Retrieval Process Research Articles

The effect of survival processing on memory for pictures depends on how memory is tested

ABSTRACT Two experiments investigated the effects of survival processing on memory for pictures of objects. In experiment 1, participants were presented with 32 pictures of common objects and rated them for their relevance to a survival scenario, a moving home scenario, or for pleasantness. In a surprise recall test, participants in the survival condition recalled more of the verbal labels of the objects than participants in the moving and pleasantness conditions. In experiment 2, participants rated 64 pictures of objects in survival, moving home, or pleasantness conditions. Memory for visual detail was assessed using a forced-choice recognition test in which participants had to decide which of two highly similar pictures was the one they rated at study. In contrast to the results of experiment 1, correct recognition scores were highest in the pleasantness condition and lowest in the survival condition. This pattern suggests that survival processing enhances memory for objects but not for precise visual detail. The findings are consistent with the view that rating objects for their survival value directs attention to the potential uses of the objects. They also emphasise the importance of the match between encoding and retrieval processes in the survival processing paradigm.

A case-based reasoning tool to recommend drinking water source protection actions

In response to a need expressed by water actors to support them in identifying appropriate actions for protecting drinking water sources, we modelled, designed, implemented, tested, and validated a prototype case-based reasoning (CBR) system. This required an intensive knowledge acquisition and structuring process which we conducted in collaboration with 102 water management and governance actors in Quebec, Canada. Knowledge was organized in a case base containing nearly 200 past experiences implementing water protection actions at different decision-making scales (local, regional, provincial), by various organizations (government, municipalities, water-related associations), over the last two decades. This paper describes our case engineering process to design case attributes based on content analysis. It presents the case edition, the case retrieval process, and the prototype's implementation and validation, conducted through a rigorous and transparent participatory procedure. This is, to our knowledge, the first successful attempt to apply a CBR approach to support water actors in protecting drinking water sources. It provides empirical evidence of the positive potential of such an approach for knowledge sharing and transfer and can be generalized to other similar contexts.

Processing noncanonical sentences: effects of context on online processing and (mis)interpretation

Prior research has shown that sentences with noncanonical argument order (e.g., patient-before- agent instead of agent-before-patient order) are associated with additional online processing difficulty, but that this difficulty can be alleviated if the discourse context licenses noncanonical order. Other studies demonstrated that noncanonical sentences are prone to misinterpretation effects: comprehenders sometimes seem to form interpretations with incorrect assignments of semantic roles to argument NPs. However, those studies tested noncanonical sentences in isolation. To further clarify the source of misinterpretation effects, we designed three experiments that investigated how discourse properties licensing noncanonical order affect online processing and final interpretation. All experiments tested unambiguous active declarative sentences in German with agentive verbs and two arguments, probing both online processing difficulty (using selfpaced reading) and accuracy of interpretation (using wh-comprehension questions). Besides word order (subject-before-object, SO vs. object-before-subject, OS), we varied the context preceding the target sentence (neutral context vs. context licensing OS, Experiment 1), the type of NP serving as object (definite vs. demonstrative NP, Experiment 2) and the type of question probing comprehension (two-argument vs. one-argument wh-questions, Experiment 3). Consistent with earlier findings, we observed that discourse properties licensing OS order facilitated online processing in early sentence regions. However, they did barely affect accuracy on comprehension questions, with accuracy instead being a function of word order and question type. Our results support models that explain misinterpretation effects in terms of task-specific retrieval processes. A retrieval mechanism capturing the effects of question type is proposed.

