Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Type-1 Fuzzy Inference Systems with Different Sugeno Polynomial Orders Applied to Diagnosis Problems

Fuzzy Logic has been implemented successfully for different kind of problems. One of the interesting problems that had been solved with Fuzzy Logic is the classification problem, however, there exist an opportunity to improve this system to be competitive in the realm of classifications problems with respect another kind of methods for example Artificial Neural Networks. The present paper is focused in a specific application of classification problems, the diagnosis systems, this problem consists in training an intelligent system to learn the relationship between symptoms and diagnosis. This kind of problems are usually based in powerful non-linear methods for example Modular Neural-Networks or complex hybrids models, however, in this paper are applied the Type-1 Takagi Sugeno Fuzzy Systems (TSK) but analyzing the improvement of their performance by increasing the order of the Sugeno polynomial, the objective is to evaluate if is possible to improve the performance of the TSK systems applied in diagnosis problems. The conventional Takagi-Sugeno Fuzzy Systems are based in the aggregation of first-order polynomial but it is interesting to observe the effect of increase the order of this polynomial, the TSK Fuzzy Diagnosis Systems are evaluated by their accuracy obtained in ten benchmark dataset of the UCI Dataset Repository, for different kind of diseases and different difficult levels.
Assessing and Improving Learning Outcomes for Power Management Experiments Using Cognitive Graph

The series of Power Management Lab Kits (PMLK), released by Texas Instruments (TI), have been globally adopted in classroom settings. We propose a cognitive graph-based method to assist better adoption of TI-PMLK in Chinese power electronics education and specifically assessment of the experiment-based learning experience. First, construct a power management cognitive graph. Then, identify knowledge weaknesses using a Deterministic Inputs Noisy And Gate (DINA) model based cognitive diagnosis method. An Automatic Items Generation System (AIG) is then developed to generate personalized experiment items for any given student. Finally, learning outcomes evaluation is generated from the AIG experimental items using Bayesian psychometric models.
An Overview of Cloud Computing Testing Research

With the rapid growth in information technology, there is a significant increase in research activities in the field of cloud computing. Cloud testing can be interpreted as (i) testing of cloud applications, which involves continuous monitoring of cloud application status to verify Service Level Agreements, and (ii) testing as a cloud service which involves using the cloud as a testing middleware to execute a large-scale simulation of real-time user interactions. This study aims to examine the methodologies and tools used in cloud testing and the current research trends in cloud computing testing.
Sundials in the Shade

ICMP timestamp request and response packets have been standardized for nearly 40 years, but have no modern practical application, having been superseded by NTP. However, ICMP timestamps are not deprecated, suggesting that while hosts must support them, little attention is paid to their implementation and use. In this work, we perform active measurements and find 2.2 million hosts on the Internet responding to ICMP timestamp requests from over 42,500 unique autonomous systems. We develop a methodology to classify timestamp responses, and find 13 distinct classes of behavior. Not only do these behaviors enable a new fingerprinting vector, some behaviors leak important information about the host e.g., OS, kernel version, and local timezone.
A Supervised Filter Feature Selection Method for Mixed Data Based on the Spectral Gap Score

Feature Selection for supervised classification plays a fundamental role in pattern recognition, data mining, machine learning, among other areas. However, most supervised feature selection methods have been designed for handling exclusively numerical or non-numerical data; so, in practical problems of fields such as medicine, economy, business, and social sciences, where the objects of study are usually described by both numerical and non-numerical features (mixed data), traditional supervised feature selection methods cannot be directly applied. This paper introduces a supervised filter feature selection method for mixed data based on the spectral gap score and a new kernel capable of modeling the data structure in a supervised way. To demonstrate the effectiveness of the proposed method, we conducted some experiments on public real-world mixed datasets.
ON THE REPRESENTATION OF TIME CATEGORY IN LANGUAGE

The paper deals with problems of time representation in language. The study of time category is based on the cognitive anthropological approach. The aim of this article is to show linguis means of representation of the temporal category. Objective (astronomical) and subjective (perceptible), conceptual and linguistic (subdivided on real and virtual) representations of time are distinguished. The virtual time is represented in works of fiction, tales, and legends. In these cases we can observe the absence of dynamism, acceleration of time or travel in time. The category of real time is expressed by lexical (definite and indefinite), word-formation and grammatical means. The comparative analysis of derivatives meaning “before/ after” in Russian and French shows that the number of the former considerably (by 4-5 times) surpasses the number of the latter ones. In each of the languages derivative antonyms are revealed. Metaphors of time representation are also analyzed.
An Application of Computer Algebra and Dynamical Systems

