Reviewerly/exHarmony huggingface dataset:
The Development Of The Information-Logical Model Of Image Recognition By The Invariant Characteristics

One of the problems of digital image processing is associated with the allocation of characteristic attributes for the compact representation and identification of images. Using the invariant features allows accomplishing the authentication and categorization of images in solving the problems of recognition, as well as the description of real scenes subjected to complex types of variability for their application in the problems identification of ground targets from the Board of unmanned flying vehicles. The paper gives the solution to the following problems: finding the invariant characteristics of images to change the scale and rotation; construction of a hierarchical system of invariants; computer simulation by using real and test images for the experimental study of the developed model of image description.
Beyond the Bare Stage

While improvised theatre (improv) is often performed on a bare stage, improvisers sometimes incorporate physical props to inspire new directions for a scene and to enrich their performance. A tech booth can improvise light and sound technical elements, but coordinating with improvisers' actions on-stage is challenging. Our goal is to inform the design of an augmented prop that lets improvisers tangibly control light and sound technical elements while performing. We interviewed five professional improvisers about their use of physical props in improv, and their expectations of a possible augmented prop that controls technical theatre elements. We propose a set of guidelines for the design of an augmented prop that fits with the existing world of unpredictable improvised performance.
OSense

Observing that, how we grasp objects is highly correlated with geometric shapes and interactions, we propose the use of hand postures and motions as an indirect source of inputs for object-activity recognition. This paradigm treats the human hand as an always-available sensor, and transforms all sensing problems to the data analysis for the "sensor hand". We envision this paradigm to be generalizable for all objects regardless of whether they are acoustically or electromagnetically active, and that it detects different motions while holding the same object. Our proof-of-concept setup consists of six IMU sensors mounted on the fingers and back of the hand. Our experiments show that when the posture is combined with the motion, the personalized object-activity detection accuracy increases from 80% to 87%.
Deformable MRI-TRUS surface registration from statistical deformation models of the prostate

Transrectal ultrasound (TRUS) is considered the standard of care for imaging the prostate during biopsy and brachytherapy procedures. However, interpretation of TRUS images is challenging due to high specularity, making it difficult to recognize prostate boundaries. Image-guided brachytherapy and fusion-guided prostate biopsies require accurate non-rigid registration of magnetic resonance pre-operative image to intra-operative TRUS. State of the art techniques suggest semi-automated segmentation of the prostate on the TRUS images. However, due to the high variability, segmentation of the prostate is challenging. Segmentation errors could lead into poor localization of the biopsy target and can impact the registration of pre-operative images. In general, this kind of registration is challenging since the prostate anatomy undergoes motion due to TRUS probe pressure. In this paper, we propose a non-rigid surface registration approach for MR-TRUS fusion based on a statistical deformation model. Our method builds a statistical deformation model (SDM) of pre-operative to intra-operative deformations on a prostate dataset. In order to compute the fusion for an unseen MR-TRUS pair, the trained SDM is incorporated into the registration process to increase the fusion accuracy. The proposed approach is evaluated on a dataset of 23 patients with prostate cancer, for which the MRI-TRUS scans were available. We compared the proposed non-rigid SDM registration to non-rigid Iterative closest point (NICP) and rigid ICP approaches. Experiments demonstrate that the proposed SDM based method outperforms both NICP and ICP approaches, yielding a mean squared distance of 0.52 &plusmn; 0.26mm at the base, 0.45 &plusmn; 0.17mm mid-gland and 0.59 &plusmn; 0.13mm at the apex. These results show the advantage of integrating prior knowledge of deformation fields due to probe pressure for MR-TRUS fusion prostate interventions.
On the Use of System-Level Benchmarks for Comparing Public Cloud Environments

The proliferation of public cloud providers and services offered necessitate that end users have benchmarking-related information that help compare the properties of the cloud computing environment being provided. System-level benchmarks are used to measure the performance of overall system or subsystem. This chapter surveys the system-level benchmarks that are used for traditional computing environments that can also be used to compare cloud computing environments. Amazon's EC2 Service is one of the leading public cloud service providers and offers many different levels of service. The research in this chapter focuses on system-level benchmarks and looks into evaluating the memory, CPU, and I/O performance of two different tiers of hardware offered through Amazon's EC2. Using three distinct types of system benchmarks, the performance of the micro spot instance and the M1 small instance are measured and compared. In order to examine the performance and scalability of the hardware, the virtual machines are set up in a cluster formation ranging from two to eight nodes.
Solubility equilibrium learning supported by PhET-SS

