Similar Items Research Articles

Heterogeneous Hypergraph Neural Network for Social Recommendation using Attention Network

Graph neural networks (GNNs) have been used extensively as a backbone for social recommendation. However, their application to a diverse range of situations is still rather limited. This is because graph structures only leverage pairwise user relationships. They cannot capture the higher-order relationships so common in the real world, and ignoring the interest friends and strangers might have in similar items is severely hampering the expressiveness of the current graph-based recommendation models. Hence, in this paper, we outline a heterogeneous hypergraph neural network for social recommendation called HHGSA that incorporates an attention network to address these issues. The hypergraph is able to represent higher-order relationships through five motifs: friend and stranger item appeal, item similarity, user similarity based on interactions with items, and social relations. Two modules, the attentive vertex aggregation module, and the attentive hyperedge aggregation module, capture user and item attention. In addition, it has been discovered that similar items have identical appeal when displayed to users. A GNN aggregates the user embedding data, including information about the friend and stranger and item embeddings. Finally, information about users and items is aggregated for social recommendations. Extensive experiments on four datasets demonstrate that the HHGSA model outperforms a wide range of baselines and can significantly improve the accuracy of recommendations.

The Cost of Operating: Analysis of Single-Use Instrument Costs for Craniotomies.

Introduction All-cause craniotomies comprise a significant portion of neurosurgical practice as well as hospital costs. While some instruments are reusable with a fixed cost, price variability for similar single-use instruments exists. A better understanding of these cost variations within cranial procedures can better inform operating physicians to be cost-sensitive stewards. Objective In this study, we examine how single-use items contribute to the overall cost of cranial procedures. Methods A de-identified institutional database containing records of all single-use items from craniotomies between July 1, 2019, and June 30, 2020, was subject to a longitudinal analysis by three independent parties (one senior surgeon, one resident, and one medical student). Four hundred and sixty-nine unique single-use items were identified and classified by function. Similar items were combined, and a range of costs was provided. Three sample cases with sum costs were reviewed for cost division and primary contributors. Results The category with the highest median cost across all cases was non-specialty implants comprising dural onlays, mesh, aneurysm clips, and plates. The category with the lowest median cost was personal protective equipment. The items with the most cost variability were sterile surgical patties due to the variety of sizes and preset multipacks. The proportion of cost generators varies from craniotomy indication. Conclusion While institution dependent, awareness of cost generators in cranial cases is important for economic stewardship. For single-use items, costs are highly variable and not insignificant.Surgeons and neurosurgical departments are responsible for allocating single-use items in a responsibleand efficient manner.

Similarity-Based Compression in Working Memory: Implications for Decay and Refreshing Models

AbstractThe ability to compress information is a fundamental cognitive function. It allows working memory (WM) to overcome its severely limited capacity. Recent evidence suggests that the similarity between items can be used to compress information, leading to a rich pattern of behavioral results. This work presents a series of simulations showing that this rich pattern of WM performance is captured using the principles of TBRS*, a decay and refreshing architecture. By assuming that similar items are compressed, the architecture can explain the beneficial effect of similarity on the items themselves. The architecture also explains the fact that when similar items are mixed with dissimilar items, this provides a proactive—but no retroactive—benefit on WM performance. In addition, the model captures fine-grained patterns of transposition errors recently reported. Several analyses are reported showing the robustness of the model’s predictions. We reached the conclusion that decay and refreshing theories provide a plausible explanation for compression effects in WM. These conclusions are discussed in light of recent experimental results. The importance of computational modeling for testing theories is emphasized.

Reduced benefit of novelty detection on subsequent memory judgments in paranoia.

