Reviewerly/exHarmony huggingface dataset:
MODERN TEACHING TECHNOLOGIES AND DEVELOPING CONSTRUCTIVE THINKING

Numerous researches in the previous period have shown the effectiveness of mathematical learning with the use of information and communication technologies (ICT) and mathematical software packages. Also, through theoretical studies and research results, the quality of mathematics teaching in schools was examined. The results of the research of the authors of this paper showed the lack of a visual-logical approach in solving mathematical problems. Primary and secondary school students are primarily trained in mastering formulas and algebraic procedures that help them solve a given task. In order to develop the ability to perceive lawfulness and logical thinking, we organized introducing elementary and secondary school students to figurative numbers and selected examples that demonstrate the observation of lawfulness among numbers. We applied work in collaborative groups using computers and GeoGebra software. The results showed the students’ ability to perceive lawfulness and to successfully solve tasks by applying the observed lawfulness. They also confirmed the effectiveness of mathematical learning with the use of computers, mathematical software and working in collaborative groups.
Low Pass Filter-Based Enhancement of Arabic Handwritten Document Images

Document image processing gained due attention during the recent few decades as a result of technological advancements in the twenty-first century pertaining to facilitating the enhancements of the pictorial data for human interpretation. During the image acquisition process, some unwanted information needs to be removed by using several preprocessing techniques. Among those, filtering helps foster the image processing through enhancing and noise removal. This paper aims to demonstrate the low pass filter which is also used in Fourier and wavelet transformations. Through applying the low pass on a specifically built database, it was found out that low pass preserves the edge details and denoises the image by preserving details, and also it produces smooth and blurs document images. In this work, we conducted experimentation on 100 images of our own handwritten dataset, and the proposed method is evaluated based on the quantitative measures.
The Use of Augmented Reality Applications in Second Grade Mathematics Course

This study aims to examine students' knowledge of shapes as well as the views of the students and their teachers about the use of AR applications in teaching and learning geometry in second-grade mathematics lessons. A convergent parallel mixed methods research design was employed. The data collected for this study come from tests on the knowledge of shapes, as well as semi-structured interviews with students and their teachers and open-ended questionnaires. The participants of the study comprised three primary school second-grade teachers and the students from these teachers' classrooms. The quantitative data were analyzed via paired sample t-test, while the data derived from the qualitative sources were analyzed by using a content analysis method. The results show that the AR application in the second-grade mathematics lessons positively affected the students' knowledge of shapes. In addition, both students and their teachers considered AR technologies as a facilitator for learning geometrical shapes.
IR-FF-GSO: Image Retrieval using Feature Fusion and Glowworm Swarm Optimization

Image retrieval plays an important role in the Digital imaging and media such as image classification, photography, medical imaging etc., in which the obtained information is crucial for the analysis of images. Extraction of representative features is a challenge due to the variations in geometric, photometric image features. The feature fusion process affords compact discriminative features of an image; this crucial information requires in analysing images accurately to increase the accuracy. Hence, Image Retrieval using feature fusion and Glowworm Swarm Optimization (IR-FF-GSO) is proposed. Multiple features are extracted with Texture, Color, Statistical and Scale Invariant Feature Transform (SIFT) descriptors to perform retrieval process. Feature vector is fused using optimized weight value which is obtained from GSO algorithm. The proposed method yields 95.5% retrieval accuracy on ImageNet database and is accurate compared to the conventional image retrieval method by over 10% [1].
The iCub Multisensor Datasets for Robot and Computer Vision Applications

Multimodal information can significantly increase the perceptual capabilities of robotic agents, at the cost of a more complex sensory processing. This complexity can be reduced by employing machine learning techniques, provided that there is enough meaningful data to train on. This paper reports on creating novel datasets constructed by employing the iCub robot equipped with an additional depth sensor and color camera. We used the robot to acquire color and depth information for 210 objects in different acquisition scenarios. At the end, the results were large scale datasets that can be used for robot and computer vision applications: multisensory object representation, action recognition, rotation and distance invariant object recognition.
Kinetic Analysis of Pushing Force in Wheelchair Operation by Caregiver

Transportation using wheelchair is one of the most frequent transportation methods at home, medical and nursing care facilities. Wheelchairs are designed to be operable by passengers but often require assistance, and wheelchair assistance is a heavy burden. In order to quantitatively evaluate the physical burden of the caregiver when operating the wheelchair, it is necessary to analyze the characteristics of the force applied to the wheelchair when the caregiver operates the wheelchair. On the other hand, the optimal posture index of the caregiver when operating a wheelchair is not known, and the operation posture differs depending on the caregiver. Therefore, in this study, we compare the posture and the mechanical characteristics of multiple caregivers to operate a wheelchair, and consider the posture of the caregiver.
Image Encryption Using Genetic Algorithm and Bit-Slice Rotation

