Reviewerly/exHarmony huggingface dataset:
Research on Project-Based Teaching Quality Evaluation Based on Fuzzy Cluster Analysis

In view of the traditional teaching quality evaluation method is not applicable to project of teaching quality evaluation problem, taking the “scientific computation and visualization” in the “Python Language Programming” course as an example, the project is divided into five sub-projects, re-construct the teaching quality evaluation system including 4 first-level indicators such as teaching attitude, teaching method, teaching content and teaching effect. It also includes 24 second-level indicators. Through the questionnaire survey of project quality teaching, the fuzzy clustering method is used to cluster the survey results, which gives the corresponding evaluation. The feasiuility and effectiveness of fuzzy clustering method in analyzing the quality of project teaching are verified.
gravityZERO, an installation work for virtual environment

This paper reports the exposition of an artistic installation, gravityZERO, and its ongoing technical development. It consists of virtual sound, VR and robotic technologies in order to simulate the state of zero gravity. Audience members can experience a floating sensation within this virtual environment.
Face Liveness Detection Benchmark based on Stereo Matching

In this paper, a face liveness detection benchmark is established and maintained, wherein 400 images pairs captured with binocular camera are made openly available for research purposes. This image dataset contains numbers of people with varied expressions, illumination, and background environment conditions, etc., among which 200 image pairs characterize lively human faces, and the other half are planar face pictures. The benchmark provides a platform for researchers to test stereo matching algorithms for liveness detection, where the detection performance is evaluated via a binary classification on the detection response for being a lively human or not. The feasibility of SIFT features are verified based on a comparative analysis of the classification result, and a set of optimal parameters for the classification is given which provides a reference for further research. * denotes the equal contributions.
Exploration on Two-Way Interaction Students Management Mode of Vocational Colleges in Emerging Engineering Education

In recent years, study and practice on emerging engineering education is developing by leaps and bounds, vocational education has made considerable strides. It brings new challenges to student management in vocational colleges. Therefore, in emerging engineering education, more student management modes must be developed to meet personalized teaching requirements. This paper first investigates and discusses current situation of students' behavior in vocational colleges. On this foundation, a “two-way interaction” student management mode of vocational colleges in emerging engineering education is proposed and explored. The “two-way interaction” management mode are equipped with some new concepts such as: “student-oriented”, “dual aspects in student managers”, “two sides of students and teachers”, and “the process of management is the process of improving students' quality”, to replace the traditional management concept that emphasizing managing students with the new one that aims at cultivating students' abilities of creative, cognitive, collaborative, and problem-solving. The theory and the practical results of this paper provide beneficial reference for effectively solving the current problems in vocational education management, and improving the qualities of students in vocational schools.
Computational Complexity of Structural Identification Method for Interval Discrete Models

One of the stages of building of discrete models based on experimental data, is structural identification. Structural identification means the procedures of finding of general form of models. In cases if experimental data are inaccurate, represented in an interval form, and mathematical models of the processes are represented in the form of difference equations, structural identification tasks have a very high computational complexity. One of the methods of structural identification of interval discrete models, is a method based on bee colony behavioral models. However, the complexity of this method has not been investigated. The problem of high computational complexity of this method constrains its wide application for a large spectrum of modeling tasks in technical, environmental, medical, economic and other fields. The paper is devoted to investigation of this problem, the theoretical substantiation of the causes of high computational complexity and ways to reduce it. The paper is of a theoretical nature, but will be widely used in the case of adding the computational procedures based on the results of the research to the method of structural identification of interval discrete models.
Initial Results from an Applied Baccalaureate Program in Software Development

Nearly half of the states in the U.S. now authorize community colleges to confer certain baccalaureate degrees, typically applied baccalaureate degrees that are workforce-oriented. Green River College, a community college located in a suburb 25 miles south of Seattle, Washington, launched a new applied baccalaureate program in Software Development in 2014, focused on preparing students, particularly those who are place-bound, underrepresented and under-served, to access entry-level career opportunities as software developers. This lightning talk will share initial results from a five-year retrospective on an applied baccalaureate program in Software Development and provides the authors an opportunity to ask for feedback from SIGITE community members on what other factors and data should be considered to provide additional value in the retrospective.
Descoberta de Subtópicos Adaptativa por Consulta para Recuperação de Imagens Diversificadas

