Reviewerly/exHarmony huggingface dataset:
PROJECT METHOD: PAST AND FUTURE

The issues of the “project method” in the context of the past and the prospects for further use in the educational process have been considered. It has been suggested the interpretation of the terms “project”, “method”, “project method” based on the generalization of definitions of many researchers. It has also been found out that a project is a comprehensive way of exploring different topics as while working on one main topic, others, which are no less important, are identified in the course of the activity and in-depth study of the issue. It has been indicated the effectiveness of the “project method”, its dependence on the level of preparation due to the subject of study since the participants must possess certain skills and experience for its implementation. The main stages of the project-based learning process are outlined. It has been highlighted the role of the teacher in project training. It is emphasized that the teacher performs contemplative and directing functions while executing the project, and his main goal is to create a working atmosphere that is comfortable for all participants. It is noted that the project method, like any other method used in the educational process, has both advantages and disadvantages. The information about the founder of William Kilpatrick's “project method” is given. It has been emphasized that W. Kilpatrick suggested organizing the educational process as an appropriate activity in the social environment, focused on the enrichment of individual experience. Information on “smart cards” and their relation to the “project method” is presented. It has been stated the essence of reference schemes, which allow to cover a certain issue in the most complete way, help in memorizing, supplementing, associating, visualizing information. It is supposed that the application of this method allows to unite children, learn how to work in a team, develop the ability to express their opinions, formulate a willingness to take different approaches to one topic.
Linear codes over finite rings are trace codes

Linear codes over finite rings are described here as trace codes for a suitable generalization of the trace called a GF-trace. Cyclic codes over Galois rings are given a trace description as well. The main tools are the notion of trace dual bases, in the case of linear codes, and of normal bases of an extension ring over a ring, in the case of cyclic codes.
Robust security framework with bit‐flipping attack and timing attack for key derivation functions

A Key Derivation Function (KDF) derives cryptographic keys from private string and public information. The security property for the cryptographic keys is indistinguishable from the random strings of equal length. The security analysis of KDFs has received increasing attention. The practice important of KDFs is reflected in the adoption of industry standards such as NIST800-135 and PKCS5. This study proposes a robust security framework that takes into consideration the side-channel attacks. The robust security framework consists of the proposed security model and existing security models. The proposed security model is known as Adaptive Chosen All Inputs Model (CAM), which analyses the security of KDFs in terms of the bit-flipping attack and timing attack. The existing security model is the Adaptive Chosen Public Inputs Model (CPM). This research shows the implication of relationship and the non-implication relationship between CAM and CPM. The simulation of security models is according to the indistinguishable game played between a challenger and an adversary. These security models are used to evaluate existing KDFs. The result shows that none of the existing KDFs are secure in CAM for both the bit-flipping attack and timing attack. Hence, this research introduces an alternative KDF that is proven secure in CAM.
PTPG: A Poisson Triangular Pattern Generator for Tokens on Tangible Surfaces

We present PTPG (Poisson triangular pattern generator), a method that generates and optimizes triangular patterns for tokens on tangible surfaces.PTPG uses spatial information of markers to form triangular patterns and processes a template-matching algorithm to identify patterns.We introduce Poisson disk sampling to search patterns in the feature vector space and set sampling constraints to obtain patterns that meet the requirements of size, shape, and pattern distance.The patterns optimized by a shape constraint address the marker-loss problem caused by a common design of capacitive tokens, and therefore improve the recognition rate.We manufacture ten sets of prototype tokens based on the patterns generated by Poisson sampling and test these tokens on different devices.Experimental results show that our method provides more unique patterns than other heuristic methods, and the patterns are applicable to different tangible surfaces with high recognition rates.
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization

Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step. Previous work shows the efficacy of jointly scoring and selecting sentences with neural sequence generation models. It is, however, not well-understood if the gain is due to better encoding techniques or better redundancy reduction approaches. Similarly, the contribution of salience versus diversity components on the created summary is not studied well. Building on the state-of-the-art encoding methods for summarization, we present two adaptive learning models: AREDSUM-SEQ that jointly considers salience and novelty during sentence selection; and a two-step AREDSUM-CTX that scores salience first, then learns to balance salience and redundancy, enabling the measurement of the impact of each aspect. Empirical results on CNN/DailyMail and NYT50 datasets show that by modeling diversity explicitly in a separate step, AREDSUM-CTX achieves significantly better performance than AREDSUM-SEQ as well as state-of-the-art extractive summarization baselines.
An open-source GIS-enabled lookup service for Nagoya Protocol party information

