Category Identification Research Articles

Electrical analogy approach to estimate material category from transient thermal response

Material identification based on thermal properties is a commonly used approach of classifying materials into different categories. In this paper, an analytical approach for estimation of two thermal properties under the transient condition and arbitrary ambient temperature is presented for material category identification. The thermal properties are estimated by exploiting the analogy between the one-dimensional thermal conduction in material with the voltage distribution in resistance-capacitance (RC) ladder network. The concept of time delay in the RC ladder network has been used in the thermal domain for deriving simple analytical expressions of thermal diffusivity and thermal conductivity. The proposed approach is simple, has less data related concerns and can identify the material category based on two thermal properties for better certainty. This approach has been verified by simulations and experiments performed on materials of different categories. The approach would be helpful in automated applications related to material identification or handling.

Exploiting bi-directional global transition patterns and personal preferences for missing POI category identification

Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Interest (POI) recommender systems. Existing POI recommendation and location prediction tasks utilize past information for future recommendation or prediction from a single direction perspective, while the missing POI category identification task needs to utilize the check-in information both before and after the missing category. Therefore, a long-standing challenge is how to effectively identify the missing POI categories at any time in the real-world check-in data of mobile users. To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users. Specifically, we delicately design an attention matching cell to model how well the check-in category information matches their non-personal transition patterns and personal preferences. Finally, we evaluate our model on two real-world datasets, which clearly validate its effectiveness compared with the state-of-the-art baselines. Furthermore, our model can be naturally extended to address next POI category recommendation and prediction tasks with competitive performance.

GaoFen-1 Remote Sensing Image Forest Extraction Using Object-based CNN

As an important natural resource, forest plays a vital role in regulating regional climatic conditions and maintaining the balance of the Earth’s ecosystem. At the same time, the major changes in China’s natural resource management system have put forward new requirements for forest surveys. Efficient and accurate grasp of the spatial distribution of forest resources and the type of forest in China have become an important part of all-weather remote sensing survey and monitoring of natural resources. In recent years, with the strong promotion of the national high-scoring project, the extraction of forest based on high-resolution remote sensing image (HSRI) has become one of the main technical means of forest resource survey in China. However, HSRI contains abundant detailed information and spatial characteristic information, and spectral heterogeneity within the object is increased, which significantly causes difficulties in HSRI processing and information extraction. Therefore, aiming at the problem of extracting forest types from China’s domestic GF-1 satellite image, this paper proposes a classification method based on feature learning using an object-oriented convolution neural network (CNN). First, the study area was under-segmented using the Multi-Resolution Segmentation (MRS) algorithm and referred Estimation of Scale Parameter algorithm to determining the segmented scale. Then, a CNN model was obtained for identifying forest type by parameter adjustment. Finally, the multi-layer feature learning of the CNN and the regional Majority Voting algorithm were combined to classify the forest types within the study region. The overall extraction accuracy of the proposed method in this paper was 0.88. The object-oriented method can avoid the “salt and pepper” phenomenon that exists in the pixel-based classification. CNN can extract the deep features contained in HSRI, which is effectively conductive to category identification. In addition, the method proposed in this paper has a greater applicability to forest extraction from HSRI.

Verbing nouns and nouning verbs: Using a balanced design provides ERP evidence against "syntax-first" approaches to sentence processing.

In this event-related potential (ERP) study we reevaluate syntax-first approaches to sentence processing by implementing a novel paradigm in French that includes correct sentences, pure syntactic category violations, lexical-semantic anomalies, and combined anomalies. Our balanced design systematically controlled for target word (noun vs. verb) and the context immediately preceding it. Group results from 36 native speakers of Quebec French revealed that, up to 300 ms, ERPs elicited by syntactic category violations were comparable with ERP responses to correct sentences, showing that there is no early activation reflecting syntactic category identification. Instead, in response to all anomalous conditions, we observed an N400 followed by a P600. Combined anomalies yielded additive effects of syntactic category and lexical-semantic anomalies on the N400, and a large P600 effect similar to the one observed in the pure syntactic condition. These results provide strong evidence against the hypothesis that (i) syntactic categories are processed first, and (ii) that syntactic category errors “block” lexical-semantic processing. Further, the N400 effect in response to pure syntactic category violations reflects a mismatch detection between a predicted word-stem and the actual target. This mechanism takes place simultaneously (and potentially in parallel) with lexical-semantic processing. In contrast, an interaction of syntax and semantics for the P600 reveals that the same neurocognitive resources are recruited for syntactic and semantic integration, both promoted by the implementation of an acceptability judgement task in our design. Additional analyses of individual data complemented these observations: during sentence processing, participants did not rely on one single cognitive mechanism reflected by either the N400 or the P600 effect but on both, suggesting that the biphasic N400-P600 ERP wave can indeed be considered to be an index of phrase-structure violations in most individuals, at least if they are realized on content words.