Novelty detection is critical to the effective employment of memory-guided behavior. While recent work has found impaired novelty detection in subclinical paranoia, other studies show different patterns. Here, we tested the hypothesis that those higher in paranoia receive less benefit from novelty in their immediate environment when making subsequent mnemonic judgments. Using a continuous recognition task (comprising Old, New, and Similar items) in a sample drawn from an online marketplace (N = 450), we found that Similar trial performance was generally enhanced by preceding judgments of "New" versus "Old"-replicating prior work. However, paranoia was associated with a reduction of this novelty-based enhancement-a novel finding. Those experiencing paranoia may thus less readily use novelty to adjudicate between the competing mnemonic processes of encoding and retrieval. We interpret this finding in light of the role of novelty detection in maintaining adaptive predictive models, suggesting that this deficit may reduce coherence between one's active predictive model and one's environment, thereby contributing to perceptions of the world as unduly uncertain and threatening. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Using retest-adjusted correlations as indicators of the semantic similarity of items.

Determining whether different items provide the same information or mean the same thing within a population is a central concern when determining whether different scales or constructs are overlapping or redundant. In the present study, we suggest that retest-adjusted correlations provide a valuable means of adjusting for item-level unreliability. More exactly, we suggest dividing the estimated correlation between items X and Y measured over measurement interval |d| by the average retest correlations of the items over the same measurement interval. For instance, if we correlate scores from items X and Y measured 1 week apart, their retest-adjusted correlation is estimated by using their 1-week retest correlations. Using data from four inventories, we provide evidence that retest-adjusted correlations are significantly better predictors of whether two items are consensually regarded as "meaning the same thing" by judges than raw-score correlations. The results may provide the first empirical evidence that Spearman's (1904, 1910) suggested reliability adjustment do-in certain (perhaps very constrained!) circumstances-improve upon raw-score correlations as indicators of the informational or semantic equivalence of different tests. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Efficient federated item similarity model for privacy-preserving recommendation

Previous federated recommender systems are based on traditional matrix factorization, which can improve personalized service but are vulnerable to gradient inference attacks. Most of them adopt model averaging to fit the data heterogeneity of federated recommender systems, requiring more training costs. To address privacy and efficiency, we propose an efficient federated item similarity model for the heterogeneous recommendation, called FedIS, which can train a global item-based collaborative filtering model to eliminate user feature dependencies. Specifically, we extend the neural item similarity model to the federated model, where each client only locally optimizes the shared item feature matrix. We then propose a fast-convergent federated aggregation method inspired by meta-learning to address heterogeneous user updates and accelerate the convergence of global training. Furthermore, we propose a two-stage perturbation method to protect both local training and transmission while reducing communication costs. Finally, extensive experiments on four real-world datasets validate that FedIS can provide more competitive performance on federated recommendations. Our proposed method also shows significant training efficiency with less performance degradation.

Separate but not independent: Behavioral pattern separation and statistical learning are differentially affected by aging

Our brains are capable of discriminating similar inputs (pattern separation) and rapidly generalizing across inputs (statistical learning). Are these two processes dissociable in behavior? Here, we asked whether cognitive aging affects them in a differential or parallel manner. Older and younger adults were tested on their ability to discriminate between similar trisyllabic words and to extract trisyllabic words embedded in a continuous speech stream. Older adults demonstrated intact statistical learning on an implicit, reaction time-based measure and an explicit, familiarity-based measure of learning. However, they performed poorly in discriminating similar items presented in isolation, both for episodically-encoded items and for statistically-learned regularities. These results indicate that pattern separation and statistical learning are dissociable and differentially affected by aging. The acquisition of implicit representations of statistical regularities operates robustly into old age, whereas pattern separation influences the expression of statistical learning with high representational fidelity and is subject to age-related decline.

Extend Tversky’s Ratio Model to an Asymmetric Similarity Measurement Model with Three Conditional Parameters: 3p-ASM Model

