Tojisha-Kenkyu of autism spectrum disorders

Accumulating evidence suggests behavioral and genetic fractionation of the autistic traits. Above it, ethnographic researches that document how the sociality of children with autism varies across different situational conditions, depict autistic sociality not as an oxymoron but, rather, as a reality. Given the discussion above, it is important to do case-based studies on the assumption of heterogeneity of sociality and of autism. In addition, subjective experience is an important outcome variable whenever considering support. As a candidate of the research methods that meet the above conditions, we would like to introduce Tojisha-Kenkyu. In a nutshell, ‘Tojisha-Kenkyu’ is studying oneself through communication with others who share similar experiences. We introduce Ayaya’s Tojisha-Kenkyu and Necco Tojisha-Kenkyu meeting as examples of Tojisha-Kenkyu on autism spectrum disorders (ASD), which illustrate that Tojisha-Kenkyu has not only recuperative effects but also academic potentials changing the concept of ASD.     

Attribute Selection for Numerical Databases that Contain Correlations

There are many correlated attributes in a database. Conventional attribute selection methods are not able to handle such correlations and tend to eliminate important rules that exist in correlated attributes. In this paper, we propose an attribute selection method that preserves important rules on correlated attributes. We rst compute a ranking of attributes by using conventional attribute selection methods. In addition, we compute two-dimensional rules for each pair of attributes and evaluate their importance for predicting a target attribute. Then, we evaluate the shapes of important two-dimensional rules to pick up hidden important attributes that are under-estimated by conventional attribute selection methods. After the shape evaluation, we re-calculate the ranking so that we can preserve the important correlations. Intensive experiments show that the proposed method can select important correlated attributes that are eliminated by conventional methods.     

Extension of grounding mechanism for abstract words: computational methods insights

The attempts to model cognitive phenomena effectively have split the research community in two paradigms: symbolic and connectionist. The extension of grounding phenomenon for abstract words is very important for social interactions of cognitive robots in real scenarios. This paper reviews the strength of symbolic and connectionist methods to address the abstract word grounding problem in cognitive robots. In particular, the presented work is focused on designing and simulating cognitive robotics model to achieve a grounding mechanism for abstract words by using the semantic network approach, as well as examining the utility of connectionist computation for the same problem. Two neuro-robotics models based on feed forward neural network and recurrent neural network are presented to see the pros and cons of connectionist approach. The simulation results and review of attributes of these methods reveal that the proposed symbolic model offers the solution to the problem of grounding abstract words with attributes like high data storage capacity with recall accuracy, structural integrity and temporal sequence handling. Whereas, connectionist computation based solutions give more natural solution to this problem with some shortcomings that include combinatorial ambiguity, low storage capacity and structural rigidity. The presented results are not only important for the advancement in communication system of cognitive robot, also provide evidence for embodied nature of abstract language.     

Key factors mediating the use of a mobile technology tool designed to develop social and life skills in children with Autistic Spectrum Disorders

Of late there has been growing interest in the potential of technology to support children with Autistic Spectrum Disorders (ASD) with social and life skills. There has also been a burgeoning interest in the potential use of mobile technology in the classroom and in the use of such technology to support children with ASD. Building on these developments, the HANDS project has developed a mobile cognitive support application for smartphones, based on the principles of persuasive technology design, which supports children with ASD with social and life skills functioning - areas of ability which tend to be impaired in this population. The software application has been piloted in four special schools for children with ASD. This paper reports on a qualitative interpretivist evaluation, which explores which factors may mediate how the software application is incorporated in to existing practice and what influence it has on practice. Kairos is identified as a key factor, which is associated with the teachers’ view of the software application as extending their reach beyond the classroom. Design guidelines are proposed for future implementations of similarly purposed technology tools.     

医疗数据发布中属性顺序敏感的隐私保护方法

隐私保护已成为包含微数据应用诸如医疗数据发布共享或数据挖掘中的一个重要问题.基于全局重编码或局部重编码的匿名性方法,通过保证每一条数据记录都至少有某个数量的其他记录与其具有同样的特征来保护隐私性.如果考虑到对处理后的数据进行属性顺序敏感的数据分析任务,这类方法并不能很好地完成任务.研究基于数据可用性指标的匿名性方法,着重考虑数据分析任务中的属性顺序对于匿名性方法的影响.从多维数据匿名的概念出发,讨论用于该类情况下的数据匿名性方法.在公开数据集上的实验结果表明,该方法对于上述问题是有效的,并且效率并未受到影响.     

Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder

Generally, an experienced therapist continuously monitors the affective cues of the children with Autism Spectrum Disorders (ASD) and adjusts the course of the intervention accordingly. In this work, we address the problem of how to make the computer-based ASD intervention tools affect-sensitive by designing therapist-like affective models of the children with ASD based on their physiological responses. Two computer-based cognitive tasks are designed to elicit the affective states of liking, anxiety, and engagement that are considered important in autism intervention. A large set of physiological indices are investigated that may correlate with the above affective states of children with ASD. In order to have reliable reference points to link the physiological data to the affective states, the subjective reports of the affective states from a therapist, a parent, and the child himself/herself were collected and analyzed. A support vector machines (SVM)-based affective model yields reliable prediction with approximately 82.9% success when using the therapist's reports. This is the first time, to our knowledge, that the affective states of children with ASD have been experimentally detected via physiology-based affect recognition technique.     

Conditional Generative Adversarial Network Approach for Autism Prediction

Autism Spectrum Disorder (ASD) requires a precise diagnosis in order to be managed and rehabilitated. Non-invasive neuroimaging methods are disease markers that can be used to help diagnose ASD. The majority of available techniques in the literature use functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. Traditional supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text, images, and videos, but their performance and robustness are restricted by the size of the accompanying training data. Deep learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering algorithms. It takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural networks. Generative modelling, also known as Generative Adversarial Networks (GANs), is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original dataset. GANs are an exciting and rapidly changing field that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains, most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the past. In this paper, we present a novel conditional generative adversarial network, or cGAN for short, which is a form of GAN that uses a generator model to conditionally generate images. In terms of prediction and accuracy, they outperform the standard GAN. The proposed model is 74% more accurate than the traditional methods and takes only around 10 min for training even with a huge dataset.     

Attributes affecting computer-aided decision making — a literature survey

In spite of the ever-increasing use of computers in decision making, few people consider the effects of human interaction on the efficacy of computerassisted decision making.This article provides a comprehensive review of current literature relating to those personal, demographic, situational and cognitive attributes that affect computer-aided decision making. In addition, the overall effectiveness of computer-aided decision making is explored as it relates to decision quality, decision effectiveness, and decision confidence.Prior studies relating to the effects on computer-aided decision making of attributes, such as age, anxiety, cognitive type, attitude toward computers, gender, and prior computer experience are discussed. Although many of the studies provide significant empirical evidence as to the importance of these attributes, this article provides a balanced presentation by also presenting opposing results.     

Issues,challenges, and a proposed theoretical core of agile software development research

Over the last two decades, agile software development (ASD) has garnered much attention in both research and practice. Several ASD methods and techniques have been developed and studied. In particular, researchers have provided several theoretical perspectives on ASD and contributed rich insights to the ASD practice. Still, despite calls for a more unified theoretical understanding of ASD, a theoretical core of ASD has not been identified. This paper offers a theoretical core of ASD research, clarifying what is essential and what is less essential for IS agility, hoping to spark a scholarly discussion, and provides implications of such a core for understanding method tailoring.     

基于视觉认知的自闭症儿童干预图卡角色造型设计研究

以自闭症儿童视觉认知特点为基础,探究自闭症儿童的视觉意象偏好,解析干预 图卡角色造型的设计要素,充分发挥干预图卡的效能。首先通过康复机构、教辅人员、特殊教 育网站等渠道整理目前市场上流通的自闭症儿童干预图卡,筛选出具有代表性的图卡角色造型, 并结合自闭症儿童视觉认知特点对角色造型的特征进行意象分析,整理视觉意象词汇;然后运 用语义差异法,将视觉意象词汇和图卡样本相结合制作评估量表,对获得数据进行统计整理, 通过结合因子分析法提取出视觉意象主因子;最终构建自闭症儿童视觉认知偏好与主意象因子 之间的映射关系。通过调研与实验,整理了具有代表性的干预图卡角色造型,结合自闭症儿童 的视觉偏好与意象词汇,有针对性地调整造型特征,能够增强干预图卡角色造型的趣味性,提 高干预训练的有效性,为教辅人员进行干预训练提供参考。     

