High-level Labels Research Articles

Children and Young People's Involvement in Designing Applied Games: Scoping Review.

User involvement is widely accepted as key for designing effective applied games for health. This especially holds true for children and young people as target audiences, whose abilities, needs, and preferences can diverge substantially from those of adult designers and players. Nevertheless, there is little shared knowledge about how concretely children and young people have been involved in the design of applied games, let alone consensus guidance on how to do so effectively. The aim of this scoping review was to describe which user involvement methods have been used in the design of applied games with children and young people, how these methods were implemented, and in what roles children and young people were involved as well as what factors affected their involvement. We conducted a systematic literature search and selection across the ACM Digital Library, IEEE Xplore, Scopus, and Web of Science databases using State of the Art through Systematic Review software for screening, selection, and data extraction. We then conducted a qualitative content analysis on the extracted data using NVivo. We retrieved 1085 records, of which 47 (4.33%) met the eligibility criteria. The chief involvement methods were participatory design (20/47, 43%) and co-design (16/47, 37%), spanning a wide range of 45 concrete activities with paper prototyping, group discussions, and playtesting being the most frequent. In only half of the studies (24/47, 51%), children and young people participated as true design partners. Our qualitative content analysis suggested 5 factors that affect their successful involvement: comprehension, cohesion, confidence, accessibility, and time constraints. Co-design, participatory design, and similar high-level labels that are currently used in the field gloss over very uneven degrees of participation in design and a wide variety of implementations that greatly affect actual user involvement. This field would benefit from more careful consideration and documentation of the reason of user involvement. Future research should explore concrete activities and configurations that can address the common challenges of involving children and young people, such as comprehension, cohesion, confidence, and accessibility.

Partial Visual-Tactile Fused Learning for Robotic Object Recognition

Currently, visual-tactile fusion learning for robotic object recognition has achieved appealing performance, due to the fact that visual and tactile data can offer complementary information. However: 1) the distinct gap between vision and touch makes it difficult to fully explore the complementary information, which would further lead to performance degradation and 2) most of the existing visual-tactile fused learning methods assume that visual and tactile data are complete, which is often difficult to be satisfied in many real-world applications. In this article, we propose a partial visual-tactile fused (PVTF) framework for robotic object recognition to address these challenges. Specifically, we first employ two modality-specific (MS) encoders to encode partial visual-tactile data into two incomplete subspaces (i.e., visual subspace and tactile subspace). Then, a modality gap mitigated (MGM) network is adopted to discover modality-invariant high-level label information, which is utilized to generate gap loss and further help updating the MS encoders for relatively consistent visual and tactile subspaces generation. In this way, the huge gap between vision and touch is mitigated, which would further contribute to mine the complementary visual-tactile information. Finally, to achieve data completeness and complementary visual-tactile information exploration simultaneously, a cycle subspace leaning technique is proposed to project the incomplete subspaces into a complete subspace by fully exploiting all the obtainable samples, where complete latent representations with maximum complementary information can be learned. A lot of comparative experiments conducted on three visual-tactile datasets validate the advantage of the proposed PVTF framework, by comparing with state-of-the-art baselines.

Classification of Negative Information on Socially Significant Topics in Mass Media

