Miracle-dz/UniFAR-FTUs huggingface dataset:
Customization of Human-Computer Interface Guided by Ontological Approach in Web 2. 0

The philosophy of web 2.0 applications is based on the democratization of access to information by providing the users, having humble technical knowledge, with the possibility to participate and improve web functions. In this very context figures this article, which is part of a series of research conducted by the team within the framework of the development of web 2.0 and participatory design of web interfaces. It aims at proposing a new design technique of web interfaces through involving the user in all stages of this process. In this respect, the internet users’ contributions will be quite useful in web interfaces design by answering the questionnaires suggested by the system. These questionnaires will be integrated within ontology of the domain of human-computer interface ergonomics. The answers to the suggested questionnaires will allow a semantic classification of profiles according to a vector model and, then, develop ontology of users. Accordingly, the system will be able to categorize users in a definite profile based on their ergonomic interests and make a decision about the interfaces recommended by each type of profile. This article consists of three sections. In the first section, HCI technologies related to web 2.0 are presented. Secondly, participatory design and interface evaluation methods will be discussed. In the third section, HCI evaluation model guided by an ontological approach will be advanced in order to help the system make decisions about interfaces. The conclusion comes in the last section.
Emerging Social Work Field Education Trends in China

ABSTRACT MSW field education in China has been adopting Western models. However, this adaptation cannot address all educational and professional training needs in China. This study used an exploratory qualitative study design and interviewed 54 MSW students regarding their field experiences at a university in Shanghai. Results revealed six key themes: field education curriculum design’s influence on student field experiences, faculty advisers’ influence on student field experiences, field agencies’ expectations toward MSW intern performance, field agencies’ capacity to host MSW interns, student perceptions of their professional competencies, and students’ coping strategies to manage challenges in field practicum. Competency-based social work field education is a potential solution to address the current social work field education challenges in China.
Tree-structured regression and the differentiation of integrals

This paper provides answers to questions regarding the almost sure limiting behavior of rooted, binary tree-structured rules for regression. Examples show that questions raised by Gordon and Olshen in 1984 have negative answers. For these examples of regression functions and sequences of their associated binary tree-structured approximations, for all regression functions except those in a set of the first category, almost sure consistency fails dramatically on events of full probability. One consequence is that almost sure consistency of binary tree-structured rules such as CART requires conditions beyond requiring that (1) the regression function be in ${\mathcal {L}}^1$, (2) partitions of a Euclidean feature space be into polytopes with sides parallel to coordinate axes, (3) the mesh of the partitions becomes arbitrarily fine almost surely and (4) the empirical learning sample content of each polytope be ``large enough. '' The material in this paper includes the solution to a problem raised by Dudley in discussions. The main results have a corollary regarding the lack of almost sure consistency of certain Bayes-risk consistent rules for classification.
Ignore Similarity If You Can: A Computational Exploration of Exemplar Similarity Effects on Rule Application

It is generally assumed that when making categorization judgments the cognitive system learns to focus on stimuli features that are relevant for making an accurate judgment. This is a key feature of hybrid categorization systems, which selectively weight the use of exemplar- and rule-based processes. In contrast, Hahn et al. (2010) have shown that people cannot help but pay attention to exemplar similarity, even when doing so leads to classification errors. This paper tests, through a series of computer simulations, whether a hybrid categorization model developed in the ACT-R cognitive architecture (by Anderson and Betz, 2001) can account for the Hahn et al. dataset. This model implements Nosofsky and Palmeri’s (1997) exemplar-based random walk model as its exemplar route, and combines it with an implementation of Nosofsky et al. (1994) rule-based model RULEX. A thorough search of the model’s parameter space showed that while the presence of an exemplar-similarity effect on response times was associated with classification errors it was possible to fit both measures to the observed data for an unsupervised version of the task (i.e., in which no feedback on accuracy was given). Difficulties arose when the model was applied to a supervised version of the task in which explicit feedback on accuracy was given. Modeling results show that the exemplar-similarity effect is diminished by feedback as the model learns to avoid the error-prone exemplar-route, taking instead the accurate rule-route. In contrast to the model, Hahn et al. found that people continue to exhibit robust exemplar-similarity effects even when given feedback. This work highlights a challenge for understanding how and why people combine rules and exemplars when making categorization decisions.
Search-As-You-Type: Opportunities and Challenges

Traditional information systems return answers after a user submits a complete query. Users often feel “left in the dark” when they have limited knowledge about the underlying data, and have to use a tryand-see approach for finding information. A recent trend of supporting autocompletion in these systems is a first step towards solving this problem. In this paper, we study a new information-access paradigm, called “search-as-you-type” or “type-ahead search,” in which the system searches the underlying data on the fly as the user types in query keywords. It extends traditional prefix-based autocompletion interfaces by supporting full-text search on the data using tokenzied query keywords. We give an overview of this information-access paradigm, discuss research challenges and opportunities, and report recently developed techniques.
Chattering-free classical variable structure direct torque controlled IPM synchronous motor drive by using PI controller within boundary layer