Learning materials about solubility and solubility products tend to discuss the calculation aspects only. Therefore, it requires media able to describe the dissolution process in sub-microscopic and symbolic, namely using computer-based simulation. A learning using inquiry model supported by simulation media, PhET-SS, is required in order to make students capable of mastering the concept and to improve learning effectiveness in the classroom. This study aims to analyze the improvement of students' mastery on the concepts of solubility and solubility products after learning using inquiry model supported by PhET. The method used in this study is pre-experimental method with one group pre-test and post-test design. The subject of this study consists of 38 science students at senior high school. The research instrument used is in the form of mastery test on the concepts of solubility and solubility products. The result data is tested using N-Gain. The results show that the application of inquiry model supported by PhET-SS can improve the students' mastery on the concepts of solubility and solubility products. It can be seen form the number of students experiencing increased mastery of the concept in high, medium and low category with average N-Gain in the medium one.
WSNs-based long-range selfie camera system

This paper proposes a WSN-based long-range selfie system, which is capable of taking selfies and landscape photos from long distances. In the proposed system, a low-power wireless sensor network is used to provide long-range communication between a camera and a sensor node. The operating range of the proposed system can be extended easily by adding more relay nodes to the network. Experimental results show that the proposed system performs efficiently in terms of network range, end-to-end network delay, and processing time.
Recurrent bidirectional visual human pose retrieval

Content‐based image retrieval technique is an essential component under various application scenarios. In this paper, instead of visual similarity defined by colors, shapes, or textures, we aim to retrieve images with respect to the visual similarity defined by the human pose. In our framework, all the poses are derived from images, inspired by the recent development of three‐dimension (3D) human pose reconstruction. Furthermore, to make the retrieval more robust against reconstruction error, we propose a recurrent bidirectional similarity measure called recurrent best‐buddies similarity (RBBS). Specifically, we treat the similarity measure between two visual poses as a distance measure between two point vectors, with each point representing one of the reconstructed 3D human pose candidates. We then recur the similarity measure by the displacement of query. As a justification, we verify the validity of RBBS in a one dimension (1D) Gaussian situation. In experiments, we build an original dataset for the retrieval task. Both the qualitative and quantitative results show the usefulness of our framework; the quantitative results evaluated by mean average precision (MAP or mAP) especially demonstrate that RBBS is improved by 14.13% compared to the most competitive alternative methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Analysis of student’s conceptual understanding on the work and energy of online hybrid learning

Conceptual understanding is needed for students to build correct knowledge. This study aims to identify the student's understanding of work and energy concepts in online hybrid learning. This research is a quantitative research by using descriptive method (descriptive-quantitative) that in the data analyzing using amount of size or frequency. The results showed the understanding of the students' initial concepts on the work and energy as a whole including in the sufficient category. Besides, it is obtained that students are adept at using mathematical formulas to do work and energy, but still difficult to understand the concept of physics. The results of the analysis also showed that students responded well to online hybrid learning.
Low Altitude, Slow Speed and Small Size Object Detection Improvement in Noise Conditions Based on Mixed Training

The low altitude, slow speed and small size object which we call LSS-object for short, such as small UAV (unmanned aerial vehicles) have become a hot issue of air defense security, which is difficult to detect and identify accurately from the image. In this paper, aiming at the problem of LSS-object detection under noise environment, the detection method based on deep learning is proposed. Firstly, a standard training dataset consisting 5 classes of typical objects is constructed. Then, the standard dataset is augmented with noise of different intensity. Finally, YOLO v3 algorithm is used to form a LSS-object detection system which can adapt to environment noise. The training and detection experiments were carried out on the GPU server. After only using the noise-free dataset for training, the mAP(mean Average Precision) of the noise-free test set detection reached 81.07%, but the mAP decreased to 20.68% when the noise variance was 0.03.After adopting the mixed training strategy of the dataset with noise variance of 0.01 and noise-free data, the mAP for the test set detection with noise variance of 0.03 was increased to 70.61%, and the mAP still reached 79.85% in noise-free test set detection. The experiment results show that the mixed training strategy can greatly improve the accuracy in the noisy images detection while maintaining a higher accuracy in noise-free images.
Snake and Ladder Game Integrated with Asmaul-husna: Development of Learning Media

