Content Retrieval Research Articles

Unlocking the potential of melting calorimetry: a field protocol for liquid water content measurement in snow

Abstract. Melting calorimetry, a classic experiment often conducted in high-school chemistry laboratories, holds significant untapped potential for scientific applications beyond its educational context. Traditionally, this technique has been applied to measure the liquid water content in snow using two different formulations: melting calorimetry and freezing calorimetry. In contrast to freezing calorimetry, which is considered the reference method for measuring liquid water content, melting calorimetry has been perceived as prone to generating significant inaccuracies. This research revisits the formulations for both melting and freezing calorimeters to assess volumetric liquid water content in snow. By incorporating the calorimetric constant, we account for heat exchange with the calorimeter, a critical factor often neglected in melting-calorimetry experiments. This paper identifies the most effective and least uncertain method for determining this constant. A central contribution of this work is the introduction of a framework for estimating uncertainty in volumetric liquid water content measurements, adhering to established guidelines for uncertainty expression. This novel framework allows us to revisit past mathematical analyses and demonstrate that melting calorimetry delivers reliable measurements with an uncertainty 0.25 % greater than freezing calorimetry. Notably, despite this slightly higher uncertainty, melting calorimetry offers significant practical advantages for field applications. Moreover, we show how the proposed uncertainty framework can be expanded beyond instrumental uncertainty and also take into account the variability from environmental factors and operators, providing a more comprehensive characterization of the uncertainty. By exploiting the proposed uncertainty framework, we finally conduct an in-depth analysis for the optimal tuning of the experiment parameters. This analysis culminates in a robust field protocol for melting calorimetry that transcends commonsense procedural guidelines. Strict adherence to this protocol will maximize measurement accuracy. Applied in field tests in Italy and Switzerland, the melting calorimetry was demonstrated to accurately track the wet front penetration in the snowpacks, producing results comparable to independent dielectric measurements. These findings highlight the accuracy and the practical advantages of melting calorimetry as a reliable field tool for quantifying snowpack liquid water content. Melting calorimetry can potentially serve as a valuable tool for the independent calibration and validation of proximal and remote sensing techniques used for liquid water content retrieval.

College Campus Tour using AR/VR

This project focuses on developing an immersive 360° virtual campus tour using augmented and virtual reality (AR/VR) technologies to enhance accessibility and engagement for prospective students, alumni, and visitors. Integrated into the college's official website, this virtual tour enables users to explore key campus locations, view event-based images and information by selecting specific dates, and experience campus life from a remote setting. By leveraging high-definition 360° video and interactive elements, the project addresses challenges in accessibility, engagement, and marketing faced by traditional static campus presentations. The system architecture includes modules for video capture, interactive feature integration, and seamless website embedding, utilizing advanced technologies like WebGL, A-Frame, and machine learning algorithms for content retrieval and event-based data integration. The project aims to increase the institution's digital presence and support recruitment and alumni engagement

Retrieval of refractive index and water content for the coating materials of aged black carbon aerosol based on optical properties: a theoretical analysis

Abstract. Water content in the coatings of aged black carbon (BC) aerosol can be reflected through the complex refractive index. In this study, the retrieval of the refractive index and water content for non-absorbing coatings of BC aerosol during hygroscopic growth (RH = 0 %–95 %) based on scattering and absorption properties is theoretically investigated. Optical properties of morphologically realistic fractal BC aerosols are simulated using the multiple-sphere T-matrix method (MSTM), the optical equivalent refractive index of coating material is retrieved based on the Mie theory, and the water content in coatings is further retrieved using effective medium theory. Results show that the scattering property performs best in retrieving the refractive index and water content. The retrieval errors of the refractive index of heavily aged BC aerosols are less than 10 % at high relative humidities (RHs), while partially coated BC and thinly coated BC have larger errors. The regularity of retrieved water content is similar to that of the refractive index retrieved, and the retrieved water content errors range from 2 % to 63 % for heavily coated BC. This study provides a helpful optical method to obtain the water content of BC coatings.

