Reviewerly/exHarmony huggingface dataset:
Edge Computing Based Miniature Maps Using Embedded Webserver For Prediction of Malignancy

Cancer detection from histopathology images is based on extraction of spatial information from whole slide images(WSI) using image processing tools. Machine learning algorithms and state-of-the art convolution neural networks based prediction of malignancy requires cloud services which are often prone to network latency. We put forward a novel technique to classify histopathology images by creating a miniature probability maps to locate the presence or absence of cancer which can be used in any portable device connected to the local network. The shortcomings of loading the high resolution images in mobile devices is resolved as the extraction of patches from WSI and prediction is performed concurrently to minimize computational overhead. The miniature map indicating the presence or absence of cancer in pathology images can be viewed in mobile screen for the uploaded WSI from same device. A custom trained model is used to perform to reduce the processing time thereby faster predictions in end-devices deployed near the medical practitioner as assisting tool in disease diagnosis.
University Digital Maturity Profile as a Tool of Higher Education System Digital Transformation

Digital transformation is an extremely popular practice when the business models of organizations are changing in all industries around the world. In the Russian Federation achieving digital maturity of key sectors of the economy and social sphere is a national development goal until the 2030 year. The development of a system for assessing the state and monitoring of digital maturity is relevant in theoretical and methodological terms. Within the framework of this study, attention is paid to the organization of the higher (university) education system. It was noted that the existing approaches and methods for assessing digital development and digital maturity, as a rule, take into account the infrastructural factors of digitalization (material-technical factors, informational resources, human resources) and the results of the digitalization of educational products. In the model proposed by the authors, it is suggested to assess the digitalization of business processes and the digitalization of the work environment of students, teachers, and administrative personnel with the allocation of models: a model of a digital university, digital teaching methods, and the formation of the student experience, research and development area, campus and student life, administrative functions, functions of the teaching staff, university management and funding model, personnel, information and communication technologies. In the context of these models, authors highlighted the parameters that characterize the conditions and results of digital transformation with a specification to the result in terms of digital parameters and socio-economic effect. The proposed model can serve as a basis for building a digital maturity profile of a university, become a tool for diagnosing the current state of digitalization, determining the strengths and priorities of digital development.
ADA: a tool for visualizing the architectural overview of open-source repositories

Writing highly maintainable and efficient software code is becoming increasingly difficult, especially while following the rapid, agile development process and working in a distributed team. One of the key indicators of that inefficient software design is a high degree of code coupling, which leads to unwanted side-effects during refactoring and acts as a burden during future development. To alleviate these problems, we developed a visualization tool, ADA, that statically analyzes an open-source repository and seeks to address the issue of code coupling by providing developers with a powerful graphic representation. ADA showcases the relationships and the degree of inter-connectivity between the classes. ADA will ultimately guide developers to instantly locate the coupled area and assist them in decoupling it.
Weakly Supervised Word Segmentation for Computational Language Documentation

Word and morpheme segmentation are fundamental steps of language documentation as they allow to discover lexical units in a language for which the lexicon is unknown.However, in most language documentation scenarios, linguists do not start from a blank page: they may already have a pre-existing dictionary or have initiated manual segmentation of a small part of their data.This paper studies how such a weak supervision can be taken advantage of in Bayesian non-parametric models of segmentation.Our experiments on two very low resource languages (Mboshi and Japhug), whose documentation is still in progress, show that weak supervision can be beneficial to the segmentation quality.In addition, we investigate an incremental learning scenario where manual segmentations are provided in a sequential manner.This work opens the way for interactive annotation tools for documentary linguists.
Practical protection against video data leakage via universal adversarial head

While online video sharing becomes more popular, it also causes unconscious leakage of personal information in the video retrieval systems like deep hashing. A snoop can collect more users' private information from the video database by querying similar videos. This paper focuses on bypassing the deep video hashing based retrieval to prevent information from being maliciously collected. We propose universal adversarial head (UAH), which crafts adversarial query videos by prepending the original videos with a sequence of adversarial frames to perturb the normal hash codes in the Hamming space. This adversarial head can be generated only with a few natural videos, and mislead the retrieval system to return irrelevant videos when it is applied to most query videos. Furthermore, to obey the principle of information protection, we expand the proposed method to a data-free paradigm to generate the UAH, without access to users' original videos. Extensive experiments demonstrate the effectiveness of our method in misleading deep video hashing under both white-box and black-box settings.
WindsPT e-Science platform for wind measurement campaigns