Given the complex search tasks imposed to multimedia retrieval systems, the similarity-based results often represent redundant item sets. Several real-world search tasks demand broad coverage of multiple implicit subtopics of a given query. Many works have proposed the use of clustering-based result diversification for addressing such problem. However, the definition of the number of clusters (subtopics) to be discovered is a long-lasting challenge. In order to attenuate such problems, this work proposes a novel diverse image retrieval approach as an unsupervised query-adaptive subtopic discovery based on intrinsic clustering quality optimization. Our experimental analysis have shown significant improvements, both in terms of relevance and diversity.
The performance of HTML5 in e-learning

After Adobe announced that in 2020 it will stop supporting Flash for mobile devices, the need for a second language is becoming necessary. HTML5 is one of the hot web technologies and is widely accepted across mobile devices and the web. It is designed to deliver everything you want to do around the Internet without the need for additional software such as components Browser. It used to create incredibly complex applications, supports multimedia on mobile devices and works on all portable operating systems and responds to all screen sizes. It is advantageous to cache offline, and the important thing is that most of the major e-learning tools available on the market are almost compatible with it.
A Hierarchical Approach for MARLÖ Challenge

Recently reinforcement learning has been showing remarkable performance in playing games. However, the majority of conventional approaches merely solve games with a single task. It is not yet well studied whether reinforcement learning is effective in games like Minecraft, where players are required to finish multiple different tasks while cooperating with other collaborators. In such games, AIs are confronted with dual challenges - finishing multiple tasks and building a multi-agent system. We propose a hierarchical approach with reinforcement learning policies to address the challenges. Experiments show that our approach performs well when dealing with multiple tasks and multiple agents simultaneously. Our approach got the second runner-up in MARLO Challenge, demonstrating its potential in tackling the challenges.
Path Puzzles: Discrete Tomography with a Path Constraint is Hard

We prove that path puzzles with complete row and column information—or equivalently, 2D orthogonal discrete tomography with Hamiltonicity constraint—are strongly NP-complete, ASP-complete, and #P-complete. Along the way, we newly establish ASP-completeness and #P-completeness for 3-Dimensional Matching and Numerical$$3$$-Dimensional Matching.
Hybrid Optimization Method and Algorithms for Monochrome Images Tone Approximation with Implementation

The chapter considers the Monochrome Multi-tone Images (MMI) Tone Approximation (TA) problem. The TA procedure consists in reducing the image single-color tones palette size by replacing the original tones values with the approximated ones. The main problem is the selection of the appropriate approximation tones; in other words, there is a need to define the optimal palette. To provide optimal TA for monochrome images a hybrid algorithm is developed and implies a 2-stage MMI processing. In the first stage the modified evolutionary-genetic algorithm is used. The main goal of the first stage is reducing the search area for the optimal approximation palette. In the second stage, the simple, but effective deterministic algorithm scans the nearest neighbourhood of the suboptimal solution, which was found in the first stage. The scanning of the nearest neighbourhood guarantees that the found extreme approximation palette is fulfilling the optimization criterion and that it is sub-optimized in respect to the total TA processing time.
Analysis of the Possibilities for the Implementation and Formation of Leadership Qualities among Teachers in the Modern Educational Environment

In modern socio-cultural conditions, there is a request for professional growth of the teacher. It is confirmed by the decree of the President of the Russian Federation (5) and teachers (1). The problem is to increase the number and level of complexity of the requirements for teachers and the lack of theoretical and practical information base for both working and future teachers. The purpose of the study: to develop a Handbook designed to help in the possible implementation of the request for professional growth of the teacher, the formation of the teacher-leader. Research problem:&#x0D; 1) to study the features of teachers ' perception of the phenomenon of leadership and understanding the role of a leader for a teacher within his profession.&#x0D; 2) to analyze the difficulties in the pedagogical sphere, relevant at the moment. 3) the opinions of teachers about the specifics of their profession are revealed. The topic of the phenomenon of leadership in the pedagogical sphere and improving the quality of education was considered in the works of M. Barber, M. Murshed [2], I.B. Bicheva, O.M. Filatova [3], S.S. Lutkin [4], I.L. Feldman [6], S.N. Yaroshenko [7].
Intelligent Auto Visual Tracking

Visual tracking is a prime requirement for any intelligent surveillance. The problem becomes more challenging when target of interest is to be auto-detected. Several algorithms have been developed for auto target detection and tracking, but most of the algorithms are not realizable for real-time applications due to high computational cost and lack of robustness. A number of false alarms are detected, or tracking window is lost during the tracking operation for these algorithms. A novel approach is developed in the proposed algorithm, which auto-detects the target of interest using weighted modified Gabor filtering followed by tracking using speeded-up robust features (SURFs). The combination of Gabor and SURF provides robustness and fast computation. The proposed algorithm is based on strong feature descriptors; hence, it is very robust and independent of illumination variation.
Research on Arm Shape of Manipulator Based on Trajectory and End Posture