Cryptography is a powerful means of delivering information in a secure manner. Over the years, many image encryption algorithms have been proposed based on the chaotic system to protect the digital image against cryptography attacks. In chaotic encryption, it jumbles the image to vary the framework of the image. This makes it difficult for the attacker to retrieve the original image. This paper introduces an efficient image encryption algorithm incorporating the genetic algorithm, bit plane slicing and bit plane rotation of the digital image. The digital image is sliced into eight planes and each plane is well rotated to give a fully encrypted image after the application of the Genetic Algorithm on each pixel of the image. This makes it less prone to attacks. For decryption, we perform the operations in the reverse order. The performance of this algorithm is measured using various similarity measures like Structural Similarity Index Measure (SSIM). The results exhibit that the proposed scheme provides a stronger level of encryption and an enhanced security level.
Text-to-Clipart using AttnGAN

The Attentional Generative Adversarial Network (AttnGAN) [1] is a state-of-the-art text-to-image generation model. One of the key factors of AttnGAN's success is the ability to evaluate the similarity between an input sentence and the generated image in the same feature space (Deep Attentional Multimodal Similarity Model, DAMSM). However, the network architecture of AttnGAN is complicated and vast, which necessitates considerable computational costs in the training process. When AttnGAN is applied to a text-to-image generation task in different image domains such as clipart, the output images are simpler than the high-resolution natural images that AttnGAN originally assumes. Therefore, we propose a lightweight AttnGAN aiming at reducing the training computational cost without compromising the quality of the generated images. In particular, we focus on the image encoder; replacing it from Inception-v3 to VGG-16 reduces the DAMSM training time by approximately half of the original implementation.
Performance Analysis of Playfair Cipher Color Substitution Variants

Different cryptographic techniques undergo different modifications to improve its weakness to different attacks that may expose hidden information to unwanted users. The use of gibberish text as the cipher is the common process. Thus color substitution is compared. The process involves the Playfair cipher seed-based algorithm, which utilizes randomizer and color block, providing an improvement of 31% encryption strength, brute-force attack resilience, and 52% storage saved. The following result shows that the Playfair cipher seed-based color substitution has a strong encryption capability utilizing regular characters only. This paper also presents some limitations and future work.
Supporting Computer Science Student Reading through Multimodal Engagement Interfaces

While many computer science (CS) curricula are increasingly addressing a demand for more communicative and ethical graduates, reports of CS student difficulties with nontechnical subjects, such as Professional Ethics, persist. These seem compounded for students learning through a second or foreign language. This paper explores the impact that multimodal engagement interfaces can have on content comprehension. 30 participants of varying English language ability were asked to engage with four unrelated articles under four different conditions: baseline reading (C1); guided reading (sentence-by-sentence) (C2); audio/listening only (C3); and concurrent (multi-modal) presentation of C2 & C3 (C4). After each engagement, participants were asked to complete a comprehension test on the material that they had just encountered. A subjective survey evaluating the “comfort” and “engagement quality” of each interface was also completed after each interaction. Our results paint a complex picture with the guided reading interface (C2) producing both the best performance, and the poorest subjective evaluation from participants. This result aligns with existing findings identified in the field of reading education. The results highlight how varying language levels in participants impact subjective and performance metrics, suggesting how future interfaces may better support readers, according to their language ability or intended outcomes of reading.
Performance Evaluation of Semantic Based Image Retrieval System from Cyberspace (SBIRSC) Framework

With the popularity and remarkable development of digital imaging technology and its usage in various domains, an extraordinary level of accuracy required in the existing image retrieval techniques. The retrieval of the requested images on the basis of the user query by convoluted features in cyberspace is a very big challenge. Various researchers were already proposed multiple techniques for image retrieval. But, after concluding the detailed research, the authors have analyzed that most of the search-engines does not produce an accurate result. In this context, a new generation of digital images retrieval from cyberspace applications must be developed. This paper presents the performance evaluation of the proposed semantic-based image retrieval system from the cyberspace (SBIRSC) framework. The proposed framework was tested on various datasets like Flickr, Shutterstock, Pixabay, Everypixel, Bing available on the cyberspace. The performance of the proposed framework is evaluated by using accuracy and response time.
Research on Distributed Energy Application Mechanism and Key Technologies Based on Hyperledger