Abstract Experimental field campaigns for collecting wind data, essential for academic research and the wind energy industry, are non-trivial due to the complex equipment and infrastructure required. This paper reports the latest developments of the WindsPT e-Science platform for planning, executing, and disseminating wind measurement campaign data. Existing e-Science platforms have been developed for more generic domains, preventing them from capturing the details and requirements of the field. Additionally, we propose a protocol for transferring large volumes of data from the in-site devices to our platform, ensuring data replication. With an easy-to-use Web interface, WindsPT promotes collaboration between participants, disseminates results among the stakeholders, publishes metadata, uses DOI, and includes metadata that enables machine-to-machine communication. The platform has multiple sections, with maps, images, and documents, where there is information about the location of the stations, positioning of the sensors, operating dates, photos, technical sheets, calibration documents, among others. The WindsPT platform has been used to host the Perdigão 2017 experimental campaign and proved to be a valuable tool during all the phases of this large field experiment. A new version of WindsPT, designed to be FAIR, host multiple campaigns, and include multiple cross-campaign shared features, as full-text search capabilities, is now developed and tested.
DISTANCE LEARNING TECHNOLOGIES FOR SCHOOLS OF THE FIRST DEGREE: STAGES OF IMPLEMENTATION

Modern society is constantly evolving, the educational community is facing new challenges and challenges, so the education system needs to innovative teaching methods that allow to achieve maximum involvement of all participants in the educational process with maximum productivity. One of the relevant forms to achieve this goal is distance learning. The purpose of the article is to define distance learning models, as well as to describe the stages and technologies of the distance learning model for school of the first degree. The research used theoretical research methods: analysis of scientific literature, generalization of information, systematization of scientific and scientific-methodological sources on the research problem. The study presents the features of distance learning technologies’ stage implementation in primary school educational process. The levels and sub-stages of distance learning implementation are revealed, which include: use of elements of distance learning technologies by the teacher; use of elements of distance learning technologies by teachers and students; independent work of students (under the guidance of a teacher) with elements of distance learning technologies; independent homework (short-term, medium-term and long-term use). The following classification of distance learning technologies is offered: according to the possibility of educational process realization, according to the directions (spheres) of the teacher’s professional activity in remote conditions and according to the environment and communication type. Thus, we have identified the following models of distance learning: minimal distancing of the subjects of interaction, significant (partial) distancing and complete distancing of the subjects of interaction. Prospects for further research are to uncover the next, broader level of computer education technology.
An Enhanced Security System Using Blockchain Technology for Strong FMC Relationship

Blockchain technology is a shared database of logs of all consumer transactions which are registered on all machines on a network.Both transactions in the system are carried out by consensus processes and to preserve confidentiality all the files contained cannot be changed.Blockchain technology is the fundamental software behind digital currencies like Bitcoin, which is common in the marketplace.Cloud computing is a method of using a network of external machines to store, monitor, and process information, rather than using the local computer or a local personal computer.The software is currently facing multiple problems including lack of data protection, data instability, and reliability.This paper aims to give the highest security for multiple user environments in cloud for storing and accessing the data in blocks.The users who are legitimate are only allowed for storing and accessing the data as like a secured block chain approach.As like the Blockchain which does not require a centralized system for transactions, the proposed system is also independent on centralized network interface.The decentralized system is developed in such a way to avoid counterfeiting.The system enables the fabricator to spend less or null resources to perform the validations for its direct operated stores.This ensures the product fabricator to avoid the circulation of its duplicate products.The customer as an end-user is also ensured to have only the genuine products from the fabricator.The Fabricator (F), Merchant (M) and consumer (C) forms an interconnected triangular structure without allowing any counterfeiting agents in their secured cloud chain.The proposed approach provides the stability in the security system of the cloud using the chaining mechanism within different blocks at each node.It takes roughly 4.7, 6.2, and 7.5 ms, respectively, to register each node in the proposed system for 5, 10, and 15 nodes.The overall registration time for each scenario is 11.9, 26.2, and 53.1 ms, despite the fact that each node's registration time was greatest for 10 nodes.By looking at the data, it's clear that the number of nodes is a function of time.
Accurate modulation classification under impaired wireless channels via shallow convolutional neural networks