Generally, the similarity between objects is often measured by symmetric operators, such as Cosine, Dice, and Jaccard similarity. However, the ratio model originally proposed by Tversky pointed out that the similarity using the feature matching method tends to be asymmetric. Furthermore, in many practical situations, the existing similarity measures using the feature matching method have some limitations: the calculation formulas are symmetrical, it is not intuitive based on binary features, and it is not easy to calculate based on fuzzy sets. To overcome such limitations, some other asymmetries have proposed to directly combine Tversky’s ratio model with the classical symmetric similarity metric, which in turn leads to the inability to identify different features between the compared objects and affects their similarity accuracy. Therefore, aiming to avoid these, this paper will focus on extending Tversky’s ratio model to a series of 3-parameter asymmetric similarity metrics (3p-ASM), using three conditional parameters to describe both common and different features. First, the set-based 3p-ASM is achieved due to the general and fuzzy set-theoretic operations, when estimating features in left{ 0,1right} and left[ 0,1right] , respectively. Then, considering that the estimated values of features can also be expressed as vectors, it will be extended to the vector-based 3p-ASM. Finally, a vector form of 3p-ASM is compared with existing classical methods and a comparative analysis is performed to demonstrate its effectiveness and validity. It is then applied to the KNN model in order to select the most similar items.

Muscular Kevlar aerogel fibers appealing to thermal insulation with a symbiotic core-sheath structure

High-performance aerogels offer a broad application prospect owing to their various features such as high specific surface area and attractive thermal insulation. However, their porous structure confers weak mechanical properties to aerogels. The strategy of coaxial wet-spinning was implemented to strengthen Kevlar aerogel fibers and improve their usability. Prepared Kevlar aerogel fibers have a symbiotic core–sheath structure, with a blurred division between core and sheath. In addition, the breaking stress of Kevlar aerogel fibers can reach 2.6 MPa, which is four times that of the core, presenting a significant improvement in tensile strength compared to those in previous studies. Moreover, Kevlar aerogel fibers exhibit superior thermal stability, which is inherited from Kevlar fibers, and decomposed after 480 °C. Moreover, their high porosity and high specific area (∼400.5 m2·g−1) endow them with outstanding thermal insulation properties. When fabrics woven from these fibers are exposed to a 300 °C hot plate, the temperature on the surface can be 110 °C lower, resulting in a significant temperature gradient of 83 °C·mm−1. In total, the resultant Kevlar aerogel fibers with a symbiotic core–sheath structure are used as fillers for protective clothing or fire insulation blankets and similar items.

Allergic Contact Dermatitis Due to Chemical Agents and Microbial Contamination in Cosmetic Products: A Review

Cosmetics are items that are used to enhance the appearance of skin complexion. Common cosmetics include lipstick, foundation, eyeliner, contour, skin primer, blushes, and other similar items. Cosmetics may cause allergies to the human body, such as bronchial asthma, allergic rhinitis, allergic dermatitis, allergic conjunctivitis (eye inflammation), and anaphylaxis. Cosmetic allergies are becoming more common, owing to the preservatives and perfumes utilized in the goods. Microbial contamination, viz. bacteria (Staphylococcus, Escherichia, Bacillus, Pseudomonas etc.), molds and yeasts are also associated with cosmetics, resulting in allergic contact dermatitis (ACD). Patches and skin prick tests were used to quickly identify allergies. Once particular allergies have been discovered, patients should be educated about which drugs may be taken safely in the future. This review aims to provide cosmetology information to understand the causative agents, detection tests, and management to enhance the knowledge about ACD due to cosmetics.

EAQ: Enabling Authenticated Complex Query Services in Sustainable-Storage Blockchain

The data query service is urgently required in sustainable-storage blockchain, where full nodes store the entire transaction data while light nodes only store block headers. Queries invariably seek data with multiple attributes. However, no existing method provides a unified authenticated data structure (ADS) to support complex query operators (e.g., range queries and data object queries) on multiattribute blockchain data. In this paper, we propose a framework EAQ that effectively supports both fast data queries on multiple attributes and authentication of the query result. We propose a new ADS, called the MR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\mathit{Bloom}$</tex-math></inline-formula> -tree, based on the Bloom filter (BF) and Merkle R-tree. We prove the decomposability of BFs, which enables the BF to be seamlessly incorporated with the Merkle R-tree. This ADS enables range-level search using multidimensional attribute ranges and object-level search using BFs. This ADS also supports querying and proving inexistent data objects. To reduce storage overhead, we improve the MR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\mathit{Bloom}$</tex-math></inline-formula> -tree using the suppressed BF structure, which constructs only one BF independent of the number of attributes. To manage string attributes, we transform them into discrete numerical attributes using density-based clustering to represent similar items with close numerical values. Experiments show that the proposed framework achieves promising results.

