Low-level Concepts Research Articles

COMPARISON OF MESSAGE PASSING SYSTEMS IN CONTEXT OF ADAPTIVE LOGGING METHOD

Computer software is an important part of technological progress. As it becomes more and more complex and sophisticated, so does the need to protect it. Apart from typical information security aspects of integrity, availability and confidentiality, the scale and complexity of modern computer systems require a high level of control and observability. The main goal of this research is to build upon the foundations laid by the general idea of an adaptive logging method and introduce the next iteration of its design in the form of an appropriate message passing system to be used to propagate required changes to corresponding implementation in an effective and performant manner. Four different message passing system models are introduced, based on different technologies such as RabbitMQ message broker, communication channels in PostgreSQL database management system, general web server architecture and Linux-based process signaling interface. For each of those an overview description and graphical model is presented. Finally, the resulting comparison is conducted, comparing aspects such as reliance on third-party software, communication medium, error surface increase and authentication related considerations. As a result, the design based on process signaling approach is determined to be the most suitable for adaptive logging method, as it does not introduce any third-party software (and as such affects error surface in a somewhat negligible manner), binds directly to an observed application, is built using low level concepts that should be present in multiple different platforms and programming languages and should be able to reuse authentication logic that is already used when accessing computational machine where observed program is executed.

Context-aware Multi-level Question Embedding Fusion for visual question answering

Question model has been widely concerned as the cornerstone of constructing Visual Question Answering (VQA) models. Existing question models attempt to exploit word context to extract multi-level concepts for modeling multi-level questions. However, they still have many defects. For example, most question models utilize simple fusion methods to fuse shallow modules and extract parameter-unshared low-level concepts, leading to poor modeling of multi-level questions; although some question models use deep bidirectional Transformer encoder in external knowledge transfer and BERT for multi-level questions, their complexity is still high. To solve these issues, we propose a novel low-complex multi-level contextual question model, termed Context-aware Multi-level Question Embedding Fusion (CMQEF). We formalize its concepts and theories, deduce its modeling process, optimization process and feature extraction process, analyze its low complexity and high expressiveness, and prove that it defines a new way to solve parameter non-sharing for extracting parameter-shared multi-level concepts and optimize the tradeoff between expressiveness and complexity in question models. Extensive experiments on VQAv2 and VQA-CPv2 validate that comparing with the state-of-the-art, our CMQEF outperforms it on SANs and UpDn, reduces the language priors of SANs and UpDn, and has preferable interpretability and applicability. Our code is available at https://github.com/lsdruc/CMQEF-for-VQA.

A Normative Methodology for Research & Development Alignment and Decision-Making in the Arctic

ERDC’s current portfolio development process introduces ill-defined and low technology readiness level (TRL) concepts before processing them through the USACE Project Portfolio Management (PFM) System to determine the list of projects for lab resourcing. This research will present a scalable decision model before the PFM, to ensure that projects are aligned with higher-level strategies and priorities. This would minimize wasted time and resources by the research community before Research and Development (R&D) projects are approved using the PFM. The multi-attribute decision aid coupled with a Kendall Tau analysis provides mechanisms to ensure alignment. The goal is to eliminate ideas that do not fall under the strategic objectives set by the White House and the Department of Defense and make sure all projects are aligned with these priorities. The model illustrates how the PFM misaligns with the more normative design created for ill-defined projects.

Improved Deep Multi-Patch Hierarchical Network For Handling Saturation In Image Deblurring

The most active study topic in the field of computer vision is dealing with saturation in image deblurring. The Deep Multi Patch Framework (DMPHN) is primarily utilized in dynamic scene deblurring, and its fine-to-coarse hierarchical representation results in lower processing costs. The contribution of the framework in gathering and processing local residual information of blur at the courser levels in the hierarchy makes the dynamic scene deblurring successful. The existing framework uses the traditional CNN with the convolutional filter, which is a generalized linear model (GLM) that goes along with a nonlinear activation function to scan the input. When the samples of latent concepts are not linearly separable then the linear CNN cannot abstract the good representations. Because saturation in an image contradicts the linear properties of the blur model, the present Deep Multi Patch architecture with linear CNN is unable to collect the necessary local information of blur in an image. In CNN, the higher-level layer maps to the larger regions of the input image by combining the lower-level concepts from each local patch. Due to the framework’s incapacity to retain information at lower levels, noticeable ringing artifacts and poor deblurring performance resulted. We designed a nonlinear function approximator for this section, which was influenced by the good abstraction capabilities of ”Network-in-Network”. The nonlinear features of saturated pixels and blur are successfully captured by this upgraded DMPHN with a nonlinear function approximator. Compared to the current DMPHN model, the suggested approach greatly reduced the number of trainable parameters while improving the time cost. On the GoPro, Synthetic, and Natural Saturated Blurred Image datasets, the upgraded DMPHN network achieves superior deblurring performance with better PSNR and SSIM than the state-of-the-art.

