaluate its performance via the simulation study. The simulation results show that HeurEmb is efficient, achieving a decrease of up to two orders of magnitude of running time compared with the latest work, and effective, improving the success ratio and navigation path length. Finally, we apply HeurEmb in a content retrieval scenario of SIoT using a real-world trace. We make only a minor modification to HeurEmb-based forwarding, and the resulting strategy can achieve similar performance as the best benchmark.

Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube

BackgroundCommon methods for extracting content in health communication research typically involve using a set of well-established queries, often names of medical procedures or diseases, that are often technical or rarely used in the public discussion of health topics. Although these methods produce high recall (ie, retrieve highly relevant content), they tend to overlook health messages that feature colloquial language and layperson vocabularies on social media. Given how such messages could contain misinformation or obscure content that circumvents official medical concepts, correctly identifying (and analyzing) them is crucial to the study of user-generated health content on social media platforms.ObjectiveHealth communication scholars would benefit from a retrieval process that goes beyond the use of standard terminologies as search queries. Motivated by this, this study aims to put forward a search term identification method to improve the retrieval of user-generated health content on social media. We focused on cancer screening tests as a subject and YouTube as a platform case study.MethodsWe retrieved YouTube videos using cancer screening procedures (colonoscopy, fecal occult blood test, mammogram, and pap test) as seed queries. We then trained word embedding models using text features from these videos to identify the nearest neighbor terms that are semantically similar to cancer screening tests in colloquial language. Retrieving more YouTube videos from the top neighbor terms, we coded a sample of 150 random videos from each term for relevance. We then used text mining to examine the new content retrieved from these videos and network analysis to inspect the relations between the newly retrieved videos and videos from the seed queries.ResultsThe top terms with semantic similarities to cancer screening tests were identified via word embedding models. Text mining analysis showed that the 5 nearest neighbor terms retrieved content that was novel and contextually diverse, beyond the content retrieved from cancer screening concepts alone. Results from network analysis showed that the newly retrieved videos had at least one total degree of connection (sum of indegree and outdegree) with seed videos according to YouTube relatedness measures.ConclusionsWe demonstrated a retrieval technique to improve recall and minimize precision loss, which can be extended to various health topics on YouTube, a popular video-sharing social media platform. We discussed how health communication scholars can apply the technique to inspect the performance of the retrieval strategy before investing human coding resources and outlined suggestions on how such a technique can be extended to other health contexts.

Soil moisture content retrieval over meadows from Sentinel-1 and Sentinel-2 data using physically based scattering models

Soil moisture content (SMC) information at field scale could have important applications in agricultural and regional water management. This study presents an operationally applicable scheme for SMC retrieval over meadows from synthetic aperture radar (SAR) backscatter (σ0) observations. We parameterized the vegetation scattering and absorption model developed at the Tor Vergata University of Rome (TV) and the integral equation method (IEM) surface scattering model for grass-covered soil surfaces. Leaf area index (LAI) estimates from a Sentinel-2 product provide field-scale vegetation information, as is demonstrated by validation against in situ measurements. The SMC retrieval scheme is applied with field-averaged Sentinel-1 σ0 observations from November 2015 to November 2018 and evaluated on 21 meadows against adjacent in situ station measurements, without (IEM) and with a vegetation correction (TV-IEM). Masking the IEM and TV-IEM SMC retrievals for dense vegetation conditions improves their performance, but this is a trade-off with the number of retrievals. By setting the SMC retrievals that exceed the upper retrieval limit of 0.75m3m−3 during the wet period to the maximum SMC, the performance metrics improve to mean Pearson correlation coefficients of 0.55 for IEM and 0.64 for TV-IEM, root mean square deviations (RMSD) of 0.14m3m−3 for IEM and 0.13m3m−3 for TV-IEM, and RMSDs relative to the range of the SMC references of 24% for both IEM and TV-IEM. The slightly better SMC retrieval performance with TV-IEM is caused by invalid SMC retrievals under dense vegetation conditions, and the performance metrics for IEM and TV-IEM are practically equal by considering the same retrieval–reference pairs. The IEM and TV-IEM retrieval performances are also similar to the performances of two other Sentinel-1 based products at field scale. They are, on average, outperformed by NASA’s Soil Moisture Active Passive (SMAP) 9km and 36km products evaluated at field scale, but these products are expected to deviate if larger regional differences are present and in field-specific situations.