CartoMark: a benchmark dataset for map pattern recognition and map content retrieval with machine intelligence

Maps are fundamental medium to visualize and represent the real word in a simple and philosophical way. The emergence of the big data tide has made a proportion of maps generated from multiple sources, significantly enriching the dimensions and perspectives for understanding the characteristics of the real world. However, a majority of these map datasets remain undiscovered, unacquired and ineffectively used, which arises from the lack of numerous well-labelled benchmark datasets, which are of significance to implement the deep learning techniques into identifying complicated map content. To address this issue, we develop a large-scale benchmark dataset involving well-labelled datasets to employ the state-of-the-art machine intelligence technologies for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. Furthermore, these well-labelled datasets would facilitate map feature detection, map pattern recognition and map content retrieval. We hope our efforts would provide well-labelled data resources for advancing the ability to recognize and discover valuable map content.

Optimization of IoT perceived content caching in F-RANs: Minimum retrieval delay and resource extension with performance sensitivity

In the Internet of Things (IoT) perceived applications of monitoring the states of the environment, a feasible technology is to use fog radio access networks (F-RANs) to alleviate the problems of long response time and cloud server bottlenecks in cloud computing. In response to the above problems, this work investigates the problem of minimizing the retrieval delay of IoT contents in F-RANs under the constraints of system resources. The problem is formulated as an integer linear programming (ILP) model. Then, a polynomial-time method with linear programming (LP) relaxation and rounding is proposed to approximate the optimal solution of the problem. Through proof, the method can obtain a feasible solution with a bounded approximation ratio in polynomial time. The conducted simulations validate that the obtained feasible solution is very close to the optimal one. On the other hand, when the system resources are not enough to meet the continuous growth of content retrieval and need to be expanded, this work further establishes an association relation between cached contents and system resources. Based on the above relation, the second method of expanding system resources with performance sensitivity is proposed to provide the service provider with an effective and economical expansion of system resources. It utilizes a predefined system parameter in balancing the trade-off between the approximation ratio to the optimal solution of the problem and the extended system resources. The solution obtained by the second method is also proved to have a bounded approximation ratio.

A Hybrid Framework for Improved Weighted Quantum Particle Swarm Optimization and Fast Mask Recurrent CNN to Enhance Phishing-URL Prediction Performance

Phishing websites are cybercrimes that aim to collect confidential data, including bank card numbers, bank accounts, and credentials. To detect phishing sites, specialists must extract the elements of the websites and utilize third-party resources. One of the drawbacks of these methods is that identifying phishing characteristics takes a lot of effort and knowledge. Second, the recognition of phishing websites is delayed when third-party services are used. A novel detecting system Improved Weighted Quantum Particle Swarm Optimisation (IWQPSO) and Fast Mask Recurrent Convolutional Neural Network (FMRCNN) proposed to strengthen and empower the technique of identifying phishing URLs. The proposed model does not require the retrieval of target website content or the use of any third-party services. Phishing attacks continue to pose significant cyber-security threats, particularly through deceptive URLs. This study proposes a novel approach to enhance phishing-URL prediction using a hybrid methodology. The method integrates an IWQPSO-FMRCNN. The IWQPSO algorithm is leveraged to optimize the weights and parameters of the FMRCNN model, enhancing its performance in distinguishing between legitimate and phishing URLs. By combining the strengths of evolutionary optimization and deep learning, the proposed approach aims to achieve higher accuracy 99.3%, precision 94.6%, F1-Score 96.7%, Recall 98.2% and AUC score 97.9% in detecting phishing URLs compared to existing methods. Experimental results demonstrate the effectiveness of the proposed hybrid method in improving phishing-URL prediction performance, providing a promising avenue for enhancing cyber-security measures against online threats.

