Reviewerly/exHarmony huggingface dataset:
AN EXTRACURRICULAR EVENT “HISTORY OF INFORMATICS IN DATES

The article presents an extracurricular event “History of informatics in dates”, conducted using infographics. The article justifies why infographics will allow us to memorize historical events associated with the development of informatics. The dates and information from the history of the development of the Internet, computers, microprocessors and operating systems are given. The work with dates is carried out in a game form.
The extraction research on evaluation rules for students based on discernibility matrix

We apply the rough set theory to the evaluation process of students. Firstly, we should create the information table of evaluation decision by using the discernibility matrix of rough set theory to do attribute reduction for the evaluation data and hence reduce unnecessary evaluation indicators. We will do value reduction and rule extraction algorithm based on this and then dig out the general rule of the evaluation for students from enormous evaluation data in order to provide decision basis for the work of students at school. This process of evaluation is totally about enabling the date talk and reduce the influence of human-dominated factors, as a result, the outcome of the evaluation will be more objective and fair.
Transforming McLuhan: Cultural, Critical, and Postmodern Perspectives, Paul Grosswiler (2010)

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A New Pointwise Convolution in Deep Neural Networks Through Extremely Fast and Non Parametric Transforms

Some conventional transforms such as Discrete Walsh-Hadamard Transform (DWHT) and Discrete Cosine Transform (DCT) have been widely used as feature extractors in image processing but rarely applied in neural networks.However, we found that these conventional transforms can serve as a powerful feature extractor in channel dimension without any learnable parameters in deep neural networks.This paper firstly proposes to apply conventional transforms on pointwise convolution, showing that such transforms can significantly reduce the computational complexity of neural networks without accuracy degradation on various classification tasks and even on face detection task.Our comprehensive experiments show that the proposed DWHT-based model gained 1.49% accuracy increase with 79.4% reduced parameters and 49.4% reduced FLOPs compared with its baseline model on the CIFAR 100 dataset while achieving comparable accuracy under the condition that 81.4% of parameters and 49.4% of FLOPs reduced on SVHN dataset.Additionally, our DWHT-based model showed comparable accuracy with 89.2% reduced parameters and 26.5% reduced FLOPs compared to the baseline models on WIDER FACE and FDDB datasets.
Generating virtual images for promoting visual artificial intelligence

Multi-label learning methods can be categorised into algorithm adaptation, problem transformation and ensemble methods. Some of these methods depend on a base classifier and the relationship is not well understood. In this paper the sensitivity of five problem transformation and two ensemble methods to four types of classifiers is studied. Their performance across 11 benchmark datasets is measured using 16 evaluation metrics. The best classifier is shown to depend on the method: Support Vector Machines (SVM) for binary relevance, classifier chains, calibrated label ranking, quick weighted multi-label learning and RAndom k-labELsets; k-Nearest Neighbours (k-NN) and Naïve Bayes (NB) for Hierarchy Of Multilabel classifiERs; and Decision Trees (DT) for ensemble of classifier chains. The statistical performance of a classifier is also found to be generally consistent across the metrics for any given method. Overall, DT and SVM have the best performance–computational time trade-off followed by k-NN and NB.
Robust Object Tracking Based on Deep Feature and Dual Classifier Trained with Hard Samples

Visual tracking has attracted more and more attention in recent years. In this paper, we proposed a novel tracker that is composed of a feature network, a dual classifier, a target location module, and a sample collecting and pooling module. The dual classifier contains two classifiers, called long-term classifier and short-term classifier, in which the long-term classifier is to maintain the long-term appearance of the target and the short-term classifier is for prompt response to the sudden change of the target. The training samples are divided into positive samples, negative samples, hard positive samples and hard negative samples and are used to train the two classifiers, respectively. Furthermore, in order to overcome the unreliability in locating the target by highest score, a density clustering method is introduced into the target locating process. Experimental results conducted on two benchmark datasets demonstrate the effectiveness of the proposed tracking method.
Context Data Preprocessing for Context-Aware Smartphone Authentication

This paper proposes an approach to carrying out context data preprocessing gathered from smartphone users to support context-aware authentication. Context-aware authentication is a technique to implicitly authenticate a smartphone user using contextual data (e.g., call log, location) without explicitly requesting the user’s any actions. In order to enable context-aware authentication, a user’s contextual data should be carefully processed for learning user’s past contextual patterns in consideration of user’s hourly, daily, weekly or monthly behaviors. In this paper, we gathered contextual data from 200 voluntary smartphone users for about 2 years and showed what the appropriate contextual data is preprocessing for performing context-aware authentication.
Using Rasch Models for Developing Fast Technology Enhanced Learning Solutions: An Example with Emojis