Mass media not only reflect the activities of state bodies but also shape the informational context, sentiment, depth, and significance level attributed to certain state initiatives and social events. Multilateral and quantitative (to the practicable extent) assessment of media activity is important for understanding their objectivity, role, focus, and, ultimately, the quality of the society’s “fourth power”. The paper proposes a method for evaluating the media in several modalities (topics, evaluation criteria/properties, classes), combining topic modeling of the text corpora and multiple-criteria decision making. The evaluation is based on an analysis of the corpora as follows: the conditional probability distribution of media by topics, properties, and classes is calculated after the formation of the topic model of the corpora. Several approaches are used to obtain weights that describe how each topic relates to each evaluation criterion/property and to each class described in the paper, including manual high-level labeling, a multi-corpora approach, and an automatic approach. The proposed multi-corpora approach suggests assessment of corpora topical asymmetry to obtain the weights describing each topic’s relationship to a certain criterion/property. These weights, combined with the topic model, can be applied to evaluate each document in the corpora according to each of the considered criteria and classes. The proposed method was applied to a corpus of 804,829 news publications from 40 Kazakhstani sources published from 01 January 2018 to 31 December 2019, to classify negative information on socially significant topics. A BigARTM model was derived (200 topics) and the proposed model was applied, including to fill a table of the analytical hierarchical process (AHP) and all of the necessary high-level labeling procedures. Experiments confirm the general possibility of evaluating the media using the topic model of the text corpora, because an area under receiver operating characteristics curve (ROC AUC) score of 0.81 was achieved in the classification task, which is comparable with results obtained for the same task by applying the BERT (Bidirectional Encoder Representations from Transformers) model.

Multilabel Text Classification With Incomplete Labels: A Safe Generative Model With Label Manifold Regularization and Confidence Constraint

In multilabel text classification, the label information of training instances may be incomplete due to various factors, such as expensively manual labeling, errors, etc. Hence, imposing big challenges to training classifiers. To tackle the problem of the incomplete labels, we proposed to exploit label correlations, and further jointly leverage manifold regularization and label confidence constraint. On one hand, the label manifold regularization preserves the local label structure to handle the high-level incomplete labels. On the other hand, the label confidence constraint, following the likelihood theory, avoids overestimating the weights of negative labels, leading to a safer inference. Upon the ideas, we propose a novel generative model, namely B ayesian M odel with L abel manifold R egularization and label C onfidence constraint (BM-LRC), solved by generalized expectation maximization. Empirical results on popular benchmark data sets show that BM-LRC achieves competitive performance, especially when the available labels are scarce.

A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels

Anomalous event detection in surveillance videos is a challenging and practical research problem among image and video processing community. Compared to the frame-level annotations of anomalous events, obtaining video-level annotations is quite fast and cheap though such high-level labels may contain significant noise. More specifically, an anomalous labeled video may actually contain anomaly only in a short duration while the rest of the video frames may be normal. In the current work, we propose a weakly supervised anomaly detection framework based on deep neural networks which is trained in a self-reasoning fashion using only video-level labels. To carry out the self-reasoning based training, we generate pseudo labels by using binary clustering of spatio-temporal video features which helps in mitigating the noise present in the labels of anomalous videos. Our proposed formulation encourages both the main network and the clustering to complement each other in achieving the goal of more accurate anomaly detection. The proposed framework has been evaluated on publicly available real-world anomaly detection datasets including UCF-crime, ShanghaiTech and UCSD Ped2. The experiments demonstrate superiority of our proposed framework over the current state-of-the-art methods.

MLNE: Multi-Label Network Embedding.

Network embedding aims to preserve topological structures of a network using low-dimensional vectors and has shown to be effective for driving a myriad of graph mining tasks (e.g., link prediction or classification) free of the stressful feature extraction procedure. Many methods have been proposed to integrate node content and/or label information, with nodes sharing similar content/labels being close to each other in the learned latent space. To date, existing methods either consider networked instances with a single label or consider a set of labels as a whole for node representation learning. Therefore, they cannot handle network of instances containing multiple labels (i.e. multi-labels), which are ubiquitous in describing complex concepts of instances. In this article, we formulate a new multi-label network embedding (MLNE) problem to learn feature representation for networked multi-label instances. We argue that the key to MLNE learning is to aggregate node topology structures, node content, and multi-label correlations. We propose a two-layer network embedding framework to couple information for effective learning. To capture higher order label correlations, we use labels to form a high-level label-label network over a low-level node-node network, in which the label network interacts with the node network through multi-labeling relations. The low-level node-node network can be enhanced by latent label-specific features from high-level label network with well-captured high-order correlations between labels. To enable the multi-label informed network embedding, we force both node and label representations being optimized under the same low-dimensional latent space by a unified training objective. Experiments on real-world data sets demonstrate that MLNE achieves better performance compared with methods with or without considering label information.