VOICE ASSISTANT FOR FINDING ITEMS IN CLOTHING STORE

The paper presents the use of machine learning in audio file processing, the use of neural networks to recognize voice commands, and the development of a voice assistant for finding products in a clothing store. &#x0D; The purpose of the work is to develop a voice assistant for finding items in a clothing store.&#x0D; As a result, a dataset containing 30 categories and 3095 audio recordings was created, a neural network model was trained using the collected data, and an accuracy of 96.02% was achieved; WER: 0.0398; CER: 0.0087. The model was integrated with the search system into the voice assistant API, which allows you to record from a microphone, convert audio to text, break the text into keywords, and search the database using the obtained keywords. The speech recognition system has shown stable and high accuracy when recording from a microphone. This provides users with reliable and accurate recognition results even when using simple microphones. Search allows you to find results by keywords and names of items, and there is a function to recommend similar items if nothing was found at the user's request. The system's flexibility allows it to understand language contexts and does not depend on the order of words in a phrase, as the search is performed by keywords separately. The research analyzes the literature and compares existing approaches to voice command recognition. The methods of audio signal processing are described. The problem of searching for items in a clothing store was analyzed, and the current state of the market and demand for the technology were investigated. The practical value is that the developed voice assistant is specialized and optimized for searching for goods in a clothing store, allows solving the task of finding goods according to specified criteria, and simplifies the search task for different groups of people.

Social vulnerability indices: a scoping review

BackgroundSocial vulnerability occurs when the disadvantage conveyed by poor social conditions determines the degree to which one’s life and livelihood are at risk from a particular and identifiable event in health, nature, or society. A common way to estimate social vulnerability is through an index aggregating social factors. This scoping review broadly aimed to map the literature on social vulnerability indices. Our main objectives were to characterize social vulnerability indices, understand the composition of social vulnerability indices, and describe how these indices are utilized in the literature.MethodsA scoping review was conducted in six electronic databases to identify original research, published in English, French, Dutch, Spanish or Portuguese, and which addressed the development or use of a social vulnerability index (SVI). Titles, abstracts, and full texts were screened and assessed for eligibility. Data were extracted on the indices and simple descriptive statistics and counts were used to produce a narrative summary.ResultsIn total, 292 studies were included, of which 126 studies came from environmental, climate change or disaster planning fields of study and 156 studies were from the fields of health or medicine. The mean number of items per index was 19 (SD 10.5) and the most common source of data was from censuses. There were 122 distinct items in the composition of these indices, categorized into 29 domains. The top three domains included in the SVIs were: at risk populations (e.g., % older adults, children or dependents), education, and socioeconomic status. SVIs were used to predict outcomes in 47.9% of studies, and rate of Covid-19 infection or mortality was the most common outcome measured.ConclusionsWe provide an overview of SVIs in the literature up to December 2021, providing a novel summary of commonly used variables for social vulnerability indices. We also demonstrate that SVIs are commonly used in several fields of research, especially since 2010. Whether in the field of disaster planning, environmental science or health sciences, the SVIs are composed of similar items and domains. SVIs can be used to predict diverse outcomes, with implications for future use as tools in interdisciplinary collaborations.

“The Turquoise Was Brought From Chorasmia” Mining, Empire, and the People of the Steppes across the Achaemenid Northeastern Borderlands

Abstract Two main scholarly trends dominate the study of the relations between the Northeastern frontier zone(s) of the Achaemenid Empire and the steppes: the assessment of the military strategies to control the Saka and the development of artistic currents within and beyond the Empire through diplomatic relations and gift-giving. This paper makes the case for a more complex and dynamic scenario, in which the Central Asian borderlands transpire to have been a fertile ground for experimentation and innovation (not only of strife and rebellion), while local actors are given back their agency. In doing so, it first of all focuses on the extraction and working of precious stones. Secondly, recent research on Achaemenid archival materials shall be discussed which suggest the importance of trade in these and similar items for both imperial agents and local elites. Thirdly, and finally, the paper considers material evidence originating from the Empire but found beyond its Northeastern territories as far as China.

Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering

Metaheuristic optimization algorithms play an essential role in optimizing problems. In this article, a new metaheuristic approach called the drawer algorithm (DA) is developed to provide quasi-optimal solutions to optimization problems. The main inspiration for the DA is to simulate the selection of objects from different drawers to create an optimal combination. The optimization process involves a dresser with a given number of drawers, where similar items are placed in each drawer. The optimization is based on selecting suitable items, discarding unsuitable ones from different drawers, and assembling them into an appropriate combination. The DA is described, and its mathematical modeling is presented. The performance of the DA in optimization is tested by solving fifty-two objective functions of various unimodal and multimodal types and the CEC 2017 test suite. The results of the DA are compared to the performance of twelve well-known algorithms. The simulation results demonstrate that the DA, with a proper balance between exploration and exploitation, produces suitable solutions. Furthermore, comparing the performance of optimization algorithms shows that the DA is an effective approach for solving optimization problems and is much more competitive than the twelve algorithms against which it was compared to. Additionally, the implementation of the DA on twenty-two constrained problems from the CEC 2011 test suite demonstrates its high efficiency in handling optimization problems in real-world applications.

ЕЩЕ ОДНА НАХОДКА ВОСТОЧНОГО ЗЕРКАЛА СО СФИНКСАМИ НА СРЕДНЕМ ЕНИСЕЕ

The article is devoted to the publication of another metal button mirror with paired figures of sphinxes and a floral ornament with a Kufic inscription, recently discovered on the territory of the Middle Yenisei. In this region, similar items are not only rare (exhibits of the Minusinsk and Abakan museums), but are also markers of the northern border of the distribution of these items in the Mongolian time. The сlarity of the decoration details of the product gives every reason to consider it far from being the latest copy from the original. Sufficiently good preservation of the images on the mirror (epigraphics, fantastic animals and floral decorations) makes it possible to analyze in detail not only individual artistic details, but also to make a probable attribution of the origin and chronology of this object. The peculiarities of the style of the Kufic inscription and images of sphinxes make it possible to date the mirror no later than the 11th–12th centuries, and also to include it in the circle of objects produced in Iran or Central Asia. The absence of such imported items in the south of Western Siberia north of the middle reaches of the Yenisei River is probably due to the historical features of the cultural ties of the medieval population in the initial period of the 2nd millennium AD. Overall, the new find of a mirror with a Kufic inscription and a paired image of the sphinxes on the background of a floral ornament makes it possible to clarify the local and general features of the distribution of such items in Eurasia in the Middle Ages. This, in turn, provides certain grounds for reconstructing the dynamics of the changes in the external relations of Southern Siberia over the past millennium.

Integrated Personalized Book Recommendation using Social Media Analysis

Today, most e-commerce sites use product-specific recommendation systems to better user experience. The algorithm used by such sites is - item-toitem collaborative filtering. This matches each user who has purchased and rated items to similar items and then combines those similar items into a recommendation list. The solution proposed in this paper is an integrated book recommendation system that maps the user’s highly rated books with books of a similar genre, maps the interactions of said user on social media to assess the kind of books one is interested in, and considers the collaborative filtering or association mapping between the items. In this model, authors used datasets for the same Goodreads book collection, Amazon and Goodreads reviews, transaction histories, and Twitter data. The proposed solution shall use a weighted measure, k-means clustering, and sentiment analysis. The collaborative filtering will be done using the Apriori mechanism to develop an integrated book recommendation list. The result is a list of 10 books that are recommended for a particular user. The proposed model met 80 percent of the user’s expected recommendations, whereas the simple collaborative model only met 60 percent of the user’s expectations. The collaborative model consisted majority of books by the same author or of a complete contrast genre as the method only considers the choice of other similar users and not similar books. So, the proposed integrated recommendation system is more accurate in its recommendations than a simple collaborative system. This model helps firms recommend the best possible book for book lovers. It also helps book lovers to find the best content as per their interests.