Language profiles in children with concussion

ABSTRACTPrimary Objective: Inform the production of a screening tool for language in children with concussion. The authors predicted that children with a recent concussion would perform the cognitive-linguistic tasks more poorly, but some tasks may be more sensitive to concussion than others.Methods & Procedures: 22 elementary school aged children within 30 days of a concussion and age-matched peers with no history of concussion were assessed on a battery of novel language and cognitive-linguistic tasks. They also completed an auditory attention task and the Raven’s Colored Progressive Matrices.Main Outcomes & Results: Children with a recent concussion scored significantly more poorly in novel tasks targeting category identification, grammaticality judgments, and recognizing target words presented in a short story than their age-matched peers with no such injury history. All observed effects had moderate sizes. Inclusion of these three tasks significantly improved prediction of concussion status over symptom score when controlling for the age of participants.Conclusions: The finding supports continued investigation of targeted linguistic tasks in children following concussion, particularly in the domains of semantic and syntactic access and verbal working memory. Future work developing brief language assessments specifically targeting children in this age range may provide a valuable addition to the existing tools for identifying the effects of concussion.

Is there an end in sight? Viewers' sensitivity to abstract event structure

People segment the stream of experience into events, or temporal segments that have a beginning and an ending. But how are such event boundaries defined? Linguistic theories of event encoding draw a distinction between bounded events that include an inherent endpoint (“eat a pretzel”) and unbounded events that lack such an endpoint (“eat cheerios”). Even though the literature on event cognition has not focused on such abstract aspects of event structure, we hypothesize that sensitivity to boundedness could shape the way events are processed. In the present study, we show that viewers are sensitive to event boundedness in a category identification task and distinguish it from event completion; furthermore, viewers identify bounded events more easily than unbounded events. Sensitivity to boundedness emerges even when viewers are prevented from encoding the events linguistically and thus does not depend on the online use of linguistic distinctions. We conclude that event cognition relies on highly abstract properties of events and their boundaries, and sketch implications of these findings for the way events are described, processed, and used to interact with the world.

Category identification of non-technical skills required for emergency medical care

Category identification of non-technical skills required for emergency medical care

Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs

Students’ feedback is an effective mechanism that provides valuable insights about teaching-learning process. Handling opinions of students expressed in reviews is a quite labour-intensive and tedious task as it is typically performed manually by the human intervention. While this task may be viable for small-scale courses that involve just a few students’ feedback, it is unpractical for large-scale cases as it applies to online courses in general, and MOOCs, in particular. Therefore, to address this issue, we propose in this paper a framework to automatically analyzing opinions of students expressed in reviews. Specifically, the framework relies on aspect-level sentiment analysis and aims to automatically identify sentiment or opinion polarity expressed towards a given aspect related to the MOOC. The proposed framework takes advantage of weakly supervised annotation of MOOC-related aspects and propagates the weak supervision signal to effectively identify the aspect categories discussed in the unlabeled students’ reviews. Consequently, it significantly reduces the need for manually annotated data which is the main bottleneck for all deep learning techniques. A large-scale real-world education dataset containing around 105k students’ reviews collected from Coursera and a dataset comprising of 5989 students’ feedback in traditional classroom settings are used to perform experiments. The experimental results indicate that our proposed framework attains inspiring performance with respect to both the aspect category identification and the aspect sentiment classification. Moreover, the results suggest that the framework leads to more accurate results than the expensive and labour-intensive sentiment analysis techniques relying heavily on manually labelled data.