A new approach to video summary generation

This paper describes a video summary approach for an actual video file that provides a rapid way to assess its information. This is a difficult task to identify relevant or informative sections. When the original video demands knowledge of its content, we build a video summary via extracting in-depth characteristics of each original video. Video summarization deals with the generation of a condensed version of the original video by including meaningful frames or segments while eliminating redundant information provides a concise and accurate representation of the original video clips by providing the most representative summary with increased interest through the application of a matching function between the current and prior frames. The primary goal of this work is to give a strategy for recording video clips using security cameras and converting them into a short video to provide a clear analysis of the video by reducing excess and extracting the key video contents into the real video. In a lengthy video clip, viewers may not have enough time to see the full thing. The viewer may be interested in seeing a specific topic under the label "Important". It may be composed of pieces that may be shared on social media and communicate the semantics of long videos, particularly video snippets that must be submitted to Internet. To browse, will be needed a network with adequate bandwidth. That is, non-affiliated video streaming, indexing, and retrieval of video content makes it valuable to viewers.

Trailblazer: A proactive caching, blockchain, minimum content retrieval latency, and energy cost-aware resource and job orchestration scheme for publisher-subscriber based 6G ICN services

With the rise of information-centric networks (ICN), the user can access the caching content from nearby caching nodes rather than the remote content server through in-network caching facilities. The existing articles did not look into minimum caching content retrieval latency, user energy consumption, user financial cost, and maximum service provider profit-aware proper resource orchestration and job scheduling policies all at once, taking into account multiple publishers and subscriber-based caching jobs, blockchain, user-owned and service provider cache, pre-caching, neighbor collaboration, and available resources. To suppress these challenges, this article offers a proactive pre-caching, collaboration, blockchain, minimum latency-aware resource, and job scheduling policy for 6G ICN services that takes into account publishers’ and subscribers’ caching job requests, user-owned and service provider cache, available resources, and deadline. The experimental results visualized that at least 16% content retrieval delay, 66% energy, and 22% service provider profit gain are attained in the proposed scheme over the existing techniques.

Proactive Content Retrieval Based on Value of Popularity in Content-Centric Internet of Vehicles

Proactive Content Retrieval Based on Value of Popularity in Content-Centric Internet of Vehicles

The impact of face masks on face-to-face neural tracking of speech: Auditory and visual obstacles

Face masks provide fundamental protection against the transmission of respiratory viruses but hamper communication. We estimated auditory and visual obstacles generated by face masks on communication by measuring the neural tracking of speech. To this end, we recorded the EEG while participants were exposed to naturalistic audio-visual speech, embedded in 5-talker noise, in three contexts: (i) no-mask (audio-visual information was fully available), (ii) virtual mask (occluded lips, but intact audio), and (iii) real mask (occluded lips and degraded audio). Neural tracking of lip movements and of the sound envelope of speech was measured through backward modeling, that is, by reconstructing stimulus properties from neural activity. Behaviorally, face masks increased perceived listening difficulty and phonological errors in speech content retrieval. At the neural level, we observed that the occlusion of the mouth abolished lip tracking and dampened neural tracking of the speech envelope at the earliest processing stages. By contrast, degraded acoustic information related to face mask filtering altered neural tracking of speech envelope at later processing stages. Finally, a consistent link emerged between the increment of perceived listening difficulty and the drop in reconstruction performance of speech envelope when attending to a speaker wearing a face mask. Results clearly dissociated the visual and auditory impact of face masks on the neural tracking of speech. While the visual obstacle related to face masks hampered the ability to predict and integrate audio-visual speech, the auditory filter generated by face masks impacted neural processing stages typically associated with auditory selective attention. The link between perceived difficulty and neural tracking drop also provides evidence of the impact of face masks on the metacognitive levels subtending face-to-face communication.