An algorithm for the symbolic computation of outer inverses of matrices is presented. The algorithm is based on the exact solution of the first order system of differential equations which appears in corresponding dynamical system. The domain of the algorithm are matrices whose elements are integers, rational numbers as well as one-variable or multiple-variable rational or polynomial expressions.
Opening Privacy Sensitive Microdata Sets in Light of GDPR

To enhance the transparency, accountability and efficiency of the Dutch Ministry of Justice and Security, the ministry has set up an open data program to proactively stimulate sharing its (publicly funded) data sets with the public. Disclosure of personal data is considered as one of the main threats for data opening. In this contribution we argue that, according to Dutch laws, the criminal data within the Dutch justice domain are sensitive data in GDPR terms and that the criminal data can only be opened if these sensitive data are transformed to have no personal information. Subsequently, having no personal information in data sets is related to two GDPR concepts: the data being anonymous in its GDPR sense or the data being pseudonymized in its GDPR sense. These two GDPR concepts, i.e., being anonymous data or pseudonymized data in a GDPR sense, can be distinguished in our setting based on whether the data controller cannot or can revert the data protection process, respectively. (Note that the terms anonymous and pseudonymized are interpreted differently in the technical domain.) We examine realizing these GDPR concepts with the Statistical Disclosure Control (SDC) technology and subsequently argue that pseudonymized data in a GDPR sense delivers a better data utility than the other. At the end, we present a number of the consequences of adopting either of these concepts, which can inform legislators and policymakers to define their strategy for opening privacy sensitive microdata sets, like those pertaining to the Dutch criminal justice domain.
An Approach to Advance STEM Education Practices Based on IoT Technologies and the CoPs Paradigm

Internet of Things and other state-of-the-art technologies like mobile and ubiquitous computing present ample opportunities for developing novel solutions almost in every domain of modern life. The research work presented here aims to leverage on the potential of such technologies in the direction of enhancing learning practices in secondary level education and promoting positive attitudes towards the corresponding scientific and engineering disciplines. The originality of the proposed approach lies on the provision of an educational platform, framed by contemporary pedagogical principles, and with an aim to stimulate collaboration between the relevant stakeholders in the form of operational communities of practice. Such communities are brought together on the basis of participating in on line activities, problem solving, exchanging reflections and experiences in the context of educational scenarios that incorporate modern technologies. The platform development is discussed in terms of the underlined conceptual models, the defined stakeholders’ requirements, the on-line services developed, the software tools integrated and the data management supported. An example educational scenario, the corresponding IoT application developed and preliminary evaluation results of this approach are also reported.
System Design and Analysis of Outdoor Obstacle Surmounting Experiments for the Robot with Foldable Wheels

A high adaptability outdoor robot with foldable wheels was described in this paper. The robot’s structure scheme and motion form have been analyzed. The kinematics model and equation of the robot has been established. The design principle of the robot’s motion control system and the adaptive control principle of the foldable wheels have been analyzed. And then the obstacle surmounting performance has been analyzed after wheels unfolding, the results indicate that the robot can surmount the obstacle easily. When the robot overcomes the obstacle with different postures, the obstacle surmounting performance of the robot will also change. At last two experiments have been carried out. The experimental results indicated that the robot with foldable wheels has a good obstacle surmounting performance and the posture of the robot will affect the obstacle surmounting performance, which will prove the feasibility of the theoretical analysis and the system design.
Augmented reality in medical devices

The emergence of the Extended Realities (XR) in medicine has resulted in a proliferation of applications spanning from education to procedural use. Advances in augmented reality (AR) technology, which allows the user to have meaningful interactions with both their native environment and with digital, or holographic images, is making a significant impact in the medical field by improving physician visualization and control. Given this expanding role, there are a critical set of design challenges that should be considered and addressed moving forward.
Subset-Guided Consistency Enhancement Assessment Criterion for an Imageset Without Reference