Concept Mapping for Noun Identification in CLIL Textbooks by Primary Education Students

This study aims at observing if initial training on concept mapping improves pupils’ skills on noun category identification from foreign language (L2) texts. To observe the null hypothesis, learners receive short-term training to manage the basics of concept mapping. After the intervention, in a classroom context of reading and writing assignments in the English language, Spanish primary students identify and circle substantives in texts from their CLIL textbooks. The sample comprises sixty fifth-grade Spanish pupils age-ranged 10 to 11. Though the school has an English-Spanish bilingual program, the students are not complete bilinguals. In the sample, participants belong to both male and female genders non randomly assigned. Concerning the identification of singular and collective nouns from texts, including plural irregular nouns, both treated and control sample groups displayed similar results. Although the instructed group was skilled in detecting and categorizing hypernyms and first hyponym categories of nouns, they did not precisely discriminate adjectives, confusing them with nouns. Dissimilarly, the untrained control group not only mistakenly identified adjectives as nouns but also other grammatical categories as verbs, conjunctions, pronouns, and adverbs. The scarce disclosed precision by the non-trained group was also perceptible in the production of the concept maps. When they had to make the concept mapping tasks, control participants tended to make flow charts, mind maps, and tree diagrams instead. Contrarily, the trained group accomplished the activities satisfactorily. Yet the outcome of this study reveals that a little training on concept mapping leads to disclosing slight achievement in discriminating information from L2 texts in students. Hence, statistical analysis reveals that concept mapping helps students differentiate lexical and grammatical categories and to synthesize the information.

Patient safety incidents reported by relatives of hospitalized children.

To know the main safety incidents reported by relatives of patients hospitalized in pediatric units. This is a qualitative research with exploratory-descriptive design. Data were collected between April 2016 and December 2017, in three hospitals in Porto Alegre/RS. A total of 91 semi-structured interviews were conducted with relatives of pediatric patients hospitalized in emergencies, infirmaries, and intensive care centers. The statements were transcribed in full and submitted to a thematic content analysis. The category Identification of safety incidents by relatives of hospitalized pediatric patients was formulated. Eight subtopics emerged: drug therapy, hand and environment hygiene, diet therapy, risk of falls, communication, patient identification, care processes/procedures and access to the institution. The family identified safety incidents in various stages of care for hospitalized children. It is necessary to think about strategies that integrate the family in the care of children, to minimize risks and complications resulting from care.

Comprehensive Eye Diagram Analysis: A Transfer Learning Approach

A deep transfer learning (TL)-based comprehensive eye diagram analysis and diagnosis scheme that can output essential eye diagram parameters, estimate fiber link length, calculate Q-factor, and diagnose device imperfection-induced impairments is proposed. TL can be used to extract system information and optical signal characteristics contained in eye diagrams and apply the learned knowledge and extracted features obtained from source tasks to related target tasks. As a source task, the proposed method estimates the transmission distance of a fiber link using convolutional neural network (CNN)-based eye diagram recognition. The feature extraction layers of the CNN are transferred to six target tasks involving the recognition of cross percentage, levels “0” and “1,” eye height and width, and Q-factor. Using TL reduces the total training times for on-off keying (OOK) and pulse amplitude modulation (PAM4) formats by $>$ 95% and 60%, respectively. We also investigated six common PAM4 impairments caused by transmitter imperfection by setting the impairment category identification as source task and the impairment-degree diagnoses as target tasks. The TL methods consistently outperformed non-TL methods, with higher accuracies and significantly reduced training times. The proposed impairment diagnosis technique should be useful in impairment healing and fault correction.

Classification and Change Detection of Tirupati Urban Area using Erosion and Dilation Based PCA Transform

In remote sensing, the identification of the land use and land cover (LULC) changes in the global and local region are developed by classification and detection algorithms. This classification system can be developed to meet the needs of state agencies, and Federal for an up-to-date analyze of LULC throughout the entire selected of region area. The multispectral images have multiple low-resolution bands due to lack of sensory acquisition problem, haze-covered on earth objects and atmospheric distributions. So difficult to analyze the full information, the user wrongly interprets the information. Image processing applications can be done for compress and enhance the details of land surface details. The Principal Component Analysis and Morphological operations are implemented for compressing and feature extract the color and earth object values with good accuracy level. Change Detection between the time difference of the proposed enhanced images for land objects classes was computed. The most extensive land cover change category identification of the Tirupati urban Agricultural and forest area for the last 14 years. The change analyzed by using the image differencemethod for obtaining the changing level of the forest and urban development areas between two-timeintervals.