Life cycle assessment (LCA) on waste management options for derelict fishing gear

PurposeDerelict fishing gear (DFG) is one of the most abundant and harmful types of marine litter that gets increasingly retrieved from the ocean. However, for this novel waste stream recycling and recovery pathways are not yet commonly established. To identify the most suitable waste management system, this study assesses the potential environmental impacts of DFG waste treatment options in Europe.MethodsThis study applies an attributional life cycle assessment (LCA) to four DFG waste treatment scenarios, namely a mechanical recycling, syngas production, energy recovery and landfill disposal. The scope spans from the retrieval and transport processes to pre- and end-treatment steps until the outputs are sent to landfill or assumed to substitute products or energy. Primary data was collected from retrieval and waste treatment trials in Europe. Contribution, sensitivity and uncertainty analyses were conducted using the LCA software SimaPro and ReCiPe as the impact methodology.Results and discussionThe results show that the mechanical recycling and energy recovery achieve the lowest potential environmental impacts. The syngas production and landfill disposal scenario are not environmentally competitive because they require too much electricity, or their avoided production credits were too small to offset their emissions. Unlike the pre-treatment and transport processes, the retrieval and end-treatment processes have a significant impact on the overall results. The transport distances, energy mix and market and technological assumptions are least sensitive, while changes to the waste composition significantly affect the results. Especially a reduced lead content benefits the human toxicity impact potential of the landfill disposal scenario. The uncertainty analysis showed that the results are very robust in nine of twelve impact categories.ConclusionsThis is the first LCA study that compares waste treatment options for marine litter. The results indicate that a disposal of DFG is hazardous and should be replaced with mechanical recycling or energy recovery. While this may be technologically possible and environmentally beneficial, economic and social factors should also be considered before a final decision is made. To further reduce environmental impacts, marine litter prevention should play a more important role.Graphical abstract

VMLH: Efficient Video Moment Location via Hashing

Video-moment location by query is a hot topic in video understanding. However, most of the existing methods ignore the importance of location efficiency in practical application scenarios; video and query sentences have to be fed into the network at the same time during the retrieval, which leads to low efficiency. To address this issue, in this study, we propose an efficient video moment location via hashing (VMLH). In the proposed method, query sentences and video clips are, respectively, converted into hash codes and hash code sets, in which the semantic similarity between query sentences and video clips is preserved. The location prediction network is designed to predict the corresponding timestamp according to the similarity among hash codes, and the videos do not need to be fed into the network during the process of retrieval and location. Furthermore, different from the existing methods, which require complex interactions and fusion between video and query sentences, the proposed VMLH method only needs a simple XOR operation among codes to locate the video moment with high efficiency. This paper lays the foundation for fast video clip positioning and makes it possible to apply large-scale video clip positioning in practice. The experimental results on two public datasets demonstrate the effectiveness of the method.

Spectral replacement using machine learning methods for continuous mapping of the Geostationary Environment Monitoring Spectrometer (GEMS)

Abstract. Earth radiances in the form of hyperspectral measurements contain useful information on atmospheric constituents and aerosol properties. The Geostationary Environment Monitoring Spectrometer (GEMS) is an environmental sensor measuring such hyperspectral data in the ultraviolet and visible spectral range over the Asia–Pacific region. After completion of the in-orbit test of GEMS in October 2020, bad pixels are found as one of remaining calibration issues resulting in obvious spatial gaps in the measured radiances as well as retrieved properties. To solve the fundamental cause of the issue, this study takes an approach reproducing the defective spectra with machine learning models using artificial neural network (ANN) and multivariate linear regression (Linear). Here the models are trained with defect-free measurements of GEMS after dimensionality reduction with principal component analysis (PCA). Results show that the PCA-Linear model has small reproduction errors for a narrower spectral gap and is less vulnerable to outliers with an error of 0.5 %–5 %. On the other hand, the PCA-ANN model shows better results emulating strong non-linear relations with an error of about 5 % except for the shorter wavelengths around 300 nm. It is demonstrated that dominant spectral patterns can be successfully reproduced with the models within the level of radiometric calibration accuracy of GEMS, but a limitation remains when it comes to finer spectral features. When applying the reproduced spectra to retrieval processes of cloud and ozone, cloud centroid pressure shows an error of around 1 %, while total ozone column density shows relatively higher variance. As an initial step reproducing spectral patterns for bad pixels, the current study provides the potential and limitations of machine learning methods to improve hyperspectral measurements from the geostationary orbit.

Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808)

The advanced imagers onboard the new generation of geostationary satellites could provide multilayer atmospheric moisture information with unprecedented high spatial and temporal resolutions, while the physical retrieval algorithm (One-Dimensional Variational, 1DVAR) is performed for operational atmospheric water vapor products with reduced resolutions, which is due to the limited computational efficiency of the physical retrieval algorithm. In this study, a typical cost-efficient machine learning (Random Forecast, RF) algorithm is adopted and compared with the physical retrieval algorithm for retrieving the atmospheric moisture from the measurements of Advance Himawari Imager (AHI) onboard the Himawari-8 satellite during the typhoon Maria (201808). It is found that the accuracy of the RF-based algorithm has much high computational efficiency and provides moisture retrievals with accuracy 35–45% better than that of 1DVAR, which means the retrieval process can be conducted at full spatial resolution for potential operational application. Both the Global Forecast System (GFS) forecasts and the AHI measurements are necessary information for moisture retrievals; they provide added value for each other.

Remote referencing strategy for high-resolution coded ptychographic imaging.

The applications of conventional ptychography are limited by its relatively low resolution and throughput in the visible light regime. The new development of coded ptychography (CP) has addressed these issues and achieved the highest numerical aperture for large-area optical imaging in a lensless configuration. A high-quality reconstruction of CP relies on precise tracking of the coded sensor's positional shifts. The coded layer on the sensor, however, prevents the use of cross correlation analysis for motion tracking. Here we derive and analyze the motion tracking model of CP. A novel, to the best of our knowledge, remote referencing scheme and its subsequent refinement pipeline are developed for blind image acquisition. By using this approach, we can suppress the correlation peak caused by the coded surface and recover the positional shifts with deep sub-pixel accuracy. In contrast with common positional refinement methods, the reported approach can be disentangled from the iterative phase retrieval process and is computationally efficient. It allows blind image acquisition without motion feedback from the scanning process. It also provides a robust and reliable solution for implementing ptychography with high imaging throughput. We validate this approach by performing high-resolution whole slide imaging of bio-specimens.

PERANAN UAV (UNMANNED AERIAL VEHICLE) DALAM IDENTIFIKASI ANCAMAN LONGSOR TINGGI DI SUNGAI CI DURIAN, DAERAH CIGUDEG, BOGOR

Technological developments in the field of photography, especially in the manufacture of 3D models from a collection of aerial photographs can be quickly and effectively with Unmanned Aerial Vehicle (UAV). This research was conducted in CI Durian River as the target object of 3D modeling. The study area located in Bogor regency, West Java province has a morphology of hills to low hills with winding rivers and composed by the Bojongmanik formation (Tmb). The dominant rock units in the study area are sandstone and claystone. The process of data retrieval using Unmanned Aerial Vehicle (UAV) can be used to perform mapping quickly and accurately at a more affordable cost. This research uses descriptive and interpretive methods. Primary Data in the study is all the data acquired through drones. Secondary Data are data such as slope maps and topographic maps. Primary Data and secondary data can be a reference in detecting landslides in the study area. The tool used in this study is to use unmanned Aerial Vehicle (UAV) technology in the form of drones by utilizing aerial photography data which is then processed into Digital Elevation Model (DEM) data. The use of Ground Control Point (GCP) can certainly add accuracy to the creation of DEM data. The Data is a reference in the interpretation of the threat of landslides that occurred in the study area. Primary data in the form of slope can validate secondary data in the form of slope maps. The dominant factors that influence the threat of landslides in the study area include: topographic factors and steep slopes. Topography and slope factors are very steep slopes are the main factors of high landslide threat in the study area.