We focus on issues related to learning analytics for predicting behavior, presenting a case in which people’s answers can be predicted on the bases of a known numeric function describing a cognitive relation. We used the Rasch scaling method following an item-response latent-trait model on questions on the valence of emojis of common use. Emojis are pictorial symbols with a high degree of humanization, nowadays increasingly frequent in computer-mediated communication (CMC). However, sometimes their meaning is ambiguous. Therefore, after quantifying how much of a property such as positive and negative valence different emojis represented, we identified the function that relates the two different judgements on the same object, specifically the positive and negative valence of each emoji. Applications of this approach can be highly relevant for learning analytics, to optimize the measurement, collection, and analysis of data about learners and their contexts for fast Technology Enhanced Learning (TEL) solutions.
Interval-Valued Methods in Medical Decision Support Systems

In this chapter we present the application of methods based on interval modeling and aggregation in OvaExpert computer support system [OvaExpert project homepage: http://ovaexpert.pl/en/ ] designed for ovarian tumor diagnosis (however applicable also in other medical fields). It was shown that such methods made it possible to reduce the negative impact of lack of data and lead to meaningful and accurate decisions [2–10]. Here the behavior of some new interval-valued operators in OvaExpert is shown, namely there are considered possible and necessary aggregation functions and aggregation functions with respect to admissible linear orders. These aggregation operators were not previously considered in OvaExpert. The results prove that these new aggregation operators may be competitive with others, especially if it comes to the cost matrix results.
A Fuzzy RDF Graph-Matching Method Based on Neighborhood Similarity

With the popularity of fuzzy RDF data, identifying correspondences among these data sources is an important task. Although there are some solutions addressing this problem in classical RDF datasets, existing methods do not consider fuzzy information which is an important property existing in fuzzy RDF graphs. In this article, we apply fuzzy graph to model the fuzzy RDF datasets and propose a novel similarity-oriented RDF graph matching approach, which makes full use of the 1-hop neighbor vertex and edge label information, and takes into account the fuzzy information of a fuzzy RDF graph. Based on the neighborhood similarity, we propose a breadth-first branch-and-bound method for fuzzy RDF graph matching, which uses a state space search method and uses truncation parameters to constrain the search. This algorithm can be used to identify the matched pairs.
The Relationship Between Root Locus and Transient Field Components of AC Machines

The dynamic analysis of induction motors is supported by well-known theories: the two-axes transformation, and the space vector theory. Yet some inconsistencies with the theory of dynamic systems exist. The machine eigenvalues suggest the existence of two damped oscillators, physically not understandable. The respective eigenfrequencies change with the angular velocity of the reference frame. This contradicts the understanding that eigenfrequencies are inherent system properties. Physically, the dynamics depend on the continuous distribution of magnetic energy and its spatial displacement during transient processes. Information on the system dynamics is lost when dividing the continuum of magnetic energy into discrete portions. Complex state variables associate the dynamics to the propagation in space of distributed magnetic fields. The dynamic analysis reveals the existence single-complex eigenvalues. These define a novel class of system identifiers. They are characterized by having only one imaginary part instead of a conjugate complex pair. The use of complex state variables conveys insight and physical understanding of the dynamic processes within the machine. The approach constitutes an extension to the theory of dynamic systems.
Extracting and Summarizing the Commonly Faced Security Issues from Community Question Answering Site

Community question-answering (CQA) sites are popular as information-seeking platforms where users communicate to their peers. Security-related posts are gaining popularity with the rapid development of information technology in these sites and. CQA sites contains wide range of posts from classic cryptography to recently popular mobile security. Investigating such posts can be useful for researchers, teachers and developers. In this article, spectral clustering and frequent term-based summarization techniques are proposed for security related posts. The proposed method is developed in three stages. In the first stage, security related folksonomies are created and security post profile matrix is built with the help of tag frequency-inverse security post frequency. In the second stage, security related posts are grouped with help of spectral clustering algorithms. Finally, in the third stage, frequent terms are extracted from each cluster for security related post summarization with the help of frequent words and semantic similarity.
SOCIAL PROJECT AS A PLATFORM FOR THE IMPLEMENTATION OF PROFESSIONAL SKILLS OF FUTURE INFORMATICS TEACHERS