SO–CNN based urban functional zone fine division with VHR remote sensing image

Functional zone reflects city's spatial structures, and as a carrier of social and economic activities, it is of critical significance to urban management, resource allocation and planning. However, most researches on functional zone division are based on a large spatial scale such as blocks or other scales larger than it. Aiming at a subtle fine functional result, the concept of Super Object (SO) was especially explained, also a Super Object - Convolutional Neural Network (SO–CNN) based urban functional zone fine division method with very high resolution (VHR) remote sensing image was proposed. The original image was firstly segmented into different SOs which correspond to the basic functional zone units in geography. A random point generation algorithm was used to generate the voting points for functional zone category identification, and then a trained CNN model was employed to assign functional attributes to those voting points. Then a statistical method was involved to count the frequency of the classified voting points of different functional attributes in each basic functional zone units. By voting process, the functional attribute with the highest frequency was assigned to the basic functional zone unit, which corrected the misclassification results of CNN to some extent. This paper also explored the scale effect of the SO on the final functional zone classification result from two aspects, spatial scale of SO and the sampling window size of CNN model. Because of the natural differences between functional zone division and land cover classification, region based overall accuracy assessment method was used to evaluate functional zone division result. Compared with other methods, SO–CNN method can generate higher accuracy and subtle result, based on which larger spatial scale results can be available by scaling-up, so SO–CNN method plays a great significant role on small scale functional space structure research.

A neural ensemble correlation code for sound category identification.

Humans and other animals effortlessly identify natural sounds and categorize them into behaviorally relevant categories. Yet, the acoustic features and neural transformations that enable sound recognition and the formation of perceptual categories are largely unknown. Here, using multichannel neural recordings in the auditory midbrain of unanesthetized female rabbits, we first demonstrate that neural ensemble activity in the auditory midbrain displays highly structured correlations that vary with distinct natural sound stimuli. These stimulus-driven correlations can be used to accurately identify individual sounds using single-response trials, even when the sounds do not differ in their spectral content. Combining neural recordings and an auditory model, we then show how correlations between frequency-organized auditory channels can contribute to discrimination of not just individual sounds but sound categories. For both the model and neural data, spectral and temporal correlations achieved similar categorization performance and appear to contribute equally. Moreover, both the neural and model classifiers achieve their best task performance when they accumulate evidence over a time frame of approximately 1–2 seconds, mirroring human perceptual trends. These results together suggest that time-frequency correlations in sounds may be reflected in the correlations between auditory midbrain ensembles and that these correlations may play an important role in the identification and categorization of natural sounds.

SOUND ASSIMILATION IN AMANATUN DIALECT OF UAB METO

This study was conducted to identify and describe the kinds of assimilation in Amanatun Dialect of Uab Meto. Descriptive qualitative was used in conducting the study. The data were collected from Uab Meto native speakers speech transcription and analyzed using category identification and integration. The result of this study indicates that there are ten kinds of assimilation in Amanatun dialect of Uab Meto which are grouped into five classifiers. They are phonological assimilation and morphophonemic assimilation which are grouped into assimilation based on form; regressive assimilation and progressive assimilation which are grouped into assimilation based on direction, contact assimilation and distance assimilation which are grouped into assimilation based on distance, assimilation in word and assimilation at word boundary which are grouped into assimilation based on position, consonant assimilation and vowel-consonant assimilation which are grouped into assimilation based on inventory of sound.DOI: 10.24071/ijhs.2019.030104

Motivational intensity and visual word search: Layout matters

Motivational intensity has been previously linked to information processing. In particular, it has been argued that affects which are high in motivational intensity tend to narrow cognitive scope. A similar effect has been attributed to negative affect, which has been linked to narrowing of cognitive scope. In this paper, we investigated how these phenomena manifest themselves during visual word search. We conducted three studies in which participants were instructed to perform word category identification. We manipulated motivational intensity by controlling reward expectations and affect via reward outcomes. Importantly, we altered visual search paradigms, assessing the effects of affective manipulations as modulated by information arrangement. We recorded multiple physiological signals (EEG, EDA, ECG and eye tracking) to assess whether motivational states can be predicted by physiology. Across the three studies, we found that high motivational intensity narrowed visual attentional scope by altering visual search strategies, especially when information was displayed sparsely. Instead, when information was vertically listed, approach-directed motivational intensity appeared to improve memory encoding. We also observed that physiology, in particular eye tracking, may be used to detect biases induced by motivational intensity, especially when information is sparsely organised.

SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples.