Many Hands Make Light Work: Transferring Knowledge From Auxiliary Tasks for Video-Text Retrieval

The problem of video-text retrieval, which searches videos via natural language descriptions or vice versa, has attracted growing attention due to the explosive scale of videos produced every day. The dominant approaches for this problem follow the pipeline that firstly learn compact feature representations of videos and texts, and then jointly embed them into a common feature space where matched video-text pairs are close and unmatched pairs are far away. However, most of them neither consider the structural similarities among crossmodal samples in a global view, nor leverage useful information from other relevant retrieval processes. We argue that both information have great potential for video-text retrieval. In this paper, we propose to extract useful knowledge from the retrieval process by exploiting structural similarities via Graph Neural Networks (GNNs) and then progressively transfer useful knowledge from relevant retrieval processes in a general-tospecific manner to assist the current retrieval process. Specifically, for the retrieval of the current query, we first construct a sequence of query-graphs whose central queries are chosen from distant to close to the current query. Then we conduct knowledgeguided message passing in each query-graph to exploit regional structural similarities and gather knowledge of different levels from the updated query-graphs with a knowledge-based attention mechanism. Finally, we transfer the extracted useful knowledge from general to specific to assist the current retrieval process. Extensive experimental results show that our model outperforms the state-of-the-arts on four benchmarks.

A unifying framework and a mathematical model for the Slab Stack Shuffling Problem

The Slab Stack Shuffling Problem (SSSP) consists of retrieving slabs, stored in stacks in a warehouse, to efficiently satisfy a processing order. The problem is relevant in the steel industry as the slab yard serves as a storage buffer between the continuous casting stage and the rolling mill. Notably, the SSSP also arises in cutting/assembly centres within the shipbuilding supply chain, where already rolled slabs must undergo further production stages. The different slabs managed in these facilities confer the problem novel practical features, such as the existence of slabs' typologies and deadlines, i.e., a maximum time beyond which their quality certifications expire and are no longer usable. In such a context, the goals of the present paper are twofold: (i) providing a comprehensive taxonomy of the main aspects involved in the problem; (ii) proposing an original mathematical formulation for the SSSP. Specifically, the model is cast as a bi-objective multi-period program, seeking to minimise the number of shuffles and expired slabs. Computational tests on randomly generated instances prove the relevance of the trade-off between the above-mentioned objectives and the impact of the yard's configuration on the retrieval process, suggesting the most suitable storage strategy to adopt under different operational settings.

Further advancing theories of retrieval of the personal past

Abstract In our target article, we presented the idea that involuntary autobiographical memories (IAMs) and déjà vu may both be based on the same retrieval processes. Our core claim was thus straightforward: Both can be described as “involuntary” or spontaneous cognitions, where IAMs deliver content and déjà vu delivers only the feeling of retrieval. Our proposal resulted in 27 commentaries covering a broad range of perspectives and approaches. The majority of them have not only amplified our key arguments but also pushed our ideas further by offering extensions, refinements, discussing possible implications and providing additional empirical, neuroscientific and clinical support. The discussion launched by the commentaries proves to us the importance of bringing IAMs and déjà vu into mainstream discussions of memory retrieval processes.

O esquema de classificação da comunidade booktube

Abstract The booktube community comprises individuals who share their literary experiences through videos posted on the YouTube platform. They are also book collectors who share their collections and their own subjective reasons for collecting books. Since their videos are published using tags, our focus was on the 'bookshelf tour' tag. This is a case study which utilized video analysis to investigate their classification criteria. We used the think-aloud protocols to collect the data. Content analysis was conducted, and the concept of classification was employed to understand the particular categories of private organization in their shelves and where these categories originated. Furthermore, we also considered studies related to book collections for interpreting the subjective connection between collecting and classification items in private collections. The results of the investigation highlighted three main aspects. Firstly, the desire to showcase their collection significantly influences their classification categories. Secondly, classification stands out as one of the primary activities of book collectors. Thirdly, the categories are influenced by their backgrounds and life stories, although many of them have been changing their classification considering their audience’s opinion. When they make these classifications visible and explain their reasons for their choices, they also engage with their audience. In conclusion, we observed that when a classification scheme is based on the classifier's identity, it can lead to some instability in the categories. However, it also demonstrates how memory plays a crucial role in the retrieval process and how their worldview is reflected in these categories. Moreover, the digital environment influences on personal classification.