Emotion Recognition from Videos Using Multimodal Large Language Models

The diffusion of Multimodal Large Language Models (MLLMs) has opened new research directions in the context of video content understanding and classification. Emotion recognition from videos aims to automatically detect human emotions such as anxiety and fear. It requires deeply elaborating multiple data modalities, including acoustic and visual streams. State-of-the-art approaches leverage transformer-based architectures to combine multimodal sources. However, the impressive performance of MLLMs in content retrieval and generation offers new opportunities to extend the capabilities of existing emotion recognizers. This paper explores the performance of MLLMs in the emotion recognition task in a zero-shot learning setting. Furthermore, it presents a state-of-the-art architecture extension based on MLLM content reformulation. The performance achieved on the Hume-Reaction benchmark shows that MLLMs are still unable to outperform the state-of-the-art average performance but, notably, are more effective than traditional transformers in recognizing emotions with an intensity that deviates from the average of the samples.

RMBCC: A Replica Migration-Based Cooperative Caching Scheme for Information-Centric Networks

How to maximize the advantages of in-network caching under limited cache space has always been a key issue in information-centric networking (ICN). Replica placement strategies aim to fully utilize cache resources by optimizing the location and quantity distribution of replicas in the network, thereby improving the performance of the cache system. However, existing research primarily focuses on optimizing the placement of replicas along the content delivery path, which cannot avoid the inherent drawback of not being able to leverage off-path cache resources. The proposals for off-path caching cannot effectively solve this problem as they introduce excessive complexity and cooperation costs. In this paper, we address the trade-off between cache resource utilization and cooperation costs by introducing a mechanism complementary to replica placement. Instead of redesigning a new caching strategy from scratch, we propose a proactive cooperative caching mechanism (called RMBCC) that involves an independent replica migration process, through which we proactively relocate replicas evicted from the local cache to neighboring nodes with sufficient cache resources. The cooperation costs are effectively controlled through migration replica filtering, migration distance limitation, as well as hop-by-hop migration request propagation. Extensive simulation experiments show that RMBCC can be efficiently integrated with different on-path caching strategies. Compared with representative caching schemes, RMBCC achieves significant improvements in evaluation metrics such as cache hit ratio and content retrieval time, while only introducing negligible cooperation overhead.

Retrieval of snow liquid water content from radiative transfer model, field data and PRISMA satellite data

The amount of liquid water (LWC) present in the snowpack is critical for predicting wet snow avalanches, forecasting meltwater release, and assessing water availability in river basins. However, measuring this variable using traditional in situ methods is challenging. Space imaging spectroscopy is emerging as a promising approach to map the spatial and temporal variations of snow parameters. While some studies suggest the potential of hyperspectral remote sensing to infer liquid water content, field validation is still lacking. In this context, we propose a new spectral index, namely Snow Surficial Water Index (SSWI), which is designed to be sensitive to the percentage of surficial liquid water content in snow. We developed the index using the BioSNICAR radiative transfer model and then we tested it on both field spectral data and satellite PRISMA imagery. Validation was performed using field data collected with a Snow Sensor during four campaigns in alpine environments, one of which simultaneously with PRISMA. Through a k-fold cross-validation analysis, we achieved a coefficient of determination of 0.7 and a Root Mean Square Error equal to 3%, demonstrating the effectiveness of the proposed index in retrieving LWC from field data and mapping LWC from PRISMA data. A spatial analysis at the catchment level reinforced the results, showing an LWC distribution consistent with orography. The proposed method can be easily applied to other space imaging spectroscopy missions.

A Level 3 monthly gridded ice cloud dataset derived from 12 years of CALIOP measurements