The article is devoted to the consideration of ways to solve one of the actual problems in theory and methodology of training and upbringing — the problem of developing professional skills of future informatics teacher. As a way to adapt students to the profession, the possibility of their involvement in social designing was chosen. Participation in social projects contributes to the approbation and introduction of new forms and methods in teaching informatics. Expanding the experience of future teachers in carrying out large-scale events contributes to the formation of a socially adapted personality competitive in modern society. The potential of a social project in consolidating the knowledge and skills obtained during the theoretical training at the university is indicated. In the article, theoretical reasoning is accompanied by examples of real social projects and activities aimed at the formation of professional competencies of future informatics teachers.
Risk-Based Elicitation of Security Requirements According to the ISO 27005 Standard

Security is of great importance for software intensive systems. Security incidents become more and more frequent in the last few years. Such incidents can lead to substantial damage, not only financially, but also in term of reputation loss. The security of a software system can be compromised by threats, which may harm assets with a certain likelihood, thus constituting a risk. All such risks should be identified, and unacceptable risks should be reduced. The task of dealing with risks is called risk management and should be performed right from the beginning of the software development process. Security requirements can be used to address security aspects during requirements engineering. We propose a risk-based method to elicit security requirements based on functional requirements. Our method complies to the ISO 27005 standard for security risk management. We provide guidance for all steps of that process, and the results are collected in a model. We also define validation conditions to support the identification of errors when carrying out the process as early as possible.
Machine Learning Methods for Imbalanced Data

We discuss high-dimensional data analysis in the framework of pattern recognition and machine learning, including single-component analysis and clustering analysis. Several boosting methods for tackling imbalances in sample sizes are investigated.
Design of Signal-Oriented Automatic Test System Software Based on Flowchart

In order to solve the low efficiency and high complexity of developing test program set (TPS) in automatic testing currently, an approach to developing TPS based on flowchart rather purely text code is proposed in this paper. By introducing test sequence module (TRM), TPS flowchart can be developed by combining and configuring the graphic test units logically according to the test requirements. Besides, the run-time module (RTS) is constructed to parse and execute the TPS flowchart developed upon. In process of executing, the RTS realizes resource mapping, signal path routing and finally implements the automatic test task. The fact has demonstrated that the method of developing TPS in this paper can reduce the complexity of developing TPS, improve efficiency and achieve the rapid development of TPS dramatically.
Social Event Management System: Users’ Recommendations

Social meetings and events pose a major part in Brazilian culture and have great importance throughout various interaction contexts: from daily neighborhood social activities to workplace relations. In a country well known by its festivities, to gather people, from small parties to huge events, can bring a certain social impact. Since the beginning of the 3rd wave of computing, the use of digital apparatuses and social medias became gradually part of the Brazilian social processes and directly affect their daily social life and work [1]. Technology has been changing social behavior and the way people interact with each other, with less direct interaction, but never the less connected. This research is part of the project “RatatApp”, a system to help organize collaborative events that can encompass multiple administrators and manage multiple events, users, schedules and finance. Although the whole research comprises many phases, this paper presents the high fidelity prototype-testing phase.
Misclassification Error Impurity Measure

One way of solving the problem of incompatibility between nonlinear split measures, like the information gain or the Gini gain, and the Hoeffding’s inequality is the application of another statistical tool, e.g. the McDiarmid’s inequality. Another way is to find a split measure which can be expressed as an arithmetic average of some random variables since the Hoeffding’s inequality is applicable in this case. In the literature, many different impurity measures, other than the information entropy or Gini index, were considered [1]. For example in [2, 3] the Kearns-Mansours index [4] was taken into account. A survey of various splitting criteria can be found in [5]. However, these functions are nonlinear and cannot be expressed as a desired sum of random variables. In this chapter, a split measure based on the misclassification error impurity measure is proposed [6, 7], which has the mentioned above property. In the case of misclassification error, the bounds obtained using the Hoeffding’s inequality and the McDiarmid’s inequality are equivalent.
Online Anomaly Detection of Streaming Data for Space Payloads Based on Improved GNG Algorithm

Space payloads are the important facilities for space exploration and scientific experiments. Real-time anomaly detection is the key technology for discovering the abnormal parameters and potential failures of space payloads. In this paper, we proposed a novel online anomaly detection method based on improved GNG algorithm (Growing Neural Gas), which could be online learning the dynamic character of streaming data without off-line training. Comparing to the existing traditional GNG algorithm, the proposed method could acquire better performance of online anomaly detection by means of adaptive adjustment of learning rate, adaptive addition and deletion of neurons. Finally, the experiment result indicates the improved GNG algorithm acquires better efficiency and accuracy in online anomaly detection of streaming data for space payloads.
Classical Algorithm vs. Machine Learning in Objects Recognition