The lockdown and its consequences-Perspectives and needs of people at increased risk of severe illness from COVID-19 : Results from aqualitative longitudinal study.

BackgroundThere is a lack of knowledge on how people at increased risk of severe illness from Coronavirus disease 2019 (COVID-19) experienced the infection control measures. This study aimed to explore their perspectives and needs during the coronavirus outbreak.MethodsA qualitative longitudinal interview study was conducted in Austria during lockdown due to COVID-19 containment and afterwards. People older than 65 years of age and/or affected by a chronic medical condition participated in individual telephone interviews at two time points. Thematic analysis was used to analyze the data and saturation was defined as no new emerging concepts in at least 10 subsequent interviews.ResultsThematic saturation was reached when 33 individuals (75.8% female, mean age ± standard deviation [SD] 73.7±10.9 years) were included. A total of 44 lower level concepts were extracted and summarized into 6 higher level concepts. They included (i) a general positive attitude toward COVID-19 measures, (ii) challenges of being isolated from the community, (iii) deterioration of health status, (iv) difficulties with measures due to their health condition, (v) lack of physical contact and (vi) lack of information versus overload. Participants suggested environmental adaptations for strengthening resilience in people at increased risk of severe illness from COVID-19.ConclusionStrategies and interventions are needed to support people at risk under pandemic conditions. Their perceptions and needs should be addressed to reduce the potential deterioration of health conditions and ensure well-being even during prolonged periods of crisis.Supplementary InformationThe online version of this article (10.1007/s00508-021-01979-9) contains supplementary material, which is available to authorized users.

LEARNING PROGRESSIONS IN LOWER-SECONDARY SCHOOL SCIENCE EDUCATION IN JAPAN

This research examined whether incorporating learning progress (LPs) in teaching can help Japanese lower-secondary school students systemically understand science concepts. A teaching plan incorporating the learning progressions (LPs) concept was developed. Next, a lesson was conducted for 36 third-year students of a public lower-secondary school in Japan (Hiroshima Prefecture) from the unit of “Ions and Atoms.” Then, the portfolios used in the class and the assessment questions after the class were analyzed. The results show that some students understood the concepts systemically at the grade level indicated by the LPs; however, some students did not. The results of this study suggest the following: (1) teachers should examine the appropriate teaching methods in the target unit and incorporate teaching to allow students to engage with what they are learning; (2) students should understand the lower-level concepts related to the target unit based on LPs, and if the level of understanding is insufficient, teachers should incorporate time to review and reconstruct the concepts. Keywords: lower-secondary school science, scientific concepts, learning progressions (LPs), systemic learning

Öğrencilerin Epistemolojik İnançları ile Farklı Fen Disiplinlerine Yönelik Öğrenme Anlayışları Arasındaki İlişkiler

This study aims to investigate the relationships between 9th grade students’ epistemological beliefs and conceptions of learning in different science domains (physics, chemistry, biology). A total of 462 students (male=247, female=215) participated in the study. Data were collected with the help of the questionnaires that measure students’ epistemological beliefs and conceptions of learning. Pearson correlation analysis was performed to determine the relationships between students’ epistemological beliefs and conceptions of learning in different science domains. The results revealed that there were positive and moderate relationships between students’ epistemological beliefs in “justification” and “development” dimensions and their higher-level conceptions of learning. However, these beliefs were weakly correlated or not correlated to the students’ lower-level conceptions of learning. Similarly, students’ epistemological beliefs in “source” and “certainty” dimensions were weakly associated or not associated with their conceptions of learning.