A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation

Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis.

PAN: Projective Adversarial Network for Medical Image Segmentation.

Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation. Unique to medical imaging, capturing 3D semantics in an effective yet computationally efficient way remains an open problem. In this study, we address this computational burden by proposing a novel projective adversarial network, called PAN, which incorporates high-level 3D information through 2D projections. Furthermore, we introduce an attention module into our framework that helps for a selective integration of global information directly from our segmentor to our adversarial network. For the clinical application we chose pancreas segmentation from CT scans. Our proposed framework achieved state-of-the-art performance without adding to the complexity of the segmentor.

Evaluation of gamma ray-induced gastrointestinal tract morphological and proliferative activity changes in rhesus monkeys.

To provide support for future pharmacology and preclinical studies, we have established a stable nonhuman primate animal model to demonstrate the histopathological changes in the gastrointestinal tract following gamma ray irradiation. In this study, 12 healthy rhesus monkeys were divided into 2 groups (control and radiation groups). Animals in the radiation group were exposed to gamma rays (cobalt 60 source) at a dose level of 6.5 Gy total body irradiation bilaterally (i.e. 3.25 Gy on each side). Control animals were sham exposed using identical procedures. After a 5-day in-life observation period, gastrointestinal tract tissues (esophagus, stomach, duodenum, jejunum, ileum, colon, and rectum) were collected and fixed in 10% neutral-buffered formalin for subsequent hematoxylin and eosin and 5-bromo-2-deoxyuridine (BrdU) immunohistochemistry processing. The results showed that the esophagus was undergoing degeneration without obvious inflammatory changes, while the stomach and duodenum exhibited both degeneration and inflammation. From the jejunum to the rectum, late-stage inflammation with glandular regeneration, as well as a high-level BrdU labeling index, was present.

Component-Level Tuning of Kinematic Features From Composite Therapist Impressions of Movement Quality.

In this paper, we propose a general framework for tuning component-level kinematic features using therapists' overall impressions of movement quality, in the context of a home-based adaptive mixed reality rehabilitation (HAMRR) system. We propose a linear combination of nonlinear kinematic features to model wrist movement, and propose an approach to learn feature thresholds and weights using high-level labels of overall movement quality provided by a therapist. The kinematic features are chosen such that they correlate with the quality of wrist movements to clinical assessment scores. Further, the proposed features are designed to be reliably extracted from an inexpensive and portable motion capture system using a single reflective marker on the wrist. Using a dataset collected from ten stroke survivors, we demonstrate that the framework can be reliably used for movement quality assessment in HAMRR systems. The system is currently being deployed for large-scale evaluations, and will represent an increasingly important application area of motion capture and activity analysis.

Fuzzy Approach for Semantic Face Image Retrieval

Our aim is to realize semantic face image retrieval (FIR)\ by bridging the semantic-gap between low-level visual features of images and the high-level labels that describe facial features. This allows a user to retrieve face images based on a description of face features rather than an example image. We approach the semantic-gap problem by developing a fuzzy-based method of finding a mapping between the low-level features and the vocabulary that describes the semantics of the face features. The contribution of this paper is a new method of using fuzzy clustering and fuzzy inference methods to derive the degree of membership for each semantic label to a new image. Our experiments show that our approach has good results for annotating images, and provides a sound foundation for local face feature-based FIR systems. We have made available a demonstration system online. Further, our system is not domain specific and can be generalized and applied to other problems in the field of image retrieval.

Multilabel Neighborhood Propagation for Region-Based Image Retrieval

Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, graph-based semi-supervised learning has attracted many researchers. However, while the related work mainly focused on global visual features, little attention has been paid to region-based image retrieval (RBIR). In this paper, a framework based on multilabel neighborhood propagation is proposed for RBIR, which can be characterized by three key properties: (1) For graph construction, in order to determine the edge weights robustly and automatically, mixture distribution is introduced into the Earth mover's distance (EMD) and a linear programming framework is involved. (2) Multiple low-level labels for each image can be obtained based on a generative model, and the correlations among different labels are explored when the labels are propagated simultaneously on the weighted graph. (3) By introducing multilayer semantic representation (MSR) and support vector machine (SVM) into the long-term learning, more exact weighted graph for label propagation and more meaningful high-level labels to describe the images can be calculated. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal.