This article focuses on two most popular methods of detecting regular shapes in the pictures. Over the past years, image processing and object recognition are entering our lives more and more. The difference between a classical algorithm and machine learning was analyzed, in case of a tennis ball recognition on photos. It has been created on own dataset to avoid a uniform background and bright colors. Images were taken with a low-cost camera in different conditions. Creating the classical algorithm and machine learning and comparing the accuracy, false flags and performance of the two methods are described.
A Fast Algorithm for Sparse Extreme Learning Machine

As a more compact network, sparse ELM is an alternative model of extreme learning machine (ELM) for classification, which requires less memory space and testing time than conventional ELMs. In this paper, a fast training algorithm (FTA-sELM) specially developed for sparse ELM is proposed, which improves training speed, achieves better generalization performance and further promotes the application of sparse ELM in large data problem. The proposed algorithm breaks the large quadratic programming (QP) problem of sparse ELM into a series of two-dimensional sub-QP problems, specifically. In every iteration, Newton’s method is employed to solve the optimal solution for each sub-QP problem. Moreover, a new clipping scheme for Lagrange multipliers is presented, which improves convergence performance.
The Qualitative Image: Urban Analytics, Hybridity and Digital Representation

High-precision analytical software, such as that used for medical imaging, can be also applied productively to the assessment of urban conditions such as pedestrian and vehicular flow. A prominent feature of this tool is its ability to offer a new and more abstract understanding of the material nature of the city. Drawing upon a range of scaled-up software procedures to illustrate capability, the chapter reveals how an analytical medical software tool can be adapted for use in alternative interdisciplinary contexts such as urban design. Using imagery captured from public domain webcams, it demonstrates how the upscaling and transferal of this digital tool from its original disciplinary role provides a new way of assessing the appropriateness of a proposed built intervention. It also reveals that the extension of this tool’s fine-grain, image-based analysis capabilities into a broader, more complex urban scale allows the more ambiguous and often-disregarded properties of city life to form part of a comprehensive and wholistic data set. The chapter concludes with the proposal that the synthesis of quantitative and qualitative data facilitated by this analytical platform exceeds the capability of urban assessment tools currently used by the discipline.
Optimization Under Fuzzy Max-t-Norm Relation Constraints

Fuzzy relation equationsLama, Reinis and inequalities play an important role in many tools of fuzzy modelling and have been extensively studied. In many practical applications they are used as constraints in optimization. Algorithms for specific objective functionsAsmuss, Svetlana have been proposed by many authors. In this paper we introduce a method to convert a system of fuzzy relation constraints with max-t-norm composition to a linear constraint system by adding integer variables. A numerical example is provided to illustrate the proposed method.
Dynamic Consolidation Based on Kth-Order Markov Model for Virtual Machines

The rapid development of cloud computing technology has led to a high level of energy consumption. The central processing unit (CPU) of the data center and other resources often use less than half the rate; therefore, if the work of the virtual machine is focused on part of the server, and the idle server switches to low power mode, the power consumption of the data center can be greatly reduced. Traditional research into virtual machine consolidation is mainly based on the high load threshold of the current host load setting or periodically migrates, and the present study made predictions based on the timing of problems of lower prediction accuracy faced. To solve these problems, we consider the impact of the multi-order Markov model and the CPU state at different times, and propose a new hybrid sequence K Markov model for the next period of time of the host CPU load forecasting. Owing to the large-scale data experiment on the CloudSim simulation platform, the host load forecasting method proposed in this paper is compared with the traditional load detection method to verify that the proposed model has a large reduction in the number of virtual machine migrations and amount of data center energy consumption, and the violation of the service level agreement (SLA) is also at an acceptable level.
Fuzzy Logic Model of Device for Generating Control Signals

In this article the authors show a fuzzy logic model of the device for generating control signals. A control device with computer numerical control uses a cooling metal cutting tool for setting a level. The algorithm of the device for generating control signals based on fuzzy logic is provided. The fuzzy logic model uses simple formulas to obtain minimum and maximum values. The model was created for the confirmation of the system work and visual representation of the device. Visual modelling with the help of software “Matlab” was considered for a better understanding of the device operation for generating control signals. For the device operation modeling Simulink was used. The experimental part of this article confirms the applicability of the fuzzy logic algorithm.