PbCP: A profit-based cache placement scheme for next-generation IoT-based ICN networks

The large number of connected devices generating a massive amount of data in the emerging technologies such as the Internet of Things (IoT), will surpass the capabilities of the current Internet infrastructure. Therefore, it is essential to design a new communication paradigm to meet the demands and requirements of new applications. Information-Centric Networking (ICN) is a new candidate for future Internet architecture that aims at addressing different challenges in the currently used host-centric Internet. In ICN, the data/content is searched, routed, located, and retrieved through the name of the content rather than the address of data source. ICN also enables in-network caching where the intermediate nodes can cache the content. In-network caching helps in improving the network scalability and reduces the content retrieval delay. To date, multiple caching schemes have been designed for IoT data management in ICN; however, most of them suffer from scalability or ignore constraints on the IoT devices. Moreover, the placement of content in a large-scale environment needs further improvement and optimization since it impacts overall network performance. To fill the gaps, in this paper, we combine various metrics that affect caching performance. We formulate the content placement as an optimization problem and then propose a profit-based caching scheme that harnesses the benefits of the Tabu local search algorithm. The simulation results and comparative analysis show that the proposed strategy outperforms the existing state-of-art schemes, notably in terms of memory resources utilization, data transmission time, content diversity ratio, and cache replacement operations.

Composing Beethoven: Not All Organizational Rhetoric is Equal

While there is a growing body of research on the benefits of rhetorical history, we know considerably less about the degree to which a firm’s chosen rhetorical history strategy successfully produces a firm benefit, especially when history may be retold using a variety of mnemonic tools for the storage and retrieval of historical content and organizational memories. Through the qualitative investigation of the most historically elite peer group of U.S. orchestras coined the “Big Five,” rhetorical history strategies emerge in the form of four distinct mnemonic practices – articulation, action, attribution, and artifactualization. The insights from this study have both theoretical and managerial implications for how these practices help mythologize personal histories, while further preserving collective beliefs and traditions.

Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things

The low earth orbit (LEO) mega constellation for the internet of thing (IoT) has become one of the hot spots for B5G and 6G concerns. Information-centric networking (ICN) provides a new approach to the interconnection of everything in the LEO mega constellation. In ICN, data objects are independent of location, application, storage and transport methods. Therefore, data naming is one of the fundamental issues of ICN, and research on the data naming mechanism of the LEO mega constellation for the IoT is thus the focus of this study. Adopting a fusion of hierarchical, multicomponent, and hash flat as one structure, a data naming mechanism is proposed, which can meet the needs of the IoT multiservice attributes and high-performance transmission. Additionally, prefix tokens are used to describe hierarchical names with various embedded semantic functions to support multisource content retrieval for in-network functions. To verify the performance of the proposed data naming mechanism, an NS-3-based simulation platform for LEO mega constellations for the IoT is designed and developed. The test simulation results show that, compared with the IP address, the ICN-HMcH naming mechanism can increase throughput by as much as 54% and reduce the transmission delay of the LEO mega satellites for the IoT by 53.97%. The proposed data naming mechanism can provide high quality of service (QoS) transmission performance for the LEO mega constellation for IoT and performs better than IP-based transmission.

Dance Art Scene Classification Based on Convolutional Neural Networks

Digital multimedia resources have become an important part of people’s daily cultural life. Automatic scene classification of a large number of dance art videos is the basis for scene semantic based video content retrieval. In order to improve the accuracy of scene classification, the videos are identified using a deep convolutional neural network based on differential evolution for dance art videos. First, the Canny operator is used in YCbCr colour space to detect the human silhouette in the key frames of the video. Then, the AdaBoost algorithm based on cascade structure is used to implement human target tracking and labelling, and the construction and updating of weak classifiers are analysed. Next, a differential evolution algorithm is used to optimise the structural parameters of the convolutional neural network, and an adaptive strategy is adopted for the scaling factor of the differential evolution algorithm to improve the optimisation solution accuracy. Finally, the improved deep convolutional neural network is used to train the classification of the labelled videos in order to obtain stable scene classification results. The experimental results show that by reasonably setting the crossover rate of differential evolution and the convolutional kernel size of the convolutional neural network, high scene classification performance can be obtained. The high accuracy and low root-mean-square error validate the applicability of the proposed method in dance art scene classification.

