Computational approach to solving problems of spatial processing of point sets on two-dimensional regular grids

The paper presents an approach to solving problems of spatial processing on sets of points on a plane. The presented method consists in plotting regions of an arbitrary geometric shape near given points of the set on a regular grid and determining the intersection points of the regions using spatial hash tables to improve the efficiency of operations. The proposed approach is implemented in the form of software for determining the spatial relationships between points as a sequence of operations with discretized sets and allows visualization of research results. Figs.: 2. Refs.: 13. Keywords: spatial processing task; point set; plane; regular grid; spatial hash table.

Holistic processing of faces and words predicts reading accuracy and speed in dyslexic readers

We compared the performance of dyslexic and typical readers on two perceptual tasks, the Vanderbilt Holistic Face Processing Task and the Holistic Word Processing Task. Both yield a metric of holistic processing that captures the extent to which participants automatically attend to information that is spatially nearby but irrelevant to the task at hand. Our results show, for the first time, that holistic processing of faces is comparable in dyslexic and typical readers but that dyslexic readers show greater holistic processing of words. Remarkably, we show that these metrics predict the performance of dyslexic readers on a standardized reading task, with more holistic processing in both tasks associated with higher accuracy and speed. In contrast, a more holistic style on the words task predicts less accurate reading of both words and pseudowords for typical readers. We discuss how these findings may guide our conceptualization of the visual deficit in dyslexia.

The Age-Related Changes in Speed of Visual Perception, Visual Verbal and Visuomotor Performance, and Nonverbal Intelligence During Early School Years

Speed of sensory information processing has long been recognized as an important characteristic of global intelligence, though few studies have concurrently investigated the contribution of different types of information processing to nonverbal IQ in children, nor looked at whether chronological age vs. months of early schooling plays a larger role. Thus, this study investigated the speed of visual information processing in three tasks including a simple visual inspection time (IT) task, a visual-verbal processing task using Rapid Automatic Naming (RAN) of objects as an accepted preschool predictor of reading, and a visuomotor processing task using a game-like iPad application, (the “SLURP” task) that requires writing like skills, in association with nonverbal IQ (Raven’s Coloured Progressive Matrices) in children (n = 100) aged 5–7 years old. Our results indicate that the rate and accuracy of information processing for all three tasks develop with age, but that only RAN and SLURP rates show significant improvement with years of schooling. RAN and SLURP also correlated significantly with nonverbal IQ scores, but not with IT. Regression analyses demonstrate that months of formal schooling provide additional contributions to the speed of dual-task visual-verbal (RAN) and visuomotor performance and Raven’s scores supporting the domain-specific hypothesis of processing speed development for specific skills as they contribute to global measures such as nonverbal IQ. Finally, RAN and SLURP are likely to be useful measures for the early identification of young children with lower intelligence and potentially poor reading.

Abstractive text summarization: enhancing sequence to sequence models using word sense disambiguation and semantic content generalization

Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based that could further enhance their efficiency. In this direction, this work presents a novel framework that combines sequence to sequence neural-based text summarization along with structure and semantic-based methodologies. The proposed framework is capable of dealing with the problem of out-of-vocabulary or rare words, improving the performance of the deep learning models. The overall methodology is based on a well defined theoretical model of knowledge-based content generalization and deeplearning predictions for generating abstractive summaries. The framework is comprised of three key elements: (i) a pre-processing task, (ii) a machine learning methodology and (iii) a post-processing task. The pre-processing task is a knowledge-based approach, based on ontological knowledge resources, word-sense-disambiguation and namedentity recognition, along with content generalization, that transforms ordinary text into a generalized form. A deep learning model of attentive encoder-decoder architecture, which is expanded to enable a coping and coverage mechanism, as well as reinforcement learning and transformer-based architectures, is trained on a generalized version of text-summary pairs, learning to predict summaries in a generalized form. The post-processing task utilizes knowledge resources, word embeddings, word-sense disambiguation and heuristic algorithms based on text similarity methods in order to transform the generalized version of a predicted summary to a final, humanreadable form. An extensive experimental procedure on three popular datasets evaluates key aspects of the proposed framework, while the obtained results exhibit promising performance, validating the robustness of the proposed approach.

AI-enabled IoT-Edge Data Analytics for Connected Living

As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the performance of data processing applications. Here, a Feasible Solution (FS) algorithm is designed to solve the problem of placing copy-free data processing tasks in the system. The FS algorithm has excellent results once considering the overall coordination. Under light load, the V value is reduced by 73% compared to the Only Data Center-available (ODC) algorithm and 41% compared to the Hash algorithm. Under heavy load, the V value is reduced by 66% compared to the ODC algorithm and 35% compared to the Hash algorithm. The algorithm has achieved good results after considering the overall coordination and cooperation and can more effectively use the bandwidth of edge nodes to transmit and process data stream, so that more tasks can be deployed in edge computing nodes, thereby saving time for data transmission to the data centers. The end-to-end collaborative real-time data processing task scheduling mechanism proposed here can effectively avoid the disadvantages of long waiting times and unable to obtain the required data, which significantly improves the success rate of the task and thus ensures the performance of real-time data processing.

Measuring toddlers non-symbolic number processing

Much research has investigated children’s non-symbolic number processing and its relation to mathematical ability. However, surprisingly few studies have investigated performance in 18-36 month-olds, where symbolic number concepts begin to emerge, and the extent results indicate poor performance. We tested 74 2 - 3.5 year-olds recruited from two sites (Ulster and Uppsala). They completed a novel dot-comparison task where children were shown, but not verbally instructed, how pushing a more numerous array resulted in reward and a Give-N task. Overall, participants performed above chance on the dot comparison task, indicating that non-symbolic number processing skills can be measured in toddlers without verbal instructions. We found no relation between performance on the non-symbolic number processing task and knower-level. Our results warrant two conclusions. First, verbal instructions involving the concept of more are not necessary to measure non-symbolic number processing skills in young children. Second, the development of a symbolic number concept seems independent of the development of non-symbolic comparison skills but may become artificially related when researchers use quantifiers such as “more” to measure the former.