Item Matching Model in E-Commerce: How Users Benefit

Abstract Research purpose. During the last decades, e-commerce sales have been rocketing, and this tendency is expected to increase over the following years. Due to the digital nature of e-commerce, one actual item can be sold on various e-commerce platforms, which leads to the exponential growth of the number of propositions. At the same time, the title and description of this item might differ. All these facts make more complicated for customers the process of searching on online platforms and change business approaches to the development of competitive strategy by e-commerce companies. The research question is how we can apply a machine learning algorithm to detect, based on the product information such as title and description, whether the items are actually relevant to the same product. Methodology. We suggest an approach that is based on a flexible textual data pipeline and the usage of a machine-learning model ensemble. Each step of the data processing is adjustable in dependence on domain issues and data features because we can achieve better results in solving the item-matching task. Findings. The item-matching model is developed. The proposed model is based on the semantic closeness of text descriptions of items and the usage of the core of keywords to present the reference item. Practical implications. We suggest an approach to improving the item searching process on different e-commerce platforms by dividing the process into two steps. The first step is searching for the related items among the set of reference items according to user preferences. The reference item description is created based on our item-matching model. The second step is surfing proposals of similar items on chosen e-commerce platforms. This approach can benefit buyers and sellers in various aspects, such as a low-price guarantee, a flexible strategy of similar products shown, and appropriate category-choosing recommendations.

Episodic memory through a social and emotional lens.

Negative emotion enhances item memory but may reduce associative memory. Social information may also enhance memory, yet how social and emotional cues combine to affect memory for focal and surrounding details is unknown. Gender differences may also exist, but whether women and men encode and remember negative and social information differently is unclear. This study examines how social information contributes to or interacts with emotion to influence item and associative memory, and whether gender plays a role in how social and/or emotional information is recognized. Discovery and replication samples (N = 706) were recruited. Stimuli included (1) images with varying social and emotional content categorized into four conditions: negative social, negative nonsocial, neutral social, neutral nonsocial and (2) neutral images of objects paired with target stimuli to assess associative memory. Participants viewed the image pairings (Encoding). Twenty-four hours later, item and associative recognition memory were tested. Item recognition memory was better for negative vs. neutral and social vs. nonsocial images. By contrast, associative recognition memory was worse for negative vs. neutral, but better for social vs. nonsocial images. Finally, women demonstrated similar item memory performance compared to men but better associative recognition memory performance overall compared to men. Emotional and social cues impart distinct effects on how we form holistic episodic memories, highlighting the importance of considering these critical factors when striving to understand how and what we remember. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Validity and reliability of the Integrated Palliative Care Outcome Scale for non-cancer patients.

This study assessed the validity and reliability of the Integrated Palliative Care Outcome Scale for non-cancer patients. We recruited 223 non-cancer patients receiving palliative care and their healthcare providers (222) across two home care facilities and two hospitals for a cross-sectional study. We assessed the construct validity and known-group validity of the Integrated Palliative Care Outcome Scale. The weighted kappa and interclass correlation coefficients were assessed to ascertain reliability. The scale scores were significantly higher for the 'non-stable' group (worsening condition group) measured in the palliative care phase than for the 'stable' group (P < 0.001). Regarding validity, Spearman's correlations between similar items on the Integrated Palliative Care Outcome Scale and Edmonton Symptom Assessment System ranged from 0.61 to 0.94. Regarding reliability, the weighted kappa coefficients ranged from 0.53 to 0.81 for patients and from 0.58 to 0.90 for healthcare providers. For inter-rater reliability between patients and healthcare providers, the weighted kappa coefficients for each item ranged from 0.03 to 0.42. This study confirmed the validity and reliability of the Integrated Palliative Care Outcome Scale for non-cancer patients requiring palliative care. However, the inter-rater reliability indicates poor agreement between the assessments of patients and healthcare providers. This highlights the discrepancies between both their assessments and the importance of the patient's assessment. Geriatr Gerontol Int 2023; 23: 517-523.