PF-ClusterCache: Popularity and Freshness-Aware Collaborative Cache Clustering for Named Data Networking of Things

Named Data Networking (NDN) has been recognized as the most promising information-centric networking architecture that fits the application model of IoT systems. In-network caching is one of NDN’s most fundamental features for improving data availability and diversity and reducing the content retrieval delay and network traffic load. Several caching decision algorithms have been proposed; however, retrieving and delivering data content with minimal resource usage, reduced communication overhead, and a short retrieval time remains a great challenge. In this article, we propose an efficient popularity and freshness caching approach named PF-ClusterCache that efficiently aggregates the storage of different nodes within a given cluster as global shareable storage so that zero redundancy be obtained in any cluster of nodes. This increases the storage capacity for caching with no additional storage resource. PF-ClusterCache ensures that only the newest, most frequent data content is cached, and caching is only performed at the edge of the network, resulting in a wide diversity of cached data content across the entire network and much better overall performance. In-depth simulations using the ndnSIM simulator are performed using a large transit stub topology and various networking scenarios. The results show the effectiveness of PF-ClusterCache in sharing and controlling the local global storage, and in accounting for the popularity and freshness of data content. PF-ClusterCache clearly outperforms the benchmark caching schemes considered, especially in terms of the significantly greater server access reduction and much lower content retrieval time, while efficiently conserving network resources.

Optimization of Content Based Image Retrieval Using Hybrid Approach

With the dawn of multimedia technology and the social web, retrieval from large image databases become possible. As a consequence of rising usage and incredible enthusiasm for inquiry about on Content-Based Image Retrieval (CBIR) systems, it needs improvement in the accuracy of CBIR systems in addition to the decline in monotonous results. Currently, the majority of research has focused on the representation and differentiation of images by an arrangement of low-level visual features. However, most of the retrieval systems produce repetitive or unnecessary retrieval results, termed a redundancy factor. In addition, content-based retrieval with reduced redundancy has a direct correlation with high-level semantics which is overlooked. Accurate content retrieval with reduced redundancy enables the user to focus on the actual problem in preference to nurture the retrieval results, augments the efficiency, and improves overall system performance. To enhance the retrieval accuracy and diminish the redundancy factor in image retrieval, we proposed an optimization-based technique that is blended with classification. In the proposed novel hybrid approach for CBIR, we used a two-tier architecture model. The first tier belongs to the feature extraction process via Particle Swarm Optimization with a Support Vector Machine as a classifier. On the way to reduce the redundancy factor, the K-Nearest Neighbor as a classifier is used with the Genetic Algorithm in the second tier. We noted a noteworthy increase in retrieval accuracy for images that is up to 25% (approx.). The proposed hybrid model is effective to enhance accuracy and reduce redundancy factors in CBIR systems. We used the WANG dataset for experimentation. Henceforward, improving the retrieval accuracy and reducing redundancy factor using unsupervised learning techniques is part of our future work.

Effects of Planning and Revising Conditions on EFL Learners’ Writing Quality

The current study aimed at investigating the effects of the four different writing conditions (with-planning/without-planning conditions and initial-draft-accessible/ initial-draft-removed conditions) on EFL learners’ writing quality. For the study 95 college freshmen were randomly assigned into four condition groups. They were asked to write an argumentative essay about the effects of Internet use, and then to revise the initial draft under the four different conditions. They were also required to respond the pre- and post-survey to find more information about them and what they thought about the experimental conditions. The findings of the study are as follows: 1) the with-planning condition turned out to be a better strategy than the without-planning (or free writing) one when the participants were engaged in the revision process, although the two strategies failed to play a different role in initial writing quality; that is planning holds out a fair chance of successful organization of the writing text, facilitative content retrieval, and lowered cognitive load for writing; and 2) those engaged in the initial-writing-accessible condition outperformed those in the initial-writing-removed condition, which suggests that the learners with their initial drafts in the revision process may write better quality of the revised drafts. Discussion and pedagogical implication were addressed.