This study aims to determine the attractiveness, feasibility, and response of students in using snake ladder game media integrated with asmaul husna on the theme of animal and human movement organs. The development research method adapted to the Borg and Gall model using seven steps: potential problems, data collection, product design, product validation, product revision, product trials, and product revision. Data collection tools used is the material expert questionnaire, media expert questionnaire and educator response questionnaire to determine product feasibility, and questionnaire responses of students to find out the attractiveness of the product. The results of the research that have been developed obtain a percentage of the feasibility of 92% by material experts, 90% by media experts, and 89% by educators. The percentage of participants' responses to product attractiveness gained 92%. Based on the results of the description above, the products developed by researchers are suitable for use as learning media.
Exploration on Web Testing of Website

As an important measure on quality assurance for web applications of website, web testing has been paid more attention in software testing recently. Based on the analysis of website and its application characteristics, from the view of affecting the quality of web applications, testing activities from the five aspects on functionality testing, performance testing, usability testing, compatibility testing and security testing are explored to assure the quality on web applications of website. Finally, further researches on comprehensive and adequate testing of the web service composition with some test techniques under the background of service-oriented technology and cloud computing technology, and how to combine exploratory testing with testing activities of functionality testing and usability testing effectively in web testing are expected in the paper.
Design and Evaluation of a Virtual Reality-Based Car Configuration Concept

The Daimler AG provided a concept preview towards the individualization of interior trim parts at the International Motor Show in September 2017, which was named unleash the color. At the show, a tablet computer was used to enable the configuration of a car. The configuration output, in turn, could be either directly previewed on the tablet computer or experienced using a virtual reality application. However, as the car configuration procedure is usually performed iteratively, a user experiences frequent context switches of the used software application, which often leads to an embittered perceived user experience and usability. To remedy these drawbacks, one promising approach constitutes the idea to integrate the configuration procedure into a proper virtual reality application. The work at hand presents Xconcept, which draws upon various state-of-the-art approaches from the field of human-computer interaction to provide a suitable car configuration procedure based on a virtual reality setting. Among other important factors, one fundamental goal of Xconcept constitutes the perceived user experience independently of age, gender, or previous virtual reality experiences. To evaluate whether or not this can be achieved with Xconcept, we conducted a study with employees of the Daimler AG. Although the results of the study reveal that with rising age, the rating of the Xconcept deteriorates, the overall user experience and usability has been rated positively. Interestingly, gender and previous experiences with virtual reality applications had no significant effect on the rating of the user experience. Altogether, Xconcept shows valuable insights to ease the car configuration procedure based on a proper virtual reality setting.
Olympiad Movement in the System of Partnership “School–University–Enterprise”

The article addresses the problem of organizing Olympiads and competitions for schoolchildren and students by the University to identify and support them in their individual educational and career trajectory, with the participation of employers in the region. To solve this problem, I.N. Ulianov Chuvash State University is implementing a number of projects in the partnership system «school – University – enterprise». The article highlights the activities of the Centre for working with the talented youth of I.N. Ulianov Chuvash State University aimed at realization of the strategic project roadmap of the University «Formation and development of the complex for popularization of promising careers, engaging and support of the talented youth in the system of multilevel anticipatory staff training». The article describes a unique experience in organizing academic Olympiads and creative design contests for schoolchildren in conjunction with innovative enterprises of the Chuvash Republic, among which are: «Hope of Chuvashia electrical engineering», «Hope of Chuvashia mechanical engineering», «Builders of the future », «Electronics 4.0», «IT-Ring». Winners and prizeholders of these academic Olympiads and contests get involved into the work of professional navigational guidance platform of the University «Center for career planning». Currently, the University is actively working on adaptation and introduction of the tutorship model. Key indicators of Olympiad movement efficiency in the network of cooperation with enterprises are provided. An analysis of these indicators makes it possible to conclude that various academic Olympiads and competitive activities for schoolchildren held together with enterprises-partners increase the number of winners and prize-holders of the highest level academic Olympiads entering the University. The article also discusses the forms of supplementary education for gifted schoolchildren and their teachers-tutors. It is stated that the value is not holding Olympiads and identifying talented schoolchildren, but regular classes with them in clubs and in supplementary education courses. It is concluded that by attracting talented graduates of secondary educational institutions to enter the University and their active participation in student Olympiad movement organized in partnership with leading innovative enterprises, the problem of professional elite developing in the region is successfully being solved.
Automatic Document Classification Using Convolutional Neural Network