In a new era for machine vision, some image enhancement algorithms can be used to improve the quality of an imageset without reference.To assess the performance of imageset enhancement, the existing average criterion utilizes a no-reference image quality metric to calculate the quality score of each enhanced image, and quantifies the performance of an enhancement algorithm by the mean value of all scores.If the quality scores of some images fluctuate greatly, their mean value difficultly reflects the degradation or worst cases during imageset enhancement.Therefore, this paper analyzes and illustrates the need and significance of consistency enhancement assessment, and then proposes a subsetguided consistency enhancement assessment criterion for an imageset without reference.By measuring the subset of an imageset, the proposed criterion firstly calculates the difference of quality scores of each image before and after enhancement and then filters the outlier data outside confidence interval, and finally quantifies the consistency enhancement performance of an enhancement algorithm according to its consistency enhancement degree.When a small subset is used to guide its large imageset, the average criterion judges a consistency or non-consistency enhancement algorithm with a 16.7% false identification ratio, and also makes one misjudgment about the optimal-consistency algorithm, while the proposed criterion always correctly judges the non-consistency or optimal-consistency enhancement algorithm.This paper can help the scientific community to select a robust enhancement algorithm in the degradation or worst cases.As compared with the average criterion, the proposed criterion is more robust in terms of subset-guided consistency enhancement assessment, which may effectively find an optimal-consistency or non-consistency enhancement algorithm for the rest of an imageset.
A Weakly-Supervised Approach for Semantic Segmentation

Deep learning shows great power in many computer vision tasks, however these methods require a massive labeled samples to train the neural networks. In the task of semantic segmentation, it is usually unrealistic to obtain such a large number of labeled training samples. The weakly supervised method provides a promising idea to solve this issue using weak supervised information such as the image level labels. In this paper, we propose a weakly supervised approach for semantic segmentation. In the proposed approach, the orthogonal non-negative matrix factorization (ONMF) is utilized to extract the features of different targets with strict sparse constraints. The gradient optimization is used to solve the objective equation of ONMF. To start the iteration with a preferable initial value, we adopt the mini-batch k-means approach to cluster the massive training samples with image level labels and use the clustering centers as the initial value of the base matrix, which accelerates the convergence of the iteration effectively. using the image label in the reconstruction of the image, select the features corresponding to the image label, then the probability of every pixel can be obtained. The effectiveness of the proposed approach is demonstrated through the open dataset.
Automating the Categorization of Learning Activities, to Help Improve Learning Design

As part of the large-scale implementation of Learning Design at The Open University, the UK’s largest higher education institution, a taxonomy of learning activities informs the development of course modules. The taxonomy is also used to map a module’s Learning Design, to categorize its learning activities, after it has been developed. This enables course teams to compare a module’s Learning Design with student outcomes, in order to determine which Learning Designs are most effective and in which circumstances. However, the mapping process is labor-intensive and open to inconsistencies, making the outcomes less trustworthy and less useful for learning analytics. In this paper, we present an exploratory study that investigates the automatization of the mapping process by means of both unsupervised and supervised machine learning approaches. For the supervised machine learning (Logistic Regression), we use a labelled set of 35,000 activity descriptions classified as either reflective or non-reflective (i.e., whether or not the activity involves student reflection) drawn from 267 modules. Our outcomes, with ~ 79% accuracy, are sufficiently promising for this approach to merit further work, extending it in particular to a larger set of Learning Design activities.
A Stereo Matching and Depth Map Acquisition Algorithm Based on Deep Learning and Improved Winner Takes All-Dynamic Programming

The stereo matching technology has been widely used nowadays for robot guidance, depth of field rendering, and video processing applications. The binocular stereo matching technology collects two images of the same scene from different views, computes the disparity, and then determines the 3-D depth of the object using the triangulation theory. In this paper, we propose a new deep-learning-based stereo matching algorithm called convolutional neural network WTADP (CNN-WTADP). The commonly used WTA method ignores the disparity constraint of the neighboring pixels and is prone to be affected by noise, so the obtained original disparity map may contain a lot of abnormal values and mismatched points. To solve this problem, we construct a CNN for matching cost computation and use a novel global method of dynamic programming based on WTA for disparity selection. Steps for the CNN-based computation of the matching cost are presented, and the post-processing algorithm is used to obtain the final determination of the disparity map. The Middlebury stereo dataset and stereo evaluation are used to compare the proposed algorithm with other methods. The experiments and its results demonstrate the ability of the proposed algorithm to match the images more effectively.
Automated Feedback on the Structure of Hypothesis Tests

Hypothesis testing is a challenging topic for many students in introductory university statistics courses. In this paper we explore how automated feedback in an Intelligent Tutoring System can foster students’ ability to carry out hypothesis tests. Students in an experimental group (N = 163) received elaborate feedback on the structure of the hypothesis testing procedure, while students in a control group (N = 151) only received verification feedback. Immediate feedback effects were measured by comparing numbers of attempted tasks, complete solutions, and errors between the groups, while transfer of feedback effects was measured by student performance on follow-up tasks. Results show that students receiving elaborate feedback solved more tasks and made fewer errors than students receiving only verification feedback, which suggests that students benefited from the elaborate feedback.
Improvement on Subjective Weighing Method in Attribute Coordinate Comprehensive Evaluation Model