Geo-Intelligent Retrieval Framework Based on Machine Learning in the Cloud Environment: A Case Study of Soil Moisture Retrieval

Soil moisture is one of the important parameters in Earth system models. In recent years, the retrieval based on machine learning and data fusion of multi-source satellite observation data has become one of the effective methods to obtain soil moisture information at a large scale. However, most retrieval studies need to download remote sensing original data first, then preprocess, train the retrieval models, and finally generate products in the offline environment. In order to meet the requirements of long temporal series of large-scale area retrieval, and with the widespread use of machine learning in retrieval studies, the amount of remote sensing data and necessary computing resources are gradually increasing. Moreover, studies usually use a single machine learning retrieval model for the entire study area, which lacks the consideration of geographical differences and spatial heterogeneity of soil moisture. Therefore, we established a geo-intelligent soil moisture retrieval framework completely based on the cloud environment. In this study, a variety of machine learning algorithms were used to fuse multi-source observation data mainly including MODIS data and other auxiliary data, and the Continental United States (CONUS) was taken as the experimental area to generate soil moisture data with a resolution of 500m. In addition, this study combines geographical correlation with machine learning models to cope with the spatial heterogeneity of surface soil moisture. Overall, on the basis of site-based validation, the retrieval model trained under the framework performed well, with estimation accuracy of 0.716 and 0.0383 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> in terms of coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and unbiased root mean square error (ubRMSE). The establishment of the cloud retrieval framework provides convenience for the whole retrieval process and also provides a new idea for other retrieval studies of geoscience parameters.

Color Image Retrieval Method Using Low Dimensional Salient Visual Feature Descriptors for IoT Applications.

Digital data are rising fast as Internet technology advances through many sources, such as smart phones, social networking sites, IoT, and other communication channels. Therefore, successfully storing, searching, and retrieving desired images from such large-scale databases are critical. Low-dimensional feature descriptors play an essential role in speeding up the retrieval process in such a large-scale dataset. A feature extraction approach based on the integration of color and texture contents has been proposed in the proposed system for the construction of a low-dimensional feature descriptor. In which color contents are quantified from a preprocessed quantized HSV color image and texture contents are retrieved from a Sobel edge detection-based preprocessed V-plane of HSV color image using a block level DCT (discrete cosine transformation) and gray level co-occurrence matrix. On a benchmark image dataset, the suggested image retrieval scheme is validated. The experimental outcomes were compared to ten cutting-edge image retrieval algorithms, which outperformed in the vast majority of cases.

Involuntary autobiographical memories and déjà vu: When and why attention makes a difference.

The target article claims that involuntary autobiographical memories and déjà vu are based on the same retrieval processes, although they result in different phenomenological states. Here we argue that the differential engagement of attention at various stages of memory may be one of the determinants of when common retrieval processes give rise to such different experiences.

Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system, the matching prediction and similarity identification take place a major role in the ontology. In that, there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time. Since, in the data recommendation system, this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process. To improve the performance of data validation, this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing. In this paper advanced model of the Logarithmic Directionality Texture Pattern (LDTP) method with a Metaheuristic Pattern Searching (MPS) system was used to estimate the similarity between the query data in the entire database. The overall work was implemented for the application of the data recommendation process. These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period. Also, with the help of a neural network, the relevancies of feature attributes in the database are predicted, and the matching index was sorted to provide the recommended data for given query data. This was achieved by using the Distributional Recurrent Neural Network (DRNN). This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set. The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset. These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric. The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%, 70%, and 80%. The parameters that are considered for performance analysis are Precision, Recall, F1-score and the accuracy of data retrieval, the query recommendation output, and comparison with other state-of-art methods.