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore prior knowledge of transcriptomes and the probable structures of the data. Moreover, cell identification heavily relies on subjective and possibly inaccurate human inspection afterwards. To address these analytical challenges, we developed SCINA (Semi-supervised Category Identification and Assignment), a semi-supervised model that exploits previously established gene signatures using an expectation–maximization (EM) algorithm. SCINA is applicable to scRNA-Seq and flow cytometry/CyTOF data, as well as other data of similar format. We applied SCINA to a wide range of datasets, and showed its accuracy, stability and efficiency, which exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocytes from mouse brain scRNA-Seq data. SCINA also detected immune cell population changes in cytometry data in a genetically-engineered mouse model. Furthermore, SCINA performed well with bulk gene expression data. Specifically, we identified a new kidney tumor clade with similarity to FH-deficient tumors (FHD), which we refer to as FHD-like tumors (FHDL). Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

Automating Intention Mining

Developers frequently discuss aspects of the systems they are developing online. The comments they post to discussions form a rich information source about the system. Intention mining, a process introduced by Di Sorbo et al., classifies sentences in developer discussions to enable further analysis. As one example of use, intention mining has been used to help build various recommenders for software developers. The technique introduced by Di Sorbo et al. to categorize sentences is based on linguistic patterns derived from two projects. The limited number of data sources used in this earlier work introduces questions about the comprehensiveness of intention categories and whether the linguistic patterns used to identify the categories are generalizable to developer discussion recorded in other kinds of software artifacts (e.g., issue reports). To assess the comprehensiveness of the previously identified intention categories and the generalizability of the linguistic patterns for category identification, we manually created a new dataset, categorizing 5,408 sentences from issue reports of four projects in GitHub. Based on this manual effort, we refined the previous categories. We assess Di Sorbo et al.'s patterns on this dataset, finding that the accuracy rate achieved is low (0.31). To address the deficiencies of Di Sorbo et al.'s patterns, we propose and investigate a convolution neural network (CNN)-based approach to automatically classify sentences into different categories of intentions. Our approach optimizes CNN by integrating batch normalization to accelerate the training speed, and an automatic hyperparameter tuning approach to tune appropriate hyperparameters of CNN. Our approach achieves an accuracy of 0.84 on the new dataset, improving Di Sorbo et al.'s approach by 171 percent. We also apply our approach to improve an automated software engineering task, in which we use our proposed approach to rectify misclassified issue reports, thus reducing the bias introduced by such data to other studies. A case study on four open source projects with 2,076 issue reports shows that our approach achieves an average AUC score of 0.687, which improves other baselines by at least 16 percent.

Working to Have a Normal Life With Cystic Fibrosis in an Adherence-Driven Health Care System.

Adults with cystic fibrosis (CF) must continuously manage their condition, while working for a living, and want a normal life. Adherence rates to treatments/medications are less than optimal. Existing theory offers little to explain adherence rates. The purpose of this study was to develop a theory to further the understanding of how people with CF manage their condition in an adherence-driven health care system. Constructivist Grounded Theory methodology was used to conduct 27 semistructured interviews with adults with CF, family members, and health care providers. Data collection and analysis were simultaneous, using constant comparative methods, initial and focused coding, and category identification and reduction to develop a theory. Doing what works to balance life and CF is the theory generated from this study. The main concern of participants was to be seen as normal. The theory depicts what participants with CF and their family members do about their concerns and involves 4 interrelated processes: working overtime, receiving support, passing as normal, and facing disease progression. Participants did not relate to the term nonadherent; rather they described working overtime to manage CF, to work, and to have a normal life. Health care provider and researcher perspectives on adherence differ from those of people with CF. Engaging adults with CF and health care providers in a dialogue in which expectations are shared may lead to individualized treatment regimens that work, because adults with CF will do what works.

Ensino de Tatos, Respostas de Ouvinte e Efeitos sobre Intraverbais em Autistas

Efeitos de metodologias de intervenção em Análise do Comportamento são investigados sobre a aprendizagemde intraverbais, que costumam ser deficientes em crianças autistas. O objetivo desta pesquisa foi medir efeitosdo ensino de repertórios de tato e ouvinte, organizado em dois diferentes pacotes de intervenções para duasduplas de crianças autistas, sobre a emergência de intraverbais de relatar membros de categorias. Para duascrianças da condição tato, foi implementado o ensino de tatos simples, tatos simples com feedback instrutivoe tatos de categorias; para duas crianças da condição ouvinte, ensino de respostas de ouvinte simples, respostascom feedback instrutivo e identificação de categorias como ouvinte. Não foi demonstrado qualquer efeito deemergência de intraverbais, e seu ensino direto demandou o uso de muitas pistas (tato e ecoico), sendo queimplicou evolução com variabilidade para um participante e dependência do tato como pista. São discutidaspossibilidades de adaptações metodológicas para pesquisas futuras.