Retrieval Of Items Research Articles

Deliberate search for analogous cases aids the retrieval and transfer of suboptimally-encoded memory items.

Even though spontaneous retrieval of analogous cases lacking surface similarity with a target situation typically requires achieving an abstract representation of the target situation, recent studies on analogical argumentation suggest that the deliberate disposition to search for analogous cases in long-term memory (LTM) suffices to increase cross-domain retrieval significantly. However, a limitation of these studies concerns the impossibility to determine whether the analogous situations reported were invented rather than retrieved, and whether there were instances of analogical retrieval that were not reflected in participants' arguments. To overcome these shortcomings, Experiment 1 resorted to a traditional transfer paradigm where a base analogue is learned prior to the presentation of the target situation during a contextually-separated phase. Results confirmed that an explicit indication to base persuasive arguments on analogous situations increases distant retrieval as compared to a baseline condition where the instruction to generate persuasive arguments did not include an indication to think of analogous cases. Experiment 2 generalised the retrieval advantage of voluntary search to the activity of generating explanatory hypotheses for a counterintuitive phenomenon, a more prototypical variety of knowledge transfer that has been somewhat overlooked within analogy research. The theoretical and educational implications of the present findings are discussed.

Opposing effects of pre-encoding stress on neural substrates of item and emotional contextual source memory retrieval

Although the mediating role of the stress hormone systems in memory for single- especially emotional- events is well-stablished, less is known about the influence of stress on memory for associated contextual information (source memory). Here, we investigated the impact of acute stress on the neural underpinnings of emotional contextual source memory. Participants underwent a stress or a control manipulation before they encoded objects paired with pleasant, neutral, or unpleasant backgrounds. One week later, item and contextual source memory were tested. Acute stress modulated the neural signature of item and contextual source memory in an opposite fashion: stressed participants showed larger activation in the precuneus and the medial prefrontal cortex (mPFC) during the retrieval of items, while the retrieval of contextual unpleasant information was associated with lower activation in the angular gyrus (AG) and mPFC. Furthermore, as revealed by cross-region representational similarity analyses, stress also reduced the memory reinstatement of the previously encoded visual cortex representations of object/unpleasant background pairings in the AG and mPFC. These results suggest that pre-encoding stress induction increases the activity of memory-related regions for single items but reduces the activity of these regions during the retrieval of contextual unpleasant information. Our findings provide new insights into the dissociative effects of stress on item and contextual source memory which could have clinical relevance for stress-related disorders.

Motor-related oscillations reveal the involvement of sensorimotor processes during recognition memory

Certain object properties may render an item as more memorable than others. One such property is manipulability, or the extent to which an object can be interacted with using our hands. This study sought to determine if the manipulability of an item modulates memory task performance on both a behavioural and neural level. We recorded electroencephalography (EEG) from a large sample of right-handed individuals (N=53) during a visual item recognition memory task. The task contained stimuli of both high and low manipulability. Analysis focused on activity in the theta rhythm (3.5-7Hz), which has been implicated in sensorimotor integration, and the mu rhythm (8-14Hz), the primary oscillation associated with sensorimotor related behaviours. At both encoding and retrieval, theta oscillations were greater over the left motor region for high manipulability stimuli, suggesting that an item's sensorimotor properties are assessed immediately upon presentation. Manipulability did not affect activity in the mu rhythm. However, mu oscillations over the left motor region were lower during the retrieval of old versus new items and response time was faster for old items, aligning with the cortical reinstatement hypothesis. These results collectively reveal an association between motor oscillations and memory processes, highlight the involvement of sensorimotor processing at both encoding and retrieval.

A new mathematical model and meta-heuristic algorithm for order batching, depot selection, and assignment problem with multiple depots and pickers

A new mathematical model and meta-heuristic algorithm for order batching, depot selection, and assignment problem with multiple depots and pickers

Examining the merits of feature-specific similarity functions in the news domain using human judgments