Abstract. Clouds play important roles in weather, climate, and the global water cycle. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft has measured global vertical profiles of clouds and aerosols in the Earth’s atmosphere since June 2006. CALIOP provides vertically resolved information on cloud occurrence, thermodynamic phase, and properties. We describe version 1.0 of a monthly gridded ice cloud product derived from over 12 years of global, near-continuous CALIOP measurements. The primary contents are monthly vertically resolved histograms of ice cloud extinction coefficient and ice water content (IWC) retrievals. The CALIOP Level 3 Ice Cloud product is built from the CALIOP Version 4.20 Level 2 5 km Cloud Profile product that, relative to previous versions, features substantial improvements due to more accurate lidar backscatter calibration, better extinction coefficient retrievals, and a temperature-sensitive parameterization of IWC. The gridded ice cloud data are reported as histograms, which provides data users with the flexibility to compare CALIOP’s retrieved ice cloud properties with those from other instruments with different measurement sensitivities or retrieval capabilities. It is also convenient to aggregate monthly histograms for seasonal, annual, or decadal trend and climate analyses. This CALIOP gridded ice cloud product provides a unique characterization of the global and regional vertical distributions of optically thin ice clouds and deep convection cloud tops, and it should provide significant value for cloud research and model evaluation. A DOI has been issued for the product: https://doi.org/10.5067/CALIOP/CALIPSO/L3_ICE_CLOUD-STANDARD-V1-00 (Winker et al., 2018).

Integrated Data Processing Architecture Applied to Learning Objects Repository for Educational Robotics: Proposing Approach

Educational Robotics has emerged as a multidisciplinary field integrating Information and Communication Technologies, Engineering, and Artificial Intelligence areas in education. Furthermore, Educational Robotics offers an innovative approach to teaching and learning by incorporating the construction and programming of robots. However, the small number of platforms for sharing Educational Robotics Learning Objects with a large amount of well-described metadata poses a challenge for efficient search and retrieval of relevant educational content. This study presents an architecture for data processing and ingestion in the RepositORE system, a Learning Objects Repository for Educational Robotics that stores educational content and associated metadata based on the Dublin Core standard. Based on microservices architecture, the proposed system aims to facilitate the location and reuse of learning objects by adapting the RepositORE system to work as a client service. The theoretical framework explores the concepts of learning objects, repositories, and the significance of Educational Robotics in education, and reviews related works. The proposed architecture and system can potentially enhance the accessibility and discoverability of Educational Robotics Learning Objects, fostering effective teaching and learning experiences.

Galor: Global view assisted localized fine-grained routing for LEO satellite networks

Low Earth Orbit (LEO) satellite networks have been expected to provide global coverage for Internet services with immediacy requirements. The dynamics of an LEO satellite network topology induce the challenges of achieving efficient content retrieval. This article takes an initial step toward achieving efficient content retrieval in LEO satellite networks from the routing perspective. We start with investigating the topology characteristics of LEO satellite networks in terms of the deterministic neighbor relation and the intermittent inter-satellite links. We then propose a Global view Assisted Localized fine-grained Routing (GALOR), which is an information-centric routing mechanism customized to LEO satellite networks. Specifically, GALOR disseminates the link state within a predefined range instead of the entire constellation, incurring less convergence time and control overhead. Therefore, GALOR can calculate the routing table (to guide interest forwarding) based on the local link state and the global neighbor relations. Moreover, GALOR improves the forwarding method of the information-centric routing by reconstructing a failed Pending Interest Table (PIT) entries in response to occasional link failures. Our packet-level experiments show that GALOR outperforms state-of-the-art mechanisms (up to 103.4%) in terms of average packet delivery ratio in content-sharing.

MCA NOTES WEBSITE

Abstract— In the contemporary educational landscape, the accessibility and availability of study materials play a pivotal role in enhancing the learning experience for students pursuing Master of Computer Applications (MCA). This research paper presents a comprehensive examination of the design, development, and implementation of an innovative online platform tailored specifically for MCA students. The platform serves as a repository of semester-wise previous question papers and subject notes, offering a centralized hub for students to access valuable resources essential for their academic success. Utilizing cutting-edge technologies such as React JS, Tailwind CSS, MongoDB, and Node.js, the website provides an intuitive and user-friendly interface, ensuring seamless navigation and efficient content retrieval. Through meticulous curation and organization, the platform categorizes study materials according to semesters, enabling students to effortlessly locate pertinent resources relevant to their academic progression. Moreover, the incorporation of features such as downloadable notes and interactive contact us page further enriches the user experience, facilitating streamlined communication between students and administrators. Index Terms – Educational Technology, React JS, Tailwind CSS, MongoDB, Node.js, Study Materials, Previous Question Papers, User Interface Design.