In this paper, a novel robust direct torque controlled interior permanent magnet synchronous motor drive is presented which is based on variable structure approach and space vector modulation technique. Basically, it has similar structure as classical SVM-DTC scheme where the linear controllers are replaced with sliding mode type of controllers. In this case, the chattering effect has been addressed by using PI controllers within the specified boundary layers of switching surfaces. Such a combination of sliding mode controller and PI controller has been employed for controlling over stator flux, electromagnetic torque and rotor speed in the proposed direct torque control scheme. In fact, the SMC is in charge of transient response control and PI controller prevails in the steady state. As a result, the lack of robustness in linear controllers is compensated by SMC while the chattering effects and steady state errors of such sliding mode controllers are suppressed by smooth control obtained through PI controllers. The drive performance improvements are also investigated through the presented comparative simulation studies.
A real-time rescheduling heuristic using decentralized knowledge-based decisions for flexible flow shops with unrelated parallel machines

In a manufacturing planning and control system, a change of system environment or of the production requirements may invalidate the current production schedule. In that case, rescheduling as a self-adaption function of the system is necessary for generating a new schedule, regarding the current state of the production system. This rescheduling process is time critical and normally requires real time solutions. In this paper we present a rescheduling approach with offline self-learning and online self-decision-making abilities. It solves the rescheduling problem of flexible flow shops (FFS) with unrelated parallel machines. The optimality criterion is the makespan. The approach uses a centralized heuristic to guarantee the generation of active schedules. In addition, it integrates a decentralized knowledge-based decision making system in the heuristic. This decision making system can learn from previous scheduling problems and their schedules. Consequently, it uses the obtained knowledge to dynamically select the most appropriate dispatching rule for scheduling the production, depending on the current system state. Computational results show that the proposed approach is superior to only using one single dispatching rule constantly. Furthermore, due to its efficient runtime the approach is suitable for real time applications.
LibGA: a user-friendly workbench for order-based genetic algorithm research

Over the years there haa been several packages developed that provide a workbench for genetic algorithm (GA) research. Most of these packages use the generational model inspired by GEN ESIS. A few have adopted the steady-state model used in Genitor. Unfortunately, they have some deficiencies when working with orderhsed problems such as packing, routing, and scheduling. This paper describes LibGA, which was developed specifically for order-baaed problems, but which also works easily with other kinds of problems. It offers an easy to use ‘user-friendly’ interface and allows comparisons to be made between both generational and steadystate genetic algorithms for a particular problem. It includes a variety of genetic operators for reproduction, crossover, and mutation. LibGA makes it easy to use these operators in new ways for particular applications or to develop and include new operators. Finally, it offers the unique new feature of a dynamic generation gap.
3 Learning Nonlinear Principal Manifolds by Self-Organising Maps

This chapter provides an overview on the self-organised map (SOM) in the context of manifold mapping. It first reviews the background of the SOM and issues on its cost function and topology measures. Then its variant, the visualisation induced SOM (ViSOM) proposed for preserving local metric on the map, is introduced and reviewed for data visualisation. The relationships among the SOM, ViSOM, multidimensional scaling, and principal curves are analysed and discussed. Both the SOM and ViSOM produce a scaling and dimension-reduction mapping or manifold of the input space. The SOM is shown to be a qualitative scaling method, while the ViSOM is a metric scaling and approximates a discrete principal curve/surface. Examples and applications of extracting data manifolds using SOM-based techniques are presented.
Differentially Private Wireless Data Publication in Large-Scale WLAN Networks

Wireless trace data play an important role in wireless network researches. However, publishing the raw WLAN traces poses potential privacy risks of network users. Therefore, it is necessary to sanitize users' sensitive information before these traces are published, and provide high data utility for wireless network researches as well. Although some existing works based on various anonymization methods have started to address the problem of sanitizing WLAN traces, the anonymization techniques cannot provide strong and provable privacy guarantees. Differential Privacy is the only framework that can provide strong and provable privacy guarantees. However, we find that existing studies on differential privacy fail to provide effective data utility on multi-dimensional and large-scale datasets. Aim at WLAN trace datasets that have unique characteristics of multi-dimensional and large-scale, this paper proposes a privacy-preserving data publishing algorithm which not only satisfies differential privacy but also realizes high data utility. Furthermore, the theoretical analysis shows the noise variance of our sanitization algorithm is O(logo(1) n/ϵ2) which indicates the algorithm can achieve a higher data utility on large-scale datasets. Moreover, from the results of extensive experiments on an large-scale WLAN trace dataset, we also show that our sanitization algorithm can provide high data utility.
Exploration and Visualization of Very Large Datasets with the Active Data Repository