Classifying the modulation type of radio signals plays an important role in current and future wireless communication systems. We present a modulation classification method based on convolutional neural networks that reaches high accuracy in face of various channel characteristics and signal conditions without requiring the network to have a very large depth. Experiment results show that the proposed method reaches accurate classification under different system impairment settings that include sampling rate offset, carrier frequency offset, multi-path fading, and additive white Gaussian noise. For instance, compared to a state-of-the-art method, accuracy is improved up to 25% in classifying difficult modulation types under system impairments. Source code of the proposed method is available online.
Application of Open Multimodal Information System in the design of Chinese Culture and Educational Network Community based on Neural Network

Application of the open multimodal information system in the design of Chinese culture and education network community based on neural network is studied in the paper. The traditional neural computing model has the challenge of complexity robustness concerns, and with the designed model, 4 challenges are fixed. (1) The processing speed is too slow when learning massive information; (2) The memory capacity is limited; (3) Repeated training is required, and the ability to learn once is not available; (4) Amnesia is prone to occur when receiving new information. The multimodal information system is designed to implement the ideas, the action object, working mode and function positioning of each link may be different, so as to form a fusion system at different levels. Then, the BBS structure is well optimized with the neural networks. Finally, the system is applied to the design of Chinese culture and educational network community. Through this implementation, the better solutions have been determined.
No-Reference Blur Assessment Based on Re-Blurring Using Markov Basis

Blur is produced in a digital image due to low pass filtering, moving objects or defocus of the camera lens during capture.Image viewers are annoyed by blur artefact and the image's perceived quality suffers as a result.The highquality input is relevant to communication service providers and imaging product makers because it may help them improve their processes.Human-based blur assessment is time-consuming, expensive and must adhere to subjective evaluation standards.This paper presents a revolutionary no-reference blur assessment algorithm based on re-blurring blurred images using a special mask developed with a Markov basis and Laplace filter.The final blur score of blurred images has been calculated from the local variation in horizontal and vertical pixel intensity of blurred and re-blurred images.The objective scores are generated by applying proposed algorithm on the two image databases i.e., Laboratory for image and video engineering (LIVE) database and Tampere image database (TID 2013).Finally, on the basis of objective and subjective scores performance analysis is done in terms of Pearson linear correlation coefficient (PLCC), Spearman rank-order correlation coefficient (SROCC), Mean absolute error (MAE), Root mean square error (RMSE) and Outliers ratio (OR).The existing no-reference blur assessment algorithms have been used various methods for the evaluation of blur from no-reference image such as Just noticeable blur (JNB), Cumulative Probability Distribution of Blur Detection (CPBD) and Edge Model based Blur Metric (EMBM).The results illustrate that the proposed method was successful in predicting high blur scores with high accuracy as compared to existing no-reference blur assessment algorithms such as JNB, CPBD and EMBM algorithms.
Integration of text and geospatial search for hydrographic datasets using the lucene search library

We present a hybrid text and geospatial search application for hydrographic datasets built on the open-source Lucene search library. Our goal is to demonstrate that it is possible to build custom GIS applications by integrating existing open-source components and data sources, which contrasts with existing approaches based on monolithic platforms such as ArcGIS and QGIS. Lucene provides rich index structures and search capabilities for free text and geometries; the former has already been integrated and exposed via our group's Anserini and Pyserini IR toolkits. In this work, we extend these toolkits to include geospatial capabilities. Combining knowledge extracted from Wikidata with the HydroSHEDS dataset, our application enables text and geospatial search of rivers worldwide.
Detection of Toxic Content on Social Networking Platforms Using Fine Tuned ULMFiT Model

Question and answer websites such as Quora, Stack Overflow, Yahoo Answers and Answer Bag are used by professionals.Multiple users post questions on these websites to get the answers from domain specific professionals.These websites are multilingual meaning they are available in many different languages.Current problem for these types of websites is to handle meaningless and irrelevant content.In this paper we have worked on the Quora insincere questions (questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers) dataset in order to identify user insincere questions, so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the platform.Previously, a research was carried out with recurrent neural network and pretrained glove word embeddings, that achieved the F1 score of 0.69.The proposed study has used a pre-trained ULMFiT model.This model has outperformed the previous model with an F1 score of 0.91, which is much higher than the previous studies.
20th European NKOS workshop

The workshop will explore the potential of Knowledge Organization Systems (KOS), such as classification systems, taxonomies, thesauri, ontologies, and lexical databases, in the context of current developments and possibilities. These tools help model the underlying semantic structure of a domain for purposes of information retrieval, knowledge discovery, language engineering, etc. The workshop provides an opportunity to discuss projects, research and development activities, evaluation approaches, lessons learned, and research findings. The main theme of the workshop is Designing for Cultural Hospitality and Indigenous Knowledge in KOS.
Enhancing Students' Motivation by STEM-Oriented, Mobile, Inquiry-Based Learning