Online news article recommendations are typically of the ‘more like this’ type, generated by similarity functions. Across three studies, we examined the representativeness of different similarity functions for news item retrieval, by comparing them to human judgments of similarity. In Study 1 (N=401\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=401$$\\end{document}), participants assessed the overall similarity of ten randomly paired news articles on politics and compared their judgments to different feature-specific similarity functions (e.g., based on body text or images). In Study 2, we checked for domain differences in a mixed-methods survey (N=45\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=45$$\\end{document}), surfacing evidence that the effectiveness of similarity functions differs across different news categories (‘Recent Events’, ‘Sport’). In Study 3 (N=173\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=173$$\\end{document}), we improved the design of Study 1, by controlling for how news articles were matched, differentiating between dissimilar news articles and articles that were matched on a shared topic, named entities, and/or date of publication, across ‘Recent Events’ and ‘Sport’ categories. Across all studies, we found that users mostly used text-based features (e.g., body text, title) for their similarity judgments, while BodyText:TF-IDF was found to be the most representative for their judgments. Moreover, the strength of similarity judgments by humans and similarity scores by feature-specific functions was strongly affected by how news article pairs were matched. We show that humans and similarity functions are better aligned when two news articles are more alike, such as in a news recommendation scenario.

Limitations to optimal search in naturalistic active learning.

Optimality in active learning is under intense debate in numerous disciplines. We introduce a new empirical paradigm for studying naturalistic active learning, as well as new computational tools for jointly modeling algorithmic and rational theories of information search. Participants in our task can ask questions and learn about hundreds of everyday items but must retrieve queried items from memory. To maximize information gain, participants need to retrieve sequences of dissimilar items. In eight experiments (N = 795), we find that participants are unable to do this. Instead, associative memory mechanisms lead to the successive retrieval of similar items, an established memory effect known as semantic congruence. The extent of semantic congruence (and thus suboptimality in question asking) is unaffected by task instructions and incentives, though participants can identify efficient query sequences when given a choice between query sequences. Overall, our results indicate that participants can distinguish between optimal and suboptimal search if explicitly asked to do so, but have difficulty implementing optimal search from memory. We conclude that associative memory processes may place critical restrictions on people's ability to ask good questions in naturalistic active learning tasks. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Automated Storage and Retrieval System for Warehouse

Manual picking and retrieval of items from storage locations can be time-consuming, leading to delays in order fulfillment. This can negatively impact customer satisfaction and the overall responsiveness of the supply chain. In addition, manual data entry and tracking systems are prone to human errors, resulting in discrepancies between recorded and actual inventory levels. This can lead to stockouts, overstocking, and difficulties in maintaining accurate inventory records. Therefore, a fully automated storage and retrieval system is proposed to tackle this problem. The overall system consists of infrared sensor as presence of item stored, LEDs indicator, warehouse model with compartments and stepper motors act as actuator to pick and place the item. This project aims to automate the storage and retrieval system with the assist of monitoring system for accurate tracking of inventory and space savings. The use of Programmable Logic Controller (PLC) in this project is a good representation of integrated automation technology in automated storage system design. Furthermore, monitoring system connected to a web-based system will be used to remotely monitor and operate the facility. Implementing an Automated Storage and Retrieval System (ASRS) in the warehouse can address these challenges by automating the storage, retrieval, and tracking of inventory.

Optimizing Recall in Deep Graph Hashing Framework for Item Retrieval (Student Abstract)

Hashing-based recommendation (HR) methods, whose core idea is mapping users and items into hamming space, are common practice to improve item retrieval efficiency. However, existing HR fails to align optimization objective (i.e., Bayesian Personalized Ranking) and evaluation metric (i.e., Recall), leading to suboptimal performance. In this paper, we propose a smooth recall loss (termed as SRLoss), which targets Recall as the optimization objective. Due to the existence of discrete constraints, the optimization problem is NP-hard. To this end, we propose an approximation-adjustable gradient estimator to solve our problem. Experimental Results demonstrate the effectiveness of our proposed method.

ICAR: Image-Based Complementary Auto Reasoning

Scene-aware Complementary Item Retrieval (CIR) is a challenging task which requires to generate a set of compatible items across domains. Due to the subjectivity, it is difficult to set up a rigorous standard for both data collection and learning objectives. To address this challenging task, we propose a visual compatibility concept, composed of similarity (resembling in color, geometry, texture, and etc.) and complementarity (different items like table vs chair completing a group). Based on this notion, we propose a compatibility learning framework, a category-aware Flexible Bidirectional Transformer (FBT), for visual ``scene-based set compatibility reasoning'' with the cross-domain visual similarity input and auto-regressive complementary item generation. We introduce a ``Flexible Bidirectional Transformer (FBT),'' consisting of an encoder with flexible masking, a category prediction arm, and an auto-regressive visual embedding prediction arm. And the inputs for FBT are cross-domain visual similarity invariant embeddings, making this framework quite generalizable. Furthermore, our proposed FBT model learns the inter-object compatibility from a large set of scene images in a self-supervised way. Compared with the SOTA methods, this approach achieves up to 5.3% and 9.6% in FITB score and 22.3% and 31.8% SFID improvement on fashion and furniture, respectively.