If only there was a Department of Fieldwork in Philosophy

Fieldwork in philosophy amounts to a second-order philosophical anthropology. It examines contemporary forms of the human by attending to lower-level concepts and practices. It departs from Michel Foucault’s gray and meticulous approach to the history of the present, which understands the transformation of high-level organizing concepts such as “Man” or “the subject” through an inquiry into scientific discourses, clinical practices, disciplinary institutions, etc. However, fieldwork in philosophy doesn’t approach the present by writing its history but by conducting anthropological fieldwork. This essay reconstructs Paul Rabinow’s conception of fieldwork in philosophy as it inspired the author’s work on the perennial philosophy of the psychedelic renaissance, a case study of neurophilosophers in a sleep laboratory, as well as research on cultural primatologists who took the Enlightenment question of human nature to the African rainforest. The essay ends with a plea for reimagining anthropology as fieldwork in philosophy.

“Penggunaan Flip Book Terhadap Peningkatan Hasil Pembelajaran Bahasa Inggris Siswa pada Aspek Kognitif Tingkat Rendah (Quasi Eksperimen Pokok Bahasan Tenses pada Siswa Kelas XII Semester I di SMAN 16 Garut)”

The research was conducted in an effort to find out the differences between students who were taught by using Flip book and those who were taught without using the Flip book in mastery of low level of cognitive aspects. This research design was a quasi-experimental using two groups. In the experimental group was applied the learning process of learning media flipbook while the control group it was not applied the learning media flipbook. The Subjects in this study were 35 students of XII IPA 5 class as an experimental class and 35 students of XII IPA 4 as the control class in SMAN 16 Garut. The research Result showed that: (1) The Mastery of the cognitive aspects of low-level concept of tenses in English Language Learning in SMAN 16 Garut before it was given the study treatment by applying the learning media flipbook in the learning process are at low qualifying on aspects of knowledge, understanding and aspects of the application, (2) The mastery of Cognitive low-level Aspects of tenses in English Language Learning in SMAN 16 Garut after applying research treatment increased. Classes that implement instructional media flipbook increased on aspects of knowledge into high qualifications, aspects of the understanding and application increased into medium qualification, whereas classes that did not implement instructional media flipbook, aspect of knowledge and understanding increased into medium qualification and low qualifying for aspects of the application , (3) There were significant different increase in the cognitive aspects of low-level in mastery of the concept of tenses between students who learn by applying the learning media flipbook with students who learnt without implementing instructional media flipbook in English learning at XII IPA class at SMAN 16 Garut. Keywords: Flipbook, Cognitive Aspects of Learning Outcomes, tenses, English Language Learning.

Young people’s perspectives on patient-reported outcome measures in inflammatory arthritis: results of a multicentre European qualitative study from a EULAR task force

IntroductionAlthough patient-reported outcome measures (PROMs) are increasingly used in clinical practice and research, it is unclear whether these instruments cover the perspective of young people with inflammatory arthritis (IA). The...

Junior Secondary School Students’ Conceptions of and Approaches to Learning Mathematics and Their Relationships in Mainland China

This study reports the findings of a study which investigated junior secondary school students’ conceptions of and approaches to learning mathematics and their relationships in Mainland China. Two questionnaires, conceptions of learning mathematics (COLM) and approaches to learning mathematics (ALM), were administered to 1590 students. Descriptive analysis results suggest that Chinese junior school students tend to hold (1) higher-level conceptions of learning mathematics rather than lower-level conceptions, and (2) deep approaches to learning mathematics with a rather mixed surface and deep motive in learning mathematics. Correlation and regression analysis results confirm a structural relationship between students’ conceptions of and approaches to learning in the subject of mathematics. Two factors of students’ lower-level conceptions of learning mathematics, “memorizing” and “testing”, were the strongest predictor for the surface approaches to mathematics learning, while students’ higher-level conceptions of learning mathematics, such as “applying” and “understanding and mathematical thinking”, had a noticeable effect on their deep approaches to learning mathematics. However, under the pressure of examination in Mainland China, “understanding and mathematical thinking” was also found to exert quite a strong influence on students’ “surface motive”.

Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering.

Models for computer vision are commonly defined either w.r.t. low-level concepts such as pixels that are to be grouped, or w.r.t. high-level concepts such as semantic objects that are to be detected and tracked. Combining bottom-up grouping with top-down detection and tracking, although highly desirable, is a challenging problem. We state this joint problem as a co-clustering problem that is principled and tractable by existing algorithms. We demonstrate the effectiveness of this approach by combining bottom-up motion segmentation by grouping of point trajectories with high-level multiple object tracking by clustering of bounding boxes. We show that solving the joint problem is beneficial at the low-level, in terms of the FBMS59 motion segmentation benchmark, and at the high-level, in terms of the Multiple Object Tracking benchmarks MOT15, MOT16 and the MOT17 challenge, and is state-of-the-art in some metrics.