Official document classification is an integral essential part of daily archives administration. The traditional manual document classification method is time-consuming and labour-intensive, and the classification effect cannot be fully guaranteed. With the popularity of computers and the development of machine learning, the convolutional neural network model is becoming more and more mature, and the CNN model is suitable for the problems encountered in the current official document classification. This paper proposed a model based on convolutional neural network to solve the problem of official document classification. To train and test the model, we manually classify the dataset into ten categories according to the classification of college archives entities. And it was trained on a dataset with size of 6765. And on the testing dataset with size of 676, it reached an accuracy of 90%. And for comparison, we also trained a LSTM model and a GRU model (they are both popular in the natural language processing field), and results showed that the automatic official document classification method based on convolutional neural network can improve the efficiency of traditional official document classification. Also, it provides a new way of thinking for the solution of the official document classification problem.
Digital Signal Processing of Experimental Pressure Signal

In modern reciprocating engines, combustion diagnosis is essential for the estimation of the combustion quality and control of combustion phasing for superior performance. Post-processing of measured cylinder pressure signal is conducted for combustion diagnostics. Four-step signal processing (phasing with respect to crank angle, absolute pressure referencing (pegging), smoothing/filtering, and cycle averaging) is often used before further processing of cylinder pressure data for calculation of different combustion parameters. All the aspects of signal processing of the cylinder pressure signal (typically measured using a piezoelectric transducer) are discussed in this chapter. The measured pressure signal from the piezoelectric sensor needs to be accurately referenced in both magnitude (absolute pressure value) and crank angle position (time). Phasing of cylinder pressure signal with a crank angle position is performed with respect to TDC position. Different methods of crank angle phasing of the cylinder pressure signal with (capacitive or microwave probe) and without additional hardware are presented. Several pegging methods including the inlet and outlet manifold pressure-based referencing, two-point referencing, and least-square method based on a variable polytropic coefficient are also discussed. To carry out combustion analysis with good accuracy, it is essential to eliminate high-frequency noise from measured pressure signal because signal noise gets amplified when the signal derivative is used during post processing. Different methods for filtering the high-frequency noise for pressure signal are discussed for online as well as offline combustion analysis application. For an accurate heat release computation, typically mean values of input parameters (fuel mass flow, engine speed, air mass flow, etc.) are used, and hence, it is important to use averaged in-cylinder pressure data so that a representative thermodynamic cycle can be analyzed. Additionally, point-to-point variations (because of signal noise) diminishes by averaging of the signal, thus, improving the combustion analysis accuracy. The methods for determination of an optimal number of cycles for ensemble averaging of cylinder pressure data is also described in this chapter.
Towards the Automatic Construction of an Intelligent Tutoring System: Domain Module

Intelligent Tutoring Systems (ITS) are software tools that mimic a teacher’s teaching methods through artificial intelligence techniques. The generalized model of these systems is divided into four main modules: tutoring, student, domain, and interface. Although it has been shown that ITS is very useful in cases where a teacher cannot be present, the development of these systems is expensive and time-consuming, since it requires experts and available programmers. Therefore, this research proposes a framework to develop an authoring tool to build ITS automatically, with a focus on the domain module. We consider that the domain model represents the most important module of the ITS because it contains the knowledge that should be taught and evaluated. Based on this module, the rest of the modules will make decisions.
Validation and Evaluation of the Mapping Process for Generating Ontologies from Relational Databases

In this paper, we propose a generalized approach for mapping a relational database into ontology. This proposed approach is composed of two main phases. The first one consists of generating the ontology from the relational database, based on a direct mapping process by using R2RML language. The second one is devoted to evaluating the correctness of the generated ontology, with regards to two validation criteria, Information preservation, and Query preservation. In addition, we evaluate the computational performance of the proposed mapping process in terms of storage space and execution time.
Universal properties for universal types in bifibrational parametricity

Abstract In the 1980s, John Reynolds postulated that a parametrically polymorphic function is an ad-hoc polymorphic function satisfying a uniformity principle. This allowed him to prove that his set-theoretic semantics has a relational lifting which satisfies the Identity Extension Lemma and the Abstraction Theorem . However, his definition (and subsequent variants) has only been given for specific models. In contrast, we give a model-independent axiomatic treatment by characterising Reynolds’ definition via a universal property, and show that the above results follow from this universal property in the axiomatic setting.