E-Coin-Based Priced Oblivious Transfer with a Fast Item Retrieval

Priced oblivious transfer (POT) is a cryptographic protocol designed for privacy-preserving e-commerce of digital content. It involves two parties: the merchant, who provides a set of priced items as input, and a customer, who acquires one of them. After the protocol has run, the customer obtains the item they chose, while the merchant cannot determine which one. Moreover, the protocol guarantees that the customer gets the content only if they have paid the price established by the merchant. In a recent paper, the authors proposed a POT system where the payments employed e-coin transactions. The strong point of the proposal was the absence of zero-knowledge proofs required in preceding systems to guarantee the correctness of payments. In this paper, we propose a novel e-coin-based POT system with a fast item retrieval procedure whose running time does not depend on the number of items for sale. This is an improvement over the aforementioned existing proposal whose execution time becomes prohibitively long when the catalog is extensive. The use of zero-knowledge proofs is neither required.

Max-C and Min-D Projection Auto-Associative Fuzzy Morphological Memories: Theory and an Application for Face Recognition

Max-C and min-D projection auto-associative fuzzy morphological memories (max-C and min-D PAFMMs) are two-layer feedforward fuzzy morphological neural networks designed to store and retrieve finite fuzzy sets. This paper addresses the main features of these auto-associative memories: unlimited absolute storage capacity, fast retrieval of stored items, few spurious memories, and excellent tolerance to either dilative or erosive noise. Particular attention is given to the so-called Zadeh’ PAFMM, which exhibits the most significant noise tolerance among the max-C and min-D PAFMMs besides performing no floating-point arithmetic operations. Computational experiments reveal that Zadeh’s max-C PFAMM, combined with a noise masking strategy, yields a fast and robust classifier with a strong potential for face recognition tasks.

Measuring Typographical Errors in Online Catalogs of Academic Libraries Using Ballard’s List: A Case Study from Egypt

Typographical errors in bibliographic records of online library catalogs are a common troublesome phenomenon, spread all over the world. They can affect the retrieval and identification of items in information retrieval systems and thus prevent users from finding the documents they need. The present study was conducted to measure typographical errors in the online catalog of the Egyptian Universities Libraries Consortium (EULC). The investigation depended on Terry Ballard’s typographical error terms list. The EULC catalog was searched to identify matched erroneous records. The study found that the total number of erroneous records reached 1686, whereas the mean error rate for each record is 11.24, which is very high. About 396 erroneous records (23.49%) have been retrieved from Section C of Ballard’s list (Moderate Probability). The typographical errors found within the abstracts of the study’s sample records represented 35.82%. Omissions were the first common type of errors with 54.51%, followed by transpositions at 17.08%. Regarding the analysis of parts of speech, the study found that 63.46% of errors occur in noun terms. The results of the study indicated that typographical errors still pose a serious challenge for information retrieval systems, especially for library systems in the Arab environment. The study proposes some solutions for Egyptian university libraries in order to avoid typographic mistakes in the future.

A Knowledge Graph-Based Approach for Assembly Sequence Recommendations for Wind Turbines

There are various forms of assembly data sources for wind turbines, which contributes to the lack of a unified and standardized expression. Moreover, the reusability of historical assembly data is low, which leads to the poor reasoning ability of a new product assembly sequence. In this paper, we propose a knowledge graph-based approach for assembly sequence recommendations for wind turbines. First, for the multimodal data (text in process manual, image of tooling, and three-dimensional (3D) model) of assembly, a multi-process assembly information representation model is established to express assembly elements in a unified way. In addition, knowledge extraction methods for different modal data are designed to construct a multimodal knowledge graph for wind turbine assembly. Further, the retrieval of similar assembly process items based on the bidirectional encoder representation from transformers joint graph-matching network (BERT-GMN) is proposed to predict the assembly sequence subgraphs. Also, a Semantic Web Rule Language (SWRL)-based assembly process items inference method is proposed to automatically generate subassembly sequences by combining component assembly relationships. Then, a multi-objective sequence optimization algorithm for the final assembly is designed to output the optimal assembly sequences. Finally, taking the VEU-15 wind turbine as the object, the effectiveness of the assembly process information modeling and part multi-source information representation is verified. Sequence recommendation results are better quality compared to traditional assembly sequence planning algorithms. It provides a feasible solution for wind turbine assembly to be optimized from multiple objectives simultaneously.