Performance evaluation of implicit and explicit SIMDization

Processor vendors have been expanding Single Instruction Multiple Data (SIMD) extensions to exploit data-level-parallelism in their General Purpose Processors (GPPs). Each SIMD technology such as Streaming SIMD Extensions (SSE) and Advanced Vector eXtensions (AVX) has its own Instruction Set Architecture (ISA) which equipped with Special Purpose Instructions (SPIs). In order to exploit these features, many programming approaches have been developed. Intrinsic Programming Model (IPM) is a low-level concept for explicit SIMDization. Besides, Compiler’s Automatic Vectorization (CAV) has been embedded in modern compilers such as Intel C++ compiler (ICC), GNU Compiler Collections (GCC) and LLVM for implicit vectorization. Each SIMDization shows different improvements because of different SIMD ISAs, vector register width, and programming approaches. Our goal in this paper is to evaluate the performance of explicit and implicit vectorization. Our experimental results show that the behavior of explicit vectorization on different compilers is almost the same compared to implicit vectorization. IPM improves the performance more than CAVs. In general, ICC and GCC compilers can more efficiently vectorize kernels and use SPI compared to LLVM. In addition, AVX2 technology is more useful for small matrices and compute-intensive kernels compared to large matrices and data-intensive kernels because of memory bottlenecks. Furthermore, CAVs fail to vectorize kernels which have overlapping and non-consecutive memory access patterns. The way of implementation of a kernel impacts the vectorization. In order to understand what kind of scalar implementations of an algorithm is suitable for vectorization, an approach based on code modification technique is proposed. Our experimental results show that scalar implementations that have either loop collapsing, loop unrolling, software pipelining, or loop exchange techniques can be efficiently vectorized compared to straightforward implementations.

Fuzzy VA-Files for multi-label image annotation based on visual content of regions

In this work, we propose an efficient image annotation approach based on visual content of regions. We assume that regions can be described using low-level features as well as high-level ones. Indeed, given a labeled dataset, we adopt a probabilistic semantic model to capture relationships between low-level features and semantic clusters of regions. Moreover, since most previous works on image annotation do not deal with the curse of dimensionality, we solve this problem by introducing a fuzzy version of the Vector Approximation Files (VA-Files). Indeed, the main contribution of this work resides in the association of the generative model with fuzzy VA-Files, which offer an accurate multi-dimensional indexing, to estimate relationships between low-level features and semantic concepts. In fact, the proposed approach reduces the computation complexity while optimizing the annotation quality. Preliminary experiments highlight that the suggested approach outperforms other state-of-the-art approaches.

Extraction of fuzzy rules at different concept levels related to image features of mammography for diagnosis of breast cancer

Mammography is an inexpensive and non-invasive method through which one can diagnose breast cancer in its early stages. As these images need interpretation by a radiologist, this may develop some problems due to fatigue, repetition, and need for a great deal of attention to details and other factors. Thus, a method capable of diagnosing breast cancer should be employed to help physicians in this regard.In this paper, The mini Mammographic Image Analysis Society (mini-MIAS) database of mammograms is used. The aim is to distinguish between normal and abnormal classes. In the preprocessing stage, noise removal, removal of labels of images, heightening the contrast, and ROI segmentation are performed, and then compactness, entropy, mean, and smoothness are extracted from the images. In addition to classification, we have come to a new approach in order to create a complete knowledge base, which then we use this knowledge base for classification. We have a comprehensive knowledge base which covers all the conceptual levels.The extracted features are referred to as fuzzy classifiers through the look-up table method. And, for evaluation of the results, the 10-fold method is used. Discretization operations are performed on training data across 2, 3, and 4 levels to develop concept hierarchy. Concept hierarchies reduce the data by replacing low-level concepts with higher-level concepts and the outcome is more meaningful and easier to interpret. Eventually, Bagging algorithm is used for finding out the majority vote and the final result of the discretization levels. The obtained accuracy is 89.37±6.62.

Monomodal and multimodal instantiations of conceptual metaphors of Brexit

Abstract This article offers a linguistic analysis of the conceptual metaphors of Brexit, in which the source and the target belong to the same semiotic mode or to different ones. It is shown that the variation of high-level cognitive models underpinning metaphoric images of Brexit reflects the author’s stance towards the event. Phases of Brexit are associated with different image-schematic cognitive models, and this impinges on the range of those metaphors of Brexit that involve low-level concepts.

