Small Set Of Items Research Articles

A bottom-up approach dramatically increases the predictability of body mass from personality traits.

Personality traits consistently relate to and allow predicting body mass index (BMI), but these associations may not be adequately captured with existing inventories' domains or facets. Here, we aimed to test the limits of how accurately BMI can be predicted from and described with personality traits. We used three large datasets (combined N ≈ 100,000) with nearly 700 personality assessment items to (a) empirically identify clusters of personality traits linked to BMI and (b) identify relatively small sets of items that predict BMI as accurately as possible. Factor analysis revealed 14 trait clusters showing well-established personality trait-BMI associations (disorganization, anger) and lesser-known or novel ones (altruism, obedience). Most of items' predictive accuracy (up to r = .24 here but plausibly much higher) was captured by relatively few items. Brief scales that predict BMI have potential clinical applications-for instance, screening for risk of excessive weight gain or related complications.

Referrals to Home Visiting: Current Practice and Unrealized Opportunities.

Evidence supports ongoing investment in maternal and early childhood home visiting in the US. Yet, a small fraction of eligible families accesses these services, and little is known about how families are referred. This report describes priority populations for home visiting programs, the capacity of programs to enroll more families, common sources of referrals to home visiting, and sources from which programs want to receive more referrals. We conducted a secondary analysis of data from a national web-based survey of members of the Home Visiting Applied Research Collaborative (HARC), focusing on a small set of items that directly addressed study aims. Survey respondents (N = 87) represented local programs implementing varying home visiting models diverse in size and geographic context. Programs prioritized enrollment of pregnant women; parents with mental health, substance abuse or intimate partner violence concerns; teen parents; and children with developmental delays or child welfare involvement. Most respondents reported capacity to enroll more families in their programs. Few reported receiving any referrals from pediatric providers, child welfare, early care and education, or TANF/other social services. Most desired more referrals, especially from healthcare providers, WIC, and TANF/other social services. Given that most programs have the capacity to serve more families, this study provides insights regarding providers with whom home visiting programs might strengthen their referral systems.

Profiles of Quality in Three Distinct Early Childhood Programs Using the Brief Early Childhood Quality Inventory (BEQI)

Quality early childhood care and education (ECCE) is important for young children’s holistic healthy development. As ECCE scales, contextually relevant and feasible measurement is needed to inform policy and programs on strengths and areas for improvement. However, few measures have been designed for use across diverse contexts. Drawing on principles of mixed methods design, this study reports on a new approach to ECCE quality measurement: the Brief Early Childhood Quality Inventory. Using data from the USA, Liberia, and Colombia, results indicate variation in the items perceived as highly relevant to each setting and in the characteristics of classrooms including the degree of child autonomy, the types of activities, and in child/educator interactions and dialogue. However, despite this variation, a small set of items indicate potential functionality as cross-country anchor items. Findings lend support to the idea that quality measures can have some common elements with room for adaptation within and across settings. Future work in this area should address the possibility that the significance of these practices for child development also varies across settings.

Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval

Abstract Current state-of-the-art approaches to cross- modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image. While offering unmatched retrieval performance, such models: 1) are typically pretrained from scratch and thus less scalable, 2) suffer from huge retrieval latency and inefficiency issues, which makes them impractical in realistic applications. To address these crucial gaps towards both improved and efficient cross- modal retrieval, we propose a novel fine-tuning framework that turns any pretrained text-image multi-modal model into an efficient retrieval model. The framework is based on a cooperative retrieve-and-rerank approach that combines: 1) twin networks (i.e., a bi-encoder) to separately encode all items of a corpus, enabling efficient initial retrieval, and 2) a cross-encoder component for a more nuanced (i.e., smarter) ranking of the retrieved small set of items. We also propose to jointly fine- tune the two components with shared weights, yielding a more parameter-efficient model. Our experiments on a series of standard cross-modal retrieval benchmarks in monolingual, multilingual, and zero-shot setups, demonstrate improved accuracy and huge efficiency benefits over the state-of-the-art cross- encoders.1

Electrophysiological Signatures of Numerosity Encoding in a Delayed Match-to-Sample Task.