New media audience and sexuality issues in big brother naija.

The new media effectively plays the role of speedily conveying media content to various online communities, and this internet technology also allows for the storage and retrieval of content. The organizers of Big Brother Naija (BBN) appear to have leveraged this facility to interact with the audience of the reality show in the most profound manner in the Nigerian media ecosystem. Adopting John Locke's Empiricist Reflection Theory (ERT), the article uses quantitative and qualitative research methods to examine if coders (the new media audience) perceived the promotion of nudity, illicit sex, inordinate kisses, and vulgarity in the 2018 edition of the Big Brother Naija (BBN), codenamed "Double Wahala." A checklist of 72 internal consistency reliability was used to obtain the quantitative data while the data were analyzed using simple percentages and a pie chart. This was complemented by qualitative research modalities of historical-analytic and document observation methods. The study found that the participants and organizers have, for economic reasons, thrown caution on morality and values to the wind with the outcome of actual sexual intercourse among the participants amounting to 52% among other studied variables. The conclusion reached is that the organizers of the program should redesign it to promote wholesome entertainment, education, and enlightenment of the audience, especially the youth in Nigeria.

A Survey on Content Retrieval on the Decentralised Web

The control, governance, and management of the web have become increasingly centralised, resulting in security, privacy, and censorship concerns. Decentralised initiatives have emerged to address these issues, beginning with decentralised file systems. These systems have gained popularity, with major platforms serving millions of content requests daily. Complementing the file systems are decentralised search engines and name-registry infrastructures, together forming the basis of a decentralised web . This survey article analyses research trends and emerging technologies for content retrieval on the decentralised web, encompassing both academic literature and industrial projects. Several challenges hinder the realisation of a fully decentralised web. Achieving comparable performance to centralised systems without compromising decentralisation is a key challenge. Hybrid infrastructures, blending centralised components with verifiability mechanisms, show promise to improve decentralised initiatives. While decentralised file systems have seen more mature deployments, they still face challenges such as usability, performance, privacy, and content moderation. Integrating these systems with decentralised name-registries offers a potential for improved usability with human-readable and persistent names for content. Further research is needed to address security concerns in decentralised name-registries and enhance governance and crypto-economic incentive mechanisms.

EduCross: Dual adversarial bipartite hypergraph learning for cross-modal retrieval in multimodal educational slides

In the digital education landscape, cross-modal retrieval (CMR) from multimodal educational slides represents a significant challenge, particularly because of the complex nature of academic content, which includes images, diagrams, equations, and tables across various subjects such as mathematics and biology. Current CMR systems are primarily designed for “(natural) image to text” interactions (or vice versa) and inadequately address real-world educational scenarios. This study presents EduCross, a novel framework devised to enhance CMR within multimodal educational slides, which is a domain in which traditional retrieval systems fall short. Recognizing the imperative for a system that is tailored to the educational context, EduCross integrates dual adversarial bipartite hypergraph learning, harnessing the capabilities of generative adversarial networks with figure-text dual channels. This powerful combination facilitates robust bidirectional mapping, allowing for the precise association of figures with their descriptive spoken language segments and ensuring a comprehensive CMR experience. Specifically, we develop framelet-based deep bipartite hypergraph neural networks that effectively manage the high-order relationships between diverse educational content types and various types of slide figures. Our experimental results underscore the superior performance of EduCross, demonstrating its effectiveness through the use of the real Multimodal Lecture Presentations dataset that mirrors authentic educational settings. These outcomes highlight the significant advancements of EduCross over existing methods, marking a leap forward in the accurate retrieval of multimodal educational content.