A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture

This study presents a method for implementing generative AI services by utilizing the Large Language Models (LLM) application architecture. With recent advancements in generative AI technology, LLMs have gained prominence across various domains. In this context, the research addresses the challenge of information scarcity and proposes specific remedies by harnessing LLM capabilities. The investigation delves into strategies for mitigating the issue of inadequate data, offering tailored solutions. The study delves into the efficacy of employing fine-tuning techniques and direct document integration to alleviate data insufficiency. A significant contribution of this work is the development of a Retrieval-Augmented Generation (RAG) model, which tackles the aforementioned challenges. The RAG model is carefully designed to enhance information storage and retrieval processes, ensuring improved content generation. The research elucidates the key phases of the information storage and retrieval methodology underpinned by the RAG model. A comprehensive analysis of these steps is undertaken, emphasizing their significance in addressing the scarcity of data. The study highlights the efficacy of the proposed method, showcasing its applicability through illustrative instances. By implementing the RAG model for information storage and retrieval, the research not only contributes to a deeper comprehension of generative AI technology but also facilitates its practical usability within enterprises utilizing LLMs. This work holds substantial value in advancing the field of generative AI, offering insights into enhancing data-driven content generation and fostering active utilization of LLM-based services within corporate settings.

MIMO Coded Generalized Reduced Dimension Fourier Algorithm for 3-D Microwave Imaging

In this paper, to accelerate data acquisition and image reconstruction procedures in a multistatic short-range microwave imaging scenario, an orthogonal coding approach with Fourier domain processing is presented. First, a special two-dimensional (2D) multiple-input multiple-output (MIMO) structure is introduced to fully electronically synthesize the 2D aperture. Then, the model of the transmitted and received signals by a MIMO stepped-frequency-modulated radar is presented, with special considerations about orthogonal, balanced and optimal sequences. On the receiver side, the backscatter frequency response extraction process is formulated with the aim of obtaining individual information of all channels. Finally, based on the introduced model, a fast Fourier-based algorithm with reduced dimensions, named MIMO coded generalized reduced dimension Fourier (CGRDF), is mathematically derived. It includes extracting phase and amplitude compensators with the aim of mapping 4D to 2D spatial data, transferring the backscatter transfer function from the spatial domain to the wavenumber domain, extracting the smoothing filter, compensating the curvature of the wavefront of all scatterers, extracting the reflectivity function and an additional range compensator. The results of numerical simulations show the satisfactory and reliable performance of the proposed approach in terms of the information retrieval process and processing speed.

Universal Multimodal Representation for Language Understanding.

Representation learning is the foundation of natural language processing (NLP). This work presents new methods to employ visual information as assistant signals to general NLP tasks. For each sentence, we first retrieve a flexible number of images either from a light topic-image lookup table extracted over the existing sentence-image pairs or a shared cross-modal embedding space that is pre-trained on out-of-shelf text-image pairs. Then, the text and images are encoded by a Transformer encoder and convolutional neural network, respectively. The two sequences of representations are further fused by an attention layer for the interaction of the two modalities. In this study, the retrieval process is controllable and flexible. The universal visual representation overcomes the lack of large-scale bilingual sentence-image pairs. Our method can be easily applied to text-only tasks without manually annotated multimodal parallel corpora. We apply the proposed method to a wide range of natural language generation and understanding tasks, including neural machine translation, natural language inference, and semantic similarity. Experimental results show that our method is generally effective for different tasks and languages. Analysis indicates that the visual signals enrich textual representations of content words, provide fine-grained grounding information about the relationship between concepts and events, and potentially conduce to disambiguation.