Along with the energy revolution and the environmental movement, the energy industry is transforming into a clean, distributed energy sector with a new energy structure featuring multiple energy flows complementing each other. Blockchain is a distributed database that is jointly maintained by the consensus of the whole network and maintains all historical transaction data. It provides a feasible path for the transaction demand of distributed energy, high frequency and low cost. Firstly the problems brought by distributed energy transaction and the business positioning of each subject is analyzed. Based on the core demand of distributed energy transaction, the fit between Blockchain technology and distributed energy transaction is analyzed. On this basis, Fabric distributed energy transaction architecture and transaction flow are proposed, and the key technologies of distributed data storage, consensus mechanism and intelligent contract are analyzed.
An Event-Oriented Neural Ranking Model for News Retrieval

Event-oriented news retrieval (ENR) is the task of retrieving news articles related to the specific event in response to the event-oriented query. Previous approaches usually focus on optimizing traditional retrieval models through hand-crafted features from the perspective of new articles. However, these approaches often fail to work well in reality, as they do not consider the essential natures of the event, i.e., dynamics, coupling. In this paper, we propose a novel and effective event-oriented neural ranking model for news retrieval (ENRMNR). Our model exploits a deep attention mechanism to tackle the dynamics and coupling derived from event evolution. Specifically, the word-level bidirectional attention allows the model to identify which query words about the subevent are related to the news article words, and vice-versa, in order to tackle the dynamics. Moreover, the hierarchical attention at passage-level and document-level allows it to capture fine-grained event representations for the coupling between different events within a news article. Experimental results on real-world datasets demonstrate that ENRMNR model significantly outperforms competitive models.
Orthogonal Super Greedy Learning for Sparse Feedforward Neural Networks

The analytic approaches for feedforward neural network, e.g., Radial Basis Function (RBF), have some attractive characteristics, such as superior theoretical properties and faster numerical implementations. However, they still have several drawbacks. The primary defect is that their generation performance and computational complexity are susceptible to the influence of irrelevant hidden variables. Thus, how to alleviate this influence has become a crucial issue for the feedforward neural network. In this paper, we propose an Orthogonal Super Greedy learning (OSGL) method for hidden neurons selection. The OSGL selects more than one hidden neurons from a given network structure in a greedy strategy until an adequate sparse network has been constructed. Theoretical analyses show it can reach the optimal learning rate. Extensive empirical results demonstrate the superiority that the proposed method can produce an excellent generalization performance with a sparse and compact feature representation within feedforward networks.
Developing an Integrated IoT Blockchain Platform: A Demonstrator

Ensuring the integrity and security of an Internet of Things (IoT) system, and maintaining and updating the firmware driving its devices, becomes critical as the number of nodes, sensors, actuators and control loops increases. The security of IoT can be addressed by deploying blockchain technology. The feasibility of building a blockchain-based IoT on a low-cost microcontroller is demonstrated. This is achieved by implementing an Ethereum node on Raspberry Pi 3 controlling an LED. The LED data of turning ON and OFF by the user are collected and stored on a smart contract. The interaction with the smart contract is performed by a DApp called, Status, developed based on Light Ethereum Subprotocol (LES). The feasibility of the concept was successfully demonstrated, showing the potential of blockchain technology in developing more secure IoT systems.
Design of real-time enhanced monitoring terminal based on FPGA

In the power system, when monitoring various devices and transmission lines, because the transmission lines are generally in the wild, the surrounding weather environment and geographical environment are very complicated. Therefore, during video monitoring and recognition, the collected video images will be Affected by the weather and light, the quality of the collected images is reduced. However, the recognition in the later stage does not consider how to filter out these influences, and it is often difficult to achieve the desired results when performing subsequent image processing such as target recognition and tracking. For ARM+FPGA, DSP+FPGA image processing system, the cost is high, Low resource utilization, it is difficult to use simple FPGA to control the process and complex branch judgment, therefore, this paper designs a real-time enhanced monitoring terminal based on FPGA based on the idea of FPGA-based hardware and software collaborative processing. Complete image acquisition and real-time enhanced preprocessing in the monitoring terminal to improve the ability to accurately identify the transmission line during subsequent processing and achieve accurate identification. Use an FPGA chip as the core of the system, cache the image through SDRAM, use Sopc as the control core, coordinate software and hardware to perform image processing, easy to use hardware implementation (such as filtering, edge detection, etc.) are implemented using Verilog hardware language, Control these image processing modules through Sopc, realize the corresponding image processing function. And the part that is difficult to realize in the hardware, use NiosII in Sopc system to realize.
Semantic Enhanced Sketch Based Image Retrieval with Incomplete Multimodal Query