Investigating the interrelationships among conceptions of, approaches to, and self-efficacy in learning science

ABSTRACTThe purpose of this study was to examine the relations between primary school students’ conceptions of, approaches to, and self-efficacy in learning science in Mainland China. A total of 1049 primary school students from Mainland China participated in this study. Three instruments were adapted to measure students’ conceptions of learning science, approaches to learning science, and self-efficacy. The exploratory factor analysis and confirmatory factor analysis were adopted to validate three instruments. The path analysis was employed to understand the relationships between conceptions of learning science, approaches to learning science, and self-efficacy. The findings indicated that students’ lower level conceptions of learning science positively influenced their surface approaches in learning science. Higher level conceptions of learning science had a positive influence on deep approaches and a negative influence on surface approaches to learning science. Furthermore, self-efficacy was also a hierarchical construct and can be divided into the lower level and higher level. Only students’ deep approaches to learning science had a positive influence on their lower and higher level of self-efficacy in learning science. The results were discussed in the context of the implications for teachers and future studies.

GRAM: Graph-based Attention Model for Healthcare Representation Learning.

Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: Data insufficiency: Often in healthcare predictive modeling, the sample size is insufficient for deep learning methods to achieve satisfactory results.Interpretation: The representations learned by deep learning methods should align with medical knowledge. To address these challenges, we propose GRaph-based Attention Model (GRAM) that supplements electronic health records (EHR) with hierarchical information inherent to medical ontologies. Based on the data volume and the ontology structure, GRAM represents a medical concept as a combination of its ancestors in the ontology via an attention mechanism. We compared predictive performance (i.e. accuracy, data needs, interpretability) of GRAM to various methods including the recurrent neural network (RNN) in two sequential diagnoses prediction tasks and one heart failure prediction task. Compared to the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely observed in the training data and 3% improved area under the ROC curve for predicting heart failure using an order of magnitude less training data. Additionally, unlike other methods, the medical concept representations learned by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits intuitive attention behaviors by adaptively generalizing to higher level concepts when facing data insufficiency at the lower level concepts.

Correlation-Assisted Imbalance Multimedia Concept Mining and Retrieval

In the past decades, we have witnessed an explosion of multimedia data, especially with the development of social media websites and blooming popularity of smart devices. As a result, multimedia semantic concept mining and retrieval whose objective is to mine useful information from the large amount of multimedia data including texts, images, and videos has become more and more important. The huge amount of multimedia data and the semantic gap between low-level features and high-level semantic concepts have made it even more challenging. To address these challenges, the correlations among the classes can provide important context cues to help bridge the semantic gap. Meanwhile, many real-world datasets do not have uniform class distributions while the minority instances actually represent the concept of interests, like frauds in transactions, intrusions in network security, and unusual events in surveillance. Despite extensive research efforts, imbalanced concept retrieval remains one of the most challenging research problems in multimedia data mining. Different from existing frameworks regarding concept correlations among labels, this paper presents a novel concept correlation analysis model using the correlation between the retrieval scores and labels. Experimental results on the TRECVID benchmark datasets demonstrate that the proposed framework can enhance imbalanced concept mining and retrieval even with trivial scores from the minority class.

Relational Analysis of High School Students’ Cognitive Self-Regulated Learning Strategies and Conceptions of Learning Biology

Background:The purpose of this study was to analyze the relation between students’ cognitive learning strategies and conceptions of learning biology.Material and methods:The two scales, “Cognitive Learning Strategies” and “Conceptions of Learning Biology”, were revised and adapted to biology in order to measure the students’ learning strategies and conceptions of learning.Results:it was found that students preferred higher-level conceptions of learning such as ‘increasing knowledge’, ‘seeing in a new way’, and ‘understanding’ to lower-level conceptions such as ‘memorizing’, ‘preparing for exams’ and ‘calculating and practicing’. According to the results of regression analysis, it was seen that ‘memorizing’ and ‘application’ was common among the high school students while predicting the cognitive learning strategies. Moreover, the students who had higher-level conceptions of learning had a tendency to use strategies such as “organization,” “elaboration,” and “critical thinking.” The students who adopted “memorizing” as a lower-level conception of learning preferred “rehearsal” learning strategy. However, “memorizing” as a lower level conception of learning also positively predicts “organization,” “elaboration” and “critical thinking.”Conclusions:The special condition, which did not show parallelism with the related literature, was taken into consideration and interpreted when discussing the relational analysis of link between conceptions of learning and cognitive learning strategies.