Exploring the Hotel Service Personnel's Cognitive Implications toward Service Attributes and Ethics

It is extremely important for service personnel to have high ethical standards, be committed to the business, and be willing to serve customers well, as this can help their employer to gain a competitive advantage. This study adopted the Kano model and proposed a service ethics scale to explore service personnel's cognitive implications toward hotel service attributes and ethics. Based on a literature review and expert interviews, this study summarized 40 service ethics variables. A total of 438 responses from hotel service personnel were collected. Through factor analysis, seven dimensions (factors) of service ethic measurement were confirmed. The analytical results showed that hotel attributes within the “service attitude” and “room facilities” factors are evaluated as attractive and one‐dimensional qualities by the service personnel with high ethical standards. Such attributes should thus be a focus of hotels to increase the level of customer satisfaction. Finally, the limitations and managerial implications of this work, as well as directions for future research, are also provided. © 2012 Wiley Periodicals, Inc.     

Applied cognitive task analysis (ACTA): a practitioner's toolkit for understanding cognitive task demands

Cognitive task analysis (CTA) is a set of methods for identifying cognitive skills, or mental demands, needed to perform a task proficiently. The product of the task analysis can be used to inform the design of interfaces and training systems. However, CTA is resource intensive and has previously been of limited use to design practitioners. A streamlined method of CTA, Applied Cognitive Task Analysis (ACTA), is presented in this paper. ACTA consists of three interview methods that help the practitioner to extract information about the cognitive demands and skills required for a task. ACTA also allows the practitioner to represent this information in a format that will translate more directly into applied products, such as improved training scenarios or interface recommendations. This paper will describe the three methods, an evaluation study conducted to assess the usability and usefulness of the methods, and some directions for future research for making cognitive task analysis accessible to practitioners. ACTA techniques were found to be easy to use, flexible, and to provide clear output. The information and training materials developed based on ACTA interviews were found to be accurate and important for training purposes.     

Neural networks for analyzing service quality in public transportation

It is essential to take into account the service quality assessment made by the passengers of a public transportation system, as well as the weight or relative importance assigned to each one of the attributes considered, in order to know its strengths and weaknesses. This paper proposes using Artificial Neural Networks (ANN) to analyze the service quality perceived by the passengers of a public transportation system. This technique is characterized by its high capability for prediction and for capturing highly non-lineal intrinsic relations between the study variables without requiring a pre-defined model. First, an ANN model was developed using the data gathered in a Customer Satisfaction Survey conducted on the Granada bus metropolitan transit system in 2007. Next, three different methods were used to determine the relative contribution of the attributes. Finally, a statistical analysis was applied to the outcomes of each method to identify groups of attributes with significant differences in their relative importance. The results show that statistical significant differences exist among several categories of attributes that have a greater or lesser impact on service quality and satisfaction. All the methods agree that Frequency is the most influential attribute in the service quality, and that other attributes such as Speed, Information and Proximity are also important.     

Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions

In this study, a dynamic screening strategy is proposed to discriminate subjects with autistic spectrum disorder (ASD) from healthy controls. The ASD is defined as a neurodevelopmental disorder that disrupts normal patterns of connectivity between the brain regions. Therefore, the potential use of such abnormality for autism screening is investigated. The connectivity patterns are estimated from electroencephalogram (EEG) data collected from 8 brain regions under various mental states. The EEG data of 12 healthy controls and 6 autistic children (age matched in 7–10) were collected during eyes-open and eyes-close resting states as well as when subjects were exposed to affective faces (happy, sad and calm). Subsequently, the subjects were classified as autistic or healthy groups based on their brain connectivity patterns using pattern recognition techniques. Performance of the proposed system in each mental state is separately evaluated. The results present higher recognition rates using functional connectivity features when compared against other existing feature extraction methods.     