Interactive 3D model extraction from a single image

Abstract We present a system at the junction between Computer Vision and Computer Graphics, to produce a three-dimensional (3D) model of an object as observed in a single image, with a minimum of high-level interaction from a user. The input to our system is a single image. First, the user points, coarsely, at image features (edges) that are subsequently automatically and reproducibly extracted in real-time. The user then performs a high level labeling of the curves (e.g. limb edge, cross-section) and specifies relations between edges (e.g. symmetry, surface or part). NURBS are used as working representation of image edges. The objects described by the user specified, qualitative relationships are then reconstructed either as a set of connected parts modeled as Generalized Cylinders, or as a set of 3D surfaces for 3D bilateral symmetric objects. In both cases, the texture is also extracted from the image. Our system runs in real-time on a PC.

Heterogeneous distribution of isoactins in cultured vascular smooth muscle cells does not reflect segregation of contractile and cytoskeletal domains.

We have previously demonstrated that alpha-smooth muscle (alpha-SM) actin is predominantly distributed in the central region and beta-non-muscle (beta-NM) actin in the periphery of cultured rabbit aortic smooth muscle cells (SMCs). To determine whether this reflects a special form of segregation of contractile and cytoskeletal components in SMCs, this study systematically investigated the distribution relationship of structural proteins using high-resolution confocal laser scanning fluorescent microscopy. Not only isoactins but also smooth muscle myosin heavy chain, alpha-actinin, vinculin, and vimentin were heterogeneously distributed in the cultured SMCs. The predominant distribution of beta-NM actin in the cell periphery was associated with densely distributed vinculin plaques and disrupted or striated myosin and alpha-actinin aggregates, which may reflect a process of stress fiber assembly during cell spreading and focal adhesion formation. The high-level labeling of alpha-SM actin in the central portion of stress fibers was related to continuous myosin and punctate alpha-actinin distribution, which may represent the maturation of the fibrillar structures. The findings also suggest that the stress fibers, in which actin and myosin filaments organize into sarcomere-like units with alpha-actinin-rich dense bodies analogous to Z-lines, are the contractile structures of cultured SMCs that link to the network of vimentin-containing intermediate filaments through the dense bodies and dense plaques.

FDA labeling requirements for disinfection of endoscopes: a counterpoint.

Endoscopes are used widely for the diagnosis and therapy of medical disorders. To prevent spread of nosocomial infection, all endoscopes should undergo thorough cleaning and high-level disinfection following each use. The Food and Drug Administration (FDA) has approved a user-friendly package label for one liquid chemical germicide that requires a 45-minute immersion at 25 degrees C to support a high-level disinfection label claim. Scientific data reviewed here suggest that one can achieve at least an 8-log reduction in M tuberculosis contamination with cleaning (4 logs) followed by chemical disinfection for 20 minutes (4 to 6 logs). The FDA should modify the label to state that if cleaning is accomplished using a standardized cleaning protocol, then a 20-minute immersion at 20 degrees C will be sufficient to achieve high-level disinfection.

The mechanism of the nicotinamide adenine dinucleotide stimulation of lipid phosphorylation in a rat liver microsome-cytosol system