Abstract The Nagoya Protocol on Access and Benefit Sharing is a transparent legal framework, which governs the access to genetic resources and the fair and equitable sharing of benefits arising from their utilization. Complying with the Nagoya regulations ensures legal use and re-use of data from genetic resources. Providing detailed provenance information and clear re-usage conditions plays a key role in ensuring the re-usability of research data according to the FAIR (findable, accessible, interoperable and re-usable) Guiding Principles for scientific data management and stewardship. Even with the framework provided by the ABS (access and benefit sharing) Clearing House and the support of the National Focal Points, establishing a direct link between the research data from genetic resources and the relevant Nagoya information remains a challenge. This is particularly true for re-using publicly available data. The Nagoya Lookup Service was developed for stakeholders in biological sciences with the aim at facilitating the legal and FAIR data management, specifically for data publication and re-use. The service provides up-to-date information on the Nagoya party status for a geolocation provided by GPS coordinates, directing the user to the relevant local authorities for further information. It integrates open data from the ABS Clearing House, Marine Regions, GeoNames and Wikidata. The service is accessible through a REST API and a user-friendly web form. Stakeholders include data librarians, data brokers, scientists and data archivists who may use this service before, during and after data acquisition or publication to check whether legal documents need to be prepared, considered or verified. The service allows researchers to estimate whether genetic data they plan to produce or re-use might fall under Nagoya regulations or not, within the limits of the technology and without constituting legal advice. It is implemented using portable Docker containers and can easily be deployed locally or on a cloud infrastructure. The source code for building the service is available under an open-source license on GitHub, with a functional image on Docker Hub and can be used by anyone free of charge.
Jupyter Notebooks for Simplifying Transfer Learning

Nowadays, the use of transfer learning, a deep learning technique, is growing to solve imaging problems in several contexts such as biomedicine where the amount of images is limited. However, applying transfer learning might be challenging for users without experience due to the complexity of the deep learning frameworks. To facilitate the task of creating and using transfer learning models, we developed FrImCla, a framework for creating image classification models. In this paper, we have developed a set of Jupyter notebooks that use FrImCla to facilitate the task of creating and using image classification models for users without knowledge in deep learning frameworks and without any special purpose hardware.
Development of Sensor-Based Blind Swimmig Aids

&lt;p class="0keywords"&gt;Blindness is a condition where the senses of vision are not functioning (low vision) or overall (total blindness). Persons with visual impairments are who can enjoy swimming with the help of sensors. The aim of the study was to create aids for sensor-based blind visual swimming that can be used during swimming exercises. The method used is a qualitative approach, the main data obtained through observation, questionnaires, interviews, and documentation and procedures of research and development used is the Borg and Gall model. This study is expected to contribute to blind swimming aids material products which are effectively used for swimming exercise skills. This study concluded that the product development sensor based blind learning swimming aids can be used for motion skills training for blind swimming athletes.&lt;/p&gt;
Face Recognition on Low Resolution Face Image With TBE-CNN Architecture

Face recognition in low resolution images has challenges in active research because face recognition is usually implemented in high resolution images (HR).In general, research leads to a combination of pre-processing and training models.Therefore, this study aims to classify low-resolution face images using a combination of pre-processing and deep learning.In addition, this study also aims to compare evaluation results based on differences in epoch values.In this research will use the Trunk Branch Ensemble -Convolutional Neural Network (TBE-CNN) as one of Convolutional Neural Network (CNN) architecture combined with the pre-processing method which is super resolution to get better accuracy compared to the state-of -art.Then the model is trained with different epoch values, that is 40 and 100, to comparing the best classification.This study was evaluated by YTD Dataset.Based on the test results, this proposed method has achieved better results compared to the previous method, which gives an increase of 1%.After that, training results from different epoch values used produce different accuracy at the training model but have no effect on validation and testing model.The training using epoch = 100 has an accuracy increase of 3% compared to training using epoch = 40 and the loss ratio obtained at the training is decreased by 20% compared to training at epoch = 40.Furthermore, optimization parameters used and reducing computing time when training models and validation will be future research.
CLASSIFICATION OF NEWS TYPES BY IMPLEMENTING ENHANCED CONFIX STRIPPING STEMMER