In order to solve the problem of reasonable distribution of joint combination in the design process of manipulator. In the design of manipulator, the target trajectory of manipulator and the requirement of end attitude should be taken into account. By dividing the change of trajectory and terminal attitude according to the three components of X-Y-Z, the corresponding joint combination mode is given. The kinematics relation of the arm shape of the manipulator is established by using the D-H representation. The forward and inverse solutions are obtained by using the Robotics Toolbox in MATLAB, and the workspace is given. By checking whether there is a reasonable inverse solution to judge the appropriate joint combination mode, according to the change of the attitude at the end of the joint, it is increased according to the change of the posture at the end of the joint. Add the corresponding joint type to complete the target task. According to this method, the design idea of manipulator can be given clearly, which provides a reference for improving the rationality of manipulator joint combination.
Multi-view Robust Gesture Recognition for Assistive Interfaces

In this paper, we propose a gesture recognition approach using a multi-view setup for assistive device applications. As smart assistances become a reality, the need to interact with them in a natural fashion, as we do with other humans, becomes crucial. Gestures are a fundamental modality of human interaction, being natural and intuitive. We propose a gesture recognition approach relying on upper-body joints’ motions, so that individuals suffering from motor dysfunctions, that need to use wheelchairs or cannot stand, can as well interact with their smart assistive devices. To achieve this goal, we propose a robust multi-view skeleton fusion through a Kalman filtering technique, followed by an upper-body handcrafted feature extraction process. Gestures are classified using a support vector machine (SVM) classifier. Experiments with our captured dataset revealed a strong generalization from our method, and an increased performance of our multi-view fusion over the individual views.
Classification of Breast Cancer Malignancy Using Machine Learning Mechanisms in TensorFlow and Keras

Classification of breast cancer malignancy using digital mammograms remains a difficult task in breast cancer diagnosis and plays a key role in early detection of breast cancer. Inspired by rapid progress in the field of artificial intelligence, we explored several machine learning mechanisms, i.e., Support Vector Machine (SVM), Logistic Regression, Decision Tree, Random Forest, and Deep Neural Network (DNN) given in TensorFlow and Keras deep learning frameworks, and used Python programming to predict if a patient case is malignant or benign. This retrospective study was based on the Breast Cancer Wisconsin (Diagnostic) Data Set that contains a set of 30 features, e.g., radius mean, texture mean, perimeter mean, etc., previously extracted from digital mammograms. In addition, breast cancer diagnosis results were provided as the gold standard for training and testing of the machine learning mechanisms. Based on verification results on the test set, the five machine learning mechanisms achieved the sensitivity of 94.4%, 94.4%, 91.9%, 90.8%, 98.5%, and the specificity of 92.7%, 90.5%, 92.3%, 94.6%, 91.1%, respectively. In conclusion, our machine learning mechanisms achieved competitive performance results with the state-of-art techniques presented by other researchers and may provide useful second opinion to radiologists in breast cancer diagnosis.
Developing a User-Friendly Tool for Executing SQL Queries Using Hadoop Framework

In a Relational Database Management System (RDBMS), we execute queries to retrieve any information or to add, update and delete information. Nowadays, the data volume is increasing in every second, and therefore, it becomes difficult to keep a regular update of this data as in the form of a relational database. In this paper, we present a tool which can execute traditional SQ queries over a large amount of unstructured data. Using this application, the user need not write any traditional queries; therefore, the user requires no background knowledge on SQL or big data processing platform such as Hadoop. It can handle any numbers of flat files for executing queries. The tool provides a user interface which is easy to understand and use. The main contribution of our presented tool is that a user can easily execute queries while utilizing the enormous processing capabilities of Hadoop framework, without having any knowledge of the used framework.
Just about managing: dealing with the threats to industrial systems

Although studies and surveys from analyst firms Gartner and IDC differ slightly, there is an assumption that organisations spend around 10% of their IT budgets on cyber security. 1, 2 When differentiated by industry sector, financial services tend to spend more, leisure and hospitality slightly less. Industrial sectors including manufacturers that deploy industrial control system (ICS) solutions sit somewhere in the middle but often face a set of cyber security challenges that are harder to overcome. This includes budgets for upgrades often measured in five- to 10-year cycles that impose a major obstacle in the fast-changing world of IT-related security. Industrial firms face some particularly difficult cyber security challenges. These include upgrades often measured in five- to 10-year cycles. Many manufacturers that have operated within closed networks have failed to adapt to targeted threats. Underfunding of cyber security along with archaic security principles that lag 10 years behind the mainstream computing world can make industrial organisations easy targets. Firms need to look at holistic solutions that include people, skills and technologies across the organisation, says Ilan Barda of Radiflow.
Static Analyzers and Potential Future Research Directions for Scala: An Overview