Attribute coordinate comprehensive evaluation model provides an evaluation method for allowing the evaluator to subjectively weigh the indexes of the evaluated object. Specifically, the process of weighing is implemented by rating the given sample data to reflect the evaluator’s psychological weight upon some indexes. However, if the evaluated object includes many indexes, it is difficult for the evaluator to intuitively judge and accurately rate the sample data, which causes the great possibilities of rating the samples randomly and further influencing the final evaluation results. To address the problem, the paper changes the quantitative rating mode into qualitative judgment, and then converts the qualitative judgment into psychological weight, and finally evaluates all objects by the attribute coordinate comprehensive evaluation method. The experiment result shows the effectiveness of the improved method.
STRESSDIAG: A Fuzzy Expert System for Diagnosis of Stress Types Including Positive and Negative Rules

A fuzzy rule based expert system STRESSDIAG is presented for diagnosis of stress types including positive and negative rules. After designing and building a suitable inference engine for this system, to create effective knowledge base consisting of more than 700 positive rules for confirmation of conclusion and of more than 100 negative rules for exclusion of the same conclusion. How the rule base is constructed, managed and used are focused on for diagnosis of diagnosis of stress types such as light stress, middle stress, serious stress and serious stress with mental disorder. The inference engine shows how to combine positive and negative rules. The first evaluation of STRESSDIAG is presented by the medical expert’s group in the field of mental diseases in Vietnam and confirmed that STRESSDIAG diagnoses with a high accuracy.
Research on the Key Technology of the Automatic Piping Layout Design for the Launch Vehicle

Abstract For the low design efficiency and poor design quality in the piping layout design of the launch vehicle, the article raises a piping automatic layout method which is based on the geometry. In order to realize the piping automatic layout design, the method need the position coordinates and direction vector for both of the initial point and termination point. The article verifies the feasibility of the method by developing the piping automatic layout design system for the launch vehicle.
Secure Satellite Database Transmission

In order to provide secure transmission over a low-bandwidth, high latency data link, and the need for cyber-security must be properly addressed. This paper focuses on utilizing two ground stations with a satellite in order to transmit data over a wireless medium. The key issue of security arises when confidential information is being transferred, which can be breached by outside forces. By syncing two databases with one another, one in the sky collecting data, and two others on ground receiving data, secure connectivity is implemented. To this end, a process utilizing AES encryption and decryption by using a secret symmetric key which is never shared with outside forces was developed and implemented. The system connects multiple databases. Database 1 generates cipher text which can only be read if it is decrypted. The encrypted message is securely transmitted and then decrypted with the secret decryption key. The original message is then inserted into ground stations: database 2 and database 3. If there is a sync problem or data loss, an error will be detected, and the two databases at ground station communicate with each other and keep the data in sync, reducing retransfer of lost data, and preventing any spoofing, or man-in-the middle attacks.
Adaptive T–S Fuzzy Control with Unknown Membership Functions

In the previous chapter, the T–S fuzzy dynamic systems were formulated into linearly parametrized models, based on which parameter adaptive laws were designed and closed-loop system stability was analyzed. However, there are two group of parameters in a T–S fuzzy system: consequent parameters and membership functions parameters. The former are usually linearly dependent parameters while the latter are usually nonlinearly dependent, i.e., parameters of Gaussian or Sigmoidal membership functions. Most adaptive control approaches assume the parameters of membership functions are accurate enough so that only the uncertainties in consequent parameters are considered. However, in practice, it is difficult to set the membership function parameters accurately in advance. In this chapter, we address such an issue using an adaptive estimation method with a gradient algorithm derived based on a nonlinearly parameterized error model resulted from the parameter nonlinearity of membership functions.
Acmeological Approach as a Factor of Professional and Pedagogical Selfrealization of Students of a High Educational Institution at Studying of Foreign Languages

Abstract In the article the authors discover historical and pedagogical aspect of training in foreign languages and pay attention to requirement of revision of conceptual and methodical bases. Through a prism of psychology and pedagogical features of understanding and perception of the person by the person in the conditions of educational and informative process. The authors prove expediency of acmeological approach when training foreign languages. Consecutive and purposeful realization in educational process of higher education institution of the revealed psychology and pedagogical conditions provides effective formation of an acmeological orientation of the identity of students that is the most important result of realization of acmeological approach in a system of vocational training.Within the research framework, the approbation of the model of acmeological approach in vocational education was carried out, using the example of teaching foreign language to students of the Tyumen Industrial University.