A review on diagnostic autism spectrum disorder approaches based on the Internet of Things and Machine Learning

Children with autism spectrum disorders (ASDs) have some disturbance activities. Usually, they cannot speak fluently. Instead, they use gestures and pointing words to make a relationship. Hence, understanding their needs is one of the most challenging tasks for caregivers, but early diagnosis of the disease can make it much easier. The lack of verbal and nonverbal communications can be eliminated by assistive technologies and the Internet of Things (IoT). The IoT-based systems help to diagnose and improve the patients’ lives through applying Deep Learning (DL) and Machine Learning (ML) algorithms. This paper provides a systematic review of the ASD approaches in the context of IoT devices. The main goal of this review is to recognize significant research trends in the field of IoT-based healthcare. Also, a technical taxonomy is presented to classify the existing papers on the ASD methods and algorithms. A statistical and functional analysis of reviewed ASD approaches is provided based on evaluation metrics such as accuracy and sensitivity.

     

Perceptions of built environment and health outcomes for older Chinese in Beijing: A big data approach with street view images and deep learning technique

Built environment attributes have been demonstrated to be associated with various health outcomes. However, most empirical studies have typically focused on objective built environmental measures. Still, perceptions of the built environment also play an important role in health and may complement studies with objective measures. Some built environment attributes, such as liveliness or beauty, are difficult to measure objectively. Traditional methods to assess perceptions of the built environment, such as questionnaires and focus groups, are time-consuming and prone to recall bias. The recent development in machine deep learning techniques and big data of street view images, makes it possible to assess perceptions of the built environment with street view images for a large-scale study area. By using online free Tencent Street View (TSV) images, this study assessed six perceptual attributes of the built environment: wealth, safety, liveliness, depression, bore and beauty. These attributes were associated with both the physical and the mental health outcomes of 1231 older adults in 48 neighborhoods in the Haidian District, Beijing, China. Results show that perceived safety was significantly associated with both the physical and mental health outcomes. Perceived depression and beauty were significant related to older adults' mental health, while perceived wealth, bore and liveliness were significantly related to their physical health. The findings carry important policy implications and hence contribute to the development of healthy cities. It is urgent to improve residents' positive perceptions and decrease their negative perceptions of the built environment, especially in neighborhoods that are highly populated by older adults.     

Extracting multiple news attributes based on visual features

The problem of automatically extracting multiple news attributes from news pages is studied in this paper. Most previous work on web news article extraction focuses only on content. To meet a growing demand for web data integration applications, more useful news attributes, such as title, publication date, author, etc., need to be extracted from news pages and stored in a structured way for further processing. An automatic unified approach to extract such attributes based on their visual features, including independent and dependent visual features, is proposed. Unlike conventional methods, such as extracting attributes separately or generating template-dependent wrappers, the basic idea of this approach is twofold. First, candidates for each news attribute are extracted from the page based on their independent visual features. Second, the true value of each attribute is identified from the candidates based on dependent visual features such as the layout relationships among the attributes. Extensive experiments with a large number of news pages show that the proposed approach is highly effective and efficient.     

Statistical Independence as Linear Independence

A contingency table summarizes the conditional frequencies of two attributes and shows how these two attributes are dependent on each other. Thus, this table is a fundamental tool for pattern discovery with conditional probabilities, such as rule discovery. In this paper, a contingency table is interpreted from the viewpoint of granular computing. The first important observation is that a contingency table compares two attributes with respect to the number of equivalence classes. The second important observation is that matrix algebra is a key point of analysis of this table. Especially, the degree of independence, rank plays a very important role in extracting a probabilistic model from a given contingency table.     

Investigating attributes affecting the performance of WBI users

Numerous research studies have explored the effect of hypermedia on learners' performance using Web Based Instruction (WBI). A learner's performance is determined by their varying skills and abilities as well as various differences such as gender, cognitive style and prior knowledge. In this paper, we investigate how differences between individuals influenced learner's performance using a hypermedia system to accommodate an individual's preferences. The effect of learning performance is investigated to explore relationships between measurement attributes including gain scores (post-test minus pre-test), number of pages visited in a WBI program, and time spent on such pages. A data mining approach was used to analyze the results by comparing two clustering algorithms (K-Means and Hierarchical) with two different numbers of clusters. Individual differences had a significant impact on learner behavior in our WBI program. Additionally, we found that the relationship between attributes that measure performance played an influential role in exploring performance level; the relationship between such attributes induced rules in measuring level of a learners' performance.