News has become a community need in the world. Managing a lot of news articles is not easy and takes a long time. Indonesia has various types of media platforms that display news, one of which is an online news portal. Automation systems that are capable of managing and grouping Indonesian language news articles are needed. This study designed and built a web-based application to classify types of Indonesian language news articles by implementing the Enhanced Confix Stripping Stemmer algorithm. The categories used in the system are entertainment, lifestyle, sports, technology, and economics. The data used is secondary data quoted from 2 online news portals in Indonesia. The system development method used is Rapid Application Development. The data used for testing amounts to 30 news. The average results obtained from the system accuracy test are 63%. This shows that the system performance for the classification of news types is good. The number of words in a news article is very influential during the classification process.
Cognitive Reading and Character Recognition in Image Processing Techniques

The trend of researches in cognitive reading has become so popular in programming area in the field of computer science from late 1990s when scientists and researchers show more interests in computational approaches are complex in nature to derive from a known algorithm of solution. For instance, in the research areas of biology, medicine and human management sciences there are various problems where we need cognitive reading to deliver a complex and in-exact solution when there is no polynomial time to arrive at an exact solution. This article explains some of the methods in cognitive reading in image processing for character recognition and briefly discusses the steps involved in the process of character recognition in image processing.
Space harmony: a knot theory perspective on the work of Rudolf Laban

Space Harmony is a theory and practice that explores universal patterns of movement in nature and of man. It is studied by artists who are interested in understanding patterns of harmony and balance. Rudolf Laban created this theory and is credited with Laban Scales; these are series of movements in space that increase spatial awareness and a sense of balance in the body. Knot Theory is a branch of Topology that studies mathematical knots. In this paper, we explore the relationship between these two seemingly unrelated fields and demonstrate some of the contributions that they make to one another. More specifically, we introduce the notion of Harmonic Embeddings as a generalization of Laban scales. This gives us an interesting mathematical context to study scales and Space Harmony in general.
Ultrasound Image Deconvolution adapted to Gaussian and Speckle Noise Statistics

In this paper, we formulate a variational framework for the deconvolution of ultrasound images obtained from pulsed-echo linear array transducers. These images, obtained as a result of acoustic response of soft-biological tissues are called Tissue Reflectivity Function (TRF). TRF suffers from speckle patterns due to non-homogeneous nature of soft tissues being interrogated. The interference of reflected beam develops random patches of bright and dark spots. The reduced spatial resolution in the axial direction worsened as a function of distance from the transducer probe affects the diagnostic significance of ultrasonography. We design an optimization framework with consideration to Gaussian and speckle noise characteristics in the form of two data fidelity terms. To preserve edges during the iterative reconstruction process, we introduce Total Variation (TV) regularization term as well. We have conducted experiments on artificially corrupted synthetic data and simulated and real ultrasound data. Experimental results using several metrics supported by the visual results show improvement over state-of-the-art techniques for ultrasound image restoration.
A Novel Framework for Denoised High Resolution Generative Adversarial Network – DHRGAN

The advent of deeper convolutional neural networks and related methodologies have made significant achievements in the area of single image super-resolution (SISR). However, none of these techniques are equipped to handle noisy images, i.e., denoising the image as well as enhancing its spatial resolution. In this paper, we propose a denoised high resolution generative adversarial network (DHRGAN), capable of handling noise removal from given sample images while trying to super-resolve it to the desired magnification. As per our knowledge, this is the first GAN framework equipped to remove noise while simultaneously trying to magnify images. To accomplish this, we propose a two-layered generator network in addition to the discriminator network. In the upper layer of the generator network (also called UDCNN), we make use of the mean square error function for training samples while in the lower layer of generator network (called LSRResNet) we make use of a perceptual loss function (comprising of content loss function and adversarial loss function) for the same. We train the discriminator network, which is responsible for separating super-resolved images and ground truth images, using the adversarial loss function, thereby making the generator more robust. The content loss function that we employ in the generator actuates perceptual similarity rather than pixel similarity, which further strengthens its robustness. The resulting DHRGAN network can recover more realistic textures from heavily downsampled noisy images. We have used speckle noise, a common noise observed in natural scenes captured by airborne acquisition devices, to check the performance of DHRGAN. Experiments performed on standard image database, both visually and quantitatively, justify the superior performance of DHRGAN over existing similar networks.
Mining Dynamic Graph Streams for Predictive Queries Under Resource Constraints