High-level labeling of glycerol-containing phospholipids from[ 32P orthophosphate ( 32Pi) in a rat liver microsome-cytosol system or in liver homogenates requires the addition of optimal amounts of NAD, glycerol, and glycolytic intermediates such as fructose diphosphate and pyruvate. The cyclic oxidation-reduction of NAD via glycolysis is sufficient to yield a high level of ATP for lipid phosphorylation. For heavy labeling of phosphatidylethanolamine from 32Pi, NAD, fructose diphosphate and glycerol are required additives. For heavy labeling of phospholipid termed GPX (similar but not identical to phospatidic acid) the addition of NADH and pyruvate is required. Fructose diphosphate inhibits the labeling of GPX by producing unlabeled glycerophosphate. The labeling of GPX from 32Pi occurs in both the microsomal and mitochondrial fractions. Cyanide and dinitrophenol strongly inhibit lipid phosphorylation from 33Pi in liver homogenates fortified with NAD. No inhibition is observed in microsomescytosol. The inhibition by these agents in homogenates was overcome to the extent of 50% by added pyruvate. Oxamic acid, iodoacetate and fluoride, effective inhibitors of glycolysis, produced a large decrease in lipid phosphorylation in the microsomescytosol system. These results demonstrate that glycolysis can provide sufficient amounts of ATP for lipid phosphorylation. [ 14C] Fructose diphosphate was incorporated into phosphatidylethanolaminc and GPX in the microsome-cytosol system thus demonstrating that fructose diphosphate can provide α-glycerophosphate for phospholipid biosynthesis. NAD stimulates the in vitro incorporation of [ 14C]glycerol and 14C-labeled fatty acids into GPX, phosphatidylethanolamine and phosphatidylcholine. With 32Pi and [ 14C]glycerol, the labeling of GPX occurs faster than that of phosphatidylethanolamine or posphatidylcholine. However, with 14C-labeled fatty acids the labeling of phosphatidylcholine is the most rapid.

Low-molecular-weight nuclear RNAs.

LOW-MOLECULAR-WEIGHT NUCLEAR RNAs HARRIS BUSCH, TAE SUK ROCHOI, ARCHIE W. PRESTAYKO, HIROTOSHI SHIBATA, STANLEY T. CROOKE, SHUKRI M. EL-KHATIB, YONG C. CHOI, and CHARLES Af. MAURITZEN* The recent development of methods for isolation, purification and high-level labeling of the low-molecular-weight nuclear (LMWN) RNAs with sedimentation coefficients from 4S to 12S has amplified the initial evidence that some of these are specifically localized to the nucleolus and others to the nucleus of mammalian and other cells [1, p. 285; 2-14]. Some RNAs smaller in size than these which contain dihydrouridine or dihydrothymidine have also been reported to be present in the nucleus, specifically in association with the chromatin fraction [15-20]. Although the functions of most of these LMWN RNAs are not defined, their specificity of localization in the nucleolus and the nuclear chromatin fraction along with the number of these molecules (which approximates that of the number of functional genes) has suggested they may have an initiating or structural role in gene readouts or a regulatory role in gene function [8, 10, 2123 ]. The recent findings which have indicated that some low-molecular -weight nucleolar RNAs have specific temporal associations with newly synthesized ribosomal precursors suggest the possibility that they may be involved in specific phases of synthesis or modification of the ribosomal precursors [24]. Number of low-molecular-weight nuclear RNAs.—In a search for a definite role for these molecules in cell function, one of the critical problems has been the clarification of the number of types of these RNA molecules. Although it is not yet possible to specify with precision the total number of low-molecular-weight RNA species in the various fractions of the nucleus, the minimal estimate is eleven (fig. 1), and the maximum is approximately forty-three [1, 25]. None of these values, however, exclude possible microheterogeneity of the various nuclear RNA species. Table 1 indicates that there are at least * Department of Pharmacology, Baylor College of Medicine, Houston, Texas 77025. These studies were supported by the USPHS Cancer Research Center grant CA-10893 and the American Cancer Society grant P-339. Perspectives in Biology and Medicine · Autumn 1971 | 117 Fig. 1.—Polyacrylamide-gel electrophoresis of LMWN RNA. Electrophoresis with these 12 percent gels was carried out for fifty hours with a current of 40 mA at a voltage of 300 ? in a buffer containing 0.02 m Tris, 0.02 m NaCl, and 0.04 m EDTA. eight major groups of the LMWN RNAs up to 8S in size which include transfer RNA molecules in the 4S group and the 5S and 7S(28S) ribosomal RNA. The remaining species of RNA include the 4.5S RNA, 5S RNAn i, Ul and U2 extranucleolar nuclear RNAs and the nucleolar U3 and 8S(28S) RNAs. Although these molecules have been separated into mobility groups by polyacrylamide-gel electrophoresis, additional methods were required for further separation of these RNA groups into individual molecular species [2527 ]. At present, 4.5S RNA1, 4.5S RNA111, UIa and U2 RNA have been obtained in highly purified form (figs. 2-3). Localization of nuclear RNAs.—Analysis of the low-molecularweight RNA of the nucleolus has provided evidence for localization of two types of these RNA fractions, that is, the 8S (28S) RNA and the U3 RNA (fig. 4), regardless of whether nucleoli were isolated by 118 I Harris Busch et al. · Nuclear RNAs TABLE 1»^-Nucleotide Compositions and 3' Terminals of Low-MolecularWeight RNA from Ribosomes, Nucleus, and Nucleolus of Novikoff Hepatoma RNA amp ump GMP CMP AMP +UMP GMP +CMP 3'-Terminal 1.Nuclear 4-8S RNA: 4S ................ Total 4. 5S RNA... 4.5Si ............ 4.5Sn ........... 4.5Sm .......... Total 5S RNA ..... SSi .............. 5Sh ............. 5Sm ............ 7S(28S) ............ Ul ................ U2 ................ U3* ............... 8S(28S)*........... 2.Nucleolar 4-8S RNA 4S* ............... 5S ................ 7SÍ28S) ............ U3* ............... 8S(28S)*........... 3.Ribosomal RNA: 4S ................ 5S ................ 7S(28S) ............ 19.6 23.9 24.8 25.4 26.7 18.8 19.2 20.7 24.0 21.2 20.0 22.0 20.2 18.4 19.5 18.2 19.8 20.2 18.4 15.4 18.2 19.7 20.7 24.6 24.4 26.0...