The number of elements in a small set of items is appraised in a fast and exact manner, a phenomenon called subitizing. In contrast, humans provide imprecise responses when comparing larger numerosities, with decreasing precision as the number of elements increases. Estimation is thought to rely on a dedicated system for the approximate representation of numerosity. While previous behavioral and neuroimaging studies associate subitizing to a domain-general system related to object tracking and identification, the nature of small numerosity processing is still debated. We investigated the neural processing of numerosity across subitizing and estimation ranges by examining electrophysiological activity during the memory retention period in a delayed numerical match-to-sample task. We also assessed potential differences in the neural signature of numerical magnitude in a fully non-symbolic or cross-format comparison. In line with behavioral performance, we observed modulation of parietal-occipital neural activity as a function of numerosity that differed in two ranges, with distinctive neural signatures of small numerosities showing clear similarities with those observed in visuospatial working memory tasks. We also found differences in neural activity related to numerical information in anticipation of single vs. cross-format comparison, suggesting a top-down modulation of numerical processing. Finally, behavioral results revealed enhanced performance in the mixed-format conditions and a significant correlation between task performance and symbolic mathematical skills. Overall, we provide evidence for distinct mechanisms related to small and large numerosity and differences in numerical encoding based on task demands.

Different Types of Subitizing Activity: A Teaching Experiment with Preconservers

Abstract Introduction: Subitizing, a quick apprehension of the numerosity of a small set of items, is consistently utilized to support early number understanding. Perceptual subitizing is the innate ability to recognize less than five items without consciously using other mental or mathematical processes. Conceptual subitizing, which requires higher-level abilities, means perceiving the quantities as groups and performing a mental process on them. Research on conceptual and perceptual subitizing indicates some limitations about the activities regarding the children’s early number development. So, MacDonald and Wilkins (2016) developed a framework that explained the types of activities that young children used while subitizing. In this framework, five sets of perceptual subitizing activity were described to explain how young children’s perceptual subitizing activity changed. Besides, two types of conceptual subitizing activities were defined to explain how children’s limited or flexible number understanding related to their subitizing activity. These seven different types of activities characterize the changes in children’s subitizing actions. The study aims to investigate the relationship between children’s number understanding and subitizing activity. Methods: A teaching experiment was conducted with two preschool-aged children to analyze what perceptual and conceptual processes children relied upon when subitizing. The teaching experiment consisted of twenty-six sessions. The interviews were conducted to determine whether children are able to conserve numbers or not, and whether they rely on a variety of different types of subitizing activity or not. After the interviews, 26 teaching sessions were carried out with two preconserver children. Results: In the experimental process, it was observed that the children rely on the color of items, the gap between items, and symmetrical aspects of items when perceptually subitizing. However, they could not manage to transition their subitizing activity from perceptual to conceptual subitizing. The study indicates that children’s subitizing skills were closely related to their number conservation development. Discussion: Based on the findings from this study, for Eren and Beren, subitizing activities were found to be perceptually limited. Specifically, it was found that four types of perceptual subitizing emerged to explain how symmetry, the gap between items, color of items, and canonical patterns promoted strategies that children relied on when constructing number understanding. During the teaching experiment, although these children carried out the activities that required the separating and combining numbers and seeing the relationship between the subgroups and the composite groups, they used perceptual units in this process. The relationship between the number conservation activity and the conceptual subitizing activity requires the coordination of thinking structures related to both ordering and classification. However, it was found that the children could not move from perceptual to the conceptual subitizing. Limitations: As all studies have some limitations, this study has, too. One of the limitations of the study is the sample size/number of participants. But teaching experiments aim to get a deep understanding, studying with a small sample is an obligation. Secondly, this study focused on some compounds of subitizing such as perceptual and conceptual ones. Conclusion: In order to make the transition from perceptual subitizing to conceptual subitizing children should have more experiences with subitizing activities.When designing mathematical games and assessments for young children, being aware of different types of subitizing categories may provide better support children’s number understanding and subitizing.

(2012-6338) Design and Analysis of Acceptance Sampling Plans Based on Intuitionistic Fuzzy Linguistic Terms