STEM education promotes scientific inquiry and engineering design, including mathematics, incorporating appropriate technologies. Portable technologies motivate active learning of students and enable accessing to learn resources, facilitating cross-disciplinary designing tasks. This chapter initially presents theoretical approaches of STEM education, mobile learning, and inquiry-based learning, and then it describes an inquiry-based short-term intervention that took advantage of portable digital devices in a STEM class. The aim of the intervention was to study its affection on students' motivation about physics. Results indicate that students who participated in the activity had higher motivation scores than their classmates who attended lessons with conventional teaching methods. The findings also show that the students involved in a guided inquiry-based process became more profoundly engaged in STEM than their classmates who followed a structured inquiry process. Other factors, such as grade point average (GPA) and gender, did not seem to affect student motivation.
Computed tomography image generation from magnetic resonance imaging using Wasserstein metric for <scp>MR</scp>‐only radiation therapy

Abstract Magnetic resonance imaging (MRI) and computed tomography (CT) are the prevalent imaging techniques used in treatment planning in radiation therapy. Since MR‐only radiation therapy planning (RTP) is needed in the future for new technologies like MR‐LINAC (medical linear accelerator), MR to CT synthesis model benefits in CT synthesis from MR images generated via MR‐LINAC. A Wasserstein generative adversarial network (WGAN) architecture with a residual UNet based generator and a patch‐based discriminator is proposed in this paper. The WGAN uses the Wasserstein metric with gradient penalty along with mean absolute error (MAE) as the loss function. The WGAN is trained and tested on an NVIDIA Tesla V100 GPU server using mutually aligned MR‐CT brain images of 26 patients. The proposed model generates synthetic CTs with an average PSNR of 31.09 dB, SSIM of 0.9265, MAE of 48.39 HU, and RMSE of 172.75 HU. The treatment planning was performed on real and synthetic CTs. The dose distributions of the synthetic CTs obtained were similar to that of the corresponding real CTs. The synthetic CTs exhibit good visual quality as well. The experimental outcomes and image quality of the model surpass many of the state‐of‐the‐art methods. The proposed CT synthesizer supports MR‐only radiation therapy while minimizing treatment costs and radiation exposure. A significant increase in the training set can further improve the CT prediction accuracy of the proposed model.
PASR: Process-Aware Middleware Framework for Dynamic Service Replacement in SOA

Service-Oriented Architecture (SOA) is a paradigm to achieve growing business needs by offering diverse capabilities on the fly. Often, services from multiple providers are glued together to achieve a specific goal. However, if any of the services involved in a process does not respond in a timely manner, the overall process gets an impact and may lead to an incomplete transaction. In such cases, the service is declared as faulty as it fails to meet one or more of the Quality of Service (QoS) parameters including response time, throughput, and availability. Numerous techniques have been proposed to replace faulty services with appropriate substitutes. However, triggering a replacement may not be suitable for situations where delay in turnaround time is caused by factors that are not under the control of service providers including intranet and route latency. This paper proposes a Process-Aware Service Replacement (PASR) approach for triggering the replacement of faulty services specifically tied to turnaround time. PASR considers the performance of participating services during the execution of a process and triggers a replacement based on user preferences. Furthermore, the proposed technique leverages the ability to switch back to the primary service and avoid the additional overhead incurred with the use of alternate services.
MetaSSD: Meta-Learned Self-Supervised Detection

Deep learning-based symbol detector gains increasing attention due to the simple algorithm design than the traditional model-based algorithms such as Viterbi and BCJR. The supervised learning framework is often employed to train a model, where true symbols are necessary. There are two major limitations in the supervised approaches: a) a model needs to be retrained from scratch when new train symbols come to adapt to a new channel status, and b) the length of the training symbols needs to be longer than a certain threshold to make the model generalize well on unseen symbols. To overcome these challenges, we propose a meta-learning-based self-supervised symbol detector named MetaSSD. Our contribution is two-fold: a) meta-learning helps the model adapt to a new channel environment based on experience with various meta-training environments, and b) self-supervised learning helps the model to use relatively less supervision than the previously suggested learning-based detectors. In experiments, MetaSSD outperforms OFDM-MMSE with noisy channel information and shows comparable results with BCJR. Further ablation studies show the necessity of each component in our framework.