Pyridine nucleotide stimulation of lipid phosphorylation

High-level labeling of glycerol-containing phospholipids from[32P orthophosphate (32Pi) in a rat liver microsome-cytosol system or in liver homogenates requires the addition of optimal amounts of NAD, glycerol, and glycolytic intermediates such as fructose diphosphate and pyruvate. The cyclic oxidation-reduction of NAD via glycolysis is sufficient to yield a high level of ATP for lipid phosphorylation. For heavy labeling of phosphatidylethanolamine from 32Pi, NAD, fructose diphosphate and glycerol are required additives. For heavy labeling of phospholipid termed GPX (similar but not identical to phospatidic acid) the addition of NADH and pyruvate is required. Fructose diphosphate inhibits the labeling of GPX by producing unlabeled glycerophosphate. The labeling of GPX from 32Pi occurs in both the microsomal and mitochondrial fractions.Cyanide and dinitrophenol strongly inhibit lipid phosphorylation from 33Pi in liver homogenates fortified with NAD. No inhibition is observed in microsomescytosol. The inhibition by these agents in homogenates was overcome to the extent of 50% by added pyruvate. Oxamic acid, iodoacetate and fluoride, effective inhibitors of glycolysis, produced a large decrease in lipid phosphorylation in the microsomescytosol system. These results demonstrate that glycolysis can provide sufficient amounts of ATP for lipid phosphorylation.[14C] Fructose diphosphate was incorporated into phosphatidylethanolaminc and GPX in the microsome-cytosol system thus demonstrating that fructose diphosphate can provide α-glycerophosphate for phospholipid biosynthesis.NAD stimulates the in vitro incorporation of [14C]glycerol and 14C-labeled fatty acids into GPX, phosphatidylethanolamine and phosphatidylcholine. With 32Pi and [14C]glycerol, the labeling of GPX occurs faster than that of phosphatidylethanolamine or posphatidylcholine. However, with 14C-labeled fatty acids the labeling of phosphatidylcholine is the most rapid.