Hippocampal activity predicts contextual misattribution of false memories

Failure of contextual retrieval can lead to false recall, wherein people retrieve an item or experience that occurred in a different context or did not occur at all. Whereas the hippocampus is thought to play a crucial role in memory retrieval, we lack understanding of how the hippocampus supports retrieval of items related to a target context while disregarding related but irrelevant information. Using direct electrical recordings from the human hippocampus, we investigate the neural process underlying contextual misattribution of false memories. In two large datasets, we characterize key physiological differences between correct and false recalls that emerge immediately prior to vocalization. By differentiating between false recalls that share high or low contextual similarity with the target context, we show that low-frequency activity (6 to 18 Hz) in the hippocampus tracks similarity between the current and retrieved context. Applying multivariate decoding methods, we were able to reliably predict the contextual source of the to-be-recalled item. Our findings elucidate one of the hallmark features of episodic memory: our ability to distinguish between memories that were formed on different occasions.

FuseBot: mechanical search of rigid and deformable objects via multi-modal perception

Mechanical search is a robotic problem where a robot needs to retrieve a target item that is partially or fully-occluded from its camera. State-of-the-art approaches for mechanical search either require an expensive search process to find the target item, or they require the item to be tagged with a radio frequency identification tag (e.g., RFID), making their approach beneficial only to tagged items in the environment. We present FuseBot, the first robotic system for RF-Visual mechanical search that enables efficient retrieval of both RF-tagged and untagged items in a pile. Rather than requiring all target items in a pile to be RF-tagged, FuseBot leverages the mere existence of an RF-tagged item in the pile to benefit both tagged and untagged items. Our design introduces two key innovations. The first is RF-Visual Mapping, a technique that identifies and locates RF-tagged items in a pile and uses this information to construct an RF-Visual occupancy distribution map. The second is RF-Visual Extraction, a policy formulated as an optimization problem that minimizes the number of actions required to extract the target object by accounting for the probabilistic occupancy distribution, the expected grasp quality, and the expected information gain from future actions. We built a real-time end-to-end prototype of our system on a UR5e robotic arm with in-hand vision and RF perception modules. We conducted over 200 real-world experimental trials to evaluate FuseBot and compare its performance to a state-of-the-art vision-based system named X-Ray (Danielczuk et al., in: 2020 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, 2020). Our experimental results demonstrate that FuseBot outperforms X-Ray’s efficiency by more than 40% in terms of the number of actions required for successful mechanical search. Furthermore, in comparison to X-Ray’s success rate of 84%, FuseBot achieves a success rate of 95% in retrieving untagged items, demonstrating for the first time that the benefits of RF perception extend beyond tagged objects in the mechanical search problem.

Data Quality- and Utility-Compliant Anonymization of Common Data Model-Harmonized Electronic Health Record Data: Protocol for a Scoping Review.

The anonymization of Common Data Model (CDM)-converted EHR data is essential to ensure the data privacy in the use of harmonized health care data. However, applying data anonymization techniques can significantly affect many properties of the resulting data sets and thus biases research results. Few studies have reviewed these applications with a reflection of approaches to manage data utility and quality concerns in the context of CDM-formatted health care data. Our intended scoping review aims to identify and describe (1) how formal anonymization methods are carried out with CDM-converted health care data, (2) how data quality and utility concerns are considered, and (3) how the various CDMs differ in terms of their suitability for recording anonymized data. The planned scoping review is based on the framework of Arksey and O'Malley. By using this, only articles published in English will be included. The retrieval of literature items should be based on a literature search string combining keywords related to data anonymization, CDM standards, and data quality assessment. The proposed literature search query should be validated by a librarian, accompanied by manual searches to include further informal sources. Eligible articles will first undergo a deduplication step, followed by the screening of titles. Second, a full-text reading will allow the 2 reviewers involved to reach the final decision about article selection, while a domain expert will support the resolution of citation selection conflicts. Additionally, key information will be extracted, categorized, summarized, and analyzed by using a proposed template into an iterative process. Tabular and graphical analyses should be addressed in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. We also performed some tentative searches on Web of Science for estimating the feasibility of reaching eligible articles. Tentative searches on Web of Science resulted in 507 nonduplicated matches, suggesting the availability of (potential) relevant articles. Further analysis and selection steps will allow us to derive a final literature set. Furthermore, the completion of this scoping review study is expected by the end of the fourth quarter of 2023. Outlining the approaches of applying formal anonymization methods on CDM-formatted health care data while taking into account data quality and utility concerns should provide useful insights to understand the existing approaches and future research direction based on identified gaps. This protocol describes a schedule to perform a scoping review, which should support the conduction of follow-up investigations. PRR1-10.2196/46471.