The analysis and exploration of scientific datasets is a challenging and increasingly important part of scientific research. Visualization is a powerful analysis tool that converts numerical values into an image, which can be more easily analyzed by a human. We have developed a framework, called the Active Data Repository, that is designed to provide support for applications that analyze, explore and visualize very large multi-dimensional datasets. ADR targets distributed memory parallel machines with one or more disks attached to each node. In this paper, we present implementations of ray-casting based volume rendering and iso-surface rendering methods using ADR for visualizing out-of-core datasets, and describe experimental performance results.
Implementing diagnostic criteria and estimating frequency of mild cognitive impairment in an urban community.

BACKGROUND Reported rates of mild cognitive impairment (MCI) range widely depending on methodologic differences, including specific sample characteristics, cognitive measures used, normative samples used for neuropsychological tests, and diagnostic criteria. OBJECTIVES To operationalize diagnostic criteria for MCI and examine the frequency of MCI in ethnically and linguistically diverse elders (individuals older than 65 years). DESIGN Prospective, community-based longitudinal cohort study. SETTING Northern Manhattan, New York, NY. PARTICIPANTS A cohort of 1315 nondemented elderly participants. MAIN OUTCOME MEASURE A diagnosis of MCI was assigned retrospectively on the basis of comprehensive neuropsychological, functional, and neurologic assessments. Amnestic MCI, as well as forms of mild impairment with other cognitive characteristics, were classified. RESULTS The frequency of amnestic MCI was 5.0% (95% confidence interval, 3.8-6.2). Other subtypes of MCI ranged in frequency from 2.1% to 6.2%. Mild cognitive impairment was more common among those older than 75 years compared with those aged 65 to 75 years. Individuals with fewer than 9 years of schooling were more likely to meet MCI criteria. Apolipoprotein (APOE) E4 allele was more frequent among those with amnestic MCI. CONCLUSIONS When proper normative values are used, only age and education, and not race or ethnicity, are associated with higher frequency of MCI. The proportion of nondemented elders with isolated memory deficits is smaller than the proportion with deficits in multiple cognitive domains. The strong association of the APOE E4 allele with only amnestic MCI suggests that there are likely to be multiple causes of cognitive impairment and differential rates of conversion to Alzheimer disease within the cognitive subtypes of MCI.
Fornix-Based versus Limbus-Based Conjunctival Flap in Trabeculectomy: A Quantitative Evaluation of the Evidence

Objective To evaluate the efficacy and tolerability of limbus-based (LBCF) compared with fornix-based conjunctival flaps (FBCF) for trabeculectomy in the treatment of patients with uncontrolled glaucoma. Methods A comprehensive literature meta-analysis was performed according to the Cochrane Collaboration methodology to identify controlled clinical trials comparing LBCF with FBCF in trabeculectomy. The efficacy measures were the weighted mean differences (WMDs) for intraocular pressure reduction (IOPR), the reduction in glaucoma medications, and the relative risks (RRs) for success rates. Tolerability estimates were measured by RR for adverse events. The pooled effects were calculated using the random effects model. Results Sixteen controlled clinical trials meeting the predefined criteria were included in the meta-analysis. A total of 1,825 eyes from 1,392 patients with medically uncontrolled glaucoma were included. The WMD of the IOPR from baseline was 1.12 (95% CI: −0.88 to 3.12) when comparing LBCF with FBCF. LBCF was associated with numerically greater but non-significant IOP lowering efficacy than FBCF (P = 0.270). LBCF was comparable with FBCF in the reduction of glaucoma medication, with a WMD of 0.15 (−0.05 to 0.36) at the follow-up endpoint (P = 0.141). The pooled RR comparing LBCF with FBCF were 1.08 (0.94, 1.23) for the complete success rate and 1.01 (0.92, 1.10) for the qualified success rate. Rates of adverse events did not differ significantly between LBCF and FBCF. Conclusions There is no significant difference in IOP lowering, number of glaucoma medications, or proportion of patients who reached the IOP target between LBCF and FBCF trabeculectomy. Both incision techniques may contribute equally to adverse events.
An Incentive Mechanism to Elicit Truthful Opinions for Crowdsourced Multiple Choice Consensus Tasks