Acceptance sampling plans (ASPs) offer inspection of a small set of items from a lot within a predefined plan to procure a certain output quality level with minimum cost in terms of time, effort, and damage to the inspected items. Although traditional ASPs use crisp plan parameters, quality characteristics of the incoming items or human evaluations about inspection process may contain uncertainties and may not always be defined as crisp values in real life problems. The fuzzy set theory (FST) is one of the most popular techniques to model these uncertainties by defining plan parameters as fuzzy numbers. Despite the advantages, traditional fuzzy sets are not flexible enough to model all kinds of uncertainties. For example, it has some disadvantages because of defining the status of any item based on defectiveness or non-defectiveness conditions and presuming the parts as non-defective whose defectiveness is not indeed determined. New extensions of FST can improve the quality of uncertainty modeling of ASPs. Intuitionistic Fuzzy Sets (IFSs) allow slackness for non-determination about the membership and give more sensitive modeling opportunity in human-related evaluations by the help of this ability. Since the inspection procedure of the ASPs depends on human-related judgements, IFSs have been used to define the defectiveness degree of the items in this study. ASPs based on interval-valued IFSs (IVIFSs) have also been designed and some characteristic functions of ASPs, such as acceptance probability ($P_a$), average sample number ($ASN$) and average total inspection ($ATI$) have been reformulated. Intuitionistic binomial and Poisson distributions have been defined to be able to formulate the ASPs. Additionally, the defectiveness of the items has been represented by using linguistic terms to overcome the difficulty of quantifying the verbal evaluation results as numerical measures. The $alpha$-cut technique has been combined with the linguistic approach to allow defining with multiple $alpha$ values for different product segments. Finally, some numerical examples have been presented to analyze the effectiveness of proposed ASPs and discuss the obtained results.

Engineering faster sorters for small sets of items

AbstractSorting a set of items is a task that can be useful by itself or as a building block for more complex operations. That is why a lot of effort has been put into finding sorting algorithms that sort large sets as efficiently as possible. But the more sophisticated and complex the algorithms become, the less efficient they are for small sets of items due to large constant factors. A relatively simple sorting algorithm that is often used as a base case sorter is insertion sort, because it has small code size and small constant factors influencing its execution time. We aim to determine if there is a faster way to sort small sets of items to provide an efficient base case sorter. We looked at sorting networks, at how they can improve the speed of sorting few elements, and how to implement them in an efficient manner using conditional moves. Since sorting networks need to be implemented explicitly for each set size, providing networks for larger sizes becomes less efficient due to increased code sizes. To also enable the sorting of slightly larger base cases, we adapted sample sort to Register Sample Sort, to break down those larger sets into sizes that can in turn be sorted by sorting networks. From our experiments we found that when sorting only small sets of integers, the sorting networks outperform insertion sort by a factor of at least 1.76 for any array size between six and 16, and by a factor of 2.72 on average across all machines and array sizes. When integrating sorting networks as a base case sorter into Quicksort, we achieved far less performance improvements over using insertion sort, which is probably due to the networks having a larger code size and cluttering the L1 instruction cache. The same effect occurs when including Register Sample Sort as a base case sorter for IPSo. But for x86 machines that have a larger L1 instruction cache of 64 KiB or more, we obtained speedups of 12.7% when using sorting networks as a base case sorter in std::sort, and of 5%–6% when integrating Register Sample Sort as a base case sorter into IPSo, each in comparison to using insertion sort as the base case sorter. In conclusion, the desired improvement in speed could only be achieved under special circumstances, but the results clearly show the potential of using conditional moves in the field of sorting algorithms.

Diego’s Number Understanding Development through His Subitizing and Counting

ABSTRACT Subitizing, a quick apprehension of the numerosity of a small set of items, has been found to explain students’ number understanding when counting. We utilized a constructivist teaching experiment methodology to investigate how the counting and subitizing activity of one student, Diego, related to his number understanding (described by his pre-numerical units construction) when solving early number tasks. Subitizing and counting tasks were designed to assess and leverage Diego’s pre-numerical units development. Findings indicated that as Diego’s pre-numerical units included more than one way to construct five, he still relied on external material. Implications of this study suggest that task design should be grounded in unique aspects of student reasoning and should be designed to promote students’ partitioning as well as use of finger patterns, pointing, and number words.

Incrementally updating the high average-utility patterns with pre-large concept

High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns and scalability.

How fast can people refresh and rehearse information in working memory?

Refreshing - briefly attending to an item in working memory - has been proposed as a domain-general maintenance process. According to the time-based resource-sharing (TBRS) theory, people refresh the contents of working memory sequentially at high speed. We measured the speed of refreshing by asking participants to sequentially refresh a small set of items in sync with a metronome, and to adjust the metronome to the fastest speed at which they could refresh. Refreshing speeds converged on about 0.2 s per item for several verbal and visual materials. This time was shorter than the speed of articulatory rehearsal measured with the same method, and - in contrast to rehearsal - did not depend on word length. We sought evidence for people refreshing in sync with the metronome by presenting recognition probes at unpredictable times. We expected that probes matching the just-refreshed item should be recognized faster and more accurately than probes matching other items. This was not the case. A parallel experiment with overt articulatory rehearsal showed poor synchronization of rehearsal with the metronome, suggesting by analogy that refreshing was equally out of sync. The results support the assumption that people can attend sequentially to items in working memory, and monitor this process. This refreshing process is probably faster than rehearsal, but it is unlikely to be as fast as the refreshing process assumed in the TBRS theory.