Retrieval of leaf protein content using spectral transformation: proximal hyperspectral remote sensing approach

Retrieval of leaf protein content using spectral transformation: proximal hyperspectral remote sensing approach

Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site Wüstebach

Abstract. Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at https://doi.org/10.23728/b2share.756ca0485800474e9dc7f5949c63b872; Heistermann et al., 2022) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land–atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms.

Report on the 1st Workshop on Audio Collection Human Interaction (AudioCHI 2022) at CHIIR 2022

This is a report from the AudioCHI 2022 workshop which was organised in conjunction with the CHIIR conference in March 2022. The workshop was organised to bring together researchers in spoken content retrieval with expertise on human computer interaction in information access to examine opportunities and challenges for advancing technologies for search and interaction with spoken content. The workshop was originally planned to be a full day event, but due to pandemic-imposed travelling constraints, it was instead held as a virtual half day event to accommodate time zone differences for participants. Date: 14 March, 2022. Website: https://speechretrievalworkshop.github.io/.

Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs.

In terms of delivery effectiveness, Vehicular Adhoc NETworks (VANETs) applications have multiple, possibly conflicting, and disparate needs (e.g., latency, reliability, and delivery priorities). Named Data Networking (NDN) has attracted the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. A vehicle in a VANET application is heavily reliant on information about the content, network, and application, which can be obtained from a variety of sources. The information gathered can be used as context to make better decisions. While it is difficult to obtain the necessary context information at the IP network layer, the emergence of NDN is changing the tide. The Pending Information Table (PIT) is an important player in NDN data retrieval. PIT size is the bottleneck due to the limited opportunities provided by current memory technologies. PIT overflow results in service disruptions as new Interest messages cannot be added to PIT. Adaptive, context-aware PIT entry management solutions must be introduced to NDN-based VANETs for effective content dissemination. In this context, our main contribution is a decentralised, context-aware PIT entry management (CPITEM) protocol. The simulation results show that the proposed CPITEM protocol achieves lower Interest Satisfaction Delay and effective PIT utilization based on context when compared to existing PIT entry replacement protocols.

Content Media Retrieval using Virtual Network Functions in Multi‐access Edge Computing architecture

SummaryThe mobile traffic grows every year. Resource requirements of mobile applications, as processing power and storage capacity, transformed the architecture of cellular networks into a centralized infrastructure, the C‐RANs. On the other hand, providing resources close to base stations, as the multi‐access edge computing suggests, allows an immediate processing for delay sensitive applications. In this work, we formulate as a mixed integer linear programming the retrieval of media contents through caches acting as virtual network functions. The optimization model minimizes both the media contents' retrieval cost and the number of instantiated virtual network functions. Results show that the characteristic that has most impact on caching is the high throughput between the node that stores the virtual network function images and the virtualized environment that hosts the image, where up to 70% of the requests are satisfied. When caches have low storage capacity and incurs high transferring delay cost for deployment, the most important characteristic is the throughput distribution between the virtualized environment and the base stations.

Computer Image Content Retrieval considering K-Means Clustering Algorithm

The traditional computer image content retrieval technology can only meet the specific requirements of customers; because of its general features, it cannot comply with the requirements of various environments, purposes, and time simultaneously. This study presents a computer image content retrieval method for a K-means clustering algorithm (KCA). The information collected by computer is preprocessed by K-means clustering algorithm, and the unacquired computer image is labeled based on the optimal learning order according to the KCA. The K-means clustering algorithm classifies the color, pattern, shape, and content of computer images and takes advantage of the invariance advantages of image content retrieval such as scale, rotation, illumination, and blur correction to effectively solve the recurring problems of computer images during retrieval and increase its accuracy. The results of the experiment indicate that the proposed K-means clustering algorithm can enhance the efficiency and performance effectively during image retrieval by computer as compared to the traditional content search algorithm and also help to quickly converge to the query content; it also shows that KCA has good robustness.