Crowdsourcing is a rapidly growing technology to harness human intelligence to solve problems that are not suitable for automation. It is especially suitable for consensus tasks which collect opinions from human workers to gain insight into real-world phenomena. In these tasks, motivating workers to provide their truthful opinions is a challenging problem. Most existing incentive mechanisms proposed to address this problem assume that workers share a common prior. However, this assumption is not always valid in practice, resulting in that the existing approaches may discourage workers to provide their truthful opinions. In this paper, we propose a novel incentive mechanism -- iMET -- to elicit truthful opinions for crowdsourced multiple-choice consensus tasks. By incorporating the concepts of worker credibility and similarity, iMET rewards workers for providing truthful opinions without assuming a common prior. Through extensive simulations on the basis of a collected real-world dataset, iMET has been demonstrated to outperform another two widely used incentive mechanisms in eliciting truthful opinions, especially when diverse truthful opinions are held by workers.
COTRASIF: conservation-aided transcription-factor-binding site finder

COTRASIF is a web-based tool for the genome-wide search of evolutionary conserved regulatory regions (transcription factor-binding sites, TFBS) in eukaryotic gene promoters. Predictions are made using either a position-weight matrix search method, or a hidden Markov model search method, depending on the availability of the matrix and actual sequences of the target TFBS. COTRASIF is a fully integrated solution incorporating both a gene promoter database (based on the regular Ensembl genome annotation releases) and both JASPAR and TRANSFAC databases of TFBS matrices. To decrease the false-positives rate an integrated evolutionary conservation filter is available, which allows the selection of only those of the predicted TFBS that are present in the promoters of the related species’ orthologous genes. COTRASIF is very easy to use, implements a regularly updated database of promoters and is a powerful solution for genome-wide TFBS searching. COTRASIF is freely available at http://biomed.org. ua/COTRASIF/.
Microbial Monitoring of Crewed Habitats in Space—Current Status and Future Perspectives

Previous space research conducted during short-term flight experiments and long-term environmental monitoring on board orbiting space stations suggests that the relationship between humans and microbes is altered in the crewed habitat in space. Both human physiology and microbial communities adapt to spaceflight. Microbial monitoring is critical to crew safety in long-duration space habitation and the sustained operation of life support systems on space transit vehicles, space stations, and surface habitats. To address this critical need, space agencies including NASA (National Aeronautics and Space Administration), ESA (European Space Agency), and JAXA (Japan Aerospace Exploration Agency) are working together to develop and implement specific measures to monitor, control, and counteract biological contamination in closed-environment systems. In this review, the current status of microbial monitoring conducted in the International Space Station (ISS) as well as the results of recent microbial spaceflight experiments have been summarized and future perspectives are discussed.
Efficacy of Self-Directed Exposure Using Smart Phone Technology: First versus Third Person Perspective

Efficacy of Self-Directed Exposure Using Smart Phone Technology: First versus Third Person Perspective Matthew C. Arias Exposure therapy, including its self-directed forms, is effective for treatment of specific phobias. Nevertheless, there are issues with patient adherence in the use of exposure therapy, including its self-directed formats. Technological advancements, as with smartphones, may improve adherence to self-directed exposure therapy, perhaps due to exposure stimuli being more readily accessible. Thus, there is a need to examine how presenting phobic material on a smartphone might promote increased adherence in conducting self-directed exposure. Additionally, exposure can incorporate phobic material from different perspectives (i.e., first-person or third-person), which is one factor that may impact treatment effectiveness. Participants (N = 36) were randomly assigned to a treatment or control condition, and completed a pre-assessment and then a post-assessment two weeks later. The assessment consisted of a multimodal approach (e.g., self-report, physiological response, and overt behavior). Participants in the treatment condition were instructed to watch a standard exposure video of a dental examination and prophylaxis three times daily for two weeks. One week of videos was shown in a first-person perspective and the same video was shown in a third-person perspective for one week. Results indicated that the smartphone-based stimuli were utilized by the participants, and that the treatment was acceptable to them. There was a significant interaction of time by condition for self-reported anxiety during a behavioral avoidance task. Participants in the treatment condition had significantly lower selfreported anxiety at the post-assessment compared to the pre-assessment, and no change in anxiety was found for the control group from preto post-assessment. A significant decrease in heart rate and trait anxiety was found from preto post-assessment for all participants. Results demonstrated the potential utility of smartphone-based self-directed exposure therapy for specific phobia.
Hybrid ensembles of decision trees and artificial neural networks

Ensemble learning is inspired by the human group decision making process, and it has been found beneficial in various application domains. Decision tree and artificial neural network are two popular types of classification algorithms often used to construct classic ensembles. Recently, researchers proposed to use the mixture of both types to construct hybrid ensembles. However, researchers use decision trees and artificial neural networks together in an ensemble without further discussion. The focus of this paper is on the hybrid ensemble constructed by using decision trees and artificial neural networks simultaneously. The goal of this paper is not only to show that the hybrid ensemble can achieve comparable or even better classification performance, but also to provide an explanation of why it works.