Sketch Based Image Retrieval (SBIR) is a challenging problem mainly due to a significant cross-domain gap between hand-drawn sketches and natural images. While extra semantic information (such as attribute details) can facilitate query-adaptive search, we still have to face two challenges: (1) an incomplete multimodal query; (2) lack of sketch-image paired training data. Toward this end, many existing multimodal sketch retrieval frameworks utilize text-based label information to augment the limited sketch query with more semantic details. However, that single word-level category information may not always reveal sufficient characteristics on the object specific fine-grained attributes. In this work, we propose a multimodal SBIR system that allows both sketch and text level attribute description for query. In order to bridge the cross-modal gaps among sketch, image, and texts, given a semantic in consideration, two mode-specific semantic networks provide layer-wise regularizer parameters to dynamically adopt the underlying semantic within the learned sketch feature representation and thereby transform the initial generic sketch into a more comprehensive Semantic Enhanced Joint Embedding (SEJE). Also the availability of the multimodal paired samples may not be always feasible; neither during training nor during test phases. Therefore, instead of relying on strict one-one cross modal correspondence, the learning of the joint sketch embedding SEJE relies on capturing the semantic relevant cross-modal correspondences between an averaged mode-specific semantic features (image and text) and the sketch feature, which facilitates SEJE's improved generalization ability. Evaluation on two benchmark datasets: Sketchy and TU-Berlin clearly validates the superiority of the proposed method, compared to the state-of-the-art methods. In fact, by allowing a user to add text attributes to make a complete multimodal query, the proposed method improves the mAP scores by 5 - 7% on the challenging sketch-attribute composition test scenario.
A True Teacher Is the One from Whom You Want to Learn All Your Life

This article is an attempt to show the beginning of Eduard Borisovich Abdullin’s creative path in the pedagogy of music education, in particular, his experience in teaching as an artistic director and conductor of the choir studio “Melodia” of the Palace of Pioneers in the city of Podolsk, Moscow Region. As a graduate student of the Lenin Moscow State Pedagogical Institute, the young teacher achieved tremendous creative success: the choir studio under his direction performed in the Great Hall of the Moscow Conservatory, the Column Hall of the House of Unions, the P. I. Tchaikovsky Concert Hall, in the Kremlin Palace of Congresses; toured in the cities of the USSR and European countries. On the first television channel, two hour-long television programs were shown. The studio of the State House of Radio Broadcasting and Sound Recording has released educational records. For almost half a century, the “Melodiya” studio continued to meet with its leader, and all members of the choir strove to attend them in order to meet their Teacher again. Special attention is paid to the significance of the fundamental works by Eduard Borisovich Abdullin, which became the basis for the pedagogical interpretation by the author in his research activities of the conceptual provisions of the methodology of pedagogy of music education. The sequential expansion of the problem field of the conducted musical pedagogical research is described: from the theoretical substantiation of the model for the development of the Central Russian folk song tradition by children to the formation of a new direction of research searches associated with the use of various vocal techniques at the junction of different genres.
An Emergency-Incident Reporting and Response Mobile Application in State University Setup

The purpose of the development of the study is to provide a new avenue for academic institutions to contribute and elevate new methods and processes to student's safety. With the fast-changing methods, immersion to the technological advancement and environmental approach to security and safety will contribute to the fulfillment of the Universities' goal in promoting the welfare and safety of the students inside and outside the school premises. Employing Rapid Application Development is the best development model used in this development. The survey shows that the students find the mobile application usable and reliable in case of emergency and incidents concerning academic and non-academic matters.
Taxondroid: Design Interactive Application for Animal Taxonomy Learning Using Teen-Computer Interaction Approach

Today teenagers are easier to learn with a visual and auditory approach. Teenagers have problems when studying biology which consists of many foreign terms, and many abstract theories. Teachers also find it difficult to convey the material so students can easily understand it and attract students' attention. To attract the attention of teenagers so that they are easy to understand, digital technology is needed that has an attractive visual and interface. Augmented Reality (AR) technology provides a new way of learning for teenagers in learning. AR has been widely applied in the delivery of learning materials such as mathematics, physics, astronomy. Therefore AR can also be used in delivering biology material. Users who, on average, need a design interface that suits their needs. Researchers are now also starting to conduct research to find suitable modeling for the needs of teenagers commonly known as Teen-Computing Interaction. To help overcome the obstacles of teenagers in learning the Animal Taxonomy, the researchers propose developing a multimedia-based application by applying Augmented Reality technology that implements the design of a Teen-Computer Interaction based interface.