Small numerosity advantage for sequential enumeration on RSVP stimuli: an object individuation-based account.

Although there is a large literature demonstrating rapid and accurate enumeration of small sets of simultaneously presented items (i.e., subitizing), it is unclear whether this small numerosity advantage (SNA) can also manifest in sequential enumeration. The present study thus has two aims: to establish a robust processing advantage for small numerosities during sequential enumeration using a rapid serial visual presentation (RSVP) paradigm, and to examine the underlying mechanism for a SNA in sequential enumeration. The results indicate that a small set of items presented in fast sequences can be enumerated accurately with a high precision and a SOA (stimulus onset asynchrony)-sensitive capacity limit, essentially generalizing the large literature on small numerosity advantage from spatial domain to temporal domain. A resource competition hypothesis was proposed and confirmed in further experiments. Specifically, sequential enumeration and other cognitive process, such as visual working memory (VWM), compete for a shared resource of object individuation by which items are segregated as individual entities. These results implied that the limited resource of object individuation can be allocated within time windows of flexible temporal scales during simultaneous and sequential enumerations. Taken together, the present study calls for attention to the dynamic aspect of the enumeration process and highlights the pivotal role of object individuation in underlying a wide range of mental operations, such as enumeration and VWM.

Bias of Two-Level Scalability Coefficients and Their Standard Errors.

Two-level Mokken scale analysis is a generalization of Mokken scale analysis for multi-rater data. The bias of estimated scalability coefficients for two-level Mokken scale analysis, the bias of their estimated standard errors, and the coverage of the confidence intervals has been investigated, under various testing conditions. It was found that the estimated scalability coefficients were unbiased in all tested conditions. For estimating standard errors, the delta method and the cluster bootstrap were compared. The cluster bootstrap structurally underestimated the standard errors of the scalability coefficients, with low coverage values. Except for unequal numbers of raters across subjects and small sets of items, the delta method standard error estimates had negligible bias and good coverage. Post hoc simulations showed that the cluster bootstrap does not correctly reproduce the sampling distribution of the scalability coefficients, and an adapted procedure was suggested. In addition, the delta method standard errors can be slightly improved if the harmonic mean is used for unequal numbers of raters per subject rather than the arithmetic mean.

HUOPM: High-Utility Occupancy Pattern Mining.

Mining useful patterns from varied types of databases is an important research topic, which has many real-life applications. Most studies have considered the frequency as sole interestingness measure to identify high-quality patterns. However, each object is different in nature. The relative importance of objects is not equal, in terms of criteria, such as the utility, risk, or interest. Besides, another limitation of frequent patterns is that they generally have a low occupancy, that is, they often represent small sets of items in transactions containing many items and, thus, may not be truly representative of these transactions. To extract high-quality patterns in real-life applications, this paper extends the occupancy measure to also assess the utility of patterns in transaction databases. We propose an efficient algorithm named high-utility occupancy pattern mining (HUOPM). It considers user preferences in terms of frequency, utility, and occupancy. A novel frequency-utility tree and two compact data structures, called the utility-occupancy list and frequency-utility table, are designed to provide global and partial downward closure properties for pruning the search space. The proposed method can efficiently discover the complete set of high-quality patterns without candidate generation. Extensive experiments have been conducted on several datasets to evaluate the effectiveness and efficiency of the proposed algorithm. Results show that the derived patterns are intelligible, reasonable, and acceptable, and that HUOPM with its pruning strategies outperforms the state-of-the-art algorithm, in terms of runtime and search space, respectively.

Subitising activity relative to units construction: a case study

ABSTRACTSubitising, a quick apprehension of the numerosity of a small set of items, has been found to change from an individual's reliance on perceptual to conceptual processes. In this study, we utilised a constructivist teaching experiment methodology to investigate how the subitising activity of one preschool student, Amy, related to her construction of prenumerical units. Subitising and counting tasks were designed to assess and perturb Amy's thinking relative to her construction of units, and to observe changes in Amy's activity associated with the different tasks. Findings indicate that as Amy's subitising activity changed from perceptual to conceptual, she constructed subitised motor units and subitised figurative units. Implications of this study suggest that the construction of subitised units may support young children's later development of arithmetic units.