Are two words recalled or recognised as one? How age-of-acquisition affects memory for compound words

Are two words recalled or recognised as one? How age-of-acquisition affects memory for compound words

Do partial and distributed tests enhance new learning?

Testing facilitates subsequent learning of new information, a phenomenon known as the forward testing effect. The effect is often investigated in multilist procedures, where studied lists are followed by a retrieval test, or a control task such as restudying, and learning is compared on the final list. In most studies of the effect, tests include all material from the preceding list. We report four experiments, three of which were preregistered, to determine whether tests that are partial (not including all studied items) and distributed (including retrieval of items from earlier lists) are effective in enhancing new learning. The results show that testing of all studied material is not necessary to produce beneficial effects on new learning or to reduce intrusions. The beneficial effects of testing were substantially mediated by reduced proactive interference. Importantly, there was minimal evidence that the forward learning benefits of partial and distributed tests are offset by a cost to untested items via retrieval-induced forgetting. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Typicality in the brain during semantic and episodic memory decisions

Concept typicality is a key semantic dimension supporting the categorical organization of items based on their features, such that typical items share more features with other members of their category than atypical items, which are more distinctive. Typicality effects manifest in better accuracy and faster response times during categorization tasks, but higher performance for atypical items in episodic memory tasks, due to their distinctiveness. At a neural level, typicality has been linked to the anterior temporal lobe (ATL) and the inferior frontal gyrus (IFG) in semantic decision tasks, but patterns of brain activity during episodic memory tasks remain to be understood. We investigated the neural correlates of typicality in semantic and episodic memory to determine the brain regions associated with semantic typicality and uncover effects arising when items are reinstated during retrieval. In an fMRI study, 26 healthy young subjects first performed a category verification task on words representing typical and atypical concepts (encoding), and then completed a recognition memory task (retrieval). In line with previous literature, we observed higher accuracy and faster response times for typical items in the category verification task, while atypical items were better recognized in the episodic memory task. During category verification, univariate analyses revealed a greater involvement of the angular gyrus for typical items and the inferior frontal gyrus for atypical items. During the correct recognition of old items, regions belonging to the core recollection network were activated. We then compared the similarity of the representations from encoding to retrieval (ERS) using Representation Similarity Analyses. Results showed that typical items were reinstated more than atypical ones in several regions including the left precuneus and left anterior temporal lobe (ATL). This suggests that the correct retrieval of typical items requires finer-grained processing, evidenced by greater item-specific reinstatement, which is needed to resolve their confusability with other members of the category due to their higher feature similarity. Our findings confirm the centrality of the ATL in the processing of typicality while extending it to memory retrieval.

MIRN: A multi-interest retrieval network with sequence-to-interest EM routing.

Vector-based retrieval have been widely adopted to process online users' diverse interests for recommendations. However, most of them utilize a single vector to represent user multiple interests (UMI), inevitably impairing the accuracy and diversity of item retrieval. In addition, existing work often does not take into account the scale and speed of the model, and high-dimensional user representation vectors need high computation cost, leading to inefficient item retrieval. In this paper, we propose a novel lightweight multi-interest retrieval network (MIRN) by incorporating sequence-to-interest Expectation Maximization (EM) routing to deal with users' multiple interests. By leveraging representation ability of the Capsule network, we design a multi-interest representation learning module that clusters multiple Capsule vectors from the user's behavior sequence to represent each of their interests respectively. In addition, we introduce a composite capsule clustering strategy for the Capsule network framework to reduce the scale of the network model. Furthermore, a Capsule-aware module incorporating an attention mechanism has been developed to guide model training by adaptively learning multiple Capsule vectors of user representations. The experimental results demonstrate MIRN outperforms the state-of-the-art approaches for item retrieval and gains significant improvements in terms of metric evaluations.