Static analyzers are tool-sets which are proving to be indispensable to modern programmers. These enable the programmers to detect possible errors and security defects present in the current code base within the implementation phase of the development cycle; rather than relying on a standalone testing phase. Static analyzers typically highlight possible defects within the 'static' source code and thus does not require the source code to be compiled or executed. The Scala programming language has been gaining wider adoption across various industries in recent years. With such a wide adoption of tools of this nature, this paper presents an overview on the static analysis tools available, both commercial and open-source, for the Scala programming language. This paper discusses in detail about the types of defects that each of these tools can detect, limitations of these tools and also provide potential research direction that can improve the current state of static analyzers for the Scala programming language.
The Role of Multiple Representations and Representational Fluency in Cryptography Education

Both the public sector and private sector have strong demand for qualified cybersecurity professionals. It is therefore imperative that higher education institutions can fill the gap by producing more skilled graduates that have deep understanding of cybersecurity. Cryptography is one of those fundamental subjects that is difficult to learn and error-prone in practice. This work aims to understand if multiple representations and representational fluency can help students better learn new cryptography concepts. Experiments were designed to present the same new cryptography concepts using three different representations to the participants: language, graph, and math notations. Assessments with different representations were then conducted after the learning phase to associate the learning gains with the learning behaviors. An eye tracker was adopted in this study to keep track of the learning behaviors of the participants'. This IRB approved study recruited 43 undergraduate students who are majored in IT. Results showed that most participants are language dominant learners. Graphic and math representations are able to help students to improve their comprehensions on new cryptography concepts.
NATURAL LANGUAGE QUESTION ANSWERING SYSTEM OVER LINKED DATA

&#x0D; &#x0D; Linked Data project is aimed to give&#x0D; more details on any subject through the big knowledge bases defined on the web.&#x0D; In this context, knowledge bases offer endpoint service user interfaces to&#x0D; query their data. Because of the SPARQL query language limitation of these&#x0D; knowledge bases, a significant number of web users are unable to benefit from&#x0D; these services. In this paper, an English natural language question answering&#x0D; system over Linked Data is proposed in order to eliminate this limitation. The proposed&#x0D; system's main processes can be listed as follows: (1) Extracting Part-Of-Speech&#x0D; (POS) tags, (2) pattern extraction &amp;amp; preparing appropriate SPARQL queries,&#x0D; (3) executing user queries &amp;amp; displaying the results. The features which are&#x0D; not provided by the endpoint services of knowledge bases such as dynamic&#x0D; paging, voice search and answer vocalization which make the usage of the proposed system to be possible by the visually-impaired&#x0D; web users, question-answer caching, social media integration, and live spell&#x0D; checking are proposed. According to experimental results, the proposed system’s&#x0D; question answering performance is improved between 2 and 12 times through the&#x0D; type of natural language question thanks to the question-answer caching&#x0D; mechanism.
The Gallery as a Location for Research-Informed Practice and Critical Reflection

Creative holography could still be considered a fringe medium or methodology, compared to mainstream art activities. Unsurprisingly, work using this technology continues to be shown together with other holographic works. This paper examines the merits of exhibiting such works alongside other media. It also explores how this can contribute to the development of a personal critical framework and a broader analytical discourse about creative holography. The perceived limitations of showing holograms in a “gallery ghetto” are explored using early critical art reviews about these group exhibitions. An international exhibition, which toured the United Kingdom (UK) and Australia, is used as a framework to expand the discussion. These exhibitions include examples of the author’s holographic work and those of artists working with other (non-holographic) media and approaches. The touring exhibition as a transient, research-informed process is investigated, as is its impact on the critical development of work using holography as a valid medium, approach, and methodology in the creative arts.
DMSS: Decision Management System for Safer Spacecrafts

The fast growing number of low earth orbit exploitation and deep space missions results in enormous volumes of telemetry data. In order to operate efficiently satellites constellations as well as spacecrafts, DMSS offers a self-learning visual platform for anomaly detection in telemetry data coming from embedded sensors. As use-case, the data of two space missions operated by the European Space Agency were analyzed: Mars Express and GAIA.