Knowledge graph streams are a data model underlying many online dynamic data applications today. Answering predictive relationship queries over such a stream is very challenging as the heterogeneous graph streams imply complex topological and temporal correlations of knowledge facts, as well as fast dynamic incoming rates and statistical pattern changes over time. We present our approach with two major components: a Count-Fading sketch and an online incremental embedding algorithm. We answer predictive relationship queries using the embedding results. Extensive experiments over real world datasets show that our approach significantly outperforms two baseline approaches, producing accurate query results efficiently with a small memory footprint.
AUGMENTED REALITY A NEW ERA IN EDUCATION

Augmented Reality is the technology using which we can integrate 3D virtual objects in our physical environment in real time. Augmented Reality helps us in bring the virtual world closer to our physical worlds and gives us the ability to interact with the surrounding. This paper will give you an idea that how Augmented Reality can transform Education Industry. In this paper we have used Augmented Reality to simplify the learning process and allow people to interact with 3D models with the help of gestures. This advancement in the technology is changing the way we interact with our surrounding, rather than watching videos or looking at a static diagram in your text book, Augmented Reality enables you to do more. So rather than putting someone in the animated world, the goal of augmented reality is to blend the virtual objects in the real world.
Balancing Exploration and Exploitation in Self-imitation Learning

Sparse reward tasks are always challenging in reinforcement learning. Learning such tasks requires both efficient exploitation and exploration to reduce the sample complexity. One line of research called self-imitation learning is recently proposed, which encourages the agent to do more exploitation by imitating past good trajectories. Exploration bonuses, however, is another line of research which enhances exploration by producing intrinsic reward when the agent visits novel states. In this paper, we introduce a novel framework Explore-then-Exploit (EE), which interleaves self-imitation learning with an exploration bonus to strengthen the effect of these two algorithms. In the exploring stage, with the aid of intrinsic reward, the agent tends to explore unseen states and occasionally collect high rewarding experiences, while in the self-imitating stage, the agent learns to consistently reproduce such experiences and thus provides a better starting point for subsequent stages. Our result shows that EE achieves superior or comparable performance on variants of MuJoCo environments with episodic reward settings.
Grammar component in the structure of students’ language education

In the article the author views the peculiarities of formation of sociocultural literacy that seems to be knowledge of national and cultural characteristics of the country of the studied language, the culture of speech behavior. Its structure contains value, cognitive, activity and subjective-personal aspects. Studies have proven that the presence of social literacy contributes to the actual self-realization of a person. However, until today in the scientific community there is no exact definition of the concept of "literacy". Today, the quality of literacy has changed in connection with the development of society and the needs of the individual; it should contribute to its inclusion in the cultural life of the country for its progress and its self-development. Sociocultural literacy is a matter of the general education of a person who is not only able to solve normative problems in a quality manner, but also those that are dictated by global social processes, integration into the world community. In the article the author defines literacy as preparedness for development, and competence as a level of culture. Thus, she proves the necessity of relying on cultural and competency-based approaches to the formation of students' sociocultural competence, which will be facilitated by the synthesis of the activity and personality components of readiness for a certain behavior during communication with representatives of other cultures.
Balochi Non Cursive Isolated Character Recognition using Deep Neural Network

The text recognition research in artificial intelligence has enabled machines not only to recognize the human spoken languages but also to interpret them.Optical character recognition is a subarea of AI that converts scanned text images into an editable document.The researchers proposed various text recognition techniques to identify cursive and connected scripts written from left to right but their correct recognition is still a challenging problem for the visual methods.The Balochi language is one of them spoken by a significant part of the world population and no research conducted on the recognition this regional language of Pakistan.In this paper, we propose a convolutional neural network based model for Balochi script recognition for non-cursive characters.Our model optimized small VGGNet model and achieved exceptional precision and speed over the state of the art methods of machine learning.We experimented and compared the proposed method with the baseline LeNet model, the results showed the proposed method improved over the baseline method with a precision of 96%.We additionally collected and processed the Balochi characters dataset and made it public to carry further research in the future.
A Measure of Personal Information in Mobile Data

This paper describes fundamental aspects of a framework for privacy-preserving data sharing in a mobile context. The principal technical challenge is measuring the level of personal information (PI) in datasets that are shared for the delivery or enhancement of mobile enabled services. Another challenge is determining the threshold delineating a “reasonable likelihood” of an individual being identifiable from the data. The risk of reidentification defines personally identifiable information (PII). The measure of PI must go beyond simply analysing personal attributes captured in data and consider preference revealed through use of services, temporal and spatial aspects of data, as well as context for use of services.