Word recognition: re-thinking prosthetic vision evaluation

Objective. Evaluations of vision prostheses and sensory substitution devices have frequently relied on repeated training and then testing with the same small set of items. These multiple forced-choice tasks produced above chance performance in blind users, but it is unclear if the observed performance represents restoration of vision that transfers to novel, untrained items. Approach. Here, we tested the generalizability of the forced-choice paradigm on discrimination of low-resolution word images. Extensive visual training was conducted with the same 10 words used in previous BrainPort tongue stimulation studies. The performance on these 10 words and an additional 50 words was measured before and after the training sessions. Main results. The results revealed minimal performance improvement with the untrained words, demonstrating instead pattern discrimination limited mostly to the trained words. Significance. These findings highlight the need to reconsider current evaluation practices, in particular, the use of forced-choice paradigms with a few highly trained items. While appropriate for measuring the performance thresholds in acuity or contrast sensitivity of a functioning visual system, performance on such tasks cannot be taken to indicate restored spatial pattern vision.

Topological multi-view clustering for collaborative filtering

Collaborative filtering is a well-known technique for recommender systems. Collaborative filtering models use the available preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. Collaborative filtering suffers from the data sparsity problem when users only rate a small set of items which makes the computation of users similarity imprecise and reduce consequently the accuracy of the recommended items. Clustering techniques include multiplex network clustering can be used to deal with this problem. In this paper, we propose a collaborative filtering system based on clustering multiplex network that predict the rate value that a user would give to an item. This approach looks, in a first step, for users having the same behavior or sharing the same characteristics. Then, use the ratings from those similar users found in the first step to predict other ratings. The proposed approach has been validated on MovieLens dataset and the obtained results have shown very promising performances.

Adjusting data sparsity problem using linear algebra and machine learning algorithm

Data sparsity is one of the most important challenges in data in which each user only rates a small set of items. This problem is critical with increasing dimensions of data. We present an idea based on linear algebra and machine learning to solve this problem. This research applies a framework to cluster users and items in similar groups simultaneously. This method imputes appropriate values for missing data based on similar ratings in each cluster. This has the advantages of more accurate process results in each cluster due to users’ similarity of interests, and the reduction of sparsity negative effect. This approach is represented on 3dimensional data of users, items and times. The experimental results on MovieLense datasets, show that the method can help to overcome data sparsity, and increase the accuracy of prediction.

Extracting Representative Information on Intra-Organizational Blogging Platforms

Content generated on intra-organizational blogging platforms may help managers understand the emerging ideas, issues, and opportunities of their companies, whereas the difficulty is how to go beyond the overload of information and obtain an overall view. This paper proposes a system framework for extracting representative information from intra-organizational blogging platforms, as well as the REPSET (REPresentative SET) method, which serves as the core component of the extraction system. Drawing from a novel clustering technique, REPSET is designed to identify a small set of items that largely represent the diversified content of a huge information base. Building on REPSET, an extraction system enables managers to locate representative articles that may serve as starting points for comprehensively understanding the hot topics, prevailing thoughts, and emerging opinions among employees. Empirical evaluations are conducted based on the massive database accumulated on an internal blogging platform at a large telecommunications company. The results from data experiments and user evaluations demonstrate that REPSET and the extraction system upon which it is based can provide outstanding performance, in comparison with benchmark methods.

Are semantic and phonological fluency based on the same or distinct sets of cognitive processes? Insights from factor analyses in healthy adults and stroke patients

Verbal fluency for semantic categories and phonological letters is frequently applied to studies of language and executive functions. Despite its popularity, it is still debated whether measures of semantic and phonological fluency reflect the same or distinct sets of cognitive processes. Word generation in the two task variants is believed to involve different types of search processes. Findings from the lesion and neuroimaging literature further suggest a stronger reliance of phonological and semantic fluency on frontal and temporal brain areas, respectively. This evidence for differential cognitive and neural contributions is, however, strongly challenged by findings from factor analyses, which have consistently yielded only one explanatory factor.As all previous factor-analytical approaches were based on very small item sets, this apparent discrepancy may be due to methodological limitations. In this study, we therefore applied a German version of the verbal fluency task with 8 semantic (i.e. categories) and 8 phonological items (i.e. letters). An exploratory factor analysis with oblique rotation in N=69 healthy young adults indeed revealed a two-factor solution with markedly different loadings for semantic and phonological items. This pattern was corroborated by a confirmatory factor analysis in a sample of N=174 stroke patients. As results from both samples also revealed a substantial portion of common variance between the semantic and phonological factor, the present data further demonstrate that semantic and phonological verbal fluency are based on clearly distinct but also on shared sets of cognitive processes.